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OBJECTIVES To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs). DESIGN Prospective cohort study. SETTING A total of 306 ICUs from 24 European countries. PARTICIPANTS Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81‐87 y]; 51.8% male). MEASUREMENTS Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30‐day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves. RESULTS The 30‐day‐mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30‐day mortality in 91.1% of all patients who die. CONCLUSION A predictive model of cumulative events predicts 30‐day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision‐making capacity.
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BRIEF REPORT
Cumulative Prognostic Score Predicting Mortality in Patients
Older Than 80 Years Admitted to the ICU
Dylan W. de Lange, MD, PhD,* Sylvia Brinkman, PhD,
Hans Flaatten, MD, PhD,
§
Ariane Boumendil, PhD,
Alessandro Morandi, MD, MPH,
k
** Finn H. Andersen, MD, PhD,
††‡‡
Antonio Artigas, MD, PhD,
§§
Guido Bertolini, MD,
¶¶
Maurizio Cecconi, MD,
kk
Steffen Christensen, MD, PhD,*** Loredana Faraldi, MD,
†††
Jesper Fjølner, MD,***
Christian Jung, MD,
‡‡‡
Brian Marsh, MD,
§§§
Rui Moreno, MD,
¶¶¶
Sandra Oeyen, MD, PhD,
kkk
Christina Agvald Öhman, MD, PhD,**** Bernardo Bollen Pinto, MD,
††††
Anne Marie G.A. de Smet, MD, PhD,
‡‡‡‡
Ivo W. Soliman, MD, PhD,*
Wojciech Szczeklik, MD, PhD,
§§§§
Andreas Valentin, MD,
¶¶¶¶
Ximena Watson, MD,
kk
Tilemachos Zafeiridis, MD,
kkkk
and Bertrand Guidet, MD,
*****
†††††
On behalf of the VIP1 Study Group
#
OBJECTIVES: To develop a scoring system model that pre-
dicts mortality within 30 days of admission of patients older
than 80 years admitted to intensive care units (ICUs).
DESIGN: Prospective cohort study.
SETTING: A total of 306 ICUs from 24 European countries.
PARTICIPANTS: Older adults admitted to European ICUs
(N = 3730; median age = 84 years [interquartile range = 81-87
y]; 51.8% male).
MEASUREMENTS: Overall, 24 variables available during
ICU admission were included as potential predictive variables.
Multivariable logistic regression was used to identify independent
predictors of 30-day mortality. Model sensitivity, specicity, and
accuracy were evaluated with receiver operating characteristic
curves.
RESULTS: The 30-day-mortality was 1562 (41.9%). In multi-
variable analysis, these variables were selected as independent
predictors of mortality: age, sex, ICU admission diagnosis,
From the *Department of Intensive Care Medicine, University Medical Center, University Utrecht, Utrecht, The Netherlands;
Department of Medical
Informatics, Amsterdam Public Health Research Institute, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands;
Department
of Clinical Medicine, University of Bergen, Bergen, Norway;
§
Department of Anaesthesia and Intensive Care, Haukeland University Hospital, Bergen,
Norway;
Assistance Publique - Hôpitaux de Paris, Hôpital Saint-Antoine, Service de Réanimation Médicale, Paris, France;
k
Department of Rehabilitation,
Hospital Ancelle di Cremona, Cremona, Italy; **Geriatric Research Group, Brescia, Italy;
††
Department of Anaesthesia and Intensive Care, Ålesund
Hospital, Ålesund, Norway;
‡‡
Department of Circulation and Medical Imaging, NTNU, Trondheim, Norway;
§§
Department of Intensive Care Medecine,
CIBER Enfermedades Respiratorias, Corporacion Sanitaria Universitaria Parc Tauli, Autonomous University of Barcelona, Sabadell, Spain;
¶¶
Laboratorio di
Epidemiologia Clinica, Centro di Coordinamento GiViTI Dipartimento di Salute Pubblica, IRCCS - Istituto di Ricerche Farmacologiche Mario Negri,
Ranica (Bergamo), Italy;
kk
St Georges University Hospital, London, United Kingdom; ***Department of Anaesthesia and Intensive Care Medicine, Aarhus
University Hospital, Denmark;
†††
ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy;
‡‡‡
Department of Cardiology, Pulmonology and Angiology,
University Hospital, Düsseldorf, Germany;
§§§
Mater Misericordiae University Hospital, Dublin, Ireland;
¶¶¶
Unidade de Cuidados Intensivos Neurocriticos e
Trauma, Hospital de São José, Centro Hospitalar Universitário de Lisboa Central Nova Medical School, Lisbon, Portugal;
kkk
Department of Intensive Care
1K12IC, Ghent University Hospital, Ghent, Belgium; ****Karolinska University Hospital, Stockholm, Sweden;
††††
Geneva University Hospitals, Geneva,
Switzerland;
‡‡‡‡
Department of Critical Care, University Medical Center Groningen, University Groningen, Groningen, The Netherlands;
§§§§
Intensive Care
and Perioperative Medicine Division, Jagiellonian University Medical College, Kraków, Poland;
¶¶¶¶
Kardinal Schwarzenberg Hospital, Schwarzach, Austria;
kkkk
Intensive Care Unit, General Hospital of Larissa Tsakalof Larissa, Larissa, Greece; *****Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1136,
Institut Pierre Louis dEpidémiologie et de Santé Publique, Paris, France; and the
†††††
ICU, hospital Saint Antoine, APHP, Paris, France.
Address correspondence to Dylan W. de Lange, MD, PhD, Department of Intensive Care Medicine, University Medical Center, University Utrecht, Utrecht,
The Netherlands. E-mail: d.w.delange@umcutrecht.nl
#
List of contributors: see Acknowledgments.
[Corrections added May 20, 2019, after rst online publication. The author name Bernadro Bollen Pintowas corrected as Bernardo Bollen Pinto]
DOI: 10.1111/jgs.15888
JAGS 00:1-5, 2019
© 2019 The Authors
Journal of the American Geriatrics Society published by Wiley Periodicals, Inc. on behalf of The American Geriatrics Society. 0002-8614/19/$15.00
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which
permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not
used for commercial purposes.
Clinical Frailty Scale, Sequential Organ Failure Score, invasive
mechanical ventilation, and renal replacement therapy. The
discrimination, accuracy, and calibration of the model were
good: the area under the curve for a score of 10 or higher was
.80, and the Brier score was .18. At a cut point of 10 or higher
(75% of all patients), the model predicts 30-day mortality in
91.1% of all patients who die.
CONCLUSION: A predictive model of cumulative events
predicts 30-day mortality in patients older than 80 years
admitted to ICUs. Future studies should include other poten-
tial predictor variables including functional status, presence
of advance care plans, and assessment of each patients
decision-making capacity. J Am Geriatr Soc 00:1-5, 2019.
Key words: critical care; prognosis; older adults; predict;
model
More than 10% of the patients admitted to the intensive
care unit (ICU) are 80 years and older.
1
This propor-
tion of very old intensive care patients(VIPs) is estimated to
increase up to 36% in 2025.
2,3
However, once VIPs have been
admitted to the ICU for an acute medical reason, their overall
30-day mortality is high. The frailer patients in particular have
ahighmortality.
4
Despite careful patient selection before ICU
admission, more than half of these VIPs will die or will experi-
ence major functional deterioration in the 6 months following
their admission.
5,6
As a result of our current uncertainty in predicting which
VIPs could potentially benet from ICU treatment, we often
offer them an ICU trial.This means admitting VIPs to an
ICU, offering them life-sustaining treatment for a period of
2 to 3 days, and then reevaluating if they show any improve-
ment. If patients deteriorate, limitations in life-sustaining ther-
apies would be required.
7
For ICU physicians, this ICU trial
postpones the difcult ICU admission triage decision by a few
days, and by then, some patients have improved. However,
some will still receive life-sustaining therapy, and a decision to
continue treatment should be discussed with the patient or his
or her legal representatives.
8
Inevitably, during such shared
decision-making processes, the question of chances of survival
emerges. Most intensivists estimate a patients chances of out-
come on experience and on preferences. Current severity scor-
ing systems are not tailored for VIPs,
1
and proposed models
for VIP are not precise enough.
9,10
We hypothesize that a cumulative prognostic score can pre-
dict 30-day mortality and thus support physicians and relatives
with the decision to continue care or start a new treatment.
METHODS
We present only a very brief discussion of methods here. A
more elaborate description of our methods, adhering to all
Transparent Reporting of a Multivariable Prediction Model
for Individual Prognosis or Diagnosis (TRIPOD) statements,
11
is provided in Supplementary Materials S1.
In short, allpatients older than 80 years who were acutely
admitted to participating ICUs in 24 European countries were
included in this study.
4,7
The mortality 30 days after ICU
admission was the primary outcome.
Based on variables present at admission (eg, age, sex, rea-
son for ICU admission, the abbreviated Clinical Frailty Scale)
12
or treatments provided during ICU stay (eg, worst Sequential
Organ Failure Assessment [SOFA], (non-)invasive mechanical
ventilation, use of vasoactive drugs, renal replacement therapy
[RRT]), a multivariable logistic regression model was con-
structed. The discrimination, accuracy, and calibration of the
model were assessed
13,14
before a simple bedside model was
constructed. This simple bedside model was based on the beta
of each predictor in the model as described previously.
15
The total number of points assigned to each patient is
called the cumulative prognostic score (CPS) and correlates
with 30-day mortality. The performance of the bedside
model (the sensitivity and specicity at several cutoff points
of the CPS) is assessed.
RESULTS
Participants
In total, 306 ICUs from 24 countries participated and included
5187 VIPs. Of these patients, 4252 were acutely admitted.
Follow-up at 30 days with complete data on all variables was
obtained in 88% (3730/4252). The median number of patients
recruited per country and per ICU was 104 and 12, respec-
tively. Demographics of the patients included in the nal ana-
lyses are presented in Table 1.
Table 1. Demographics of Included Nonelective Patients
No. of patients %
Total 3730 100
ICU mortality 1065 28.6
30-day mortality 1562 41.9
Male 1932 51.8
Age, y, median (25th percentile-75th
percentile)
84 (81-87) -
CFS, median (25th percentile-75th
percentile)
4 (3-6) -
SOFA score, median (25th
percentile-75th percentile)
7 (4-11) -
Intubation and mechanical ventilation 1924 51.6
Vasoactive drugs 2155 57.8
NIV 984 26.4
RRT 405 10.9
Reason for ICU admission
Respiratory failure 900 24.1
Circulatory failure 537 14.4
Respiratory and circulatory failure 448 12
Sepsis 480 12.9
Multi-trauma without head injury 55 1.5
Multi-trauma with head injury 57 1.5
Head injury 110 2.9
Intoxication 13 0.3
Nontrauma 293 7.9
Emergency surgery 379 10.2
Other 458 12.3
Abbreviations: CSF, Clinical Frailty Scale; ICU, intensive care unit; NIV,
noninvasive ventilation; RRT, renal replacement therapy; SOFA, Sequential
Organ Failure Assessment.
2 DE LANGE ET AL. MONTH 2019-VOL. 00, NO. 00 JAGS
Multivariable Logistic Regression Model
No multicollinearity was found between the variables avail-
able for model development, and there was no interaction
between age and the Clinical Frailty Scale.During model devel-
opment, the following variables were selected using the Lasso
procedure: age, sex, reason for ICU admission categorized into
11 options (Supplementary Materials S1), vasoactive drugs,
Clinical Frailty Scale, SOFA score, intubation with mechanical
ventilation, and RRT. The continuous variable SOFA score
was included as restricted cubic spline. The nal regression
model is presented in Table 2, where the SOFA score for sim-
plication is presented as categorical data instead of splines.
The overall discrimination and accuracy of the nal regres-
sion model that predicts mortality within 30 days after ICU
admission was good; the area under the curve (AUC) was .80
(.80-.81). The Brier score was .18 (.18-.18). In the different diag-
nostic subgroups, the performance of the model was also good
(Supplementary Materials S2). The calibration belt of the sepsis
patients showed more uncertainty for the low- and high-risk
patients; for the emergency surgery patients, the model is not
able to predict high mortality risks. However, the calibration
belt of the total population and all other diagnostic subgroups
showed no abnormalities (Supplementary Materials S3).
BedsideModel
The bedsidemodel, based on a point system (Supplementary
Materials S4), shows parameters that inuence mortality
30 days after ICU admission and the weight assigned to these
variables. In theory, the minimum and maximum scores a
patient can obtain are 0 and 26 points, respectively. The mini-
mum and maximum scores obtained in the study population
were 1 and 26, respectively. The reason for ICU admission and
the SOFA score were the two most important factors associated
with 30-day mortality. Among the reasons for ICU admission,
multi-trauma with head injury and nontrauma central nervous
system causes were associated with a high 30-day mortality.
The sensitivity and specicity for various thresholds is
based on the assigned number of points in the prediction model
(ie, CPS of the patients) (Figure 1). When all patients with a
30-day CPS higher than 10 points are selected (corresponding
to 76.0% of all patients), 91.8% of all patients who died dur-
ing the 30 days after ICU admission are captured (sensitivity).
Of these 76.0% selected patients, 50.6% will die during the
30 days after ICU admission (positivepredicted value).
Supplementary Materials S5 lists the characteristics of
the patients with a 30-day CPS higher than 10 points and
the patients with a 30-day mortality score higher, lower, or
equal to 10 points.
DISCUSSION
This study demonstrates that a cumulative event model corre-
lates with outcome in acutely admitted ICU patients older than
80 years. This model is based on reason for ICU admission,
age, sex, frailty, SOFA score (all available at admission), and
the organ support during ICU stay: invasive mechanical venti-
lation and RRT. Although this model discriminates between
those dying and surviving 30 days after ICU admission rather
accurately (AUC = .80), it remains difcult to predict which
patients are going to die with 100% certainty. Even if we use a
high cutoff (eg, >19 points), these patients still have a 25%
chance of survival in the rst 30 days after ICU admission.
Table 2. Final Multivariable Regression Model
Covariate Odds ratio 30-day mortality
Age 1.05 (1.03-1.07)
Sex, male 1.26 (1.09-1.47)
Mechanical ventilation 2.07 (1.72-2.50)
RRT 1.70 (1.33-2.18)
CFS 1.19 (1.14-1.25)
SOFA score
<4 Reference
4 and <7 1.80 (1.37-2.37)
7 and <10 2.70 (2.02-3.60)
10 4.40 (3.26-5.92)
Reason for ICU admission
Respiratory failure 3.58 (.73-17.48)
Circulatory failure 3.74 (.76-18.39)
Respiratory and circulatory failure 4.55 (.92-22.43)
Sepsis 3.20 (.65-15.77)
Multi-trauma without head injury 3.62 (.66-19.77)
Multi-trauma with head injury 5.06 (.94-27.23)
Isolated head Injury 4.30 (.84-22.09)
Intoxication Reference
Nontrauma CNS causes 5.45 (1.10-27.01)
Emergency surgery 1.87 (.38-9.27)
Other 2.0 (.40-9.88)
Abbreviations: CFS, Clinical Frailty Scale; CNS, central nervous system;
ICU, intensive care unit; RRT, renal replacement therapy; SOFA, Sequential
Organ Failure Assessment.
1.0
0.8
0.6
0.4
0.2
0
0 0.2 0.4 0.6 0.8 1.0
sensitivity
1 - specificity
CPS > 6
CPS > 8
CPS > 10
CPS > 12
CPS > 14
CPS > 16
CPS > 18
CPS > 20
Figure 1. Sensitivity and specicity of the simple bedside model
for 30-day mortality. When a patient has >10 points on the
Cumulative Prognostic Score (CPS), that patient has a 50.6%
positive predictive value (PPV) on 30-day mortality and a nega-
tive predictive value (NPV) of 85.8%. The other values are as
follows: at >12 points, a PPV of 55.9% and an NPV of 81.1%;
at >16 points, a PPV of 67.6% and an NPV of 69.2%; and at
>20 points, a PPV of 77.0% and an NPV of 60.5%.
JAGS MONTH 2019-VOL. 00, NO. 00 PREDICTING MORTALITY IN OLDER ICU PATIENTS 3
Previous research identied variables associated with a
poor outcome in this very old patient group: age,
1,4
sex,
4,9
mechanical ventilation,
16
circulatory shock,
17
acute kidney
injury,
18
and the presence of comorbidities.
19
However, up to
now, only a few studies have tried to build a model from these
variables to predict outcome after ICU admission. Ball et al
developed a model based on age, serum creatinine, Glasgow
Coma Scale, and pH.
10
The data in that study were collected
during the rst day of ICU admission and did not include the
response to active treatment for which the ICU trial is
intended. Heyland et al made a model to predict functional
outcome 1 year after ICU admission.
9
Although that model
had a good performance (AUC = .81), the selected variables
were less easily collected at the bedside because they were
derived from more complex scores, for example, the Acute
Physiology, Age, Chronic Health Evaluation II score and the
Charlson Comorbidity Index.
It is very difcult to predict outcome in acutely ill patients
older than 80 years, and many are admitted to the ICU for an
ICU trial. However, such an admission should be reevaluated in
a shared decision-making conference with the patient or, when
the patients lack decision-making capacity, with the family or
designated surrogate. Our model can be used to estimate, in a
more objective way than just subjective clinical intuition, what
the chances of 30-day mortality will be. We believe this informa-
tion can help both the intensivist and the family members to put
treatment into perspective.
20
Indeed, although almost all intensi-
vists claim that they value the opinions of surrogates (eg, rela-
tives, family, legal representatives, and caregivers),
21
these family
opinions on ICU admissions are, in reality, rarely sought.
8,22
One of the reasons why this is omitted is the uncertainty that
intensivists feel during the prognostication of patients.
22-25
And yet, a poor prognosis is one of the most important
reasons to implement limitations in life-sustaining thera-
pies.
26,27
Indeed, familymembers reported that what was most
important to them was that the patient should be comfortable
and suffer as little as possible.The belief that life should be
preserved at all costswas their least important value consid-
ered in making treatment decisions. A substantial proportion
of the caregivers (24%) reported that comfort care without
life supportwas their most preferred treatment goal, but
14% were unsure about their treatment preferences.
28
Care-
givers and surrogates who had a shared decision-making con-
ference with their intensivist experienced less decisional
conicts than family members who had not talked to a physi-
cian.
28
Strikingly, despite family members only prioritizing
comfort measures,83.7% of these patients still received life-
sustaining treatments, and approximately 20% received such
treatments for more than a week.
28
Our bedside model for
30-day mortality can assist both intensivists and family mem-
bers in the decision-making process to continue or cease fur-
ther treatment.
The strong feature of this study is the international inclu-
sion of more than 3700 very old patients and the high follow-up
rate at 30 days after ICU admission. However, some limitations
need to be discussed. First, we did not collect data on the timing
of discussions to withhold or withdraw life-sustaining treat-
ment. For example, some patients had advanced directives not
to instigate certain treatments that might have inuenced out-
come. A second limitation of the model is that it does not predict
patients who will have a denite poor outcome (100% mortal-
ity). The predicted mortality at more than 19 points is 75%, but
this means that 25% of those patients do survive the rst
30 days. A longer follow-up will undoubtedly show there is
substantial additional mortality in these very old patients. For
this reason a new study (the so-called Very old Intensive care
Patient study 2 [VIP2]) will look at mortality at day 180 after
ICU admission. Third, we combined data from admission with
treatment data from the subsequent treatment days. This
simplies and strengthens prognostication but prevents the
model being used for triage purposes before admission. Fourth,
we only looked at survival, although we know that many
patients do not fully recover, and many older adult patients pri-
oritize quality of lifeabove quantity of life.Future research
should focus on functional outcome and quality of life. And,
nally, we did not include data on nutritional status, functional
status (activities of daily living and instrumental activities of
daily living), cognitive impairment, dementia, delirium, depres-
sion, and comorbidities (eg, active cancer). These variables will
undoubtedly inuence outcome. Our CPS can become more
comprehensive if such variables are included.
In conclusion, this relatively simple cumulative events
model can help assess the chances of 30-day mortality in
very old patients who were acutely admitted to the ICU.
This model may assist both intensivists and family members
during the shared decision-making process to estimate the
otherwise subjective chances of survival.
ACKNOWLEDGMENTS
This study could not be performed without the help of
many contributors. These VIP1 study contributors are listed
in Supplementary Materials S6.
Conict of Interest: The authors have declared no con-
icts of interest for this article.
Author Contributions: All authors contributed to
obtaining ethical clearance for their countries, including patients,
and corrected the nal draft of the manuscript. Statistical ana-
lyses: Brinkman, Soliman, Bertolini, and Boumendil. Writing the
rst draft: de Lange.
Sponsors Role: There was no sponsor for this article.
The Society of Intensive Care Medicine (ESICM) endorsed
the study and was awarded a scientic prize.
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the
online version of this article.
Supplementary Appendix S1. Supplementary Materials.
Supplementary Materials S1. Expanded methods.
Supplementary Materials S2. Performance of multivari-
ate logistic regression model.
Supplementary Materials S3. GiViTi calibration belt
for the developed prediction model.
Supplementary Materials S4. Prediction model based
on a point system.
Supplementary Materials S5. Demographics for patients
with 10 and >10 Cumulative Prognostic Score.
Supplementary Materials S6. VIP1 study contributors.
JAGS MONTH 2019-VOL. 00, NO. 00 PREDICTING MORTALITY IN OLDER ICU PATIENTS 5
... Six of these studies included the definition of "vulnerable" with a CFS of 4 [2,10,32,37,[52][53][54]. Four studies did not define a "cut-off"-level for frailty but worked with graded scales [33,34,41,45]. The study by Orsini et al. [35] used a simplified version of the CFS, and Darvall [65] used a modified eight category CFS. ...
... Eight studies (17%) established a frailty diagnosis based on their own criteria [10,21,33,45,[59][60][61]79] -4 of them exclusively [21,60,61,79] -but the others in combination with established scores. The most common criterion was a combination of decreased cognitive function and functional status and disability in daily life, with the exception of Ball et al. [79] who only referred to phys-Gerontology DOI: 10.1159/000523674 iological parameters. ...
... In these studies, the pre-ICU frailty assessment had either been reconstructed from the staff notes from the clinic where the patients were hospitalized [57,64,70] or was based on external datasets containing medical records of inpatients and outpatients, skilled nursing facilities, home health agencies, nursing homes, and permanent medical equipment [58,60,67,72], In one study [61], pre-ICU frailty status was adopted from a national registry, and in two cases [54,56], it was extracted from another study. In the remaining studies, a pre-ICU frailty or functional performance assessment was carried out at the unit where the patient was hospitalized previous to ICU admission, but without specifying exactly the method at the time of triage [2], time of inclusion [22,42], or at time of ICU admission [33,39,45]. ...
Article
Introduction: As new treatments have become established, more frail pre-ICU patients are being admitted to intensive care units (ICUs); this is creating new challenges to provide adequate care and to ensure that resources are allocated in an ethical and economical manner. This systematic review evaluates the current standard for assessing frailty on the ICU, including methods of assessment, time point of measurements, and cut-offs. Methods: A systematic search was conducted on MEDLINE, Clinical Trials, Cochrane Library, and Embase. Randomized and non-randomized controlled studies were included that evaluated diagnostic tools and ICU outcomes for frailty. Exclusion criteria were the following: studies without baseline assessment of frailty on ICU admission, studies in paediatric patients or pregnant women, and studies that targeted very narrow populations of ICU patients. Eligible articles were included until January 31, 2021. Methodological quality was assessed using the Newcastle-Ottawa Scale. No meta-analysis was performed, due to heterogeneity. Results: N = 57 articles (253,376 patients) were included using 19 different methods to assess frailty or a surrogate. Frailty on ICU admission was most frequently detected using the Clinical Frailty Scale (CFS) (n = 35, 60.3%), the Frailty Index (n = 5, 8.6%), and Fried's frailty phenotype (n = 6, 10.3%). N = 22 (37.9%) studies assessed functional status. Cut-offs, time points, and manner of baseline assessment of frailty on ICU admission varied widely. Frailty on ICU admission was associated with short- and long-term mortality, functional and cognitive impairment, increased health care dependency, and impaired quality of life post-ICU discharge. Conclusions: Frailty assessment on the ICU is heterogeneous with respect to methods, cut-offs, and time points. The CFS may best reflect frailty in the ICU. Frailty assessments should be harmonized and performed routinely in the critically ill.
... Other factors specific to older patients are known to influence ICU outcomes, such as the Clinical Frailty Scale, its components being items reflecting functional ability [10]. Age and pre-existing comorbidities weigh heavily in mortality prediction models [37], thus leading to lower discrimination of severity of these models in older patients [38]. We observed a difference between observed mortality and probability of mortality using SAPS-3 which suggests that this score is not suited for older patients. ...
Article
Full-text available
Background Little is known about the impact of hospital trajectory on survival and functional decline of older critically ill patients. We evaluate 6-month outcomes after admission to: intensive care units (ICU), intermediate care units (IMCU) or acute medical wards (AMW). Methods Data from the randomised prospective multicentre clinical trial ICE-CUB2 was secondarily analysed. Inclusion criteria were: presenting at emergency departments in critical condition; age ≥ 75 years; activity of daily living (ADL) ≥ 4; preserved nutritional status; and no active cancer. A Cox model was fitted to compare survival according to admission destination adjusting for patient characteristics. Sensitivity analysis using multiple imputation for missing data and propensity score matching were performed. Results Among 3036 patients, 1675 (55%) were women; median age was 85 [81–99] years; simplified acute physiology score (SAPS-3) 62 [55–69]; 1448 (47%) were hospitalised in an ICU, 504 in IMCU (17%), and 1084 (36%) in AMW. Six-month mortality was 629 (44%), 155 (31%) and 489 (45%) after admission in an ICU, IMCU and AMW ( p < 0.001), respectively. In multivariate analysis, AMW admission was associated with worse 6-month survival (HR 1.31, 95% CI 1.04–1.63) in comparison with IMCU admission, after adjusting for age, gender, comorbidities, ADL, SAPS-3 and diagnosis. Survival was not significantly different between patients admitted in an ICU and an IMCU (HR 1.17, 95% CI 0.95–1.46). Sensitivity analysis using multiple imputation for missing data and propensity score matching found similar results. Hospital destination was not significantly associated with the composite criterion loss of 1-point ADL or mortality. Physical and mental components of the 12-Item Short-Form Health Survey were significantly lower in the acute medical ward group (34.3 [27.5–41.7], p = 0.037 and 44.3 [38.6–48.6], p = 0.028, respectively) than in the ICU group (34.7 [28.4–45.3] and 45.5 [40.0–50.0], respectively) and IMCU group (35.7 [29.7–43.8] and 44.5 [39.7–48.4], respectively). Conclusions Admission in an AMW was associated with worse 6-month survival in older critically ill patients in comparison with IMCU admission, with no difference of survival between ICU and IMCU admission. There were no clinically relevant differences in quality of life in each group. These results should be confirmed in specific studies and raise the question of dedicated geriatric IMCUs.
... Typical outcomes were overall mortality [57][58][59][60][61][62], survival [63], and long-term quality of life [64]. Mortality [65,66] and survival status at 1 year [67] in critically ill patients aged 80 years and older were also studied using machine learning methods. ...
Article
Full-text available
Background Modern clinical care in intensive care units is full of rich data, and machine learning has great potential to support clinical decision-making. The development of intelligent machine learning–based clinical decision support systems is facing great opportunities and challenges. Clinical decision support systems may directly help clinicians accurately diagnose, predict outcomes, identify risk events, or decide treatments at the point of care. Objective We aimed to review the research and application of machine learning–enabled clinical decision support studies in intensive care units to help clinicians, researchers, developers, and policy makers better understand the advantages and limitations of machine learning–supported diagnosis, outcome prediction, risk event identification, and intensive care unit point-of-care recommendations. Methods We searched papers published in the PubMed database between January 1980 and October 2020. We defined selection criteria to identify papers that focused on machine learning–enabled clinical decision support studies in intensive care units and reviewed the following aspects: research topics, study cohorts, machine learning models, analysis variables, and evaluation metrics. Results A total of 643 papers were collected, and using our selection criteria, 97 studies were found. Studies were categorized into 4 topics—monitoring, detection, and diagnosis (13/97, 13.4%), early identification of clinical events (32/97, 33.0%), outcome prediction and prognosis assessment (46/97, 47.6%), and treatment decision (6/97, 6.2%). Of the 97 papers, 82 (84.5%) studies used data from adult patients, 9 (9.3%) studies used data from pediatric patients, and 6 (6.2%) studies used data from neonates. We found that 65 (67.0%) studies used data from a single center, and 32 (33.0%) studies used a multicenter data set; 88 (90.7%) studies used supervised learning, 3 (3.1%) studies used unsupervised learning, and 6 (6.2%) studies used reinforcement learning. Clinical variable categories, starting with the most frequently used, were demographic (n=74), laboratory values (n=59), vital signs (n=55), scores (n=48), ventilation parameters (n=43), comorbidities (n=27), medications (n=18), outcome (n=14), fluid balance (n=13), nonmedicine therapy (n=10), symptoms (n=7), and medical history (n=4). The most frequently adopted evaluation metrics for clinical data modeling studies included area under the receiver operating characteristic curve (n=61), sensitivity (n=51), specificity (n=41), accuracy (n=29), and positive predictive value (n=23). Conclusions Early identification of clinical and outcome prediction and prognosis assessment contributed to approximately 80% of studies included in this review. Using new algorithms to solve intensive care unit clinical problems by developing reinforcement learning, active learning, and time-series analysis methods for clinical decision support will be greater development prospects in the future.
... it is consistently included as an important parameter in predictive mortality models, supported by an extensive body of sound evidence showing that, both during and after a stay in the icU, elderly patients are at higher risk than their younger counterparts for complications and mortality. [58][59][60][61][62][63][64] These findings are consistent with the concept of "frailty", a clinically recognizable state of increased vulnerability stemming from diminished reserve The accountable approach the "accountable" approach, 54 in contrast, acknowledges the extraordinary nature of the circumstances and addresses the need for healthcare rationing by developing and openly discussing the criteria to be used for this purpose (as replacements for or adjuncts to the "ordinary" criteria used under non-emergency situations). Decisions on these "crisis criteria" can then be spelled out, justified, and shared with the scientific community and the public. ...
Article
Full-text available
The CoViD-19 pandemic has shattered the illusion that healthcare resource shortages that require rationing are problems restricted to low- and middle-income countries. During the pandemic surges, many high-income countries have been confronted with unprecedented demands for healthcare that dramatically exceeded available resources. Hospitals capacities were overwhelmed, and physicians working in intensive care units (ICUs) were often forced to deny admissions to patients in desperate need of intensive care. To support these difficult decisions, many scientific societies and governmental bodies have developed guidelines on the triage of patients in need of mechanical ventilation and other lifesupport treatments. The ethical approaches underlying these recommendations were grounded on egalitarian or utilitarian principles. Thus far, however, consensus on the approaches used, and, above all, on the solutions adopted have been limited, giving rise to a clash of opinions that has further complicated health professionals' ability to respond optimally to their patients' needs. As the CoViD-19 crisis moves toward a phase of what some have called "pandemic normalcy", the need to debate the merits and demerits of the individual decisions made in the allocation of ICU resources seems less pressing. Instead, the aims of the authors are: 1) to critically review the approaches and criteria used for triaging patients to be admitted in ICU; 2) to clarify how macroand micro-allocation choices, in their interdependance, can condition decision-making processes regarding the care of individual patients; and 3) to reflect on the need for decision-makers and professionals working in ICUs to maintain a proper degree of "honesty" towards citizens and patients regarding the causes of the resource shortages and the decision-making processes, which, in different ways routinely and in crisis times, involve the need to make "tragic choices" at both levels.
... No changes in decision-making were seen regarding patient age, cost of care and patient social status. Even though high age is a well-known risk factor for poor survival after an acute illness, age alone is not seen as an acceptable reason for withholding or withdrawing lifesustaining treatment in acute care setting [67,68]. A big challenge with the aging population involves learning to identify elderly patients who still benefit from intensive care procedures. ...
Article
Full-text available
Background: Physicians' decision-making for seriously ill patients with advanced dementia is of high importance, especially as the prevalence of dementia is rising rapidly, and includes many challenging ethical, medical and juridical aspects. We assessed the change in this decision-making over 16 years (from 1999 to 2015) and several background factors influencing physicians' decision. Methods: A postal survey including a hypothetical patient-scenario representing a patient with an advanced dementia and a life-threatening gastrointestinal bleeding was sent to 1182 and 1258 Finnish physicians in 1999 and 2015, respectively. The target groups were general practitioners (GPs), surgeons, internists and oncologists. The respondents were asked to choose between several life-prolonging and palliative care approaches. The influence of physicians' background factors and attitudes on their decision were assessed. Results: The response rate was 56%. A palliative care approach was chosen by 57 and 50% of the physicians in 1999 and 2015, respectively (p = 0.01). This change was statistically significant among GPs (50 vs 40%, p = 0.018) and oncologists (77 vs 56%, p = 0.011). GPs chose a palliative care approach less often than other responders in both years (50 vs. 63% in 1999 and 40 vs. 56% in 2015, p < 0.001). In logistic regression analysis, responding in 2015 and being a GP remained explanatory factors for a lower tendency to choose palliative care. The impact of family's benefit on the decision-making decreased, whereas the influence of the patient's benefit and ethical values as well as the patient's or physician's legal protection increased from 1999 to 2015. Conclusions: Physicians chose a palliative care approach for a patient with advanced dementia and life-threatening bleeding less often in 2015 than in 1999. Specialty, attitudes and other background factors influenced significantly physician decision-making. Education on the identification and palliative care of the patients with late-stage dementia are needed to make these decisions more consistent.
... During the last two decades, improvements in intensive care therapy have lowered the mortality from sepsis. However, critically ill old (>64 years), and very old (>79 years) patients are more at risk, with older patients developing sepsis more frequently and with greater severity (6,7). ...
Article
Purpose: Old (>64 years) and very old (>79 years) intensive care patients with sepsis have a high mortality. In the very old, the value of critical care has been questioned. We aimed to compare the mortality, rates of organ support, and the length of stay in old vs. very old patients with sepsis and septic shock in intensive care. Methods: This analysis included 9,385 patients, from the multi-center eICU Collaborative Research Database, with sepsis; 6184 were old (aged 65–79 years), and 3,201 were very old patients (aged 80 years and older). A multi-level logistic regression analysis was used to fit three sequential regression models for the binary primary outcome of ICU mortality. A sensitivity analysis in septic shock patients ( n = 1054) was also conducted. Results: In the very old patients, the median length of stay was shorter (50 ± 67 vs. 56 ± 72 h; p < 0.001), and the rate of a prolonged ICU stay was lower (>168 h; 9 vs. 12%; p < 0.001) than the old patients. The mortality from sepsis was higher in very old patients (13 vs. 11%; p = 0.005), and after multi-variable adjustment being very old was associated with higher odds for ICU mortality (aOR 1.32, 95% CI 1.09–1.59; p = 0.004). In patients with septic shock, mortality was also higher in the very old patients (38 vs. 36%; aOR 1.50, 95% CI 1.10–2.06; p = 0.01). Conclusion: Very old ICU-patients suffer from a slightly higher ICU mortality compared with old ICU-patients. However, despite the statistically significant differences in mortality, the clinical relevance of such minor differences seems to be negligible.
Chapter
Probably the most frequently reported outcome in healthcare in general, and in intensive care in particular, is survival or its counterpart mortality. Obviously, other patient-centered outcomes are very often connected and even dependent on a patient that survives. It makes no meaning to talk about quality of life in patients not surviving the ICU stay, but for survivors post-hospital discharge, other issues than merely survival become more and more important.
Chapter
Learning objectives of this chapter is to review existent risk score applicable to the very old patient. Problems, challenges and ongoing developments are discussed, with particular emphasis on the importance of previous health status over the presence and degree of physiological derangements in this particular population when developing or applying one of these methods.
Article
Background Each year, approximately one million older adults die in American intensive care units (ICUs) or survive with significant functional impairment. Inadequate symptom management, surrogates’ psychological distress and inappropriate healthcare use are major concerns. Pioneering work by Dr. J. Randall Curtis paved the way for integrating palliative care (PC) specialists to address these needs, but convincing proof of efficacy has not yet been demonstrated. Design We will conduct a multicenter patient-randomized efficacy trial of integrated specialty PC (SPC) vs. usual care for 500 high-risk ICU patients over age 60 and their surrogate decision-makers from five hospitals in Pennsylvania. Intervention The intervention will follow recommended best practices for inpatient PC consultation. Patients will receive care from a multidisciplinary SPC team within 24 hours of enrollment that continues until hospital discharge or death. SPC clinicians will meet with patients, families, and the ICU team every weekday. SPC and ICU clinicians will jointly participate in proactive family meetings according to a predefined schedule. Patients in the control arm will receive routine ICU care. Outcomes Our primary outcome is patient-centeredness of care, measured using the modified Patient Perceived Patient-Centeredness of Care scale. Secondary outcomes include surrogates’ psychological symptom burden and health resource utilization. Other outcomes include patient survival, as well as interprofessional collaboration. We will also conduct prespecified subgroup analyses using variables such as PC needs, measured by the Needs of Social Nature, Existential Concerns, Symptoms, and Therapeutic Interaction scale. Conclusions This trial will provide robust evidence about the impact of integrating SPC with critical care on patient, family, and health system outcomes.
Article
Background: The benefit of a stay in an intensive care unit (ICU) is not certain for older patients, particularly in the surgical context. Aims and objectives: The objective of this study was to identify the factors associated with an unfavourable outcome in this population. Design: Prospective, descriptive, monocentric study conducted in the surgical ICU of a French university hospital. Methods: Patients aged ≥75 years admitted in the surgical ICU for a predicted length of stay ≥48 hours were included. Patients received an initial and a 6-months nutritional and functional assessment performed by physicians and nurses. The outcome was considered as favourable if the Katz Activities of Daily Living (ADL) variation (ADL delta = 6-months ADL - ICU admission ADL) was between 0 and -0.5 point 6 months after ICU discharge and unfavourable if the ADL delta decreased by more than 0.5 points or if the patient had died 6 months after ICU discharge. Results: Fifty-six patients-32 (57%) male-aged 79 [77; 83] y were included. ICU mortality was 19%; 6-month mortality was 22%. Median ADL delta was -0.5 [-0.5-0] points. A low ADL score (P = .0438) and a low albumin level (P = .0213) at admission were the two independent factors associated with an unfavourable outcome. Conclusion: Mortality and loss of independence were high in this elderly population during and after their surgical ICU stay. The benefit of a systematic collaboration between intensive care specialists, ICU nurses, and geriatricians, to assess and manage nutritional and functional problems and to prevent a pejorative outcome in patients over 75 years old admitted in surgical ICU needs to be studied. Relevance to clinical practice: There should be systematic screening for objective markers of undernutrition and frailty on ICU admission of older patients as they are associated with a poor prognosis.
Article
Full-text available
Purpose: To document and analyse the decision to withhold or withdraw life-sustaining treatment (LST) in a population of very old patients admitted to the ICU. Methods: This prospective study included intensive care patients aged ≥ 80 years in 309 ICUs from 21 European countries with 30-day mortality follow-up. Results: LST limitation was identified in 1356/5021 (27.2%) of patients: 15% had a withholding decision and 12.2% a withdrawal decision (including those with a previous withholding decision). Patients with LST limitation were older, more frail, more severely ill and less frequently electively admitted. Patients with withdrawal of LST were more frequently male and had a longer ICU length of stay. The ICU and 30-day mortality were, respectively, 29.1 and 53.1% in the withholding group and 82.2% and 93.1% in the withdrawal group. LST was less frequently limited in eastern and southern European countries than in northern Europe. The patient-independent factors associated with LST limitation were: acute ICU admission (OR 5.77, 95% CI 4.32-7.7), Clinical Frailty Scale (CFS) score (OR 2.08, 95% CI 1.78-2.42), increased age (each 5 years of increase in age had a OR of 1.22 (95% CI 1.12-1.34) and SOFA score [OR of 1.07 (95% CI 1.05-1.09 per point)]. The frequency of LST limitation was higher in countries with high GDP and was lower in religious countries. Conclusions: The most important patient variables associated with the instigation of LST limitation were acute admission, frailty, age, admission SOFA score and country. Trial registration: ClinicalTrials.gov (ID: NTC03134807).
Article
Full-text available
Purpose: Very old critical ill patients are a rapid expanding group in the ICU. Indications for admission, triage criteria and level of care are frequently discussed for such patients. However, most relevant outcome studies in this group frequently find an increased mortality and a reduced quality of life in survivors. The main objective was to study the impact of frailty compared with other variables with regards to short-term outcome in the very old ICU population. Methods: A transnational prospective cohort study from October 2016 to May 2017 with 30 days follow-up was set up by the European Society of Intensive Care Medicine. In total 311 ICUs from 21 European countries participated. The ICUs included the first consecutive 20 very old (≥ 80 years) patients admitted to the ICU within a 3-month inclusion period. Frailty, SOFA score and therapeutic procedures were registered, in addition to limitations of care. For measurement of frailty the Clinical Frailty Scale was used at ICU admission. The main outcomes were ICU and 30-day mortality and survival at 30 days. Results: A total of 5021 patients with a median age of 84 years (IQR 81-86 years) were included in the final analysis, 2404 (47.9%) were women. Admission was classified as acute in 4215 (83.9%) of the patients. Overall ICU and 30-day mortality rates were 22.1% and 32.6%. During ICU stay 23.8% of the patients did not receive specific ICU procedures: ventilation, vasoactive drugs or renal replacement therapy. Frailty (values ≥ 5) was found in 43.1% and was independently related to 30-day survival (HR 1.54; 95% CI 1.38-1.73) for frail versus non-frail. Conclusions: Among very old patients (≥ 80 years) admitted to the ICU, the consecutive classes in Clinical Frailty Scale were inversely associated with short-term survival. The scale had a very low number of missing data. These findings provide support to add frailty to the clinical assessment in this patient group. Trial registration: ClinicalTrials.gov (ID: NCT03134807).
Article
Background: Very elderly patients are one of the fastest growing population in ICUs worldwide. There are lots of controversies regarding admission, discharge of critically ill elderly patients, and also on treatment intensity during the ICU stay. As a consequence, practices vary considerably from one ICU to another. In that perspective, we collected opinions of experienced ICU physicians across Europe on statements focusing on patients older than 80. Methods: We sent an online questionnaire to the coordinator ICU physician of all participating ICUs of an recent European, observational study of Very old critically Ill Patients (VIP1 study). This questionnaire contained 12 statements about admission, triage, treatment and discharge of patients older than 80. Results: We received answers from 162 ICUs (52% of VIP1-study) spanning 20 different European countries. There were major disagreements between ICUs. Responders disagree that: there is clear evidence that ICU admission is beneficial (37%); seeking relatives' opinion is mandatory (17%); written triage guidelines must be available either at the hospital or ICU level (20%); level of care should be reduced (25%); a consultation of a geriatrician should be sought (34%) and a geriatrician should be part of the post-ICU trail (11%). The percentage of disagreement varies between statements and European regions. Conclusion: There are major differences in the attitude of European ICU physicians on the admission, triage and treatment policies of patients older than 80 emphasizing the lack of consensus and poor level of evidence for most of the statements and outlining the need for future interventional studies.
Article
One of the most important decisions that a physician makes is whether to admit a patient to the intensive care unit (ICU). The modern ICU provides a capacity for advanced monitoring and life support that is typically unavailable elsewhere in the hospital and is lifesaving for patients with a wide array of acute deteriorations in health. However, ICU care is also one of the most expensive, intensive, and intrusive endeavors in health care. Although patients admitted to the ICU account for approximately one-quarter of hospitalized patients, they account for half of total hospital expenditures in the United States, with costs estimated at $110 to $260 billion per year or approximately 1% of the gross domestic product.¹- 3 Furthermore, ICU care can be unnecessary, harmful, or futile. Importantly, the provision of ICU services is increasing. In an era when efforts to contain health care costs have decreased total hospital beds, the number of ICU beds continues to increase.⁴ An important question is whether this growth in ICU services and beds is necessary to meet the demands of an expanding population of critically ill patients or whether ICU beds are being oversupplied and subsequently are being filled with patients who might be cared for in less-intense settings at lower cost with similar or better outcome.
Article
Importance The high mortality rate in critically ill elderly patients has led to questioning of the beneficial effect of intensive care unit (ICU) admission and to a variable ICU use among this population. Objective To determine whether a recommendation for systematic ICU admission in critically ill elderly patients reduces 6-month mortality compared with usual practice. Design, Setting, and Participants Multicenter, cluster-randomized clinical trial of 3037 critically ill patients aged 75 years or older, free of cancer, with preserved functional status (Index of Independence in Activities of Daily Living ≥4) and nutritional status (absence of cachexia) who arrived at the emergency department of one of 24 hospitals in France between January 2012 and April 2015 and were followed up until November 2015. Interventions Centers were randomly assigned either to use a program to promote systematic ICU admission of patients (n=1519 participants) or to follow standard practice (n=1518 participants). Main Outcomes and Measures The primary outcome was death at 6 months. Secondary outcomes included ICU admission rate, in-hospital death, functional status, and quality of life (12-Item Short Form Health Survey, ranging from 0 to 100, with higher score representing better self-reported health) at 6 months. Results One patient withdrew consent, leaving 3036 patients included in the trial (median age, 85 [interquartile range, 81-89] years; 1361 [45%] men). Patients in the systematic strategy group had an increased risk of death at 6 months (45% vs 39%; relative risk [RR], 1.16; 95% CI, 1.07-1.26) despite an increased ICU admission rate (61% vs 34%; RR, 1.80; 95% CI, 1.66-1.95). After adjustments for baseline characteristics, patients in the systematic strategy group were more likely to be admitted to an ICU (RR, 1.68; 95% CI, 1.54-1.82) and had a higher risk of in-hospital death (RR, 1.18; 95% CI, 1.03-1.33) but had no significant increase in risk of death at 6 months (RR, 1.05; 95% CI, 0.96-1.14). Functional status and physical quality of life at 6 months were not significantly different between groups. Conclusions and Relevance Among critically ill elderly patients in France, a program to promote systematic ICU admission increased ICU use but did not reduce 6-month mortality. Additional research is needed to understand the decision to admit elderly patients to the ICU. Trial Registration clinicaltrials.gov Identifier: NCT01508819
Article
Purpose: To assess the reliability of physicians' prognoses for intensive care unit (ICU) survivors with respect to long-term survival and health related quality of life (HRQoL). Methods: We performed an observational cohort-study in a single mixed tertiary ICU in The Netherlands. ICU survivors with a length of stay >48h were included. At ICU discharge, one-year prognosis was estimated by physicians using the four-option Sabadell score to record their expectations. The outcome of interest was poor outcome, which was defined as dying within one-year follow-up, or surviving with an EuroQoL5D-3L index <0.4. Results: Among 1399 ICU survivors, 1068 (76%) subjects were expected to have a good outcome; 243 (18%) a poor long-term prognosis; 43 (3%) a poor short-term prognosis, and 45 (3%) to die in hospital (i.e. Sabadell score levels). Poor outcome was observed in 38%, 55%, 86%, and 100% of these groups respectively (concomitant c-index: 0.61). The expected prognosis did not match observed outcome in 365 (36%) patients. This was almost exclusively (99%) due to overoptimism. Physician experience did not affect results. Conclusions: Prognoses estimated by physicians incorrectly predicted long-term survival and HRQoL in one-third of ICU survivors. Moreover, inaccurate prognoses were generally the result of overoptimistic expectations of outcome.
Article
The “very old intensive care patients” (abbreviated to VOPs; greater than 80 years old) are probably the fastest expanding subgroup of all intensive care unit (ICU) patients. Up until recently most ICU physicians have been reluctant to admit these VOPs. The general consensus was that there was little survival to gain and the incremental life expectancy of ICU admission was considered too small. Several publications have questioned this belief, but others have confirmed the poor long-term mortality rates in VOPs. More appropriate triage (resource limitation enforced decisions), admission decisions based on shared decision-making and improved prediction models are also needed for this particular patient group. Here, an expert panel proposes a research agenda for VOPs for the coming years.
Article
Context: Intoxicated patients are frequently admitted from the emergency room to the ICU for observational reasons. The question is whether these admissions are indeed necessary. Objective: The aim of this study was to develop a model that predicts the need of ICU treatment (receiving mechanical ventilation and/or vasopressors <24?h of the ICU admission and/or in-hospital mortality). Materials and methods: We performed a retrospective cohort study from a national ICU-registry, including 86 Dutch ICUs. We aimed to include only observational admissions and therefore excluded admissions with treatment, at the start of the admission that can only be applied on the ICU (mechanical ventilation or CPR before admission). First, a generalized linear mixed-effects model with binominal link function and a random intercept per hospital was developed, based on covariates available in the first hour of ICU admission. Second, the selected covariates were used to develop a prediction model based on a practical point system. To determine the performance of the prediction model, the sensitivity, specificity, positive, and negative predictive value of several cut-off points based on the assigned number of points were assessed. Results: 9679 admissions between January 2010 until January 2015 were included for analysis. In total, 632 (6.5%) of the patients admitted to the ICU eventually turned out to actually need ICU treatment. The strongest predictors for ICU treatment were respiratory insufficiency, age >55 and a GCS <6. Alcohol and ?other poisonings? (e.g., carbonmonoxide, arsenic, cyanide) as intoxication type and a systolic blood pressure ?130?mmHg were indicators that ICU treatment was likely unnecessary. The prediction model had high sensitivity (93.4%) and a high negative predictive value (98.7%). Discussion and conclusion: Clinical use of the prediction model, with a high negative predictive value (98.7%), would result in 34.3% less observational admissions.