ArticlePDF Available

Early prognostication of COVID-19 to guide hospitalisation versus outpatient monitoring using a point-of-test risk prediction score

Authors:

Abstract and Figures

Introduction Risk factors of adverse outcomes in COVID-19 are defined but stratification of mortality using non-laboratory measured scores, particularly at the time of prehospital SARS-CoV-2 testing, is lacking. Methods Multivariate regression with bootstrapping was used to identify independent mortality predictors in patients admitted to an acute hospital with a confirmed diagnosis of COVID-19. Predictions were externally validated in a large random sample of the ISARIC cohort (N=14 231) and a smaller cohort from Aintree (N=290). Results 983 patients (median age 70, IQR 53–83; in-hospital mortality 29.9%) were recruited over an 11-week study period. Through sequential modelling, a five-predictor score termed SOARS ( S pO2, O besity, A ge, R espiratory rate, S troke history) was developed to correlate COVID-19 severity across low, moderate and high strata of mortality risk. The score discriminated well for in-hospital death, with area under the receiver operating characteristic values of 0.82, 0.80 and 0.74 in the derivation, Aintree and ISARIC validation cohorts, respectively. Its predictive accuracy (calibration) in both external cohorts was consistently higher in patients with milder disease (SOARS 0–1), the same individuals who could be identified for safe outpatient monitoring. Prediction of a non-fatal outcome in this group was accompanied by high score sensitivity (99.2%) and negative predictive value (95.9%). Conclusion The SOARS score uses constitutive and readily assessed individual characteristics to predict the risk of COVID-19 death. Deployment of the score could potentially inform clinical triage in preadmission settings where expedient and reliable decision-making is key. The resurgence of SARS-CoV-2 transmission provides an opportunity to further validate and update its performance.
Content may be subject to copyright.
Original research
Early prognostication of COVID-19 to guide
hospitalisation versus outpatient monitoring using a
point- of- test risk predictionscore
Felix Chua ,1,2 Rama Vancheeswaran,3 Adrian Draper,4 Tejal Vaghela,5
Matthew Knight,3 Rahul Mogal,3 Jaswinder Singh,6 Lisa G Spencer ,7
Erica Thwaite,8 Harry Mitchell,3 Sam Calmonson,3 Noor Mahdi,3 Shershah Assadullah,3
Matthew Leung,3 Aisling O’Neill,3 Chhaya Popat,3 Radhika Kumar,3
Thomas Humphries,7 Rebecca Talbutt,7 Sarika Raghunath,7 Philip L Molyneaux ,1,2
Miriam Schechter,5 Jeremy Lowe,5 Andrew Barlow3
Respiratory infection
To cite: ChuaF,
VancheeswaranR, DraperA,
etal. Thorax Epub ahead of
print: [please include Day
Month Year]. doi:10.1136/
thoraxjnl-2020-216425
Prepublication history
and additional material is
published online only. To view
please visit the journal online
(http:// dx. doi. org/ 10. 1136/
thoraxjnl- 2020- 216425).
For numbered affiliations see
end of article.
Correspondence to
Dr Felix Chua, Interstitial Lung
Disease Unit, Department of
Respiratory Medicine, Royal
Brompton and Harefield NHS
Foundation Trust, London SW3
6NP, UK; f. chua@ rbht. nhs. uk
FC, RV and AD contributed
equally.
Received 19 October 2020
Revised 17 January 2021
Accepted 18 January 2021
© Author(s) (or their
employer(s)) 2021. No
commercial re- use. See rights
and permissions. Published
by BMJ.
ABSTRACT
Introduction Risk factors of adverse outcomes in
COVID-19 are defined but stratification of mortality using
non- laboratory measured scores, particularly at the time
of prehospital SARS- CoV-2 testing, is lacking.
Methods Multivariate regression with bootstrapping
was used to identify independent mortality predictors in
patients admitted to an acute hospital with a confirmed
diagnosis of COVID-19. Predictions were externally
validated in a large random sample of the ISARIC cohort
(N=14 231) and a smaller cohort from Aintree (N=290).
Results 983 patients (median age 70, IQR 53–83;
in- hospital mortality 29.9%) were recruited over an
11- week study period. Through sequential modelling,
a five- predictor score termed SOARS (SpO2, Obesity,
Age, Respiratory rate, Stroke history) was developed to
correlate COVID-19 severity across low, moderate and
high strata of mortality risk. The score discriminated
well for in- hospital death, with area under the receiver
operating characteristic values of 0.82, 0.80 and 0.74
in the derivation, Aintree and ISARIC validation cohorts,
respectively. Its predictive accuracy (calibration) in both
external cohorts was consistently higher in patients
with milder disease (SOARS 0–1), the same individuals
who could be identified for safe outpatient monitoring.
Prediction of a non- fatal outcome in this group was
accompanied by high score sensitivity (99.2%) and
negative predictive value (95.9%).
Conclusion The SOARS score uses constitutive and
readily assessed individual characteristics to predict
the risk of COVID-19 death. Deployment of the score
could potentially inform clinical triage in preadmission
settings where expedient and reliable decision- making
is key. The resurgence of SARS- CoV-2 transmission
provides an opportunity to further validate and update
its performance.
INTRODUCTION
Rapid and accurate prediction of the probability of
adverse clinical outcomes is central to the manage-
ment of global outbreaks of infection.1–3 Stratifica-
tion by predicted risk, most commonly for death,
can support clinical judgement and potentially assist
clinicians in community settings to decide how
urgently to refer patients to hospital. Used appro-
priately, predictive scores can also help inform
treatment- related decision- making. The pandemic
caused by SARS- CoV-2 lends itself to predictive
modelling by having a large at- risk population and
a high adverse event rate including death.4
Although the recent incidence of COVID-19 has
decreased in some parts of the world at the time
of writing, many countries are already experiencing
a ‘second wave’ of new cases.5–8 An increase in
incident cases in many localities is already evident
in the UK. It is widely anticipated that viral trans-
mission will continue to surge in the months
ahead, particularly with the onset of winter in the
northern hemisphere. Not all patients infected
with SARS- CoV-2 will require hospitalisation but
even among those who initially experience mild
symptoms, a sizeable proportion remain at risk
of subsequent life- threatening clinical decline.
The availability of a practical prehospital predic-
tive tool to triage patients for safe discharge to an
outpatient (virtual) monitoring system versus direct
Key messages
What is the key question?
Can patients with COVID-19 be risk stratified
in the prehospital setting without laboratory-
measured data?
What is the bottom line?
A five- predictor risk prediction score
(SOARS) based on demographic and clinical
characteristics can quickly and reliably identify
COVID-19- positive patients who have a
low probability of mortality for outpatient
monitoring and management.
Why read on?
Information from the prognostication of SARS-
CoV-2- infected individuals early in their illness
can be used to guide clinical decision- making
with respect to the level of subsequent care.
1ChuaF, etal. Thorax 2021;0:1–8. doi:10.1136/thoraxjnl-2020-216425
on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from
Respiratory infection
admission to hospital for observation or treatment would be
highly advantageous.
Reliable prediction tools to differentiate between levels and
sites of clinical care already exist and have been successfully
implemented in prehospital practice. For example, both the
CURB-65 and the CRB-65 scoring systems for the assessment
of community- acquired pneumonia include recommendations
for out- of- hospital care.9 10 Recent research by the ISARIC- 4C
Consortium has provided an accurate tool to similarly prognos-
ticate for COVID-19- attributed death in hospitalised patients
but its reliance on laboratory- measured indices limits its applica-
bility outside the institutional environment.11 Prognostic evalu-
ation of individuals with suspected SARS- CoV-2 infection at the
time of diagnostic testing is potentially achievable but has yet to
be examined.
Our objective was to develop and evaluate an easy- to- apply
and accurate prognostic score to predict mortality and aid early
clinical decision- making by identifying patients infected with
SARS- CoV-2 who might benefit from an urgent hospital assess-
ment. To develop the initial risk score, we used multivariate
logistic regression to explore the relationships between a large
panel of candidate predictors and COVID-19 death. Iterative
modelling resulted in a pragmatic predictive score based on five
widely available patient variables. The scoring of patients against
these selected predictors permitted three distinct risk classes
to be defined. The performance of the score was then assessed
against two validation cohorts—a large subgroup of the ISARIC
study patients and a smaller single- hospital cohort, the latter to
better reflect local population characteristics and practice.
METHODS
Study design and characteristics of the derivation cohort
All individuals aged 18 or older who tested positive for
SARS- CoV-2 nucleic acid by real- time reverse transcriptase PCR
between 1 March and 16 May 2020 after presenting to the emer-
gency department (ED) at Watford Hospital, West Hertfordshire
NHS Hospitals Trust were prospectively recruited. Baseline
clinical characteristics and investigation results were collected
according to a prespecified protocol. Patients were either
referred to the virtual hospital (VH) for outpatient monitoring
or admitted to a medical ward.
Laboratory, physiologic and radiographic data
All laboratory tests were performed as part of routine clinical
care. Nasopharyngeal mucosal swabs for rRT- PCR were couri-
ered to the regional UK Public Health England laboratory.
Baseline vital observations included all the parameters recom-
mended by the National Early Warning Score.12 Chest radio-
graphs acquired in ED were collated and scored at the end of the
recruitment period.
Location and level of care
After presentation, patients who were clinically judged to have
mild illness were referred to the VH for subsequent moni-
toring. To avoid missing early clinical deterioration in the post-
assessment period, they were observed for up to 24 hours in
hospital. Patients who remained admitted after the first 24 hours
but who did not require additional respiratory support beyond
wall- based oxygen were managed on designated medical wards.
Where clinically indicated, continuous positive airway pressure
(CPAP) was provided on such wards or on the intensive care unit
(ICU); intubation and mechanical ventilation were undertaken
on the ICU.
Identifying predictors of death in the derivation cohort
The primary outcome of the study was in- hospital death.
TRIPOD (Transparent Reporting of a multivariable predic-
tion model for Individual Prognosis or Diagnosis) recommen-
dations were followed for multivariate model evaluation and
reporting.13 Seventy- five baseline clinical and non- clinical vari-
ables were initially collected based on their reported association
with COVID-19 and analysed by univariate and multivariate
logistic regression with bootstrap resampling.14 15 Of these, vari-
ables with numerically small ORs or a p value of >0.05 were not
included in the final analysis. Candidate predictors of death were
assessed for potential clustering effects and missing at random
values were addressed by multiple imputation with chained
equations (MICE),16 with 10–20 random draws to account for
data variability.
Development and external validation of the clinical risk score
The large external cohort comprised a randomly selected
subpopulation of the ISARIC 4C derivation population
(N=20 000 provided; 14 231 with complete data for scoring).
The primary data of these individuals were submitted by 260
hospitals across England, Scotland and Wales to the prospective
ISARIC WHO Clinical Characterization Protocol UK (CCP- UK)
study.11 We also tested our score against a smaller population
of SARS- CoV-2- positive cases from Aintree Hospital, Liverpool
(N=303 provided; N=290 with complete data for scoring) as a
single- setting validation control.
In the initial stages, a preliminary score comprising 12 inde-
pendent predictors of death, including care home residency, was
developed; to enable external validation against the ISARIC
cohort (which did not include residential data), care home status
was excluded as a variable to yield an 11- predictor score. Its
ability to discriminate for in- hospital mortality was assessed by
the area under the receiver operating characteristic (AUROC).
From this score, a condensed version comprising five clinical
predictors was developed for prehospital application. Mortality
cut- points at each risk level were assessed to define mild,
moderate and high risk classes, followed by determination of
positive and negative predictive values, as well as sensitivity and
specificity thresholds. Model performance was further assessed
by calibration using a graphical representation of the Hosmer-
Lemeshow ‘goodness- of- fit’ test to depict agreement between
the expected (predicted) and observed (actual) outcome across
the entire COVID-19 severity range in both validation cohorts.17
The summary relationship between the dependent variable
(death) and different levels of disease severity in the external
ISARIC population was expressed as McFadden’s R2.18
Statistical analysis
Categorical variables were expressed as frequency (%), with
significance determined by the χ2 test. Continuous variables
were expressed as median (IQR) or mean (SD) and analysed
by the t- test, Kruskal- Wallis or Mann- Whitney U test, as appro-
priate. ORs were assessed as unadjusted and adjusted values with
respect to in- hospital death, the latter determined by multivar-
iate regression with bootstrapping of 1000 resamples. We used
this method as internal validation to improve statistical infer-
ence by deriving a better estimate of the sampling distribution.
Bootstrapping involves randomly drawing repeat samples from
the core dataset to calculate SEs and CIs for the final regres-
sion analysis. MICE was used to generate valid estimates of
randomly missing values in the derivation model. A p value
of <0.05 was considered statistically significant. All statistical
2ChuaF, etal. Thorax 2021;0:1–8. doi:10.1136/thoraxjnl-2020-216425
on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from
Respiratory infection
analyses including risk modelling calculations were performed
using STATA, V.16 (Stata, Texas, USA).
RESULTS
Baseline demographic and clinical characteristics of the
derivation cohort
Nine hundred eighty- three patients (52.5% male) confirmed as
SARS- CoV-2 rRT- PCR positive were recruited over the 11- week
study period. Five patients remained in hospital at the time of
data cut- off on 31 May 2020. The median age of the cohort was
70 (IQR 53–83; range 23–99); median age was lowest in the
virtual hospital pathway (53; IQR 43–67) and highest among
hospitalised patients who did not receive CPAP (77; IQR 61–86)
(p<0.001) (online supplemental appendix 1).
The most common comorbidities were hypertension (48.4%),
pulmonary disease (30.0%), cardiac disease (26.6%), diabetes
mellitus (23.6%), chronic kidney disease (CKD; 20.0%)
and dementia (15.4%). Obesity, defined as body mass index
(BMI)>30, was present in 24.7% (243) of the cohort and is asso-
ciated with an unadjusted OR for death of 1.40 (95% CI 1.18
to 2.68, p<0.05).
Overall, 294 out of 983 (29.9%) patients died in hospital, the
vast majority (97.3%) aged 50 or older. The mortality rates of
different age brackets in the cohort (compared with the ISARIC
and Aintree validation cohorts) are shown in online supplemental
appendix 2. The univariate OR for death increased with rising
age, and was 14.86 (95% CI 6.89 to 32.04) for those aged 70–79
and 20.87 (95% CI 9.93 to 43.86) for those aged 80 or older
(table 1). When stratified by maximal levels of care, mortality
rate was lowest in the VH (1.8%) and highest in the ICU group
(62.1%) (p<0.001) (online supplemental appendix 3).
White Caucasian ethnicity constituted 77.3% (760/983) of
the whole cohort, while Asian, Black and other minor ethnici-
ties (BAME) represented 16.5%, 4.5% and 1.7%, respectively.
Overall, white ethnicity was associated with the highest propor-
tion of non- survivors (85.0%); in comparison, patients of Asian
(OR 0.57, 95% CI 0.38 to 0.85, p<0.01) or black (OR 0.39,
95% CI 0.17 to 0.88, p<0.05) background in this cohort had
lower ORs for death from COVID-19. The proportion of non-
survivors within each ethnic group was also highest in white
(32.9%), followed by Asian (21.6%), Black (15.9%) and other
minority groups (17.6%) (p<0.01). Of note, white patients were
significantly older by median age (74, IQR 58–85) compared
with Asian (57, IQR 46–71; p<0.0001) or black (58, IQR
50–72; p<0.001).
Care home residency (204/983; 20.8%) was more common
among non- survivors (p<0.001) and was associated with
an unadjusted OR for death of 3.14 (95% CI 2.28 to 4.32,
p<0.001). Based on data from 644 patients aged 65 or older,
the univariate OR of frailty for death was 2.52 (95% CI 1.73 to
3.69; p<0.001) in those with a group 1 frailty score and 2.56
(95% CI 1.62 to 4.06; p<0.001) in those with a group 2 frailty
score (table 1).
The median time from symptom onset to presentation for the
derivation cohort was 6 days (IQR 2.0–11.0), with no difference
between survivors and non- survivors. The four most common
reported symptoms were fever (61%), breathlessness (57.9%),
cough (52.8%) and myalgia (21.7%). Tachypnoea (respiratory
rate>24/minute) and hypoxia (SpO2≤92% on  ambient  air) 
were evident in 35.9% and 31.4% of patients, respectively, and
is associated with crude OR for death of 2.15 (95% CI 1.63
to 2.84, p<0.001) and 3.74 (95% CI 2.73 to 5.12, p<0.001),
respectively. C reactive protein>50 mg/L was more frequently
documented in non- survivors (76.7% vs 58.5%; p<0.001) and
associated with an unadjusted OR for mortality of 2.40 (95%
CI 1.73 to 3.32, p<0.001). Lymphopenia was similarly more
common in non- survivors (44.7% vs 29.1%; p<0.001), with
an unadjusted OR for death of 1.97 (95% CI 1.47 to 2.64;
p<0.001). A baseline chest radiograph (CXR) was available in
91% (895/983) patients; abnormalities in ≥4 radiographic zones 
were evident in 338 (37.8%) of cases and was associated with
increased mortality on univariate analysis (OR 1.89, 95% CI
1.42 to 2.52, p<0.001).
Multivariate regression for independent risk factors of
mortality
The bootstrapped multivariate regression analysis included
the whole derivation cohort of 983 patients comprising 689
(70.1%) survivors and 294 (29.9%) non- survivors with complete
or multiply imputed values for data. Older age, CKD stage 5,
baseline hypoxia, elevated BMI, tachypnoea, leucocytosis and a
history of stroke were identified as the strongest independent
predictors of mortality (table 1).  In  particular,  age  ≥70 had 
the highest OR for death over the other individual variables.
Following multivariate regression, care home residency no
longer independently predicted mortality.
Iterative modelling to construct an 11-predictor and
5-predictor risk prediction scores
ORs and their respective p values from the multivariate logistic
regression model were used to identify constituent variables for
developing risk prediction scores. We began by constructing an
initial 11- predictor score that ranged from 1 to 18 points, using
data from 770 patients in the derivation cohort with a complete
dataset (online supplemental appendix 4). The lowest score of
1 point reflected the KDIGO (kidney disease improving global
outcomes) categorisation of CKD.19 In this score, the correla-
tion between increasing COVID-19 severity and in- hospital
mortality followed a linear dose–response relationship, particu-
larly between a score of 3 (below which no deaths occurred) and
12 (above which no patient survived), and an accuracy (AUROC)
of 0.84 (figure 1). A shorter five- predictor score based solely
on clinical parameters and scaled from 0 to 8 points was also
developed (table 2; figure 1). This short score, abbreviated as
SOARS (SpO2, Obesity, Age, Respiratory rate, Stroke history),
demonstrated an AUROC of 0.82 and was retained for further
evaluation as a practical prehospital risk stratification tool.
External validation and performance of the SOARS score
The performance metrics of the long 11- predictor and 5- predictor
(SOARS) scores were assessed by their ability to discriminate
for in- hospital mortality against both the ISARIC and Aintree
validation cohorts (table 3). The longer score showed higher
discrimination in the Aintree (AUROC 0.87) than the ISARIC
cohort (0.77). The SOARS score had a slightly lower AUROC
against both cohorts, namely, 0.80 (Aintree) and 0.74 (ISARIC).
In comparison, the performance of other scores based solely on
different cut- offs of age were associated with inferior discrimina-
tory ability. Comparison of some of the main population param-
eters between the derivation and both external cohorts is shown
in online supplemental appendix 5.
The mortality rate at each level of the SOARS score in the
derivation and both validation cohorts are shown in table 4. For
increased applicability, the SOARS score results were further
categorised into three risk classes: low (SOARS 0–1), moderate
(SOARS 2)  and  high (SOARS≥3) (table 5). Between 2.3% and
3ChuaF, etal. Thorax 2021;0:1–8. doi:10.1136/thoraxjnl-2020-216425
on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from
Respiratory infection
Table 1 Risk factors of mortality in the derivation cohort (N=983)
Univariate OR
P value
Likelihood ratio
χ2 of five final
predictors
Multivariate OR
P value(95% CI) (95% CI)
Age (years) 2177.27
<50 1.00 1.00
50–59 1.30 (1.28 to 7.02) 0.01 2.39 (1.49 to 3.84) <0.001
60–69 6.10 (2.73 to 13.66) <0.001 2.15 (1.32 to 3.50) <0.01
70–79 14.86 (6.89 to 32.04) <0.001 7.40 (4.67 to 11.74) <0.001
≥80 20.87 (9.93 to 43.86) <0.001 10.73 (6.82 to 16.90) <0.001
Male sex (vs female) 1.23 (0.94 to 1.62) 0.137
Ever smoked (vs never smoked) 2.14 (1.52 to 3.01) <0.001 2.51 (1.91 to 3.29) <0.001
Ethnicity (vs White)
Asian 0.57 (0.38 to 0.85) 0.006 1.44 (0.71 to 2.94) 0.301
Black 0.39 (0.17 to 0.88) 0.024 0.51 (0.16 to 1.64) 0.257
Symptoms (vs none)
Breathlessness 0.80 (0.59 to 1.07) 0.135
Fever 0.84 (0.62 to 1.14) 0.26
Cough 0.61 (0.45 to 0.82) 0.001 0.67 (0.41 to 1.09) 0.108
Myalgia 0.41 (0.28 to 0.63) <0.001 0.79 (0.44 to 1.41) 0.423
Headache 0.51 (0.26 to 0.97) 0.039 1.70 (0.64 to 4.51) 0.286
Clinical parameters
SpO2 (≤92% on air) 3.74 (2.73 to 5.12) <0.001 31.87 2.69 (1.80 to 4.01) <0.001
Respiratory rate (>24/min) 2.15 (1.63 to 2.84) <0.001 158.21 2.12 (1.35 to 3.32) 0.001
Systolic BP (≤90 mm Hg) 2.19 (0.96 to 5.03) 0.064
BMI (>30) 1.40 (1.03 to 1.90) 0.033 11.13 2.18 (1.46 to 3.20) <0.001
Frailty (vs not frail, CFS 0–4)
1 (CFS 5–6) 2.53 (1.73 to 3.70) <0.001 1.26 (0.82 to 1.96) 0.294
2 (CFS 7–9) 2.56 (1.62 to 4.06) <0.001 1.28 (0.77 to 2.13) 0.341
Residency in care home (vs own home) 3.14 (2.28 to 4.32) <0.001 1.38 (0.90 to 2.11) 0.137
Peripheral blood markers
CRP (>50 mmol/L) 2.40 (1.73 to 3.32) <0.001 1.42 (0.87 to 2.31) 0.16
Total white cell count
≤4 × 109/L 0.93 (0.56 to 1.55) 0.79
>11 × 109/L 2.04 (2.09 to 4.15) <0.001 1.76 (1.18 to 2.61) <0.01
Lymphocytes (<0.7 × 109/L) 1.97 (1.47 to 2.64) <0.001 1.67 (1.17 to 2.37) <0.01
Chronic kidney disease stage (vs 1, eGFR≥90 mL/min/1.73 m2) 1.00
2 eGFR 60–89 2.35 (1.53 to 3.62) <0.001 0.91 (0.49 to 1.69) 0.769
3 eGFR 30–44 and 45–59 6.09 (3.90 to 9.50) <0.001 1.46 (0.78 to 2.71) 0.234
4 eGFR 15–29 8.88 (4.82 to 16.34) <0.001 1.78 (0.82 to 3.85) 0.145
5 eGFR<15 15.73 (6.85 to 36.14) <0.001 3.82 (1.43 to 10.26) <0.01
CXR (≥4 zones affected, vs no abnormal zones) 1.89 (1.42 to 2.52) <0.001 1.73 (1.22 to 2.46) <0.01
Medications (≥5 different) 2.59 (1.93 to 3.48) <0.001 0.90 (0.53 to 1.52) 0.695
Co- morbidities (vs none)
Dementia 3.53 (2.46 to 5.08) <0.001 1.54 (0.97 to 2.44) 0.066
CVA/stroke 3.50 (2.32 to 5.28) <0.001 44.98 1.92 (1.18 to 3.11) <0.01
Cardiac disease 2.84 (2.13 to 3.80) <0.001 1.15 (0.70 to 1.91) 0.579
Cancer 2.60 (1.77 to 3.83) <0.001 1.85 (0.91 to 3.75) 0.091
Hypertension 2.46 (1.85 to 3.26) <0.001 1.10 (0.65 to 1.88) 0.717
Diabetes mellitus 1.40 (1.02 to 1.91) 0.036 1.00 (0.58 to 1.75) 0.981
Anxiety/psychosis 1.20 (0.84 to 1.71) 0.318
Lung disease 1.15 (0.86 to 1.55) 0.35
Out of the starting 75 variables, those with numerically small ORs or a p value of >0.05 following univariate regression were not included in table1.
BMI, body mass index; BP, blood pressure; CFS, Clinical Frailty Scale; CRP, c- reactive protein; CVA, cerebrovascular accident; CXR, chest radiograph; eGFR, estimated glomerular filtration rate.
4ChuaF, etal. Thorax 2021;0:1–8. doi:10.1136/thoraxjnl-2020-216425
on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from
Respiratory infection
3.2% of patients scoring 0 or 1 (low risk) in each of the three
cohorts died due to COVID-19, whereas 2.3%–6.3% of those
scoring 2 (moderate risk) failed to survive to discharge. Overall,
9 out of every 10 deaths in each of the derivation and validation
cohorts had a SOARS score of 3 or greater. Within this broad
range of increasing scores, the highest proportion of deaths was
encountered at SOARS score 4 in both the derivation cohort and
the ISARIC validation cohort (28.3% and 34.5%, respectively)
and at SOARS score 5 in the Aintree validation cohort (30.9%).
Sensitivity thresholds calculated for the larger validation
(ISARIC) cohort showed that the low risk class (SOARS 0–1)
comprised 16.6% of patients with an in- hospital mortality of
5.4%, sensitivity of 97% and negative predictive value (NPV)
of 94.6% (table 6). By comparison, 13.2% of patients were clas-
sified as moderate risk; their mortality rate was 14.5%. When
combining the low and moderate risk groups (SOARS 0–2), the
sensitivity reduces to 90.7% and the NPV to 90.5%. The high-
risk group comprised 70.2% of the validation cohort who scored
across a wide range of SOARS (scores 3–8). The specificity for
a prediction of death was thus more variable, from 58.2% for a
SOARS score of 3 to 99.8% at the other end of the scale when
the SOARS score was 7. Only one patient scored 8; they survived
to discharge.
Reliability of the risk estimates in the ISARIC cohort,
modelled as calibration or goodness- of- fit between expected
(predicted) and observed outcomes using SOARS, showed a cali-
bration slope of 0.70, calibration- in- the- large (CiTL) 0.02 and
an expected- to- observed (E:O) ratio of 0.990 (figure 2). Cali-
bration was slightly improved in the Aintree cohort with a slope
of 0.80, CiTL −0.16 and E:O ratio of 1.06. A LOWESS (locally 
weighted scatterplot smoothing algorithm) curve was generated
to show differences between these outcomes in both cohorts.
The plot characteristics suggested that the model, while demon-
strating good concordance, had greater predictive accuracy in
low- risk to moderate- risk patients whose predicted probability
of mortality was under 40%. Conversely, overestimation of
mortality risk was evident in patients in the high- risk group.
DISCUSSION
We show that prognostic evaluation of a small panel of base-
line clinical and demographic characteristics of patients with
COVID-19 enables their subsequent risk of in- hospital death to
be quantified across three strata of risk. Findings were obtained
by applying the five- predictor SOARS (SpO2, Obesity, Age,
Respiratory rate, Stroke history) score to a large random sample
of the ISARIC cohort and a smaller single- hospital cohort
from Aintree, both with individual- level data. Our objective
was to enable risk stratification to be undertaken early, ideally
prehospitalisation (eg, in the community), during the encounter
Figure 1 (A) In- patient mortality stratified according to the
11- predictor (SOARS) scores; (B) In- patient mortality stratified according
to the 5- predictor (SOARS) scores.
Table 2 SOARS score (five predictors; range: 0–8 points) for
predicting in- hospital COVID-19 death
Predictor Points
SpO2
>92% on air 0
≤92% on air 1
Obesity (BMI>30)
Absent 0
Present 1
Age (years)
<50 0
50–59 1
60–69 2
70–79 3
≥80 4
Respiratory rate
≤24/min 0
>24/min 1
Stroke/CVA
Absent 0
Present 1
CVA, cerebrovascular accident; SOARS, SpO2, Obesity, Age, Respiratory rate, Stroke
history.
Table 3 Discriminatory performance (area under the receiver
operating characteristic; AUROC) of different risk stratification models
for predicting COVID-19 in- hospital mortality
Cohort 11- predictor
5- predictor
(SOARS)
Age
≥80
Age
70–79 Age ≥50
Derivation 0.84 0.82 0.73 0.76 0.73
Aintree
(validation)
0.87 0.80 0.78 0.69 0.57
ISARIC
(validation)
0.77 0.74 0.68 0.70 0.63
SOARS, SpO2, Obesity, Age, Respiratory rate, Stroke history.
5ChuaF, etal. Thorax 2021;0:1–8. doi:10.1136/thoraxjnl-2020-216425
on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from
Respiratory infection
with COVID-19- suspected individuals. This role is not met
by currently available prediction tools that rely on laboratory
measurements.
The SOARS score discriminated well for COVID-19 mortality
and its simplicity obviated the need for complex calculations. It
also retained good predictive accuracy in two external validation
cohorts, with performance metrics that were primarily reflected
in its high negative predictive values for mortality among patients
with the lowest risk scores (0 or 1). This characteristic is consis-
tent with a high accuracy for predicting a non- fatal outcome in
its key target group, namely, individuals with milder COVID-19.
Patients who score SOARS 0 or 1 could be discharged home
with advice to re- establish urgent contact if their symptoms
worsened. Patients stratified as moderate risk (SOARS 2) could
be virtually monitored with a predefined plan for care escala-
tion if specific thresholds relating to deteriorating symptoms or
self- recorded SpO2 were triggered. Patients in the high- risk class
(score  ≥3)  are  highly  likely  to  be  symptomatic  and  would,  in 
all probability, be referred directly to the ED for hospital- based
management. Thus, the target individuals for the SOARS score
are those with a low or moderate risk of COVID-19 mortality.
Our data concur with other reports that advanced age is the
strongest predictor of death from COVID-19.20–23 The increase
in mortality in patients within the derivation cohort who were
in or beyond their seventh decade of life was reflected in the
magnitude of their respective adjusted ORs for in- hospital death,
namely, 7.4 (aged 70–79) and 10.7 (aged ≥80). Even so, predic-
tive models generated with age as the lone variable showed
poorer discriminatory ability than the SOARS score.
The early development of physiological abnormalities in
COVID-19 does not always result in timely clinical presenta-
tion. In our study, two measures of acute physiological perturba-
tion proved to be important predictors of COVID-19 mortality:
hypoxia and tachypnoea. Although persistent hypoxia is more
common in non- survivors of COVID-19, its relationship with
tachypnoea remains incompletely understood.24 25 Fewer than
half of patients with COVID-19 who present to hospital with
decreased oxygen saturation report experiencing subjective
breathlessness.26–29 One reason for this observation might be the
so- called ‘silent hypoxia’ where a blunted symptomatic percep-
tion of the effects of hypoxaemia is apparent even when low
arterial oxygen tension is evident.30 This phenomenon may be
responsible for delays in seeking clinical attention. Such danger
could be mitigated by accurate risk assessment including the
measurement and tracking of SpO2 in patients who are deemed
to not require immediate hospitalisation. The absence of oxygen
determination in the CURB-65 score has been cited as limiting
its utility in stratifying patients with COVID-19 for outpatient
management.31
The SOARS score was constructed with data from hospitalised
patients as the very low adverse event rate among non- admitted
cases (eg, in the VH pathway) curtailed the development of a
prognostic tool. This issue has previously been highlighted in the
context of CRB-65 where low event rates in community studies
of pneumonia made predictive inferences difficult to conclude.9
Other scores that have been used in COVID-19 studies have
either not been designed for this disease or have relied heavily
on laboratory- measured data.32–35
Table 4 Mortality in the derivation and validation cohorts at different levels of SOARS
Score
Derivation cohort
(N=821; deaths=258)
Aintree validation cohort
(N=290; deaths=94)
ISARIC validation cohort
(N=14 231; deaths=4319)
No of deaths
(n/N)
Mortality rate
(%)
Proportion of
deaths (%)
No of deaths
(n/N)
Mortality rate
(%)
Proportion of
deaths (%)
No of deaths
(n/N)
Mortality rate
(%)
Proportion
of deaths
(%)
01/69 1.4% 0.4% 1/12 8.3% 1.1% 34/833 4.1% 0.8%
15/94 5.3% 1.9% 2/23 8.7% 2.1% 94/1529 6.1% 2.2%
26/102 5.9% 2.3% 3/37 8.1% 3.2% 273/1879 14.5% 6.3%
329/124 23.4% 11.2% 11/60 18.3% 11.7% 650/2577 25.2% 15.1%
473/206 35.4% 28.3% 24/72 33.3% 25.5% 1490/4013 37.1% 34.5%
563/117 53.9% 24.4% 29/48 60.4% 30.9% 1170/2402 48.7% 27.1%
658/80 72.5% 22.6% 16/30 53.3% 17.0% 571/939 60.8% 13.2%
722/28 78.6% 8.5% 6/6 100.0% 6.4% 36/58 62.1% 0.8%
81/1 100.0% 0.4% 2/2 100.0% 2.1% 0/1 0.0% 0.0%
The proportion of deaths at each score is defined as the number of deaths at that score divided by the total number of deaths in that particular cohort.
SOARS, SpO2, Obesity, Age, Respiratory rate, Stroke history.
Table 5 COVID-19 mortality risk stratification based on SOARS score
Risk class
(score level)
Derivation cohort
(N=821 with full data; deaths=258)
Aintree validation cohort
(N=290 with full data; deaths=94)
ISARIC validation cohort
(N=14 231; deaths=4319)
Mortality by risk
class (%)
Proportion of
deaths by risk
class (%)
Mortality by risk
class (%)
Proportion of
deaths by risk
class (%)
Mortality by risk
class (%)
Proportion of deaths
by risk class (%)
Low (0–1) 6/163 (3.7%) 6/258 (2.3%) 3/35 (8.6%) 3/94 (3.2%) 128/2362 (5.4%) 128/4319 (3.0%)
Moderate (2) 6/102 (5.9%) 6/258 (2.3%) 3/37 (8.1%) 3/94 (3.2%) 273/1879 (14.5%) 273/4319 (6.3%)
High (≥3) 246/556 (44.2%) 246/258 (95.3%) 88/218 (40.4%) 88/94 (93.6%) 3917/9990 (39.2%) 3917/4319 (90.7%)
SOARS, SpO2, Obesity, Age, Respiratory rate, Stroke history.
6ChuaF, etal. Thorax 2021;0:1–8. doi:10.1136/thoraxjnl-2020-216425
on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from
Respiratory infection
Our multivariate regression model was bootstrapped to
reduce overfitting but not penalised prior to external valida-
tion. In common with other severity scores for COVID-19, we
dichotomised several continuous data parameters which may
have potentially obscured non- linear effects between predictors
and outcome, contributing to the difference in AUROC values
between our derivation and validation cohorts and between
both validation cohorts.11 32 34 Other prediction systems, notably
CURB-65 and the Pneumonia Severity Index for pneumonia
similarly categorise some of their score parameters.10 36 We also
used in- hospital mortality as an unambiguous disease- related
primary outcome rather than 30- day or 60- day mortality. The
better performance of the SOARS score in the smaller Aintree
validation cohort compared with the much larger ISARIC
cohort may have been due to its more homogeneous case- mix.
This comparison suggested that, on balance, the simplicity of a
prehospital risk prediction tool, provided it retained acceptable
accuracy, may outweigh any minor diminution of its perfor-
mance arising from improved practicality.
Other limitations in the study include the occurrence of
missing information despite prospective data collection. The use
of multiple imputation to estimate missing values for multivariate
regression and the availability of nearly 85% of observations for
constructing the risk stratification rule helped to mitigate against
underestimating their role. The modest sample size of our deri-
vation cohort was dictated by the incident caseload during the
pandemic. However, selective sampling of the pandemic timeline
was avoided by including all COVID-19 cases from the initial
rise to the subsequent decline in new case numbers over the
11- week study period. Finally, reduced score calibration at the
high- risk end suggests that SOARS may overestimate the prob-
ability of death in the highest risk cases. However, the principal
objective of this score was to enhance frontline decision- making
in patients with a low predicted risk of mortality at a time when
demand for in- patient resources is likely to be high.
In summary, prognostication using the SOARS score can be
undertaken concomitantly with SARS- CoV-2 diagnostic testing
to inform clinical triaging, including decisions about the place-
ment of the patient for ongoing care. Analysis of the ISARIC
validation cohort in this study showed that between 16.6% and
29.8% (those scoring up to SOARS 1 or 2, respectively) could
potentially have avoided admission provided a safe alternative
to hospitalisation was in place. Prospective studies of SOARS
implementation will enable the score to be calibrated against
other independent cohorts of patients with COVID-19 to
examine its performance under conditions that may be unique
to different localities. Such an opportunity may soon present
itself if SARS- CoV-2 transmission continues to increase in the
UK and beyond.
Author affiliations
1Interstitial Lung Disease Unit, Department of Respiratory Medicine, Royal Brompton
and Harefield NHS Foundation Trust, London, UK
2National Heart and Lung Institute, Imperial College London, London, UK
3Respiratory Medicine, West Hertfordshire Hospitals NHS Trust, Watford, UK
4Respiratory Medicine, St. George’s Hospital, London, UK
5Information Governance, West Hertfordshire Hospitals NHS Trust, Watford, UK
6Radiology, West Hertfordshire Hospitals NHS Trust, Watford, UK
7Respiratory Medicine, Aintree site, Liverpool Hospitals NHS Foundation Trust,
Liverpool, UK
8Radiology, Aintree site, Liverpool Hospitals NHS Foundation Trust, UK, Liverpool, UK
Twitter Matthew Knight @mjknight0380 and Andrew Barlow @Andyatfrogmore
Acknowledgements We are grateful to the healthcare teams whose efforts in the
clinical field were fundamental to this work. We would like to thank all the patients
involved for their vital contributions. We are very grateful to the ISARIC Coronavirus
Clinical Characterisation Consortium (ISARIC- 4C) Investigators, in particular, J
Table 6 Sensitivity analysis of the SOARS score for predicting mortality in the ISARIC validation cohort
Score cut- off
Patients
N (% of total)
True
positive
True
negative
False
positive
False
negative Sensitivity Specificity PPV NPV Cumulative mortality (%)
>0 833 (5.9%) 4284 799 9114 34 99.2% 8.1% 32.0% 95.9% 34/833 (4.1%)
>1 2362 (16.6%) 4190 2234 7679 128 97.0% 22.5% 35.3% 94.6% 128/2362 (5.4%)
>2 4241 (29.8%) 3917 3840 6073 401 90.7% 38.7% 39.2% 90.5% 401/4241 (9.5%)
>3 6818 (47.9%) 3267 5767 4146 1051 75.7% 58.2% 44.1% 84.6% 1051/6818 (15.4%)
>4 10 831 (76.1%) 1777 8290 1623 2541 41.2% 83.6% 52.3% 76.5% 2541/10 831 (23.5%)
>5 13 233 (93.0%) 607 9522 391 3711 14.1% 96.1% 60.8% 72.0% 3711/13 233 (28.0%)
>6 14 172 (99.6%) 36 9890 23 4282 0.8% 99.8% 61.0% 69.8% 4282/14 172 (30.2%)
>7 14 231 (100.0%) 0 9912 1 4318 0.0% 100.0% 0.0% 69.7% 4318/14 231 (30.3%)
SOARS, SpO2, Obesity, Age, Respiratory rate, Stroke history.
Figure 2 Calibration accuracy of the SOARS (SpO2, Obesity, Age,
Respiratory rate, Stroke history) score on external validation cohorts.
7ChuaF, etal. Thorax 2021;0:1–8. doi:10.1136/thoraxjnl-2020-216425
on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from
Respiratory infection
Kenneth Baillie (lead investigator) and Malcolm G Semple (chief investigator) for
providing access to data for the external validation of our model.
Contributors RV, AB, MK and RM developed the clinical algorithm and supervised
patient management. FC, RV and AD conceived and designed the investigational
plan. FC drafted the manuscript with contributions of intellectual content from RV,
AD, LGS, PLM and AB. RV, TV, MK, RM, JS, LS, ET, HM, SC, NM, SA, ML, AO, CP, RK,
TH, RT, SR, MS and JL collected the data at respective sites. AD, FC and RV examined
the data and undertook statistical analyses. All authors approved the final version of
the manuscript for submission. RV is guarantor and attests that all named authors
and contributors meet authorship criteria and that no others meeting such criteria
have been omitted.
Funding The authors have not declared a specific grant for this research from any
funding agency in the public, commercial or not- for- profit sectors.
Competing interests None declared.
Patient consent for publication Not required.
Ethics approval Ethical approval was provided by Stanmore Research Ethics
Committee, London, England (IRAS ID: 283888). The study is registered by the
National Health Service Health Research Authority under the reference 20/
HRA/2344.
Provenance and peer review Not commissioned, externally peer reviewed.
No part of this work has been written by a medical writer or published in printed
or electronic form. Some of the findings of this study have been accepted for
presentation at the British Thoracic Society Winter Meeting 2020 to be held in
February 2021, at which point the abstract will be published in printed format in a
supplement of Thorax. A copy of the originally submitted manuscript was uploaded
to the medRixv preprint website; https:// doi. org/ 10. 1101/ 2020. 10. 19. 20215426.
Data availability statement Data are available upon reasonable request.
Deidentified participant data may be requested from the corresponding author
following publication of the study (ORCID identifier: 0000-0001-7845-0173)
This article is made freely available for use in accordance with BMJ’s website
terms and conditions for the duration of the covid-19 pandemic or until otherwise
determined by BMJ. You may use, download and print the article for any lawful,
non- commercial purpose (including text and data mining) provided that all copyright
notices and trade marks are retained.
ORCID iDs
FelixChua http:// orcid. org/ 0000- 0001- 7845- 0173
Lisa GSpencer http:// orcid. org/ 0000- 0003- 3558- 992X
Philip LMolyneaux http:// orcid. org/ 0000- 0003- 1301- 8800
REFERENCES
1 Challen K, Goodacre SW, Wilson R, etal. Evaluation of triage methods used to select
patients with suspected pandemic influenza for hospital admission. Emerg Med J
2012;29:383–8.
2 PL H, Chau PH, PSF Y. A clinical prediction rule for clinical diagnosis of severe acute
respiratory syndrome. Eur Resp J;200:474–9.
3 Wynants L, Van Calster B, Collins GS, etal. Prediction models for diagnosis and
prognosis of covid-19 infection: systematic review and critical appraisal. BMJ
2020;369:m1328.
4 Intensive Care National Audit and Research Centre. ICNARC reports on COVID-19 in
critical care: England, Wales and Northern Ireland. Available: https://www. icnarc. org
[Accessed 4 Dec 2020].
5 Dighe A, Cattarino L, Cuomo- Dannenburg G, etal. Response to COVID-19 in South
Korea and implications for lifting stringent interventions. BMC Med 2020;18:321.
6 Chen B, Zhong H, Ni Y. Epidemiological trends of coronavirus disease 2019 in China.
Front Med 2020;7:250.
7 Center for Strategic & International Studies Southeast Asia national response to
Covid-19 tracker. Available: https://www. csis. org/ programs/ southeast- asia- program/
southeast- asia- covid- 19- tracker-0 [Accessed 15 Nov 2020].
8 Cacciapaglia G, Cot C, Sannino F. Second wave COVID-19 pandemics in Europe: a
temporal playbook. Sci Rep 2020;10:15514.
9 McNally M, Curtain J, O’Brien KK, etal. Validity of British thoracic Society guidance
(the CRB-65 rule) for predicting the severity of pneumonia in general practice:
systematic review and meta- analysis. Br J Gen Pract 2010;60:e423–33.
10 Lim WS, van der Eerden MM, Laing R, etal. Defining community acquired pneumonia
severity on presentation to hospital: an international derivation and validation study.
Thorax 2003;58:377–82.
11 Knight SR, Ho A, Pius R, etal. Risk stratification of patients admitted to hospital with
covid-19 using the ISARIC who clinical characterisation protocol: development and
validation of the 4C mortality score. BMJ 2020;370:m3339.
12 National Early Warning Score (NEWS)2. Royal College of Physicians, London.
Available: https://www. rcplondon. ac. uk/ projects/ outputs/ national- early- warning-
score- news-2 [Accessed 30 May 2020].
13 Collins GS, Reitsma JB, Altman DG, etal. Transparent reporting of a multivariable
prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD
statement. Ann Intern Med 2015;162:55–63.
14 Shipe ME, Deppen SA, Farjah F, etal. Developing prediction models for clinical use
using logistic regression: an overview. J Thorac Dis 2019;11:S574–84.
15 Leisman DE, Harhay MO, Lederer DJ, etal. Development and reporting of prediction
models: guidance for authors from editors of respiratory, sleep, and critical care
journals. Crit Care Med 2020;48:623-633.
16 Resche- Rigon M, White IR. Multiple imputation by chained equations for
systematically and sporadically missing multilevel data. Stat Methods Med Res
2018;27:1634–49.
17 Hosmer DW, Lemesbow S. Goodness of fit tests for the multiple logistic regression
model. Commun Stat Theory Methods 1980;9:1043–69.
18 Hu B, Shao J, Palta M. Pseudo- R2 in logistic regression model. Statistica Sinica
2006;16:847–60.
19 Kidney Disease, Improving Clinical Outcomes. KDIGO 2012 clinical practice guideline
for the evaluation and management of chronic kidney disease. Available: https://
kdigo. org/ wp- content/ uploads/ 2017/ 02/ KDIGO_ 2012_ CKD_ GL. pdf [Accessed 1 Jun
2020].
20 Williamson EJ, Walker AJ, Bhaskaran K. OpenSAFELY: factors associated with
COVID-19 death in 17 million patients. Nature 2020;584:430–4.
21 Mikami T, Miyashita H, Yamada T, etal. Risk factors for mortality in patients with
COVID-19 in New York City. J Gen Intern Med 2020. doi:10.1007/s11606-020-
05983-z. [Epub ahead of print: 30 Jun 2020].
22 Feng Y, Ling Y, Bai T, etal. COVID-19 with different severities: a multicenter study of
clinical features. Am J Respir Crit Care Med 2020;201:1380–8.
23 Onder G, Rezza G, Brusaferro S. Case- Fatality rate and characteristics of patients dying
in relation to COVID-19 in Italy. JAMA 2020;323:1775–6.
24 Xie J, Covassin N, Fan Z, etal. Association between hypoxemia and mortality in
patients with COVID-19. Mayo Clin Proc 2020;95:1138–47.
25 Wu C, Chen X, Cai Y, etal. Risk factors associated with acute respiratory distress
syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan,
China. JAMA Intern Med 2020;180:934–43.
26 Richardson S, Hirsch JS, Narasimhan M, etal. Presenting characteristics, comorbidities,
and outcomes among 5700 patients hospitalized with COVID-19 in the new York City
area. JAMA 2020;323:2052.
27 Guan W, Ni Z, Hu Y. For the China medical treatment expert group for
Covid-19. clinical characteristics of coronavirus disease in China. N Engl J Med
2020;382:1708–20.
28 Zhou F, Yu T, Du R, etal. Clinical course and risk factors for mortality of adult
inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet
2020;395:1054–62.
29 Huang C, Wang Y, Li X. Clinical features of patients infected with 2019 novel
coronavirus in Wuhan, China. Lancet 2020.
30 Tobin MJ, Laghi F, Jubran A. Why COVID-19 silent hypoxemia is Baffling to physicians.
Am J Respir Crit Care Med 2020;202:356–60.
31 Nguyen Y, Corre F, Honsel V, etal. Applicability of the CURB-65 pneumonia severity
score for outpatient treatment of COVID-19. J Infect 2020;81:e96–8.
32 Fan G, Tu C, Zhou F, etal. Comparison of severity scores for COVID-19 patients with
pneumonia: a retrospective study. Eur Respir J 2020;56:2002113.
33 Liang W, Liang H, Ou L. Development and validation of a clinical risk score to predict
the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern
Med.
34 Satici C, Demirkol MA, Sargin Altunok E, etal. Performance of pneumonia severity
index and CURB-65 in predicting 30- day mortality in patients with COVID-19. Int J
Infect Dis 2020;98:84–9.
35 Liu S, Yao N, Qiu Y, etal. Predictive performance of SOFA and qSOFA for in- hospital
mortality in severe novel coronavirus disease. Am J Emerg Med 2020;38:2074–80.
36 Fine MJ, Auble TE, Yealy DM, etal. A prediction rule to identify low- risk patients with
community- acquired pneumonia. N Engl J Med 1997;336:243–50.
8ChuaF, etal. Thorax 2021;0:1–8. doi:10.1136/thoraxjnl-2020-216425
on March 15, 2021 by guest. Protected by copyright.http://thorax.bmj.com/Thorax: first published as 10.1136/thoraxjnl-2020-216425 on 10 March 2021. Downloaded from
SUPPLEMENTARY MATERIAL
Appendix 1. Baseline clinical characteristics of the derivation cohort
All patients (N = 983)
Cohort
Survivors
(N=689)
P value
Age, years median (interquartile range)
70 (53 83)
61 (50 78)
<0.0001
Age range no. (%)
18 49 years
176 (17.9)
168 (24.4)
<0.001*
50 59 years
160 (16.3)
140 (20.3)
--
60 69 years
151 (15.4)
117 (17.0)
--
70 - 79 years
181 (18.4)
106 (15.4)
--
≥ 80 years
315 (32.0)
158 (22.9)
--
Age median (IQR) by level of care
Virtual hospital
53 (43 67)
53 (42 64)
0.007
Ward
77 (61 86)
70 (55 82)
<0.0001
Ward + received CPAP
71 (61 75)
61 (58 68)
<0.0001
Intensive Care Unit (ICU)
60 (52 67)
59 (50 62)
0.0349
Male sex no. (%)
516 (52.5)
351 (50.9)
0.137
Ethnic background
White
760 (77.3)
511 (74.1)
0.003*
Asian
162 (16.5)
127(18.4)
--
Black
44 (4.5)
37 (5.4)
--
Other
17 (1.7)
14 (2.0)
--
Smoking history no. (%)
Former or current smoker
168 (17.1)
94 (13.7)
<0.001
BMI > 30 no. (%)
243 (24.7)
156 (23.3)
0.033
Care Home residency no. (%)
204 (20.8)
101 (14.7)
<0.001
Clinical frailty score no./total scored (%)
1 4
250/644 (38.8)
192/415 (46.3)
<0.001*
5 6
268/644 (41.6)
152/415 (36.6)
--
7 9
126/644 (19.6)
71/415 (17.1)
--
Symptoms at presentation no. (%)
Fever (temperature >37.3°C)
508 (61.0)
357(62.2)
0.259
Breathlessness
482 (57.9)
342 (59.6)
0.135
Cough
440 (52.9)
325 (56.7)
0.001
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
Myalgia
181 (21.7)
148 (25.9)
<0.001
Headache
62 (7.4)
50 (8.8)
0.036
Symptom duration, days median (IQR)
6 (2 11)
7 (3 11)
0.454
Vital baseline observations no./total (%)
Respiratory rate >24/min
294/819 (35.9)
155/562 (27.6)
<0.001
SpO2 ≤92% (on ambient air)
258/822 (31.4)
125/564 (22.2)
<0.001
Systolic blood pressure <90 mm Hg
23/811 (2.8)
12/556 (2.2)
0.086
Pulse rate >120/min
81/818 (9.9)
44/560 (7.9)
0.004
Laboratory findings no./total (%)
C-reactive protein >50 mg/L
524/812 (64.5)
306/528 (58.0)
<0.001
Total white cell count >11 x 109/L
175/891 (19.6)
82/600 (13.7)
<0.001
Lymphocyte count ≤0.7 x 109/L
304/890 (34.2)
174/599 (29.1)
<0.001
Chronic kidney disease no./total (%)
Stage 1 (eGFR ≥ 90 ml/min/1.73 m2)
118/832 (14.2)
94/547 (17.2)
<0.001*
Stage 2 (eGFR 60-89 ml/min/1.73 m2)
380/832 (45.7)
286/547 (52.3)
--
Stage 3 (eGFR 30-59 ml/min/1.73m2)
237/832 (28.5)
128/547 (23.4)
--
Stage 4 (eGFR 15-29 ml/min/1.73m2)
65/832 (7.8)
29/547 (5.3)
--
Stage 5 (eGFR <15 ml/min/1.73m2)
32/832 (3.9)
10/547 (1.8)
--
≥4 abnormal CXR zones – no./total (%)
338/895 (37.8)
203/615 (33.0)
<0.001
Co-morbid conditions no. (%)
Hypertension
475 (48.4)
288 (41.8)
<0.001
Ischaemic heart disease
194 (19.7)
103 (15.0)
<0.001
Cardiac failure
33 (3.4)
21 (3.1)
0.41
Cardiac arrhythmias
34 (3.5)
20 (2.9)
0.144
Diabetes mellitus
232 (23.6)
150(21.8)
0.036
Respiratory disease
293 (30.0)
199 (29.1)
0.35
Chronic kidney disease
196 (20.0)
102 (14.8)
<0.001
Cerebrovascular disease
107 (10.9)
47 (6.8)
<0.001
Mental health/behavioural disorders
Dementia
157 (16.0)
73 (10.6)
<0.001
Anxiety, depression or both
156 (15.9)
113 (16.4)
0.486
Prescribed medications ≥5 – no. (%)
550 (56.1)
340 (49.4)
<0.001
Median length of stay days (IQR)
7 (3.0 13.5)
7 (3.0 15)
0.564
Abbreviations: BMI body mass index; CXR chest radiograph; ED Emergency Department; GFR glomerular filtration
rate; IQR inter-quartile range. P values denote comparisons between survivors and non-survivors. *χ2 test comparing
all subcategories.
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
Appendix 2. In-patient mortality rate by age bracket in the derivation and validation cohorts
Age group
Derivation cohort
Validation cohorts
(N=983)
ISARIC (N=14,231)
Aintree (N=303)
< 50
4.6%
5.6%
9.7%
50 - 59
12.5%
13.8%
7.9%
60 69
22.5%
25.4%
27.0%
70 79
41.4%
37.5%
44.1%
80
49.8%
46.2%
64.9%
Overall mortality
29.9%
30.9%
31.7%
Appendix 3. In-patient mortality rate by level of care in the derivation cohort (N=983)
LEVEL OF MAXIMAL CARE
Whole cohort
VH
Ward
Ward CPAP
ICU
N
983
228
627
41
87
Median age (IQR)
70 (53 83)
53 (43 67)
77 (61 86)
71 (61 75)
60 (52 67)
Deaths
294/983
(29.9%)
4/228
(1.8%)
216/627
(34.4%)
20/41
(48.8%)
54/87
(62.1%)
% of non-
survivors by
ethnicity at each
level of care
W 3 (75.0%)
A 1 (25.0%)
B 0
OTH 0
W 194 (90.0%)
A 17 (7.9%)
B 4
OTH 1
W 16 (80.0%)
A 3 (15.0%)
B 0
OTH 1
W 37 (68.5%)
A 13 (24.1%)
B 3
OTH 1
The difference between the median age of patients by level of care, expressed as the Chi-square
value with ties, is 150.542 (with 80 degrees of freedom) (P <0.001).
Abbreviations: A Asian; B Black; CPAP continuous positive airway pressure; ICU Intensive
Care Unit; IQR interquartile range; OTH other ethnicity; VH virtual hospital; W White.
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
Appendix 4. 11-predictor score (1 18 points) for predicting in-hospital COVID-19 death
PREDICTOR
POINTS
SpO2
> 92% on air
92% on air
0
1
Obesity (BMI >30)
absent
present
0
1
Age
< 50
50 59
60 69
70 79
80
0
1
2
3
4
Respiratory rate
24/min
> 24/min
0
1
Stroke/CVA
absent
present
0
1
Ever smoked
no
yes
0
1
Dementia
no
yes
0
1
CKD stage
1
2
3
4
5
1
2
3
4
5
White cell count >11 x109
no
yes
0
1
Lymphocytes 0.7 x109
no
yes
0
1
CXR ( 4 zones affected)
no
yes
0
1
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
Appendix 5. Comparison of key parameters between the derivation and validation cohorts
Derivation cohort
N = 983
Validation (ISARIC)
N = 14,231
P value
Median age (IQR)
Age range (years)
70 79
≥ 80
70 (53 83)
18.4%
32.5%
73 (59 83)
21.9%
34.8%
NS
<0.05
NS
Mortality
29.9%
30.9%
NS
Ethnicity (% of cohort)
White
BAME
77.3%
22.7%
69.0%
31.0%
<0.01
Male sex (% of cohort)
52.5%
55.7%
<0.05
Derivation cohort
N = 983
Validation (Aintree)
N = 303
P value
Median age (IQR)
Age range (years)
70 79
≥ 80
70 (53 83)
18.4%
32.5%
67 (57 77)
22.4%
18.8%
NS
NS
<0.001
Mortality
29.9%
31.7%
NS
Ethnicity (% of cohort)
White
BAME
77.3%
22.7%
95.7%
4.3%
<0.001
Male sex (% of cohort)
52.5%
61.1%
<0.01
Abbreviations: BAME Black Asian or other Minor Ethnicity Black; IQR interquartile range
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
Appendix 6. Receiver operating curves (ROCs) of the 11-predictor and 5-predictor (SOARS)
scores on the derivation and validation cohorts
Area under the ROC (AUROC) for the 11-predictor score on the derivation cohort (0.84)
Area under the ROC (AUROC) for the 11-predictor score on the ISARIC validation cohort (0.77)
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
Area under the ROC (AUROC) for the 11-predictor score on the Aintree validation cohort (0.87)
Area under the ROC (AUROC) for the 5-predictor score on derivation cohort (0.82)
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
Area under the ROC (AUROC) for the 5-predictor score on ISARIC validation cohort (0.74)
Area under the ROC (AUROC) for the 5-predictor score on Aintree validation cohort (0.80)
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
Appendix. Pre-specified study protocol for clinical data collection
PREDICT COVID Clinical Data Collection protocol
Date: 1st March 2020
Aims
1. To prospectively collect data on all adults (> 18 years) with laboratory-confirmed SARS-CoV-2 infection (COVID-19)
presenting to Watford Hospital, West Hertfordshire NHS Trust, during the first wave of the SARS-CoV-2 pandemic,
including an outcome of in-hospital death or hospital discharge,
2. To develop a prognostic (risk prediction) score using the above derivation data,
3. To construct a practical clinical scoring system for predicting mortality (and risk of morbidity) after validation of
score against external, i.e. independent cohorts of COVID-19 patients from other UK sites,
4. To include and assess outcomes of patients referred to the COVID-19 Virtual Hospital (out-of-hospital monitoring)
in the same NHS Trust,
5. To monitor and characterise surviving patients for up to 12 months from the time of confirmation of SARS-CoV-2
infection, including the domains of psychological, physiological and radiological impairment and recovery.
Primary outcome
In-hospital mortality with minimum 30-day follow-up data.
Secondary outcomes
Longer term mortality and morbidity: radiological, psychological and cardiorespiratory. This will also be assessed
against ongoing health care needs.
Patient inclusion
All adult patients (aged >18 years) with SARS-CoV-2 real-time reverse transcriptase polymerase chain reaction (rRT-PCR)
confirmation.
Completed admission and outcomes at 3 months and 12 months.
Inclusion criteria:
Readily available patient or clinical characteristic to attending clinicians upon presentation to hospital
(Accident & Emergency department, Acute Medical Receiving Unit)
Blood markers should be commonly measured and results available for review within the first 24 hours of
admission
All parameters relating to oxygen supplementation and advanced respiratory support including one or more
of: continuous positive airway pressure (CPAP), bilevel non-invasive ventilation (NIV), high-flow nasal cannula
(HFNC) oxygenation and invasive mechanical ventilation (IMV) needs
Exclusion criteria
All SARS-CoV-2 rRT-PCR negative patients irrespective of clinical suspicion of COVID-19.
All individuals aged <18y.
Selection of candidate variables for initial data collection
Candidate variables were chosen based on knowledge of their potential association/s with SARS-CoV-2 infection and
clinical disease (COVID-19). A systematic literature search was undertaken to identify these variables with respect to
their predictive association for mortality and other adverse outcomes including COVID-19 severity and disease-related
complications such as requirement for critical care and the development of COVID-19-associated acute respiratory
distress syndrome (ARDS).
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
Systematic literature for English language articles in the following search databases: PubMed, EMBASE, WHO Medicus,
Web of Science and Google Scholar (particularly for pre-print publications on medRxiv). Search terms included SARS-
CoV-2; COVID-19; coronavirus; ARDS; pneumonia; sepsis; influenza; risk prediction; risk score; prognosis; validation. No
date restrictions were imposed.
Statistical analysis for derivation and validation modelling
In analysing the data collected from the derivation (West Herts) cohort, categorical variables will expressed as
frequency (%), with significance determined by the Chi-squared test. Continuous variables will be analysed for median
(interquartile range) or mean (standard deviation) outcomes and analysed by the t-test, Kruskal-Wallis or Mann-
Whitney U test, as appropriate. Missing data in the derivation cohort will be expected even with prospective data
collection; missingness of data will be assumed to be at random and handled by multiple imputation by chained
equations (MICE) with at least ten imputations, provided the proportion of missingness for the defined parameter
constitutes no more than 20% of the cohort. Collated data will be subjected to univariate and multivariate logistic
regression in order to determine odds ratios (OR) for in-hospital mortality. The latter will also be internally validated by
bootstrapping using a minimum of 1000 re-samples. Predictor interactions will be analysed by appropriate
methodology such as the likelihood ratio (LR) test comparing broad and narrow (constrained) models.
All performance metrics against external validation cohorts will be analysed using the prediction score and not with the
multivariate regression model. The performance of the derivation model will be assessed for discriminatory ability (area
under the receiver operating characteristic, AUROC) and calibration (graphical representation of Hosmer-Lemeshow
analysis).
All statistical analyses including risk modelling calculations will be performed using STATA, version 16 (Stata Corp.,
Texas, USA).
R Vancheeswaran, F Chua, A Draper, T Vaghela and A Barlow (study design and responsible persons for data analysis
and interpretation)
MARCH 2020
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance
Supplemental material placed on this supplemental material which has been supplied by the author(s) Thorax
doi: 10.1136/thoraxjnl-2020-216425–8.:10 2021;Thorax, et al. Chua F
... 24 SOARS, a rapid clinical assessment only score with multisite validation including the ISARIC cohort, is a peer-reviewed model that enables safe, reliable and expedient discharge on presentation to any urgent care area, making it invaluable during peak pressures. 25 Scores stratifying mortality and deterioration are all based on the first wave of the pandemic without updates advocated by the evolving pandemic. ...
... Comparison data representing the first wave (March-May 2020) with similar inclusion criteria was previously collected (online supplemental file 1). 25 Baseline clinical characteristics and investigations in the ED were collected according to a prespecified protocol (online supplemental file 1) in a National Health Service Health Research Authority (NHS HRA). 25 Patients were either discharged, referred to the VH for outpatient monitoring or admitted to the hospital. 27 As per the previously published protocol (online supplemental file 1) there was a minimum of 30-day follow-up for all patients recruited, including patients deemed safe for early discharge. ...
... Comparison data representing the first wave (March-May 2020) with similar inclusion criteria was previously collected (online supplemental file 1). 25 Baseline clinical characteristics and investigations in the ED were collected according to a prespecified protocol (online supplemental file 1) in a National Health Service Health Research Authority (NHS HRA). 25 Patients were either discharged, referred to the VH for outpatient monitoring or admitted to the hospital. 27 As per the previously published protocol (online supplemental file 1) there was a minimum of 30-day follow-up for all patients recruited, including patients deemed safe for early discharge. ...
Article
Full-text available
Objective Prospectively validate prognostication scores, SOARS and 4C Mortality Score, derived from the COVID-19 first wave, for mortality and safe early discharge in the evolving pandemic with SARS-CoV-2 variants (B.1.1.7 replacing D614) and healthcare responses altering patient demographic and mortality. Design Protocol-based prospective observational cohort study. Setting Single site PREDICT and multisite ISARIC (International Severe Acute Respiratory and Emerging Infections Consortium) cohorts in UK COVID-19 second wave, October 2020 to January 2021. Participants 1383 PREDICT and 20 595 ISARIC SARS-CoV-2 patients. Primary outcome measures Relevance of SOARS and 4C Mortality Score determining in-hospital mortality and safe early discharge in the evolving UK COVID-19 second wave. Results 1383 (median age 67 years, IQR 52–82; mortality 24.7%) PREDICT and 20 595 (mortality 19.4%) ISARIC patient cohorts showed SOARS had area under the curve (AUC) of 0.8 and 0.74, while 4C Mortality Score had AUC of 0.83 and 0.91 for hospital mortality, in the PREDICT and ISARIC cohorts respectively, therefore, effective in evaluating safe discharge and in-hospital mortality. 19.3% (231/1195, PREDICT cohort) and 16.7% (2550/14992, ISARIC cohort) with SOARS of 0–1 were candidates for safe discharge to a virtual hospital (VH) model. SOARS implementation in the VH pathway resulted in low readmission, 11.8% (27/229) and low mortality, 0.9% (2/229). Use to prevent admission is still suboptimal, as 8.1% in the PREDICT cohort and 9.5% in the ISARIC cohort were admitted despite SOARS score of 0–1. Conclusions SOARS and 4C Mortality Score remains valid, transforming complex clinical presentations into tangible numbers, aiding objective decision making, despite SARS-CoV-2 variants and healthcare responses altering patient demographic and mortality. Both scores, easily implemented within urgent care pathways for safe early discharge, allocate hospital resources appropriately to the pandemic’s needs while enabling normal healthcare services resumption.
... We selected candidate predictor variables based on a literature review of risk factors for COVID-19 mortality, review of other COVID-19 riskprediction models, the availability of candidate predictor variables on patient arrival and the clinical knowledge of the investigator team. 9,10,12,13,[25][26][27][28][29] The candidate predictor variables included age, sex, pregnancy, type of residence, mode of arrival at the emergency department, comorbidities, symptoms, heart rate on arrival, systolic blood pressure, oxygen saturation level, respiratory rate, Glasgow Coma Scale score, oxygen delivery in the emergency department, lowest oxygen saturation level, physician or nurse impression of respiratory distress, and use of alcohol, tobacco, vaping or illicit substances (Appendix 1, Table S2). ...
... In contrast to prior models, we excluded patients with palliative goals of care, for whom invasive mechanical ventilation was not offered, to ensure our model did not predict risk of death among patients who were expected to die or were ineligible for the highest level of critical care. [9][10][11]13,14,27,28,33,35,36 This avoids the potential for selffulfilling prophecy bias. 22 Prior models were derived or validated early in the pandemic, when COVID-19 testing was restricted to those with severe disease, and they did not include consecutive eligible patients; both these factors may have resulted in selection bias. ...
... 22 Prior models were derived or validated early in the pandemic, when COVID-19 testing was restricted to those with severe disease, and they did not include consecutive eligible patients; both these factors may have resulted in selection bias. The mortality rate in prior studies ranged from 13% to 30%, 9,11,12,14,27,28,36 in contrast to 7% in our study. Our study was also able to capture patients who were readmitted to CCEDRRN sites and subsequently died in hospital. ...
Article
Background: Predicting mortality from COVID-19 using information available when patients present to the emergency department can inform goals-of-care decisions and assist with ethical allocation of critical care resources. The study objective was to develop and validate a clinical score to predict emergency department and in-hospital mortality among consecutive nonpalliative patients with COVID-19; in this study, we define palliative patients as those who do not want resuscitative measures, such as intubation, intensive care unit care or cardiopulmonary resuscitation. Methods: This derivation and validation study used observational cohort data recruited from 46 hospitals in 8 Canadian provinces participating in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN). We included adult (age ≥ 18 yr) nonpalliative patients with confirmed COVID-19 who presented to the emergency department of a participating site between Mar. 1, 2020, and Jan. 31, 2021. We randomly assigned hospitals to derivation or validation, and prespecified clinical variables as candidate predictors. We used logistic regression to develop the score in a derivation cohort and examined its performance in predicting emergency department and in-hospital mortality in a validation cohort. Results: Of 8761 eligible patients, 618 (7.0%) died. The CCEDRRN COVID-19 Mortality Score included age, sex, type of residence, arrival mode, chest pain, severe liver disease, respiratory rate and level of respiratory support. The area under the curve was 0.92 (95% confidence interval [CI] 0.90-0.93) in derivation and 0.92 (95% CI 0.90-0.93) in validation. The score had excellent calibration. These results suggest that scores of 6 or less would categorize patients as being at low risk for in-hospital death, with a negative predictive value of 99.9%. Patients in the low-risk group had an in-hospital mortality rate of 0.1%. Patients with a score of 15 or higher had an observed mortality rate of 81.0%. Interpretation: The CCEDRRN COVID-19 Mortality Score is a simple score that can be used for level-of-care discussions with patients and in situations of critical care resource constraints to accurately predict death using variables available on emergency department arrival. The score was derived and validated mostly in unvaccinated patients, and before variants of concern were circulating widely and newer treatment regimens implemented in Canada. Study registration: ClinicalTrials.gov, no. NCT04702945.
... The cutoff of the risk factor is set at 4. The patients with a score < 4 are categorized as low-risk, having a more favorable outcome, while those with a score ≥ 4 were more likely to have an undesirable outcome grant a better allocation of resources and a wider window for interventions. This tool also potentially helps to reduce hospital care costs and improves its quality in the health care units [17][18][19]. Applying such prediction models is a useful strategy for the early screening of high-risk patients in crowded care centers during the COVID-19 outbreak. Risk prediction models are increasingly utilized in medical practice to help practitioners promote healthcare quality. ...
... Recently, several studies have been conducted in different parts of the world to develop a simple scoring system to predict the prognosis and outcome of COVID-19 [19,[22][23][24][25]. The first scoring system to predict the severity of COVID-19, incorporating age, glomerular filtration rate (GFR), WBC, neutrophil count, and myoglobin, was developed by Zhang et al. in 2020 among 80 patients [22]. ...
Article
Full-text available
Background Corona Virus Disease 2019 (COVID-19) presentations range from those similar to the common flu to severe pneumonia resulting in hospitalization with significant morbidity and/or mortality. In this study, we made an attempt to develop a predictive scoring model to improve the early detection of high risk COVID-19 patients by analyzing the clinical features and laboratory data available on admission. Methods We retrospectively included 480 consecutive adult patients, aged 21–95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were collected from the medical records and analyzed using multiple logistic regression analysis. The final data analysis was utilized to develop a simple scoring model for the early prediction of mortality in COVID-19 patients. The score given to each associated factor was based on the coefficients of the regression analyses. Results A novel mortality risk score (COVID-19 BURDEN) was derived, incorporating risk factors identified in this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84–90%, and less than 84%), increased PT (> 16.2 s), diastolic blood pressure (≤ 75 mmHg), BUN (> 23 mg/dL), and raised LDH (> 731 U/L) were the features constituting the scoring system. The patients are triaged to the groups of low- (score < 4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting mortality in patients with a score of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively. Conclusions Using this scoring system in COVID-19 patients, the patients with a higher risk of mortality can be identified which will help to reduce hospital care costs and improve its quality and outcome.
... A pre-specified study protocol including data collection methodology has been published elsewhere. 12 Patients with air leak were identified after reviewing both chest radiographs (CXRs) and axial imaging (CT Thorax) by 2 respiratory physicians with confirmatory radiology reports inclusive of the terms pneumothorax and/or pneumomediastinum. Any scans performed for trauma or other likely secondary causes were excluded, for example, surgical or iatrogenic PTX and PM. ...
... We compared these cases to data from 993 randomly selected COVID-19 patients without air leak were drawn from the electronic records recruited as part of the PREDICT study. 12 For all patients with and without air leak we obtained demographic details, co-morbidities, laboratory investigations, radiological imaging, clinical management including modes of ventilation, survival and outcome. ...
Article
Background Pneumothorax (PTX) and pneumomediastinum (PM), collectively termed here “air leak”, are now well described complications of severe COVID-19 pneumonia across several case series. The incidence is thought to be approximately 1% but is not definitively known. Objectives To report the incidence and describe the demographic features, risk factors and outcomes of patients with air leak as a complication of COVID-19. Methods A retrospective observational study on all adult patients with COVID-19 admitted to Watford General Hospital, West Hertfordshire NHS Trust between March 1st 2020 and Feb 28 th 2021. Patients with air leak were identified after reviewing both chest radiographs (CXRs) and axial imaging (CT Thorax) with confirmatory radiology reports inclusive of the terms PTX and/or PM. Results Air leak occurred with an incidence of 0.56%. Patients with air leak were younger and had evidence of more severe disease at presentation, including a higher median CRP and number of abnormal zones affected on chest radiograph. Asthma was a significant risk factor in the development of air leak (OR 13.4 [4.7-36.4]), both spontaneously and following positive pressure ventilation. CPAP and IMV were also associated with a greater than six fold increase in the risk of air leak (OR 6.4 [2.5-16.6] and 9.8 [3.7-27.8] respectively). PTX, with or without PM, in the context of COVID-19 pneumonia was almost universally fatal whereas those with alone PM had a lower risk of death. Conclusion Despite the global vaccination programme, patients continue to develop severe COVID-19 disease and may require respiratory support. This study demonstrates the importance of identifying that deterioration in such patients may be resultant from PTX or PM, particularly in asthmatics and those managed with positive pressure ventilation.
... The identification of well-defined COVID-19 prognostic markers has advanced the clinical understanding of this disease. [1][2][3] Hypocalcaemia has been reported in the context of acute COVID- 19 where it has been associated with an increased risk of hospitalisation and morbidity. [4][5][6][7][8][9] However, its role in predicting mortality has been less clear, with reports of a conflicting effect. ...
... More detailed data collection methodology has been published elsewhere. 19 Control cases diagnosed with community-acquired pneumonia (CAP) and viral pneumonia (VP) were identified through a clinical database for three defined intervals over two preceding winters prior to the emergence of COVID-19 (January to February 2018, January to February 2019 and September to December 2019). ...
Article
Full-text available
Objectives To investigate whether calcium derangement was a specific feature of COVID-19 that distinguishes it from other infective pneumonias, and its association with disease severity. Design A retrospective observational case–control study looking at serum calcium on adult patients with COVID-19, and community-acquired pneumonia (CAP) or viral pneumonia (VP). Setting A district general hospital on the outskirts of London, UK. Participants 506 patients with COVID-19, 95 patients with CAP and 152 patients with VP. Outcome measures Baseline characteristics including hypocalcaemia in patients with COVID-19, CAP and VP were detailed. For patients with COVID-19, the impact of an abnormally low calcium level on the maximum level of hospital care, as a surrogate of COVID-19 severity, was evaluated. The primary outcome of maximal level of care was based on the WHO Clinical Progression Scale for COVID-19. Results Hypocalcaemia was a specific and common clinical finding in patients with COVID-19 that distinguished it from other respiratory infections. Calcium levels were significantly lower in those with severe disease. Ordinal regression of risk estimates for categorised care levels showed that baseline hypocalcaemia was incrementally associated with OR of 2.33 (95% CI 1.5 to 3.61) for higher level of care, superior to other variables that have previously been shown to predict worse COVID-19 outcome. Serial calcium levels showed improvement by days 7–9 of admission, only in survivors of COVID-19. Conclusion Hypocalcaemia is specific to COVID-19 and may help distinguish it from other infective pneumonias. Hypocalcaemia may independently predict severe disease and warrants detailed prognostic investigation. The fact that decreased serum calcium is observed at the time of clinical presentation in COVID-19, but not other infective pneumonias, suggests that its early derangement is pathophysiological and may influence the deleterious evolution of this disease. Trial registration number 20/HRA/2344.
... Most inpatients have a favourable course with non-invasive supplemental oxygen, but 15%-30% of them eventually require invasive mechanical ventilation (IMV). [4][5][6][7][8] Limitations in ICU capacity have urged the necessity for hospitals to optimise their ICU admission criteria. 9 In the context of shortage of resources and bed capacities, early identification of patients who would benefit most from ICU admission and care is of utmost importance. ...
Article
Full-text available
Background The SARS-CoV-2 pandemic led to a steep increase in hospital and intensive care unit (ICU) admissions for acute respiratory failure worldwide. Early identification of patients at risk of clinical deterioration is crucial in terms of appropriate care delivery and resource allocation. We aimed to evaluate and compare the prognostic performance of Sequential Organ Failure Assessment (SOFA), Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Uraemia, Respiratory Rate, Blood Pressure and Age ≥65 (CURB-65), Respiratory Rate and Oxygenation (ROX) index and Coronavirus Clinical Characterisation Consortium (4C) score to predict death and ICU admission among patients admitted to the hospital for acute COVID-19 infection. Methods and analysis Consecutive adult patients admitted to the Geneva University Hospitals during two successive COVID-19 flares in spring and autumn 2020 were included. Discriminative performance of these prediction rules, obtained during the first 24 hours of hospital admission, were computed to predict death or ICU admission. We further exluded patients with therapeutic limitations and reported areas under the curve (AUCs) for 30-day mortality and ICU admission in sensitivity analyses. Results A total of 2122 patients were included. 216 patients (10.2%) required ICU admission and 303 (14.3%) died within 30 days post admission. 4C score had the best discriminatory performance to predict 30-day mortality (AUC 0.82, 95% CI 0.80 to 0.85), compared with SOFA (AUC 0.75, 95% CI 0.72 to 0.78), qSOFA (AUC 0.59, 95% CI 0.56 to 0.62), CURB-65 (AUC 0.75, 95% CI 0.72 to 0.78) and ROX index (AUC 0.68, 95% CI 0.65 to 0.72). ROX index had the greatest discriminatory performance (AUC 0.79, 95% CI 0.76 to 0.83) to predict ICU admission compared with 4C score (AUC 0.62, 95% CI 0.59 to 0.66), CURB-65 (AUC 0.60, 95% CI 0.56 to 0.64), SOFA (AUC 0.74, 95% CI 0.71 to 0.77) and qSOFA (AUC 0.59, 95% CI 0.55 to 0.62). Conclusion Scores including age and/or comorbidities (4C and CURB-65) have the best discriminatory performance to predict mortality among inpatients with COVID-19, while scores including quantitative assessment of hypoxaemia (SOFA and ROX index) perform best to predict ICU admission. Exclusion of patients with therapeutic limitations improved the discriminatory performance of prognostic scores relying on age and/or comorbidities to predict ICU admission.
... Our results confirm that low oxygen saturation is a relevant predictor of mortality, even independently of some socio-demographic characteristics, medical background and other clinical findings. Low oxygen saturation measured by pulse oximetry has demonstrated to be useful for early prediction of outcomes, monitoring, and guiding hospitalization in other scenarios [38][39][40]. The presence of low oxygen saturation in patients requiring high flow oxygen delivery has been demonstrated to be more reliable than the remaining signs and symptoms for predicting clinical deterioration during the course of COVID-19 [41] and is a predictor of mortality associated with severe disease. ...
Article
Full-text available
Background: Peru is the country with the world's highest COVID-19 death rate per capita. Characteristics associated with increased mortality among adult patients with COVID-19 pneumonia in this setting are not well described. Methods: Retrospective, single-center cohort study including 1537 adult patients hospitalized with a diagnosis of SARS-CoV-2 pneumonia between May 2020 and August 2020 at a national hospital in Lima, Peru. The primary outcome measure was in-hospital mortality. Results: In-hospital mortality was 49.71%. The mean age was 60 ± 14.25 years, and 68.38% were males. We found an association between mortality and inflammatory markers, mainly leukocytes, D-dimer, lactate dehydrogenase, C-reactive protein and ferritin. A multivariate model adjusted for age, hypertension, diabetes mellitus, and corticosteroid use demonstrated that in-hospital mortality was associated with greater age (RR: 2.01, 95%CI: 1.59-2.52) and a higher level of oxygen requirement (RR: 2.77, 95%CI: 2.13-3.62). Conclusions: In-hospital mortality among COVID-19 patients in Peru is high and is associated with greater age and higher oxygen requirements.
... [12][13][14] Several scores have been proposed to assess the risk in COVID-19 positive patients progressing to more severe disease in the outpatient and inpatient settings, but they are limited by bias. 15 Of the available outpatient-based scores assessing risk to progress to hospitalization (Table 3), [16][17][18][19][20] all require symptoms or objective signs at the time of diagnosis except for SARS2 and MCC19-RS. The primary purpose of developing the MCC19-RS was to allow the clinical practice to utilize real-time EHR data to classify patients into the appropriate risk category and to assess risk for need of admission based on the risk score. ...
Article
Full-text available
Objective To evaluate the performance of an Electronic Health Record (EHR) integrated risk score for COVID-19 positive outpatients to predict 30-day risk of hospitalization. Patients and Methods A retrospective observational study of 67 470 patients with COVID-19 confirmed by polymerase chain reaction (PCR) test between March 12, 2020 and February 8, 2021. Risk scores were calculated based on data in the chart at the time of the incident infection. Results The Mayo Clinic COVID-19 risk score consisted of 13 components included age, sex, chronic lung disease, congenital heart disease, congestive heart failure, coronary artery disease, diabetes mellitus, end stage liver disease, end stage renal disease, hypertension, immune compromised, nursing home resident, and pregnant. Univariate analysis showed all components, except pregnancy, have significant ( P < .001) association with admission. The Mayo Clinic COVID-19 risk score showed a Receiver Operating Characteristic Area Under Curve (AUC) of 0.837 for the prediction of admission for this large cohort of COVID-19 positive patients. Conclusion The Mayo Clinic COVID-19 risk score is a simple score that is easily integrated into the EHR with excellent predictive performance for severe COVID-19. It can be leveraged to stratify risk for severe COVID-19 at initial contact, when considering therapeutics or in the allocation of vaccine supply.
Article
Full-text available
https://onlinelibrary.wiley.com/share/author/FQ3QEKVFJX62Z7AMQYFV?target=10.1111/anae.15635 link to the paper
Article
Full-text available
Background: After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the USA. This has led to substantial interest in their “test, trace, isolate” strategy. However, it is important to understand the epidemiological peculiarities of South Korea’s outbreak and characterise their response before attempting to emulate these measures elsewhere. Methods: We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, Rt, using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources.Results: We estimated that after the initial rapid growth in cases, Rt dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64–2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, Rt was back below one where it remained until the end of our study (July 13th). Despite less stringent “lockdown” measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly; however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%. Conclusions: Whilst early adoption of testing and contact tracing is likely to be important for South Korea’s successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult
Article
Full-text available
A second wave pandemic constitutes an imminent threat to society, with a potentially immense toll in terms of human lives and a devastating economic impact. We employ the epidemic Renormalisation Group (eRG) approach to pandemics, together with the first wave data for COVID-19, to efficiently simulate the dynamics of disease transmission and spreading across different European countries. The framework allows us to model, not only inter and extra European border control effects, but also the impact of social distancing for each country. We perform statistical analyses averaging on different level of human interaction across Europe and with the rest of the World. Our results are neatly summarised as an animation reporting the time evolution of the first and second waves of the European COVID-19 pandemic. Our temporal playbook of the second wave pandemic can be used by governments, financial markets, the industries and individual citizens, to efficiently time, prepare and implement local and global measures.
Article
Full-text available
Objective: To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19). Design: Prospective observational cohort study. Setting: International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium-ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020. PARTICIPANTS: Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction. Main outcome measure: In-hospital mortality. Results: 35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). Conclusions: An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations. Study registration: ISRCTN66726260.
Article
Full-text available
Background Use of existing disease severity scores would greatly contribute to risk stratification and rationally resource allocation in COVID-19 pandemic. However, the performance of these scores in COVID-19 hospitalised patients with pneumonia was still unknown. Methods In this single center, retrospective study, all hospitalised patients with COVID-19 pneumonia from Wuhan Jin Yin-tan Hospital who had discharged or died as of February 15, 2020 were enrolled. Performance of PSI, CURB-65, A-DROP, CRB-65, SMART-COP, qSOFA and NEWS2 were validated. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were also estimated. Results Among the 654 patients enrolled, 133 patients died and 521 were discharged. Areas of under curves (AUCs) of A-DROP, CURB-65, PSI, SMART-COP, NEWS2, CRB-65 and qSOFA in the prediction of in-hospital death were 0.87, 0.85, 0.85, 0.84, 0.81, 0.80 and 0.73 respectively. Conclusion ADROP is a reliable tool for risk stratification of death in COVID-19 hospitalised patients on admission.
Article
Full-text available
COVID-19 has rapidly affected mortality worldwide¹. There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England, here we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59, 95% confidence interval (CI) 1.53–1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people with white ethnicity, Black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.30–1.69 and 1.44, 1.32–1.58, respectively). We have quantified a range of clinical risk factors for COVID-19-related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
Preprint
Full-text available
Objective The aim of the study was to analyze the usefulness of the CURB-65 and pneumonia severity index (PSI) in predicting 30-day mortality in patients with COVID-19 and to identify other factors associated with higher mortality. Methods A retrospective study was performed at a pandemic hospital in Istanbul, Turkey and 681 laboratory-confirmed patients with COVID-19 were included. Data on characteristics, vital signs and laboratory parameters were recorded form electronic medical records. We used receiver operating characteristic analysis to quantify the discriminatory abilities of the prognostic scales. Univariate and multivariate logistic regression analyses were performed to identify other predictors of mortality. Results Higher CRP levels were associated with an increased risk for mortality (OR:1.015, 95% CI 1.008 to 1.021, p < 0.001). The PSI performed significantly better than the CURB-65 (AUC: 0.91, 95% CI 0.88-0.93 vs AUC:0.88, 95% CI:0.85-0.90; p = 0.01) and the addition of CRP levels to PSI did not improve the performance of PSI in predicting mortality (AUC: 0.91, 95% CI 0.88-0.93 vs AUC:0.92, 95% CI:0.89-0.94; p = 0.29). Conclusion In a large group of hospitalized patients with COVID-19, we found that PSI performed better than CURB-65 in predicting mortality. Adding CRP levels to PSI did not improve the 30-day mortality prediction.
Article
Objectives The assessment of illness severity at admission can contribute to decreased mortality in patients with the coronavirus disease (COVID-19). This study was conducted to evaluate the effectiveness of the Sequential Organ Failure Assessment (SOFA) and Quick Sequential Organ Failure Assessment (qSOFA) scoring systems at admission for the prediction of mortality risk in COVID-19 patients. Methods We included 140 critically ill COVID-19 patients. Data on demographics, clinical characteristics, and laboratory findings at admission were used to calculate SOFA and qSOFA against the in-hospital outcomes (survival or death) that were ascertained from the medical records. The predictive accuracy of both scoring systems was evaluated by the receiver operating characteristic (ROC) curve analysis. Results The area under the ROC curve for SOFA in predicting mortality was 0.890 (95% CI: 0.826–0.955), which was higher than that of qSOFA (0.742, 95% CI 0.657–0.816). An optimal cutoff of ≥3 for SOFA had sensitivity, specificity, positive predictive value, and negative predictive value of 90.00%, 83.18%, 50.00%, and 97.80%, respectively. Conclusions This novel report indicates that SOFA could function as an effective adjunctive risk-stratification tool at admission for critical COVID-19 patients. The performance of qSOFA is accepted but inferior to that of SOFA.
Article
Background New York City emerged as an epicenter of the coronavirus disease 2019 (COVID-19) pandemic.Objective To describe the clinical characteristics and risk factors associated with mortality in a large patient population in the USA.DesignRetrospective cohort study.Participants6493 patients who had laboratory-confirmed COVID-19 with clinical outcomes between March 13 and April 17, 2020, who were seen in one of the 8 hospitals and/or over 400 ambulatory practices in the New York City metropolitan areaMain MeasuresClinical characteristics and risk factors associated with in-hospital mortality.Key ResultsA total of 858 of 6493 (13.2%) patients in our total cohort died: 52/2785 (1.9%) ambulatory patients and 806/3708 (21.7%) hospitalized patients. Cox proportional hazard regression modeling showed an increased risk of in-hospital mortality associated with age older than 50 years (hazard ratio [HR] 2.34, CI 1.47–3.71), systolic blood pressure less than 90 mmHg (HR 1.38, CI 1.06–1.80), a respiratory rate greater than 24 per min (HR 1.43, CI 1.13–1.83), peripheral oxygen saturation less than 92% (HR 2.12, CI 1.56–2.88), estimated glomerular filtration rate less than 60 mL/min/1.73m2 (HR 1.80, CI 1.60–2.02), IL-6 greater than 100 pg/mL (HR 1.50, CI 1.12–2.03), D-dimer greater than 2 mcg/mL (HR 1.19, CI 1.02–1.39), and troponin greater than 0.03 ng/mL (HR 1.40, CI 1.23–1.62). Decreased risk of in-hospital mortality was associated with female sex (HR 0.84, CI 0.77–0.90), African American race (HR 0.78 CI 0.65–0.95), and hydroxychloroquine use (HR 0.53, CI 0.41–0.67).Conclusions Among patients with COVID-19, older age, male sex, hypotension, tachypnea, hypoxia, impaired renal function, elevated D-dimer, and elevated troponin were associated with increased in-hospital mortality and hydroxychloroquine use was associated with decreased in-hospital mortality.
Article
Patients with COVID-19 are described as exhibiting oxygen levels incompatible with life without dyspnea. The pairing-dubbed happy hypoxia, but more precisely termed silent hypoxemia-is especially bewildering to physicians and is considered as defying basic biology. This combination has attracted extensive coverage in media but has not been discussed in medical journals. It is possible that coronavirus has an idiosyncratic action on receptors involved in chemosensitivity to oxygen, but well-established pathophysiological mechanisms can account for most, if not all, cases of silent hypoxemia. These mechanisms include how dyspnea and the respiratory centers respond to low levels of oxygen, how prevailing carbon dioxide tensions (PaCO2) blunt the brain's response to hypoxia, effects of disease and age on control of breathing, inaccuracy of pulse oximetry at low oxygen saturations, and temperature-induced shifts in the oxygen dissociation curve. Without knowledge of these mechanisms, physicians caring for hypoxemic patients free of dyspnea are operating in the dark-placing vulnerable COVID-19 patients at considerable risk. In conclusion, features about COVID-19 that physicians find baffling become less strange when viewed in the light of long-established principles of respiratory physiology; an understanding of these mechanisms will enhance patient care if the much-anticipated second wave emerges. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).