Secondary Logo

Journal Logo

Systematic Review

Characteristics and Outcomes of Patients With Frailty Admitted to ICU With Coronavirus Disease 2019: An Individual Patient Data Meta-Analysis

Subramaniam, Ashwin MBBS, MMed, FRACP, FCICM1,2; Anstey, Christopher MBBS, MSc, FANZCA, FCICM3,4; Curtis, J. Randall MD, MPH5,6; Ashwin, Sushma BBA, MHE Candidate7; Ponnapa Reddy, Mallikarjuna MBBS, FCICM, DCH, DECMO, MCTR1,8; Aliberti, Márlon Juliano Romero MD, PhD9,10; Avelino-Silva, Thiago Junqueira MD, PhD9,11; Welch, Carly MBCHB, MRCP(UK), MRes12; Koduri, Gouri MRCP(UK), MD13; Prowle, John R. MA, MB BChir, MSc, MD, FRCP, FFICM14,15; Wan, Yize I. BMedSci(Hons), PhD, MRCP, FRCA, AFHEA14,15; Laurent, Michaël R. MD, PhD16; Marengoni, Alessandra MD, PhD17; Lim, Jun Pei MBBS, MRCP(UK), MMed18,19; Pilcher, David MBBS, MRCP(UK), FRACP, FCICM20,21,22; Shekar, Kiran MBBS, FCICM, FCCCM, PhD23,24,25

Author Information
doi: 10.1097/CCE.0000000000000616

Abstract

BACKGROUND

Coronavirus disease 2019 (COVID-19) causes severe respiratory illness in about 13% of cases and can rapidly transform into a life-threatening illness in about 4% of cases, particularly in those with comorbidities. The life-threatening form of the disease is characterized by severe acute respiratory distress syndrome, cytokine release, metabolic acidosis, and venous thromboembolism and/or disseminated intravascular coagulopathy (1). The surge in critically ill patients with respiratory failure has overwhelmed ICU capacity in many healthcare systems across the world (2,3). Studies published during the early phase of the pandemic have shown poor outcomes in mechanically ventilated patients with COVID-19 (4), although some studies suggest survival has improved over time (5,6). Older people, particularly patients with frailty, were unequally affected (7) with those with a higher degree of frailty, and cumulative comorbidities were linked with higher mortality in patients with COVID-19 (7–11). It was also postulated that patients with frailty have a compromised immune response to severe acute respiratory syndrome coronavirus 2, which led to higher short-term mortality, slower recovery, and further functional decline in patients (12). Given that healthcare resources worldwide were overstretched by the unprecedented COVID-19 pandemic, there has been interest in reliable assessment tools to inform patient prioritization for scarce intensive care resources.

Frailty tools, such as the Clinical Frailty Scale (CFS), have found clinical utility as an adjunct to age-based criteria for critical care triage decisions (The National Institute for Health and Care Excellence, NICE triage guidelines) (13). However, the guideline has been criticized as it was extrapolated from prepandemic data (14). Many studies have recently been published on the impact of frailty in patients with COVID-19 with some reporting on patients in the ICU. Due to these limitations in existing information, variations in study design, limitations of published data, and the heterogeneity in the measures of frailty, conventional meta-analyses based on these studies will have limited accuracy. We conducted an individual patient data meta-analysis to evaluate the characteristics and outcomes across the range of frailty in patients with COVID-19 admitted to ICU.

MATERIALS AND METHODS

The study was registered with The International Prospective Register of Systematic Reviews (CRD42020224255) and conducted in adherence with the Preferred Reporting Items for Systematic Reviews and Meta-analyses Statement (15).

Search Strategy, Information Sources, and Study Selection

Two authors (A.S., S.A.) independently searched the publicly available COVID-19 living systematic review (16). It is updated daily to provide a dynamic database of research papers related to COVID-19 that are indexed by PubMed, Excerpta Medica dataBASE, MedRxiv, and BioRxiv. Studies were extracted between December 1, 2019, and February 28, 2021, using the search terms “frail” and “frailty” within the title and the abstract columns of the systematic review list (Supplementary Table 1, https://links.lww.com/CCX/A896). Due to the rapidly evolving pandemic, preprint studies that were yet to be peer-reviewed were included to capture as much data as possible. These terms were combined with the Boolean operator “OR”.

Eligibility Criteria

The corresponding authors of eligible studies (17–33) (Supplementary Table 2, https://links.lww.com/CCX/A896) were invited to participate and share their original individual patient data. We included studies that reported on adults greater than or equal to 18 years old with laboratory-confirmed symptomatic COVID-19 patients, a documented CFS score, and admitted to ICU. Only the patients with hospital outcomes were included in the final analysis.

Data Extraction

Data collection included patient demographics (age, sex, comorbidities, ethnicity, ICU admission source, smoking status), CFS score, ICU organ supports, such as the need for mechanical ventilation (MV), noninvasive ventilation, vasopressors, and/or renal replacement therapy, medical treatment limitation order, ICU and hospital mortality, and ICU and hospital length of stay (LOS). The treatment limitation order implied that medical treatment would be constrained by either patient wishes or medical futility but not necessarily implying that the patient was expected to die during this ICU admission (34). These were independently extracted, tabulated, and verified by the two authors (A.S., S.A.).

Quality Assessment and Risk of Bias in Individual Studies

The quality of studies was assessed using the Newcastle-Ottawa Scale (NOS) tool (35) by two authors (S.A., M.P.R.) independently assessed selected based on the predefined criteria for nonrandomized study selection, comparability, and the ascertainment of the outcomes of interest. Any discrepancies from the NOS were reviewed and resolved by a third author (A.S.).

Explanatory Variable—Frailty

In the Canadian Study of Health and Aging, CFS based on a nine-point judgment-based categorical scale was used for frailty measurement (36). This scale has demonstrated validity and reliability in frailty assessment in ICU patients and other populations (36,37). This scale includes CFS = 1 (very fit), 2 (well), 3 (managing well), 4 (vulnerable), 5 (mildly frail), 6 (moderately frail), 7 (severely frail), 8 (very severely frail), and 9 (terminally ill) (36). The modified eight-category CFS is the most used frailty assessment in the critically ill (38). Frailty scores were also dichotomized as non-frail (CFS = 1–4) or frail (CFS = 5–8) according to accepted definitions (37), with the frail cohort further considered in terms of mild/moderate frailty (CFS = 5–6) and severe/very severe frailty (CFS = 7–8).

Ethical Issues

The individual patient data meta-analysis was exempt from ethics approval because we obtained deidentified data from previously published and ethically approved individual studies.

Other Covariates

Exposure variables such as the CFS, age, sex, chronic respiratory disease, chronic kidney disease, ischemic heart disease, admission source, Sequential Organ Failure Assessment (SOFA), and Acute Physiology and Chronic Health Evaluation (APACHE) 2 scores were investigated as risk factors for hospital mortality in patients with COVID-19.

Main Outcome(s)

This was a one-stage individual patient data meta-analysis to assess the ordinal approximation of continuous variable covariates (CFS and age) (39). The primary aim was to evaluate whether frailty scores predicted outcomes for patients with COVID-19 admitted to ICU, including ICU mortality, hospital mortality, and discharge destination after adjusting for age and gender. The primary outcome was hospital mortality. We examined the following secondary outcomes: organ support within the ICU (MV, noninvasive ventilation, renal replacement therapy, vasoactive infusion, and extracorporeal membrane oxygenation); length of ICU and hospital stay; ICU bed days; and discharge destination.

Missing Data

There were minimal missing data for the primary outcome (0.2%). However, there were missing data with illness severity scores (42.9%), comorbidities (11.4%), presenting symptoms (8.2%), biochemistry within 24 hours of ICU admission (10.5%), use of noninvasive ventilation (37.8%), use of invasive MV (IMV, 32.9%), hospital LOS (0.7%), and discharge destination (31.3%). For the main predictors, the data were generally complete, and imputation was deemed not necessary.

Statistical Analysis

In this study, the data were initially checked for completeness and validity with queries directed back to the contributing institutions. Normality was assessed in continuous data by employing both normal quantile (probit) plots and the Shapiro-Wilk test. Normally distributed data were reported using the mean (sd). Nonnormal, categorical, and dichotomous data were reported using either the median (interquartile range [IQR]) or number (frequency [%]), respectively. The final dataset included 2,001 patients drawn from seven discrete institutions with data for 80 variables collected for each patient. All analyses were clustered by the institution. An initial analysis was conducted between survivors and nonsurvivors to identify the independent predictors of mortality in critically ill patients with COVID-19. The selection of predictors was based on the clinical experience of the investigators with comparisons conducted on demographic, comorbidity, symptomatology, illness severity score, and biochemical and hematological data that were available within the first 24 hours of admission. ICU and hospital mortality were examined using a logistic model. When specifically analyzing from a clinical frailty point of view, the binary CFS categories nonfrail/frail, data were compared using either a standard t test for normally distributed data, the Wilcoxon rank-sum test for categorical data, or the Fisher exact test for dichotomous data. Our primary analysis included the CFS scale (1–8) with subsequent adjustment for age and sex. Secondary multivariable logistic models were constructed to examine the effects of age, CFS, obesity measured as body mass index less or greater than 30 kg/m2, presence or absence of comorbidities including active cancer, dementia, and the neutrophil-lymphocyte ratio as a marker of chronic inflammation on ICU mortality with the predictors selected from the results of the univariate analysis described above. Two-way interactions were also tested between the CFS and the significant predictors. Although overfitting was possible (40), we employed a sequential deletion of nonsignificant predictors using backward stepwise regression. Potential misspecification was tested using the linktest was conducted at each step of model development. Postestimation checks for model specification and presence of collinearity were conducted using the link test and variance inflation factor, respectively. Results were reported as the odds ratio of death with its 95% CI and p value. A competing risk analysis was performed next to examine the marginal probability of death using both the presence of ventilation and CFS. The method of Fine and Gray was used to generate the cumulative prevalence function (41). The significant predictors from the logistic model were used, and results were expressed as subhazard ratios and their 95% CIs. Youden’s Y statistic was calculated for each CFS score thus yielding individual sensitivity and specificity results. All analyses were conducted using STATA (Version 16.0; StataCorp LLC, College Station, TX) with the level of significance set at α less than 0.05.

RESULTS

Of the 616 studies identified, 16 studies (17–31) (Supplementary Table 2, https://links.lww.com/CCX/A896) met all eligibility criteria. All 16 corresponding authors were invited to participate and share their original individual patient data. We included seven studies (17,21,24,26,27,31,32) that provided individual patient data on 9,332 hospitalizations (Supplementary Table 3, https://links.lww.com/CCX/A896; Supplementary Fig. 1, https://links.lww.com/CCX/A896); of these, 3,690 patients (39.5%) were deemed frail. Although considered, no non-English articles were included. One of the included studies was from the preprint server (24). All included studies were observational cohort studies: three prospective (17,31,32), whereas the other four were retrospective (21,24,27,33). The CFS was documented prospectively by clinicians in six studies at hospitalization (17,21,24,27,31,32), whereas one study retrospectively scored the CFS based on patient information found in electronic medical records (33). Based on the NOS criteria, two studies (17,31) were good, three were fair (27,32,33), and two studies (21,24) were of poor quality. There were a total of 2,804 patients from the excluded nine studies, of which 476 (17%) were admitted to ICU. It was unclear as to what proportion of these patients were frail. Of the 2,003 patients with CFS scores, two patients with CFS scores of 9 were removed. A total of 2,001 patients (21.4%; 2,001/9,332) admitted to the ICU were included in the final analysis.

Survivor Versus Nonsurvivor Analysis

The initial analysis identified that 54.1% of patients (1,083/2,001) admitted to the ICU died (Supplementary Table 4, https://links.lww.com/CCX/A896). The independent predictors that increase the probability of death in these patients with COVID-19 were increasing age, CFS greater than or equal to 4, increasing SOFA score, use of IMV, dialysis and vasopressors, and rising or high lactate; a history of hypertension was associated with a lower likelihood of death (Supplementary Tables 5 and 6, https://links.lww.com/CCX/A896; Supplementary Fig. 2, https://links.lww.com/CCX/A896).

Analysis Based on Frailty Status

One of the major predictors of mortality in patients with COVID-19 was their frailty status. Of the 2,001 included patients admitted to the ICU, 80.6% (1,613/2,001) were nonfrail and 19.4% (388/2,001) were frail. The baseline characteristics, presenting symptoms, biochemistry, and acid-base between frail and nonfrail patients are presented in Table 1. The demographics based on the CFS score are presented in Supplementary Table 7 (https://links.lww.com/CCX/A896). Frailty increased with advancing age (Supplementary Fig. 3, https://links.lww.com/CCX/A896). Nonfrail patients were more likely to have presenting symptoms such as fever and myalgia/lethargy, whereas patients with frailty were more likely to present with delirium. The time from symptoms to hospitalization was shorter for patients with frailty when compared with nonfrail patients (median [IQR] days, 7 [4–10] vs 8 [5–11]; p = 0.001). Patients with frailty were more likely to have an accompanying acute kidney injury. They were also more lymphopenic with a higher neutrophil-to-lymphocyte ratio than nonfrail patients. A lower proportion of male patients were frail (47% vs 54%; p < 0.001). Although home residence was similar, there was a higher proportion of frail than nonfrail patients residing in a 24-hour long-term facility (8.8% vs 2.0%; p = 0.002) before the index hospitalization. The patients classified as frail were older and had higher illness severity scores (Simplified Acute Physiology Score 2). The patients with frailty also had higher chronic comorbidities, particularly hypertension, cardiovascular disease, diabetes mellitus, active cancer, and dementia, but were less likely to be obese (body mass index [BMI] ≥ 30 kg/m2).

TABLE 1. - Demographics of Patients With Coronavirus Disease 2019 Admitted to ICU Based on Frailty Status
Characteristics Nonfrail (CFS 1–4) Frail (CFS 5–8) pa
n 1,613 388
General demographics
 Male sex, n (%) 870 (54) 182 (47) 0.008
 Age (yr), mean (sd) 62.5 (11.3) 70.1 (11.9) < 0.001
Age categories, yr, n (%)
 < 50 164 (10.2) 17 (4.4) < 0.001
 50–64.9 731 (45.3) 91 (23.4) < 0.001
 65–74.9 516 (32.0) 150 (38.7) 0.001
 ≥ 75 202 (12.5) 130 (33.5) < 0.001
Admission source, n (%)
 Home 506 (90.5) 81 (90.0) 0.85
 24-hr long-term facility 11 (2.0) 8 (8.8) 0.002
 Other 42 (7.5) 1 (1.1) 0.020
Smoking status, n (%)
 Current smoker 287 (28.9) 77 (26.3) 0.42
 Ex or nonsmoker 707 (71.1) 215 (73.6)
Documented comorbidities, n (%)
 Hypertension 693 (66.4) 220 (73.8) 0.017
 Cardiovascular disease 241 (15.5) 141 (36.4) < 0.001
 Cerebrovascular accident 46 (4.4) 53 (17.7) < 0.001
 Active cancer 133 (8.6) 89 (23.0) < 0.001
 Chronic respiratory diseaseb 251 (15.7) 67 (17.2) 0.44
 Obesity (body mass index ≥ 30 kg/m2) 496 (35.1) 82 (23.0) < 0.001
 Chronic kidney disease 134 (13.7) 78 (26.2) < 0.001
 Diabetes mellitus 643 (40.1) 180 (46.4) 0.025
 Dementia 11 (0.7) 41 (10.6) < 0.001
 Charlson Comorbidity Index, median (IQR) 1 (0–3) 3 (1, 6) < 0.001
 No comorbidities 237 (14.7) 71 (18.3) 0.002
 Number of comorbidities ≤ 2 390 (24.2) 63 (16.2)
 Number of comorbidities > 2 986 (61.1) 254 (65.5)
 CFS, median (IQR) 3 (2–3) 6 (5–6) < 0.001
Illness severity scores, median (IQR)
 APACHE 2 14 (6, 23) 14 (9–23) 0.07
 APACHE 3 No data No data -
 Simplified Acute Physiology Score 2 38 (24–56) 41 (30–57) 0.006
 Sequential Organ Failure Scale 7 (5–12) 8 (5–12) 0.09
Symptoms, n (%)
 Respiratory 1,344 (91.7) 329 (89.9) 0.25
 Sputum 35 (4.0) 14 (5.2) 0.39
 Fever 921 (62.9) 183 (50.0) < 0.001
 Lethargy/myalgia 416 (45.9) 97 (35.0) 0.001
 Delirium 126 (8.6) 72 (19.8) < 0.001
 Gastrointestinal 120 (13.3) 27 (9.8) 0.15
 Symptom time (d) 8 (5–11) 7 (4–10) 0.001
 Time to ICU (hr) 3 (1–5) 3 (2–5) 0.46
Pathology results (first 24 hr), median (IQR)
 Acid base status
  pH 7.41 (7.33–7.46) 7.39 (7.33–7.45) 0.20
  Pao 2 (mm Hg) 70 (60–84) 73 (59–90) 0.18
  Paco 2 (mm Hg) 38 (33–46) 38 (32–44) 0.42
  Hco 3 (mmol/L) 24 (21–26) 23 (20–27) 0.024
  Arterial O2 saturation 93 (89–96) 94 (90––96) 0.023
  L-lactate (mmol/L) 11 (2–16) 12 (7–18) < 0.001
 Biochemistry
  C-reactive protein 154 (78–248) 144 (52–260) 0.11
  Urea 33 (9–66) 62 (25–103) < 0.001
  Creatinine 97 (71–164 115 (79–195) 0.002
  Lactate dehydrogenase 471 (365–629) 433 (316–551) < 0.001
   d-dimer 1,670 (784–5,193) 2,116 (1,023–5,861) 0.002
  Troponin 0.08 (0.02–8.00) 0.05 (0.03–0.16) 0.044
 Hematology
  Neutrophils 7.6 (5.0–11.4) 7.8 (5.0–11.5) 0.98
  Lymphocytes 0.83 (0.57–1.19) 0.72 (0.47–1.1) < 0.001
  Neutrophil-lymphocyte ratio 8.8 (5.2–15.6) 10.0 (5.6–18.9) 0.015
  Platelets 217 (159–300) 195 (131–260) < 0.001
 Radiology, n (%)
  Abnormal chest radiograph 1,237 (76.7) 285 (73.5) 0.102
APACHE = Acute Physiology and Chronic Health Evaluation, CFS = Clinical Frailty Scale, IQR = interquartile range.
aSome of the results will be statistically significant because of the large sample size but may not be clinically significant.
bChronic obstructive pulmonary disease and/or asthma.
Dashes indicate number of patients included in frail and nonfrail group.

Primary Analyses of Primary Outcome

Patients with frailty were more likely to die in ICU (unadjusted mortality 26.8% vs 17.9%; p = 0.044) and in hospital (unadjusted mortality 65.2% vs 41.8%; p < 0.001). Frailty status, after adjusting for age and sex, was independently associated with hospital mortality but not ICU mortality (Table 2 and Fig. 1, A and B). In secondary analyses, the relationship between frailty and hospital mortality remained significant independent of age, BMI, and neutrophil-lymphocyte ratio (Table 3).

TABLE 2. - Unadjusted and Adjusted (for Age and Sex) for ICU and Hospital Mortality (Primary Outcome)
Clinical Frailty Scale No. of Patients ICU Mortalitya, n (%) Unadjusted ICU Mortality, OR (95% CI; p) Adjusted ICU Mortality, OR (95% CI; p) Hospital Mortalitya, n (%) Unadjusted Hospital Mortality, OR (95% CI; p) Adjusted Hospital Mortality, OR (95% CI; p)
1 193 20 (10.4) Reference Reference 53 (27.5) Reference Reference
2 450 59 (13.1) 0.82 (0.44–1.51; p = 0.52) 0.90 (0.48–1.67; p = 0.73) 165 (37.6) 1.43 (0.98–2.06; p = 0.06) 1.37 (0.94–2.00; p = 0.10)
3 669 143 (21.4) 0.87 (0.48–1.52; p = 0.58) 0.92 (0.51––1.64; p = 0.77) 295 (44.1) 1.98 (1.40–2.81; p < 0.001) 1.57 (1.10–2.25; p = 0.014)
4 301 67 (22.3) 0.90 (0.49–1.65; p = 0.74) 0.88 (0.47–1.63; p = 0.68) 161 (53.5) 3.04 (2.06–4.48; p < 0.001) 2.21 (1.48–3.30; p < 0.001)
5 180 49 (27.2) 1.16 (0.61–2.19; p = 0.65) 0.99 (0.51–1.90; p = 0.97) 109 (60.6) 4.04 (2.62–6.24; p < 0.001) 2.70 (1.71–4.25; p < 0.001)
6 124 33 (26.6) 1.28 (0.64–2.55; p = 0.48) 1.15 (0.57–2.33; p = 0.70) 89 (64.5) 4.62 (2.85–7.51; p < 0.001) 3.16 (1.91––5.23; p < 0.001)
7 70 17 (24.3) 0.90 (0.41–1.97; p = 0.80) 0.80 (0.36–1.78; p = 0.59) 43 (61.4) 3.91 (2.20–6.94; p < 0.001) 2.61 (1.44–4.73; p = 0.002)
8 14 5 (35.7) 1.26 (0.36–4.35; p = 0.72) 1.05 (0.30–3.74; p = 0.94) 12 (85.7) 14.73 (3.19–67.9; p = 0.001) 14.20 (2.98–67.6; p = 0.001)
OR = odds ratio.
aDichotomized unadjusted analysis for frail vs nonfrail patients: ICU mortality: 26.8% vs 17.9%; p = 0.044; hospital mortality: 65.2% vs 41.8%; p < 0.001.

TABLE 3. - Secondary Analysis of Primary Outcome With Multipredictor Modeling for Hospital Mortality
Variables Multivariable Model Interaction With CFS
OR 95% CI p p
Age 1.04 1.02–1.06 < 0.001 0.65
CFS 1.23 1.12–1.35 < 0.001 -
Mechanical ventilation 4.45 3.20–6.18 < 0.001 0.07
Dialysisa 3.98 2.95–5.37 < 0.001 0.18
Obesity (body mass index ≥ 30 kg/m2) 0.59 0.44–0.79 0.001 0.29
Active cancer 1.71 1.15–2.52 0.007 0.80
CFS = Clinical Frailty Scale.
aDialysis and chronic kidney disease highly correlated.

F1
Figure 1.:
Hospital mortality according to Clinical Frailty Scale (CFS) score for all patients adjusted for age and sex. A, Is the raw (unadjusted) data by CFS. B, Is the data adjusted for age and sex. C, Is as for (B) with ventilation included.

Secondary Outcomes

The raw outcomes are presented in Supplementary Table 8 (https://links.lww.com/CCX/A896) and based on the CFS score in Supplementary Table 9 (https://links.lww.com/CCX/A896).

Mechanical Ventilation.

Excluding the 658 patients (32.9%) with missing data, a total of 1,014 of the 1,343 patients (75.5%) received MV, most of them had a CFS score between 2 and 4 (Fig. 1C). Of these 1,014 patients who received MV, there was no difference between frail and non-frail patients (68.2% [199/292] vs 77.5% [815/1,051]; p = 0.21). However, patients with frailty spent a shorter median (IQR) duration on MV (9 d [5–16 d] vs 11 d [6–18 d]; p = 0.012). The unadjusted mortality rates were higher in patients requiring MV than those who did not (Supplementary Table 8, https://links.lww.com/CCX/A896; Supplementary Fig. 4, https://links.lww.com/CCX/A896). All patients with CFS score of 8 who were mechanically ventilated died. Figure 2 describes the cumulative risk of death over time among patients who received MV, which demonstrated that the cumulative risk of death decreases with more days on MV. Multivariable linear regression in ICU survivors indicated that the adjusted geometric mean duration of MV reduced significantly with increasing CFS score (from 9.5 d [8.3–10.7] for CFS 1 to 3.6 [2.3–5.0] for CFS 7 and 8 combined) (Supplementary Table 10, https://links.lww.com/CCX/A896).

F2
Figure 2.:
Graphical representation of cumulative incidence of death for frailty.

ICU and Hospital LOS.

Patients with frailty admitted to ICU were more likely to have shorter median (IQR) LOS in ICU (8 d [4–16 d] vs 11 d [5–20 d]; p < 0.001). Of the total 24.3 × 1,000 ICU bed days, patients with frailty only contribute 12.3% of the ICU bed days (Supplementary Fig. 4, https://links.lww.com/CCX/A896). Similar findings were observed when only survivors were analyzed. Patients with CFS score of 7 and 8 spent the shortest time in ICU (0.6 × 1,000 ICU bed days, 2.5%). However, the ICU bed days occupied by patients with frailty who died were almost double that of survivors for CFS score of 5–7. Patients with frailty also had shorter median (IQR) hospital LOS (13 d [8–23 d] vs 16 d [10–28 d]; p < 0.001) when compared with nonfrail patients (Supplementary Table 8, https://links.lww.com/CCX/A896). Heat map comparing age- and CFS-stratified data based on ICU survivors and ICU nonsurvivors demonstrated that most patients admitted to ICU were younger than 80 years old and CFS less than or equal to 6 (Supplementary Fig. 5, https://links.lww.com/CCX/A896).

Other Organ Support.

Patients with frailty were less likely to receive noninvasive ventilation (27% vs 35%; p = 0.011) or renal replacement therapy (25% vs 32%; p = 0.026) when compared with nonfrail patients. There was no difference in vasopressor infusion use between frail and non-frail patients (Supplementary Table 9, https://links.lww.com/CCX/A896).

Discharge Destination.

Patients with frailty were less likely to be discharged home (unadjusted 23% vs 45%; p < 0.001) and rehabilitation (unadjusted 23% vs 35%; p < 0.001). However, the unadjusted new discharges to 24-hour long-term facility discharge (1.5% vs 2.3%; p = 0.17) were similar between frail and nonfrail patients (Supplementary Table 9, https://links.lww.com/CCX/A896).

DISCUSSION

This multinational, individual patient data meta-analysis included 2,001 patients with COVID-19 admitted to an ICU from seven studies identified the following five key findings. First, increasing age and SOFA score, CFS score greater than or equal to 4, use of MV, vasopressors, renal replacement therapy, and hyperlactatemia were independent predictors of mortality. Second, A fifth of the patients admitted to ICU were frail, with almost two-thirds of these patients with frailty dying in hospital. The odds of hospital mortality increased from a CFS score of 3 when compared with patients with scores less than or equal to 2. Multivariable analysis demonstrated that older age and increasing frailty were independently associated with hospital mortality. Third, the impact of frailty on the use of MV differed with age, with younger and nonfrail patients being more likely to receive MV. Fourth, the frail ICU survivors received a shorter duration of MV and had a shorter ICU LOS. Fifth, the ICU bed days occupancy in patients with frailty was only 16% and spent a shorter time in ICU. This final finding may relate to decisions to limit invasive and burdensome treatments.

The hospital mortality of patients with COVID-19 admitted to ICU has ranged widely depending on the geographical location and the different levels of strain on the critical care system (18,22,24,25,31,42–50). A large cohort study of patients with COVID-19 treated in ICU during periods of peak COVID-19 found a two-fold increase in mortality compared with those treated during periods of low demand (42). Furthermore, several studies have demonstrated an association of higher mortality with an increased hospital or regional COVID-19 caseloads, regardless of whether the patients were frail or not (51). Having said that, hospital and ICU mortalities in patients with COVID-19 have improved over time (52). Although not specifically studied in patients with frailty, large-scale randomized trials identified treatments, such as dexamethasone and tocilizumab, have demonstrated improvements in overall mortality (53–55). Our study findings suggest the importance of caution in interpreting results from different time periods.

A single-center retrospective study from Italy of 105 patients observed that the frailty index was an independent predictor of both higher in-hospital mortality and lower proportions with ICU admission (56). A recent large prospective multinational study (Outcomes and Prognostic Factors in COVID-19) identified that frailty was independently associated with lower survival (57). Similarly, our individual patient data meta-analysis also observed that frailty was independently associated with hospital mortality among patients with COVID-19 admitted to ICU. A nationwide Turkish study of patients 65 years old and older, using the hospital frailty risk score, observed that frailty was independently associated with hospital mortality, ICU admission, and use of MV (44). These studies may suggest that the high risk of mortality in older patients along with ICU resource constraints may raise the question of triaging ICU admissions. Even in times of nonconstrained resources, shared decision-making may be informed by the risk of mortality assessments, to allow patients and their caregivers to make informed choices about their care. A recent systematic review and meta-analysis recommended that frailty screening may be helpful to stratify high-risk groups (58).

Our study observed MV was used more often among younger and nonfrail patients. This was consistent with a recent study in non-COVID patients that investigated the impact of frailty on the duration of ventilation where they observed frail young patients had a longer duration of ventilation but not old patients with frailty (59). In a recent large systematic review, the reported mortality in mechanically ventilated patients was 45% and was significantly higher with increasing age and higher among those receiving MV (4). In our meta-analysis, patients with frailty, both survivors and nonsurvivors, spent a shorter time on MV. This finding implies that the patients with frailty may have died sooner or may have had treatment limitations.

We identified that nonfrail patients accounted for 85% of the ICU bed days. This is contrary to the findings of a recent study of older non–COVID-19 ICU patients with pneumonia which found a significantly higher ICU bed occupancy by patients with frailty (60). Furthermore, the findings that frail ICU survivors received a shorter duration of MV and had shorter ICU LOS are noteworthy and somewhat counterintuitive. This finding may be influenced by differing patterns of care for frail older adults between countries, resource constraints related to patient triage, and possibly earlier decisions to limit life-sustaining treatments in these patients.

Even before the COVID-19 pandemic began, frailty was recognized as a predictive factor for adverse outcomes, such as mortality (61), hospitalizations (62), and readmission (63). Consequently, frailty was proposed as an important aspect of patient assessment early in the pandemic. Despite the stringent guidelines, the patients with COVID-19 remained eligible for ICU admission under the NICE guidelines, particularly following a ward deterioration. However, triaging patients just based on the frailty status is not justified by the current data (64,65). Indeed, an odds ratio of ~2 by itself is not useful clinically. A patient-centered approach to triage that incorporates frailty screening could be developed to rapidly assess patients for the severity of the presenting acute illness and the likelihood of medical interventions being successful (66). An option of triage committee involvement to provide an ethical framework to guide clinicians to equitable rationing under crisis standards of care has been proposed (67,68).

This individual patient data meta-analysis has several notable strengths. First, we had high-quality data from the seven included studies from diverse countries, both resource rich and limited, at their peak of the pandemic. Second, the CFS, which is the most used frailty assessment tool for critically ill patients, was reported in all of the included studies. Third, we incorporated prespecified several secondary analyses, including the competing risk analyses, to assess the impact of frailty on several important patient-centered ICU outcomes.

However, some of the limitations should be noted. First, the datasets from the seven included studies had missing data for a proportion of covariates, such as APACHE scores. This may introduce potential bias in parameter estimation and weaken the generalizability of the results. Second, it is important to acknowledge that the results may have been predominantly influenced by two main studies, one of which was a single-center study. This single-center study patients were from a single reference hospital that cared for the severe cases of COVID-19. As a result, this may not impact on generalizability. Third, the differences in the healthcare systems across the different studies included in this individual patient data meta-analysis may introduce variability that is difficult to address with clustering by institution. Fourth, although there is evidence that COVID-19 has a disproportionate impact on disease severity and mortality in minority racial and ethnic groups, we did not have adequate data on the patient’s race or ethnicity (17). Fifth, although patients with frailty tend to have treatment limitations (do not resuscitate, do not intubate, etc.) (69) which may guide their management, we did not have data on treatment limitations or pre-ICU triage decisions which undoubtedly influenced our results. Sixth, limitations of the NOS in terms of interrater reliability and external validation should be acknowledged (70). Finally, although imprecise CFS scoring is possible (71), there is evidence that the CFS has acceptable interindividual variation in the critically ill population and is validated to stratify older adults according to the level of vulnerability (36) and predict poor short- and long-term outcomes in critically ill patients (37,72–74).

CONCLUSIONS

In this multinational individual patient data meta-analysis, almost two thirds of patients with frailty with COVID-19 who were admitted to ICU died in hospital. Patients with frailty spent a shorter amount of time in ICU suggesting decisions to limit life-sustaining treatments play a role in our findings. Frailty captures risks beyond other known risk factors in those with COVID-19 admitted to the ICU. Future studies should consider incorporating frailty into the patient assessment process alongside other commonly used measures (age, sex, comorbidities, illness acuity) to support clinicians in making better decisions for severe forms of COVID-19.

REFERENCES

1. Ciceri F, Beretta L, Scandroglio AM, et al.: Microvascular COVID-19 lung vessels obstructive thromboinflammatory syndrome (MicroCLOTS): An atypical acute respiratory distress syndrome working hypothesis. Crit Care Resusc 2020; 22:95–97
2. Phua J, Weng L, Ling L, et al.: Intensive care management of coronavirus disease 2019 (COVID-19): Challenges and recommendations. Lancet Respir Med 2020; 8:506–517
3. Abate SM, Ahmed Ali S, Mantfardo B, et al.: Rate of intensive care unit admission and outcomes among patients with coronavirus: A systematic review and meta-analysis. PLoS One 2020; 15:e0235653
4. Lim ZJ, Subramaniam A, Reddy MP, et al.: Case fatality rates for COVID-19 patients requiring invasive mechanical ventilation: A meta-analysis. Am J Respir Crit Care Med 2021; 203:54–66
5. Dennis JM, McGovern AP, Vollmer SJ, et al.: Improving survival of critical care patients with coronavirus disease 2019 in England: A national cohort study, March to June 2020*. Crit Care Med 2021; 49:209–214
6. Seligman B, Charest B, Gagnon DR, et al.: Trends in 30-day mortality from COVID-19 among older adults in the Veterans Affairs system. J Am Geriatr Soc 2021; 69:1448–1450
7. Hagg S, Jylhava J, Wang Y, et al.: Age, frailty, and comorbidity as prognostic factors for short-term outcomes in patients with coronavirus disease 2019 in geriatric care. J Am Med Dir Assoc 2020; 21:1555–1559 e1552
8. Davis P, Gibson R, Wright E, et al.: Atypical presentations in the hospitalised older adult testing positive for SARS-CoV-2: A retrospective observational study in Glasgow, Scotland. Scott Med J 2021; 66:89–97
9. Wang D, Hu B, Hu C, et al.: Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020; 323:1061–1069
10. Guan WJ, Ni ZY, Hu Y, et al.: Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020; 382:1708–1720
11. Chen N, Zhou M, Dong X, et al.: Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 2020; 395:507–513
12. Pan D, Sze S, Minhas JS, et al.: Frailty and mortality in patients with COVID-19. Lancet Public Health 2020; 5:e581
13. National Institute for Health and Care Excellence: COVID- 19 rapid guideline: Critical care in adults 2020. Available at: https://www.nice.org.uk/guidance/ng159
14. Azoulay E, Beloucif S, Guidet B, et al.: Admission decisions to intensive care units in the context of the major COVID-19 outbreak: Local guidance from the COVID-19 Paris-region area. Crit Care 2020; 24:293
15. Moher D, Liberati A, Tetzlaff J, et al.; PRISMA Group: Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ 2009; 339:b2535
16. Counotte M, Imeri H, Ipekci M, et al.: Living evidence on COVID-19. Berne, Switzerland, Institute of Social and Preventive Medicine, 2020. Available at: https://ispmbern.github.io/covid-19/living-review/index.html. Accessed August 23, 2021
17. Apea VJ, Wan YI, Dhairyawan R, et al.: Ethnicity and outcomes in patients hospitalised with COVID-19 infection in East London: An observational cohort study. BMJ Open 2021; 11:e042140
18. Aw D, Woodrow L, Ogliari G, et al.: Association of frailty with mortality in older inpatients with Covid-19: A cohort study. Age Ageing 2020; 49:915–922
19. Brill SE, Jarvis HC, Ozcan E, et al.: COVID-19: A retrospective cohort study with focus on the over-80s and hospital-onset disease. BMC Med 2020; 18:194
20. Chinnadurai R, Ogedengbe O, Agarwal P, et al.: Older age and frailty are the chief predictors of mortality in COVID-19 patients admitted to an acute medical unit in a secondary care setting- A cohort study. BMC Geriatr 2020; 20:409
21. De Smet R, Mellaerts B, Vandewinckele H, et al.: Frailty and mortality in hospitalized older adults with COVID-19: Retrospective observational study. J Am Med Dir Assoc 2020; 21:928–932.e1
22. Fagard K, Gielen E, Deschodt M, et al.: Risk factors for severe COVID-19 disease and death in patients aged 70 and over: A retrospective observational cohort study. Acta Clin Belg 2021 Feb 21. [online ahead of print]
23. Hoek RAS, Manintveld OC, Betjes MGH, et al.; Rotterdam Transplant Group: COVID-19 in solid organ transplant recipients: A single-center experience. Transpl Int 2020; 33:1099–1105
24. Koduri G, Gokaraju S, Darda M, et al.: Clinical characteristics and outcomes of 500 patients with COVID pneumonia – Results from a single center (Southend University Hospital). medRxiv [Preprint posted online August 14, 2020]. doi: 10.1101/2020.08.13.20163030
25. Kokoszka-Bargieł I, Cyprys P, et al.: Intensive care unit admissions during the first 3 months of the COVID-19 pandemic in Poland: A single-center, cross-sectional study. Med Sci Monit 2020; 26:e926974
26. Lim JP, Low KYH, Lin NJJ, et al.: Predictors for development of critical illness amongst older adults with COVID-19: Beyond age to age-associated factors. Arch Gerontol Geriatr 2021; 94:104331
27. Marengoni A, Zucchelli A, Grande G, et al.: The impact of delirium on outcomes for older adults hospitalised with COVID-19. Age Ageing 2020; 49:923–926
28. Owen RK, Conroy SP, Taub N, et al.: Comparing associations between frailty and mortality in hospitalised older adults with or without COVID-19 infection: A retrospective observational study using electronic health records. Age Ageing 2021; 50:307–316
29. Poco PCE, Aliberti MJR, Dias MB, et al.: Divergent: Age, frailty, and atypical presentations of COVID-19 in hospitalized patients. J Gerontol A Biol Sci Med Sci 2021; 76:e46–e51
30. Tehrani S, Killander A, Åstrand P, et al.: Risk factors for death in adult COVID-19 patients: Frailty predicts fatal outcome in older patients. Int J Infect Dis 2021; 102:415–421
31. Welch C; Geriatric Medicine Research and Collaborative: Age and frailty are independently associated with increased mortality in COVID-19: Results of an international multi-centre study. Age Ageing 2020; 50:617–630
32. Aliberti MJR, Szlejf C, Avelino-Silva VI, et al.; COVID HCFMUSP Study Group: COVID-19 is not over and age is not enough: Using frailty for prognostication in hospitalized patients. J Am Geriatr Soc 2021; 69:1116–1127
33. Lim JP, Low KYH, Lin NJJ, et al.: Predictors for development of critical illness amongst older adults with COVID-19: Beyond age to age-associated factors. Arch Gerontol Geriatr 2021; 94:104331
34. ANZICS: APD Data Dictionary ANZICS Core - Adult Patient Database Version 5.12. 2021. Available at: https://www.anzics.com.au/wp-content/uploads/2018/08/ANZICS-APD-Dictionary.pdf. Accessed November 27, 2021
35. Wells GA, Shea B, O’Connell D, et al.: The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. Ontario, Canada, The Ottawa Hospital. Available at: http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
36. Rockwood K, Song X, MacKnight C, et al.: A global clinical measure of fitness and frailty in elderly people. CMAJ 2005; 173:489–495
37. Bagshaw SM, Stelfox HT, McDermid RC, et al.: Association between frailty and short- and long-term outcomes among critically ill patients: A multicentre prospective cohort study. CMAJ 2014; 186:E95–102
38. Bagshaw M, Majumdar SR, Rolfson DB, et al.: A prospective multicenter cohort study of frailty in younger critically ill patients. Crit Care 2016; 20:175
39. Johnson DR, Creech JC: Ordinal measures in multiple indicator models: A simulation study of categorization error. Am Sociol Rev 1983; 48:398–407
40. Leisman DE, Harhay MO, Lederer DJ, et al.: Development and reporting of prediction models: Guidance for authors from editors of respiratory, sleep, and critical care journals. Crit Care Med 2020; 48:623–633
41. Fine JP, Gray RJ: A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94:496–509
42. Bravata DM, Perkins AJ, Myers LJ, et al.: Association of Intensive Care Unit patient load and demand with mortality rates in US department of Veterans affairs hospitals during the COVID-19 pandemic. JAMA Netw Open 2021; 4:e2034266
43. Burrell AJ, Pellegrini B, Salimi F, et al.: Outcomes for patients with COVID-19 admitted to Australian intensive care units during the first four months of the pandemic. Med J Aust 2021; 214:23–30
44. Kundi H, Çetin EHÖ, Canpolat U, et al.: The role of frailty on adverse outcomes among older patients with COVID-19. J Infect 2020; 81:944–951
45. Oliveira E, Parikh A, Lopez-Ruiz A, et al.: ICU outcomes and survival in patients with severe COVID-19 in the largest health care system in central Florida. PLoS One 2021; 16:e0249038
46. Baker KF, Hanrath AT, van der Loeff IS, et al.: COVID-19 management in a UK NHS Foundation Trust with a high consequence infectious diseases centre: A detailed descriptive analysis. Med Sci (Basel) 2021; 9:6
47. Bhatraju PK, Ghassemieh BJ, Nichols M, et al.: Covid-19 in critically ill patients in the Seattle region - Case series. N Engl J Med 2020; 382:2012–2022
48. Arentz M, Yim E, Klaff L, et al.: Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State. JAMA 2020; 323:1612–1614
49. Wang Y, Lu X, Li Y, et al.: Clinical course and outcomes of 344 intensive care patients with COVID-19. Am J Respir Crit Care Med 2020; 201:1430–1434
50. Cummings MJ, Baldwin MR, Abrams D, et al.: Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: A prospective cohort study. Lancet 2020; 395:1763–1770
51. Asch DA: Opening hospitals to more patients during the COVID-19 pandemic-making it safe and making it feel safe. JAMA Intern Med 2020; 180:1048–1049
52. Armstrong RA, Kane AD, Kursumovic E, et al.: Mortality in patients admitted to intensive care with COVID-19: An updated systematic review and meta-analysis of observational studies. Anaesthesia 2021; 76:537–548
53. Group RC; Horby P, Lim WS, Emberson JR, et al.: Dexamethasone in hospitalized patients with Covid-19 - Preliminary report. N Engl J Med 2021; 384:693–704
54. Salama C, Mohan SV: Tocilizumab in patients hospitalized with Covid-19 pneumonia. Reply. N Engl J Med 2021; 384:1473–1474
55. Salvarani C, Dolci G, Massari M, et al.; RCT-TCZ-COVID-19 Study Group: Effect of Tocilizumab vs standard care on clinical worsening in patients hospitalized with COVID-19 pneumonia: A randomized clinical trial. JAMA Intern Med 2021; 181:24–31
56. Bellelli G, Rebora P, Citerio G: The role of frailty in COVID-19 patients. Intensive Care Med 2020; 46:1958–1959
57. Jung C, Flaatten H, Fjølner J, et al.; COVIP study group: The impact of frailty on survival in elderly intensive care patients with COVID-19: The COVIP study. Crit Care 2021; 25:149
58. Zhang XM, Jiao J, Cao J, et al.: Frailty as a predictor of mortality among patients with COVID-19: A systematic review and meta-analysis. BMC Geriatr 2021; 21:186
59. Okahara S, Subramaniam A, Darvall JN, et al.: The relationship between frailty and mechanical ventilation: A population-based cohort study. Ann Am Thorac Soc. 2021 Jul 2. [online ahead of print]
60. Darvall JN, Bellomo R, Bailey M, et al.: Frailty and outcomes from pneumonia in critical illness: A population-based cohort study. Br J Anaesth 2020; 125:730–738
61. Zhang X, Dou Q, Zhang W, et al.: Frailty as a predictor of all-cause mortality among older nursing home residents: A systematic review and meta-analysis. J Am Med Dir Assoc 2019; 20:657–663.e4
62. Chang SF, Lin HC, Cheng CL: The relationship of frailty and hospitalization among older people: Evidence from a meta-analysis. J Nurs Scholarsh 2018; 50:383–391
63. Zhao F, Tang B, Hu C, et al.: The impact of frailty on posttraumatic outcomes in older trauma patients: A systematic review and meta-analysis. J Trauma Acute Care Surg 2020; 88:546–554
64. Chong E, Chan M, Tan HN, et al.: COVID-19: Use of the clinical frailty scale for critical care decisions. J Am Geriatr Soc 2020; 68:E30–E32
65. Ho EP, Neo HY: COVID 19: Prioritise autonomy, beneficence and conversations before score-based triage. Age Ageing 2021; 50:11–15
66. Hubbard RE, Maier AB, Hilmer SN, et al.: Frailty in the face of COVID-19. Age Ageing 2020; 49:499–500
67. Supady A, Curtis JR, Abrams D, et al.: Allocating scarce intensive care resources during the COVID-19 pandemic: Practical challenges to theoretical frameworks. Lancet Respir Med 2021; 9:430–434
68. Supady A, Brodie D, Curtis JR: Ten things to consider when implementing rationing guidelines during a pandemic. Intensive Care Med 2021; 47:605–608
69. Subramaniam A, Tiruvoipati R, Green C, et al.: Frailty status, timely goals of care documentation and clinical outcomes in older hospitalised medical patients. Intern Med J 2021; 51:2078–2086
70. Hartling L, Milne A, Hamm MP, et al.: Testing the Newcastle Ottawa Scale showed low reliability between individual reviewers. J Clin Epidemiol 2013; 66:982–993
71. Darvall JN, Boonstra T, Norman J, et al.: Retrospective frailty determination in critical illness from a review of the intensive care unit clinical record. Anaesth Intensive Care 2019; 47:343–348
72. Fisher C, Karalapillai DK, Bailey M, et al.: Predicting intensive care and hospital outcome with the Dalhousie clinical frailty scale: A pilot assessment. Anaesth Intensive Care 2015; 43:361–368
73. Bagshaw SM, Stelfox HT, Johnson JA, et al.: Long-term association between frailty and health-related quality of life among survivors of critical illness: A prospective multicenter cohort study. Crit Care Med 2015; 43:973–982
74. Le Maguet P, Roquilly A, Lasocki S, et al.: Prevalence and impact of frailty on mortality in elderly ICU patients: A prospective, multicenter, observational study. Intensive Care Med 2014; 40:674–682
Keywords:

coronavirus disease 2019; clinical frailty scale; frailty; hospital-related mortality; individual patient data meta-analysis; invasive mechanical ventilation

Supplemental Digital Content

Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.