Predictors of ICU Admission in Adult Cancer Patients Presenting to the Emergency Department for COVID-19 Infection: A Retrospective Study

Abstract

Study Objective:
Adult cancer patients with COVID-19 were shown to be at higher risk of ICU admission. Previously published prediction models showed controversy and enforced the importance of heterogeneity among different populations studied. The aim of this study was to detect the predictors of ICU admission for adult COVID-19 patients with cancer who present to the emergency department (ED).
Methods:
Theis a retrospective cohort study. It was conducted on adult cancer patients older than 18 years who presented to the EDof the American University of Beirut MedicalCenter from February 21, 2020, till February 21, 2021, and were found to have COVID-19 infection. Relevant electronic data were extracted. The association between different variables and ICU admission was tested. Logistic regression was done to adjust for confounding variables. P value less than 0.05 was considered significant.
Results:
Eighty-nine distinct patients were included. About 37% were admitted to the ICU (n=33). Higher ICU admission was seen in patients who had received chemotherapy within one month, had a respiratory rate at triage > 22 breaths per minute, an oxygen saturation < 95%, and/or a higher CRP upon presentation to the ED. After adjusting for confounding variables only recent chemotherapy and higher respiratory rate at triage were significantly associated with ICU admission.
Conclusion:
Physicians need to be vigilant when taking care of covid infected oncology patients. Patients who are tachypneic at presentation and those who have had chemotherapy within one month are at high risk for ICU admission.

Full text

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Predictors of ICU Admission in Adult Cancer
Patients Presenting to the Emergency Department
for COVID-19 Infection: A Retrospective Study
Imad Majzoub (  im26@aub.edu.lb )
American University of Beirut Medical Center https://orcid.org/0000-0002-1182-2435
Nour kalot
American University of Beirut Medical Center
Malak Khalifeh
American University of Beirut Medical Center
Natalie Estelly
American University of Beirut Medical Center
Tharwat El Zahran
American University of Beirut Medical Center
Research Article
Keywords:
Posted Date: March 22nd, 2022
DOI: https://doi.org/10.21203/rs.3.rs-1392228/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License

Page 2/17
Abstract
Study Objective:
Adult cancer patients with COVID-19 were shown to be at higher risk of ICU admission. Previously
published prediction models showed controversy and enforced the importance of heterogeneity among
different populations studied. The aim of this study was to detect the predictors of ICU admission for
adult COVID-19 patients with cancer who present to the emergency department (ED).
Methods:
Theis a retrospective cohort study. It was conducted on adult cancer patients older than 18 years who
presented to the EDof the American University of Beirut MedicalCenter from February 21, 2020, till
February 21, 2021, and were found to have COVID-19 infection. Relevant electronic data were extracted.
The association between different variables and ICU admission was tested. Logistic regression was done
to adjust for confounding variables. P value less than 0.05 was considered signicant.
Results:
Eighty-nine distinct patients were included. About 37% were admitted to the ICU (n=33). Higher ICU
admission was seen in patients who had received chemotherapy within one month, had a respiratory rate
at triage > 22 breaths per minute, an oxygen saturation < 95%, and/or a higher CRP upon presentation to
the ED. After adjusting for confounding variables only recent chemotherapy and higher respiratory rate at
triage were signicantly associated with ICU admission.
Conclusion:
Physicians need to be vigilant when taking care of covid infected oncology patients. Patients who are
tachypneic at presentation and those who have had chemotherapy within one month are at high risk for
ICU admission.
Background
One of the most vulnerable patients to critical illness from respiratory viral infections are cancer patients
[1]. It is postulated that patients with cancer who are infected with the SARS-CoV-2 coronavirus may have
worse outcomes than others [2]. Published work reported higher morbidity and mortality rates from
COVID-19 among cancer patients compared to their cancer-free counterparts [2–6].
Admission to the Intensive care units (ICU) plays a signicant role in the management of COVID-19
patients with some reports showing a reduced mortality rate among those admitted to critical units [7–9].
Several studies developed prediction models and risk scores of ICU admission in COVID-19. Nevertheless,
these studies have shown various results that were sometimes controversial [10]. This controversy might
be due to a composite of causes including methodological differences, regional care differences, SARS-

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CoV-2 variants, heterogeneity of the evaluated population, as well as the large heterogeneity embedded
within cancer and COVID-19 diseases [10].
During the COVID-19 pandemic, emergency departments (EDs) have been on the frontlines, playing an
essential role in detecting infected patients, providing urgent medical care [11], and deciding on the proper
disposition of patients. Worldwide, these departments have been challenged and overwhelmed by the
increasing number of Covid-19 cases. Consequently, it has been of utmost importance, in ED settings, to
be able to predict which cancer patients with COVID-19 are at risk of deteriorating and having worse
outcomes. The knowledge of these predictors can be used to assure a proper and timely risk
stratication, adjusting management accordingly, avoiding ICU admission delay [12], and prioritizing the
admission of more needy patients.
To our knowledge, there are no studies conducted in Lebanon that aim to determine the predictors of ICU
admission in our cancer patient population with COVID-19. Therefore, the objective of the present study
was to identify these predictors. In addition to that, we explored the impact of targeted COVID-19 drugs
administered in the ED on COVID-19 ICU patients.
Methods
Study Design:
The present study is a retrospective cohort study conducted in the American University of Beirut Medical
Center (AUBMC), a tertiary care academic hospital in Lebanon. The study enrolled all cancer patients who
presented from February 21, 2020, till February 21, 2021, to the emergency department (ED) of AUBMC
and were diagnosed with COVID-19 infection. The ED is run as a closed unit by onsite coverage of
emergency medicine specialists 24-hr/7.
The study was approved by the Institutional Review Board (IRB) at AUBMC under the protocol number
(BIO-2021-0015).
Participants:
Patients included were only adult (> 18 years old) cancer patients who presented to the ED of AUBMC
from February 21, 2020, till February 21, 2021, and were found to have COVID-19 infection. Patients not
tting any of the above criteria, as well as those who presented dead on arrival to the ED were excluded.
We dened COVID-19 infection as a positive result of SARS-COV-2 nucleic acid RT-PCR test using the
nasal swab samples.
Data Collection:
Eligible patients were identied through the electronic health system (Epic Systems, Verona, WI, USA). The
patients’ charts were then reviewed by the research team members who entered the relevant data into

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REDCap, a free, secure, web-based platform designed to support data capture for research studies that is
Health Insurance Portability and Accountability Act compliant.
The data collection form was divided into multiple sections. The rst section encompassed the
demographic and medical history of the patients. These were sex, date of birth, smoking status,
medication, and comorbidities. It also included a subsection about the cancer history of the patient (type
of cancer, its spread, and treatment modalities including chemotherapy and immunotherapy). The second
section was about the details of the ED visit where the patient was conrmed to be COVID-19 infected.
We collected information about vital signs, treatment given in the ED and ED disposition. Finally, the third
section was about the patients’ hospital stay which included all complications (Sepsis, AKI, cardiac and
respiratory complications like ARDS and PE.) along with the procedures done (central line or chest tube
insertion, dialysis, tracheostomy) and hospital discharge date, and disposition. The data collection sheet
is attached as an appendix.
Statistical Analysis
Statistical analysis was performed using SPSS version 25.0 (Armonk, NY: IBM Corp). Categorical
variables were described using frequencies and percentages. Continuous variables were reported using
means, standard deviations, ranges, and percentiles.
The dependent variable was ICU admission versus no ICU admission. The association between different
variables and ICU admission was tested using Pearson’s Chi-square or Fisher’s exact test and Student’s
t-
test where appropriate.
Later, Logistic regression was done to adjust for confounding variables and to identify factors that were
associated with ICU admissions in these patients. P-value less than 0.05 was considered signicant.
Results
1. Demographics And Clinical Characteristics of COVID
Oncology Patients:
A total of 89 oncology covid patients were included in the study. Their average age was 66 years (± 13.6).
The majority were males (64%), and with solid cancer (74.2%). About half of them were smokers (52.8%)
and had chemotherapy within 1 month of presentation (52.8%). Only 6 patients did BMT within 1 year of
presentation. Hypertension was the main comorbidity among patients (39.3%), followed by
cardiovascular diseases (25.8%), dyslipidemia (23.6%), diabetes mellitus (14.6%). About 34.8% died (n =
31) and 37% were admitted to the ICU (n = 33). Of the total 33 patients admitted to ICU (37%), the mean
age of patients admitted to ICU was 67 years (± 11.2) and were mainly males (69.7%) (Table1)
Table 1: Association of Baseline Characteristics of Oncology COVID Patients with ICU Admission

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Characteristics Total
N=89 No ICU
n=56
(63%)
ICU
n=33
(37%)
p
value OR 95%
CI
Age (years) 66.3
(13.6) 65.9
(14.8) 67
(11.2) 0.711  
Sex Female 32
(36%) 22
(39.3%) 10
(30.3%) 
0.394

Ref 
Male 57
(64%) 34
(60.7%) 23
(69.7%) 1.488 0.596-
3.719
History of smoking 47
(52.8%) 28 (50%) 19
(57.6%) 0.489 1.357 0.571-
3.228
Type of
Cancer Liquid 23
(26.7%) 14
(25.5%) 9 (29%) 
0.801

Ref 
Solid 63
(73.3%) 41
(74.5%) 22
(71%) 0.835 0.312-
2.234
Metastatictumor 34
(52.3%) 20
(47.6%) 14
(60.9%) 0.306 1.711 0.609-
4.809
Bone Marrow Transplant within 1 year 6
(6.8%) 3 (5.4%) 3 (9.4%) 0.664 1.828 0.346-
9.642
Chemo within 1 month 47
(52.8%) 26
(46.4%) 21
(63.6%) 0.116 2.019 0.835-
4.88
Immunotherapy 19
(21.3%) 12
(21.4%) 7
(21.2%) 0.981 0.987 0.345-
2.823






Comorbidities
Cardiovascular
Diseases 23
(25.8%) 17
(30.4%) 6
(18.2%) 0.205 0.51 0.178-
1.46
Diabetes Mellitus 13
(14.6%) 6 (10.7%) 7
(21.2%) 0.219 2.244 0.683-
7.367
Hypertension 35
(39.3%) 22
(39.3%) 13
(39.4%) 0.992 1.005 0.416-
2.423
Dyslipidemia 21
(23.6%) 14 (25%) 7
(21.2%) 0.684 0.808 0.288-
2.264
Cerebrovascular
accident/TIA 2
(2.2%) 1 (1.8%) 1 (3%) 1 1.719 0.104-
28.43
Chronic Obstructive
Pulmonary Disease 8 (9%) 7 (12.5%) 1 (3%) 0.249 0.219 0.026-
1.863
Chronic Kidney
Disease 16
(18%) 10
(17.9%) 6
(18.2%) 0.969 1.022 0.334-
3.127
Hemiplegia 1
(1.1%) 1 (1.8%) 0 (0%) 1  

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Peptic ulcer disease 2
(2.2%) 1 (1.8%) 1 (3%) 1 1.719 0.104-
28.43
Liver Disease 3
(3.4%) 1 (1.8%) 2 (6.1%) 0.552 3.548 0.309-
40.73
Other* 58
(65.2%) 40
(71.4%) 18
(54.5%) 0.106 0.48 0.196-
1.178
Data are presented as numbers with percentages.
P-value for difference between two adjacent columns is calculated by chi-square or Fisher´s exact test
where appropriate.
Abbreviations: OR: odds ratio, 95%CI: 95% Condence Interval, Ref=Reference,ICU=intensive care unit,
ED=emergency department
*Other comorbidities are thyroid disease, psychiatric disorders, and rheumatologicdiseases.
Most of the patients had tachycardia (n = 79, 89.8%) and 40.4% had low oxygen saturation at triage <
95mmHg (n = 36, 40.4%). (Table2)

Page 7/17
Table 2
Association of Vital Signs and ED treatment of COVID Oncology Patients with ICU Admission
Total n =
89 No ICU n =
56 (63%) ICU n = 33
(37%) p
value OR 95%CI
ED treatment
Mechanical Ventilation in
ED 11
(12.4%) 0 11
(33.3%) < .001  
Vasopressors 7 (7.9%) 1 (1.8%) 6 (18.2%) 0.01 12.222 1.4-
106.674
Steroid 50
(56.2%) 26 (46.4%) 24
(72.7%) 0.016 3.077 1.215–
7.789
Antibiotics 43
(48.3%) 31 (55.4%) 12
(36.4%) 0.083 0.461 0.19–
1.115
Anticoagulants 42
(47.2%) 22 (39.3%) 20
(60.6%) 0.052 2.378 0.986–
5.735
Plasma 6 (6.7%) 4 (7.1%) 2 (6.1%) 1 0.839 0.145–
4.849
Remdesivir 17
(19.1%) 13 (2 3.2%) 4 (12.1%) 0.198 0.456 0.135–
1.539
Ivermectin 13
(14.6%) 7 (12.5%) 6 (18.2%) 0.54 1.556 0.475–
5.099
Tocilizumab 8 (9%) 2 (3.6%) 6 (18.2%) 0.048 6 1.134–
31.735
Baricitinib 3 (3.4%) 1 (1.8%) 2 (6.1%) 0.552 3.548 0.309–
40.73
Vital Signs
Heart rate at
triage <=100 46
(51.7%) 32 (57.1%) 14(42.4%) 0.180 Ref 
> 100 43(48.3%) 24(42.9%) 19(57.6%) 1.180 0.758–
4.319
Systolic blood
pressure at triage <=100 9 (10.2%) 7 (12.5%) 2 (6.3%) 0.478 Ref 
> 100 79
(89.8%) 49 (87.5%) 30
(93.8%) 2.143 0.417–
11.001
Data are presented as numbers with percentages.
P-value for difference between two adjacent columns is calculated by chi-square or Fisher´s exact test
where appropriate.
Abbreviations: OR: odds ratio, 95%CI: 95% Condence Interval, Ref = Reference, SpO2 = Oxygen
saturation, ICU = intensive care unit, ED = emergency department

Page 8/17
Total n =
89 No ICU n =
56 (63%) ICU n = 33
(37%) p
value OR 95%CI
Respiratory rate
at triage <=22 72
(83.7%) 53 (94.6%) 19
(63.3%) 0.001 0.098 0.025–
0.389
> 22 14
(16.3%) 3 (5.4%) 11
(36.7%) Ref 
Temperature
(°C) at triage
< 37.5 50
(57.5%) 30 (53.6%) 20
(64.5%) 0.323 Ref 
>=37.5 37
(42.5%) 26 (46.4%) 11
(35.5%) 0.635 0.257–
1.567
Oxygen
Saturation level
(mmHg)
SpO2
< 95 36
(40.4%) 16 (28.6%) 20
(60.6%) 0.003 3.846 1.552–
9.523
SpO2
> = 95 53
(59.6%) 40 (71.4%) 13
(39.4%) Ref 0.105–
0.644
Data are presented as numbers with percentages.
P-value for difference between two adjacent columns is calculated by chi-square or Fisher´s exact test
where appropriate.
Abbreviations: OR: odds ratio, 95%CI: 95% Condence Interval, Ref = Reference, SpO2 = Oxygen
saturation, ICU = intensive care unit, ED = emergency department
Patients with liquid or solid tumors were homogenous in terms of age, smoking status, and presence of
comorbidities. However, patients with liquid tumors were mainly males (95.7% vs 50.8%, p < 0.001) and
had more moderate to severe kidney diseases (34.8% vs 11.1%, p = .021).
2. Treatments And Health Related Complications Of COVID
Oncology Patients
In the emergency department, the patients were treated mainly with steroids (56.2%), antibiotics (48.3%),
and anticoagulants (47.2%). They were also treated with Remdesivir (19.1%), Ivermectin (14.6%),
Tocilizumab (9%), or convalescent plasma (6.7%). Only7.9% of patients were treated with vasopressors
(n = 7). (Table2)
As for the complications during their hospital stay, 33.7% developed respiratory complications including
ARDS, pneumothorax, or respiratory failure while 15.7% had septic shock, and 7.9% developed
cardiovascular complications. Only 9% of patients required dialysis (n = 8). About 28.1% were
endotracheal intubated (n = 25). The average length of hospital stay was 30.7 days (+- 65.1).
2.1 Characteristics Of Patients Who Required Intubation In
The Ed

Page 9/17
There were 11 patients intubated in the ED (12.4%). There was no signicant difference in gender, age,
smoking status, and presence of comorbidities between patients who were endotracheal intubated in the
ED and those who were not. The average age of intubated patients was 66.7 years (± 10.2) and were
mainly males (81.8%).
For vital signs, patients who were intubated in the ED had signicantly more low oxygen saturation level
at triage < 95 mmHg (81.8% vs 34.6%, p = .006), tachypnea with a RR > 22 breaths/minute (72.7% vs 8%, p
< .001), or tachycardia (HR > 100 beats/minute) (81.8% vs 43.6%, p = .018).
Patients who were intubated were more on Ivermectin (36.4% vs 11.5%, p = .051), vasopressors (54.5% vs
1.3%, p < .001), or anticoagulants (81.8% vs 42.3%, p = .014). Intubated patients were less on antibiotics
(9.1% vs 53.8%, p = .005). The CRP level was signicantly higher in intubated patients (187.5 ± 93.3 vs
85.5 ± 74.6, p < .001).
2.2 Characteristics Of Patients Who Had Respiratory
Complications
About 30 patients developed respiratory complications (33.7%) including pneumothorax, acute
respiratory distress syndrome, and respiratory failure. However, patients with respiratory complications or
not didn’t show signicant differences in terms of gender, age, smoking status, or presence of
comorbidities.
For vital signs, patients with respiratory complications had signicantly lower oxygen saturation level at
triage < 95 mmHg (56.7% vs 32.2%, p = .026) or tachypnea RR > 22 (35.7% vs 6.9%, p = .001).
Patients with respiratory complications were signicantly more on Tocilizumab (20% vs 3.4%, p = .016),
steroids (76.7% vs 45.8%, p = .005) or anticoagulants (66.7% vs 37.3%, p = .009). They had signicantly
elevated CRP level (132.8 ± 94.2 vs 82.6 ± 74.5, p = .011). They were also more admitted to the ICU (75.8%
vs 13.6%, p < .001) and more died (23.3% vs 1.7%, p = .002).
3. Predictors Of ICU Admission in Covid Oncology Patients
None of the baseline characteristics including gender, age, smoking status, and presence of comorbidities
signicantly associated with ICU admission (p > 0.05). (Table1)
Patients in ICU were signicantly more on vasopressors (18.2% vs 1.8%, p = .01) and more mechanically
ventilated in the ED (p < .001) than patients who were not admitted to the ICU. They were also
signicantly 6 times more on Tocilizumab (18.2% vs 12.5%, p = .048) and 3 times more on steroids
(72.7% vs 46.4%, p = .016). (Table2)
For vital signs, low oxygen saturation level at triage < 95 mmHg (60.6% vs 28.6%, p = .003) and elevated
respiratory rate (> 22 breaths/min) (36.7% vs 5.4%, p = .001) were signicantly associated with ICU

Page 10/17
admission. However, there was no signicant difference in systolic blood pressure and temperature of
patients who were admitted to the ICU compared to patients who were not admitted to the ICU (p > 0.05).
(Table2)
The CRP level upon ED presentation was signicantly higher in patients admitted to ICU than patients
who didn’t require an ICU admission (140.8 ± 98.2 vs 76.1 ± 65.9, p = .003). (Table3)

Table 3
Association of ED laboratory data of COVID Oncology with ICU Admission
Laboratory Data Total N = 89 No ICU n = 56
(63%) ICU n = 33 (37%) p
value
White blood cells.
count 8735.830
(11719.0215) 7548.5 (7071.2) 10714.7 (16808.96) 0.31
Hemoglobin 11 (1.9456) 11.1 (1.9) 10.8 (2.1) 0.393
Platelets 184323.864
(92905.1327) 178514.55
(86596.1) 194006.1
(103234.996) 0.452
Lactate
Dehydrogenase 568.77 (560.581) 658.8 (738.7) 465.3 (204.2) 0.239
Lactic acidVenous 1.9024 (1.39752) 1.8 (1.7) 2.1 (0.82) 0.445
C-Reactive Protein 99.5 (84.5) 76.1 (65.9) 140.8 (98.2) 0.003
d-dimer 1379.9 (3418.1) 944 (1061.9) 2142.7 (5482.1) 0.3
Procalcitonin 1 (2.9) 0.8 (3.5) 1.2 (1.8) 0.552
Troponin T 0 (0.1) 0 (0.1) 0 (0) 0.539
Data are presented as mean with standard deviation.
P-value for difference between two adjacent columns is calculated by T test.
Additionally, patients admitted to ICU signicantly develop more respiratory complications (75.8% vs
8.9%, p < .001), AKI (42.4% vs 7.1%, p < .001), pulmonary embolism (p = .048), septic shock (p < .001).
They were signicantly more on Dialysis (21.2% vs 1.8%, p = 0.004) and more died (p < .001).
3.1 Predictors Of ICU Admission in Covid Oncology Patients
Using Logistic Regression
After adjusting for confounding variables using logistic regression, Remdesivir (aOR = .05, 95%CI
= .005-.463) and antibiotics (aOR = .15, 95%CI = .031-.73) were found to reduce the risk of ICU admission.
Patients admitted to ICU were more on steroids (aOR = 13.4, 95%CI = 2.3–78.2) and more on Tocilizumab
(aOR = 18.5, 95%CI = 1.9-179.6). They had signicantly more respiratory rate at triage (aOR = 17.431,

Page 11/17
95%CI = 2.4-125.1). They had also signicantly received more chemotherapy within 1 month of
presentation (aOR = 5.5, 95%CI = 1.2–25.8). (Table3)


Table 4
Logistic Regression: Factors associated with mortality in COVID ICU patients
p-value aOR 95% C.I.
Remdesivir 0.008 0.05 0.005 .463
Tocilizumab 0.012 18.481 1.902 179.595
Steroid 0.004 13.399 2.297 78.159
Antibiotics 0.019 .15 .031 .73
RR at triage 0.004 17.431 2.429 125.111
Chemotherapy within 1 month of
presentation 0.029 5.545 1.193 25.78
Variable(s) entered on step 1: Vasopressors, Remdesivir, Tocilizumab, Steroid, Antibiotics,
Anticoagulant, CRP, RR at triage (reference ≤ 22), O2 at triage (reference ≥ 95 mmHg),
Chemotherapywithin1monthofpresentation.
Omnibus < .001, R2 = .577, Hosmer = 0.918
95%C.I.: 95% Condence Interval, aOR: adjusted Odds Ratio
Discussion
In the present study, chemotherapy within one month, a respiratory rate at triage > 22 breaths per minute,
an oxygen saturation < 95%, and a higher CRP were shown to be signicantly associated with ICU
admission for cancer patients who present to the ED with Covid-19 infection. Out of these, after
multivariate analysis, only high respiratory rate and recent chemotherapy were top predictors of ICU
admission. We didn’t nd signicant associations between any of the demographic variables of our
included sample and the risk of ICU admission.
The signicance of our study is that it focuses on cancer outpatients who present to the emergency
department for COVID-19 infection which might aid ED staff with a better specic assessment and
management of the patients they encounter. Furthermore, to the best of our knowledge, this is the rst
study done in Lebanon to evaluate the morbidity of COVID-19 in cancer patients and given the fact of
heterogeneity of Coronavirus and cancer diseases among geographical regions, this might be of use to
ensure an optimal practice tailored to this specic population.

Page 12/17
Studies that evaluated the role of recent chemotherapy on covid-19 outcomes have shown controversial
results. Zhang et al. showed that rates of severe respiratory COVID-19 were associated with recent
chemotherapy [13]. On the contrary, Jee et al found that cytotoxic chemotherapy administered between
90 and 14 days before testing positive for covid-19 have no increased HR for ICU admission[14]; a nding
that is consistent with previous data[15, 16]. This controversy in results may be explained by the high
heterogeneity of chemotherapy drugs which differ in terms of their mechanisms. Interestingly, some
agents were found to have anti cytokine storm effects. Among these drugs which have shown promise in
patients with COVID-19 were the Janus kinase (JAK) inhibitors and Bruton’s tyrosine kinase (BTK)
inhibitors [17, 18]. These antineoplastic drugs revealed the ability to prevent the cytokine storm
generation thus suppressing the immune system response along with multiple organ failure[19]. Another
explanation of the contradictory results regarding chemotherapy could possibly be due to different study
models that have not accounted for factors that may affect the results[14].
As we have previously mentioned, presenting to the ED with a respiratory rate exceeding 22 breaths per
minute was a top predictor for ICU admission. Since COVID-19 has the potential to affect the respiratory
system[20], it sounds rational to suggest that changes in resting respiratory rate might occur in the early
stages of infection[21]. Respiratory rate changes is an important marker often preceding major
complications, including respiratory depression, and failure[22]. High respiratory rates displayed the
ability to predict most of in-hospital cardiac arrests as well as admission to the intensive care unit[23].
When compared to heart rate, respiratory rate is found to be a better indicator of the patient’s stability[24].
Furthermore, Subbe et. al showed that respiratory rate is superior not only to pulse rate but also to both
blood pressure in detecting high-risk patient groups[25].
It is certain that vital signs play a fundamental role in getting an overall idea of a patient’s status. Oxygen
saturation compared to the invasive arterial blood-gas measurement serves as a more accessible
indicator for oxygenation[26]for triage purposes. From what we observed in the univariate analysis of this
study, an oxygen saturation< 95% at presentation to ED was signicantly associated with admission to
ICU. Akhavan et al found that a lower ambulatory oxygen saturation was strongly correlated with
requiring high oxygen supplementation and mechanical ventilation among admitted ED covid-19
patients[27].Severe respiratory failure and death associated with coronavirus infection may be the result
of damaged alveoli and edema formation, which hinders the lung’s ability to oxygenate blood, as
reected in reduced oxygen saturation[28, 29].
Our results regarding CRP were consistent with Wang et al ndings. Higher CRP levels were associated
with aggravated COVID-19 cases, and these levels occurred before disease progression[30]. C-reactive
protein is a well-known marker of systemic inammation and severe infection[31]. In COVID-19 infection,
CRP was established as an independent outcome predictor as well as an independent discriminator of
severity of disease[32-35]. As a matter of fact, high levels of CRP were considered the most important
predictor of COVID-19 severity in oncology patients[36]. Still, it’s good to keep in mind, especially when
looking at the multivariate analysis of our study, that high CRP do commonly occur in oncology patients,

Page 13/17
which implies that it might be questionable whether or not it should be considered to be an independent
prognostic factor in cancer COVID-19 patients[37].
As in other studies, Remdesivir displayed potential benets in terms of reducing the risk of ICU admission
in our study. When given alone to cancer patients with covid-19, this drug was associated with a reduced
30-day all-cause mortality(aOR, 0.41; 95% CI: 0.17–0.99)[38]. This nucleotide analog ribonucleic acid
(RNA) polymerase inhibitor has shown promising results. In a cohort of severe COVID-19 patients clinical
improvement was observed in 68% of 53 patients[39]. In a double-blind, randomized, placebo-controlled
trial in hospitalized adults with Covid-19, intravenous Remdesivir was shown to signicantly speed the
time to improvement versus placebo (
p
< 0.001)[40].Moreover, the fact that Remdesivir usage in treating
outpatients with mild to moderate covid-19 was given approval by the US Food and Drug Administration
is an additional verication of its ecacy[41].
While the literature suggests that early antibiotic administration in Covid-19 has no impact on mortality
rates[42]and can actually increase the risk of adverse outcomes[43], we found that it is signicantly
associated with lower risk of ICU admission of infected cancer patients. To clear up this issue and draw
denitive conclusions, large multi-centric studies are urgently required[44].
Large multi-centered studies are also needed to investigate the impact of other treatments including
Tocilizumab and steroids on the morbidity and mortality of cancer patients with covid-19. While limited
evidence is available on treatment with tocilizumab for COVID-19[45], data on steroids impact is
conicting[38, 46]. In our study population, these drugs were given by ED physicians to patients who are
deemed sicker. We presume that this is the explanation for these treatment options to be associated with
increased ICU admission.
Limitations:
The present study had several limitations and should be interpreted cautiously. The rst limitation is the
small sample size included and the retrospective nature of our study that was done in a single tertiary
care center. However, AUBMC has the largest cancer center in Lebanon for oncology patients and treats
patients from all over the MENA region. Another limitation is the evolving nature of the virus and its
variants, and the discovery of new effective treatment methods along with vaccination that would affect
our observations.
Conclusion
In conclusion, we have explored several factors that would aid in the early prognosis of cancer patients
with COVID-19 infection. We found that patients who have received chemotherapy within one month of
the infection, and/or whose respiratory rate at triage exceeds 22 breaths per minute are signicantly at
greater risk of requiring an ICU admission.Higher CRP levels were associated with aggravated COVID-19
cases. Remdesivir displayed potential benets in terms of reducing the risk of ICU admission in our
study.Early antibiotic administration was signicantly associated with lower risk of ICU admission of

Page 14/17
infected cancer patients. ED physicians should be vigilant when treating oncology patients infected with
covid and should look for predictors of disease progression and ICU admission and start prompt therapy
early on.
Declarations
Funding: The authors declare that no funds, grants, or other support were received during the preparation
of this manuscript.
Conicts of interest/Competing interests: The authors have no conicts of interest to declare.
Availability of Data and material: No
Code availability: NA
Author contributions:Imad El Majzoub and Tharwat El Zahran were responsible for the study concept,
intellectual expert contribution, and design. Nour Kalot and Natalie Estelly collected the data. Malak
Khalifeh performed all statistical analysis. Nour Kalot, Nathalie, and Malak Khalifeh drafted the
manuscript.Tharwat El Zahran guided the analysis part, revised the whole manuscript, and supervised the
whole study. Imad El Majzoub revised the manuscript and supervised the whole study
Ethics approval: This observational study was approved by the Institutional Review Board (IRB) at
AUBMC under the protocol number (BIO-2021-0015).
Consent to participate: A waiver of consent was obtained given the retrospective nature of our study.
Consent to publish: A waiver of consent was obtained given the retrospective nature of our study.
References
1. Hijano, D.R., G. Maron, and R.T. Hayden,
Respiratory viral infections in patients with cancer or
undergoing hematopoietic cell transplant.
Frontiers in microbiology, 2018. 9: p. 3097.
2. Dai, M., et al.,
Patients with cancer appear more vulnerable to SARS-CoV-2: a multicenter study during
the COVID-19 outbreak.
Cancer discovery, 2020. 10(6): p. 783-791.
3. Liang, W., et al.,
Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China.
The lancet
oncology, 2020. 21(3): p. 335-337.
4. Yang, L., et al.,
Effects of cancer on patients with COVID-19: a systematic review and meta-analysis
of 63,019 participants.
Cancer Biology & Medicine, 2021. 18(1): p. 298.
5. Elkrief, A., et al.,
High mortality among hospital-acquired COVID-19 infection in patients with cancer:
A multicentre observational cohort study.
European Journal of Cancer, 2020. 139: p. 181-187.
. Giannakoulis, V.G., E. Papoutsi, and I.I. Siempos,
Effect of cancer on clinical outcomes of patients
with COVID-19: a meta-analysis of patient data.
JCO global oncology, 2020. 6: p. 799-808.

Page 15/17
7. Phua, J., et al.,
Intensive care management of coronavirus disease 2019 (COVID-19): challenges and
recommendations.
The lancet respiratory medicine, 2020. 8(5): p. 506-517.
. Halacli, B., A. Kaya, and A.T. İSKİT,
Critically ill COVID-19 patient.
Turkish Journal of medical sciences,
2020. 50(SI-1): p. 585-591.
9. Rajani HF, A.F., Anushiravani A. ,
SARS-CoV-2; What We Know so far.
Arch Iran Med, 2020 23((7)): p.
498-502.
10. Caruso, P., et al.,
Cancer-Related Characteristics Associated With Invasive Mechanical Ventilation or
In-Hospital Mortality in Patients With COVID-19 Admitted to ICU: A Cohort Multicenter Study.
Frontiers
in oncology, 2021. 11: p. 746431-746431.
11. Quah, L.J.J., et al.,
Reorganising the emergency department to manage the COVID-19 outbreak.
International journal of emergency medicine, 2020. 13(1): p. 1-11.
12. Mokart, D., et al.,
Delayed intensive care unit admission is associated with increased mortality in
patients with cancer with acute respiratory failure.
Leukemia & lymphoma, 2013. 54(8): p. 1724-
1729.
13. Zhang, H., et al.,
Clinical characteristics and outcomes of COVID-19–infected cancer patients: a
systematic review and meta-analysis.
JNCI: Journal of the National Cancer Institute, 2021. 113(4): p.
371-380.
14. Jee, J., et al.,
Chemotherapy and COVID-19 outcomes in patients with cancer.
Journal of Clinical
Oncology, 2020. 38(30): p. 3538-3546.
15. Mehta, V., et al.,
Case fatality rate of cancer patients with COVID-19 in a New York hospital system.
Cancer discovery, 2020. 10(7): p. 935-941.
1. Luo, J., et al.,
Impact of PD-1 blockade on severity of COVID-19 in patients with lung cancers.
Cancer
discovery, 2020. 10(8): p. 1121-1128.
17. Treon, S.P., et al.,
The BTK inhibitor ibrutinib may protect against pulmonary injury in COVID-19–
infected patients.
Blood, 2020. 135(21): p. 1912-1915.
1. Seif, F., et al.,
JAK inhibition as a new treatment strategy for patients with COVID-19.
International
archives of allergy and immunology, 2020. 181(6): p. 467-475.
19. Heimfarth, L., et al.,
Drug repurposing and cytokine management in response to COVID-19: A review.
International Immunopharmacology, 2020: p. 106947.
20. Huang, C., et al.,
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.
The lancet, 2020. 395(10223): p. 497-506.
21. Miller, D.J., et al.,
Analyzing changes in respiratory rate to predict the risk of COVID-19 infection.
PLoS
One, 2020. 15(12): p. e0243693.
22. Addison, P.S., et al.,
Pulse oximetry-derived respiratory rate in general care oor patients.
Journal of
clinical monitoring and computing, 2015. 29(1): p. 113-120.
23. Cohen, C.A., et al.,
Clinical manifestations of inspiratory muscle fatigue.
The American journal of
medicine, 1982. 73(3): p. 308-316.

Page 16/17
24. Kyriacos, U., et al.,
Monitoring vital signs: development of a modied early warning scoring (MEWS)
system for general wards in a developing country.
PloS one, 2014. 9(1): p. e87073.
25. Subbe, C., et al.,
Effect of introducing the Modied Early Warning score on clinical outcomes, cardio‐
pulmonary arrests and intensive care utilisation in acute medical admissions.
Anaesthesia, 2003.
58(8): p. 797-802.
2. Collins, J.-A., et al.,
Relating oxygen partial pressure, saturation and content: the haemoglobin–
oxygen dissociation curve.
Breathe, 2015. 11(3): p. 194-201.
27. Akhavan, A.R., et al.,
Risk stratication of covid-19 patients using ambulatory oxygen saturation in
the emergency department.
Western Journal of Emergency Medicine, 2020. 21(6): p. 5.
2. Chan, J.F.-W., et al.,
A familial cluster of pneumonia associated with the 2019 novel coronavirus
indicating person-to-person transmission: a study of a family cluster.
The lancet, 2020. 395(10223):
p. 514-523.
29. Xu, Z., et al.,
Pathological ndings of COVID-19 associated with acute respiratory distress syndrome.
The Lancet respiratory medicine, 2020. 8(4): p. 420-422.
30. Wang, G., et al.
C-reactive protein level may predict the risk of COVID-19 aggravation
. in
Open forum
infectious diseases
. 2020. Oxford University Press US.
31. Sproston, N.R. and J.J. Ashworth,
Role of C-reactive protein at sites of inammation and infection.
Frontiers in immunology, 2018. 9: p. 754.
32. Ahnach, M., et al.,
C-reactive protein as an early predictor of COVID-19 severity.
Journal of Medical
Biochemistry, 2020. 39(4): p. 500.
33. Chen, W., et al.,
Plasma CRP level is positively associated with the severity of COVID-19.
Annals of
clinical microbiology and antimicrobials, 2020. 19: p. 1-7.
34. Luo, X., et al.,
Prognostic value of C-reactive protein in patients with coronavirus 2019.
Clinical
Infectious Diseases, 2020. 71(16): p. 2174-2179.
35. Chalmers, S., et al.,
Diagnosis and treatment of acute pulmonary inammation in critically ill
patients: the role of inammatory biomarkers.
World journal of critical care medicine, 2019. 8(5): p.
59.
3. Lee, R.J., et al.,
Establishment of CORONET; COVID-19 Risk in Oncology Evaluation Tool to identify
cancer patients at low versus high risk of severe complications of COVID-19 infection upon
presentation to hospital.
MedRxiv, 2020.
37. Russell, B., et al.,
Risk of COVID-19 death in cancer patients: an analysis from Guy’s Cancer Centre
and King’s College Hospital in London.
British journal of cancer, 2021. 125(7): p. 939-947.
3. Rivera, D.R., et al.,
Utilization of COVID-19 treatments and clinical outcomes among patients with
cancer: a COVID-19 and Cancer Consortium (CCC19) cohort study.
Cancer discovery, 2020. 10(10): p.
1514-1527.
39. Grein, J., et al.,
Compassionate use of remdesivir for patients with severe Covid-19.
New England
Journal of Medicine, 2020. 382(24): p. 2327-2336.

Page 17/17
40. Tomashek, K. and L. Dodd,
Remdesivir for the treatment of covid-19–nal report.
N Engl J Med,
2020. 383: p. 1813-1826.
41. Administration, F.a.D.,
<EUA 046 Gilead Remdesivir LOA Outpatients (01212022).pdf>.
2022.
42. Buetti, N., et al.,
Early administered antibiotics do not impact mortality in critically ill patients with
COVID-19.
Journal of Infection, 2020. 81(2): p. e148-e149.
43. Yin, X., et al.,
Evaluation of early antibiotic use in patients with non‐severe COVID‐19 without bacterial
infection.
International journal of antimicrobial agents, 2022. 59(1): p. 106462.
44. Clancy, C.J. and M.H. Nguyen,
Coronavirus disease 2019, superinfections, and antimicrobial
development: what can we expect?
Clinical Infectious Diseases, 2020. 71(10): p. 2736-2743.
45. Bonomi, M., et al.,
Tocilizumab for COVID-19 Pneumonia in a Patient With Non–Small-cell Lung
Cancer Treated With Chemoimmunotherapy.
Clinical Lung Cancer, 2021. 22(1): p. e67.
4. Horby, P., et al.,
Effect of dexamethasone in hospitalized patients with COVID-19: preliminary report.
medRxiv.
Preprint, 2020. 10(2020.06): p. 22.20137273.
Appendix
Appendix is not available with this version.