Background

Understanding COVID-19 epidemiology is crucial to clinical care and to clinical trial design and interpretation.

Objective

To describe characteristics, treatment, and outcomes among patients hospitalized with COVID-19 early in the pandemic.

Methods

A retrospective cohort study of consecutive adult patients with laboratory-confirmed, symptomatic SARS-CoV-2 infection admitted to 57 US hospitals from March 1 to April 1, 2020.

Results

Of 1480 inpatients with COVID-19, median (IQR) age was 62.0 (49.4-72.9) years, 649 (43.9%) were female, and 822 of 1338 (61.4%) were non-White or Hispanic/Latino. Intensive care unit admission occurred in 575 patients (38.9%), mostly within 4 days of hospital presentation. Respiratory failure affected 583 patients (39.4%), including 284 (19.2%) within 24 hours of hospital presentation and 413 (27.9%) who received invasive mechanical ventilation. Median (IQR) hospital stay was 8 (5-15) days overall and 15 (9-24) days among intensive care unit patients. Hospital mortality was 17.7% (n=262). Risk factors for hospital death identified by penalized multivariable regression included older age; male sex; comorbidity burden; symptoms-to-admission interval; hypotension; hypoxemia; and higher white blood cell count, creatinine level, respiratory rate, and heart rate. Of 1218 survivors, 221 (18.1%) required new respiratory support at discharge and 259 of 1153 (22.5%) admitted from home required new health care services.

Conclusions

In a geographically diverse early-pandemic COVID-19 cohort with complete hospital folllow-up, hospital mortality was associated with older age, comorbidity burden, and male sex. Intensive care unit admissions occurred early and were associated with protracted hospital stays. Survivors often required new health care services or respiratory support at discharge.

Initial investigations have yielded a consensus understanding of the most common phenotypes and transmission dynamics of COVID-19 as well as preliminary identification of factors associated with adverse outcomes.16  Many studies, however, have been constrained by short observation periods and scarce data on hospital trajectory. Additionally, few well-designed analyses of risk factors for adverse outcomes have been conducted in diverse, multicenter patient populations.

A more granular and geographically diverse nationwide analysis of the epidemiology, clinical trajectory, and heterogeneity of patients with COVID-19 is necessary to aid clinical decision-making, help clinicians situate specific cases relative to expected variation, inform clinical trial design and interpretation, and enhance health system planning.

To address these issues, we leveraged a nationwide acute care trials network to conduct an observational study of adult patients with symptomatic SARS-CoV-2 infection admitted to 57 geographically diverse US hospitals. We used high-fidelity clinical data collected during the entire hospital course (from admission to hospital discharge) to identify risk factors for in-hospital mortality and for early and late respiratory failure. We also describe patients’ illness trajectory, patterns of organ failure, therapies, and the distribution of several clinical outcomes meaningful to patients, clinicians, health system planners, and researchers.

Design and Setting

We conducted a retrospective, multicenter cohort study of adult patients admitted to US hospitals with laboratory-confirmed SARS-CoV-2 infection and symptomatic COVID-19. Participating hospitals were members of the National Heart, Lung, and Blood Institute Prevention and Early Treatment of Acute Lung Injury (PETAL) Network and included 57 geographically diverse US hospitals organized within 12 clinical centers (Figure 1). The PETAL Network central institutional review board at Vanderbilt University and the institutional review boards at each participating hospital approved the study or determined that the study was exempt from review.

Participants

Patients aged 18 years or older admitted to a study hospital from March 1 to April 1, 2020, were eligible for inclusion if they had a positive polymerase chain reaction test result for SARS-CoV-2 during their admission or within the preceding 14 days and exhibited clinical or radiological evidence of acute infection (including fever, cough, dyspnea, hypoxemia, or bilateral airspace opacities). We excluded prisoners and patients with prior hospital admission for COVID-19. Each clinical center contributed data from up to 125 consecutive patients drawn from that center’s contributing hospitals. Because some clinical centers admitted fewer than 125 eligible patients during the study period, clinical centers with excess eligible patients contributed additional participants toward a total study inclusion target of 1500 patients.

The 57 geographically diverse hospitals participating in this retrospective cohort study were members of the NHLBI-sponsored PETAL Network.

Data Collection

Trained personnel obtained data on demographic and clinical characteristics, interventions, and outcomes by manual review of medical records according to a standardized protocol. Abstracted data were entered into a structured data capture interface with integrated real-time data validation.7  Manual medical record review was supplemented at some sites by electronic data abstraction. Patients were followed until hospital discharge. Additional assessments were performed on hospital days 1, 4, 8, 15, 21, and 28 and (if applicable) on day 1 in the intensive care unit (ICU). Each site was also asked to provide counts and basic demographics of all patients hospitalized with COVID-19 during the study window.

To quantify illness severity, we adapted an 8-point ordinal outcomes scale recommended by the World Health Organization (Supplemental Table 1, available online only at ajcconline.org).8  Scale values used the worst available value for the calendar day or, if data were missing, from an adjacent day. Sequential Organ Failure Assessment score calculation did not incorporate urine output but otherwise used standard methods, including assigning component scores of 0 when data were missing.9  Respiratory support was defined by treatment with supplemental oxygen or positive pressure ventilation. When PaO2 data were unavailable, values were estimated from peripheral oxygen saturation (SpO2) values using a validated nonlinear formula.10  For patients receiving oxygen by nasal cannula or face mask, the fraction of inspired oxygen (FIO2) was estimated using the formula FIO2 =0.21+(0.03×[oxygen flow rate in liters per minute]). Comorbidities, symptoms and their duration, and complications were obtained from clinical documentation.

The vast majority of patients who received invasive mechanical ventilation were also treated with vasopressors.

Outcomes

The primary outcome was hospital mortality. Prespecified secondary outcomes included respiratory failure (defined as treatment with oxygen at ≥11 L/min delivered by face mask, high-flow nasal cannula, noninvasive positive pressure ventilation, or invasive mechanical ventilation) occurring early (≤24 hours) or late (>24 hours) after hospital presentation. Other secondary outcomes included 7-, 14-, and 28-day hospital mortality; COVID-19 ordinal outcome scale values on hospital days 4, 8, 15, and 28; length of hospital stay; respiratory, cardiovascular, and renal support therapies; and survivors’ discharge health status.

Statistical Analysis

Continuous data are reported as medians and interquartile ranges (IQRs). Categorical data are reported as numbers and percentages. For descriptive analyses, we did not perform statistical hypothesis testing.

We employed L1 (lasso)–penalized logistic mixed-effects regression11,12  to identify risk factors for mortality, early-onset respiratory failure, and late-onset respiratory failure from outcome-specific sets of candidate risk factors identified a priori by a team of experienced epidemiologists and clinical researchers on the basis of previously reported association, plausibility, clinical utility, and data availability. To manage missingness among candidate risk factors, we performed penalized regression after multiple imputation of missing data using chained equations.13  Adjusted effect sizes for selected risk factors were estimated in the multiply imputed data using multi-variable mixed-effects logistic regression and combined coefficients using Rubin’s rules.1316  To account for site-level clustering of patient characteristics, care practices, and outcomes as well as between-site variation in resource strain,17  we employed a random effect for study site during both penalized regression18  and multivariable logistic regression model refitting for effect size estimation. Additional details are available in Supplement 1 (available online only).

We assessed our findings’ robustness in prespecified sensitivity analyses by reestimating effect sizes after (1) reclassifying patients discharged with hospice services as having the primary outcome (in-hospital mortality); (2) excluding patients who died without respiratory failure from the secondary analysis of late respiratory failure; and (3) excluding support with oxygen by face mask from the definition of respiratory failure. As an additional measure of variable importance, we also report the percentage of models in which each candidate variable was ultimately selected during cross-validation.19  Analyses were performed with R, version 4.0.3 (R Foundation for Statistical Computing); Stata, version 16.1 (StataCorp); and SAS, version 9.4 (SAS Institute Inc).

Among the 1480 patients included in our cohort, the median (IQR) age was 62.0 (49.4-72.9)years, 649 patients (43.9%) were female, and 822 of 1338 (61.4%) patients with known race/ethnicity were Hispanic/Latino or non-White (Table 1; Supplemental Table 2, available online only). The demographic profile of included patients was similar to that of the source population of all patients with COVID-19 admitted to study hospitals from March 1 to April 1, 2020 (Supplemental Table 3, available online only). Each of the 57 enrolling hospitals contributed a median (IQR) of 21 (8-41) patients. Most patients had at least 1 comorbidity included in the Charlson Comorbidity Index (n=843; 57.0%). The median (IQR) preadmission symptom duration was 6 (3-9) days and was longer in survivors than in patients who died in the hospital (median [IQR], 7 [4-9] days vs 4 [2-7] days). The first-available PaO2 to FIO2 ratio was less than 300 in 514 of 1452 patients (35.4%) with available data, and most patients (n=1203; 81.3%) had a Sequential Organ Failure Assessment score of 2 or greater within 24 hours of hospital arrival (Supplemental Table 4, available online only).

Common pharmacologic treatments included hydroxychloroquine (54.3% of patients), azithromycin (65.4%), other antibiotics (78.6%), and therapeutic anticoagulation (23.6%) (Table 2). Systemic corticosteroid therapy was relatively rare (13.9%). Clinically diagnosed acute respiratory distress syndrome was the most common complication, affecting 483 patients (32.6%), including 200 of the 262 (76.3%) patients who died in the hospital (Supplemental Table 5, available online only). Among patients not receiving dialysis before admission, acute renal failure was also more common in nonsurvivors (148 of 249 patients; 59.4%) than in survivors (173 of 1188 patients; 14.6%). Venous thromboembolism was diagnosed in 46 patients (3.1%).

Overall, 575 patients (38.9%) received care in an ICU during their hospitalization. Of these, 369 (64.2%) were admitted to the ICU within 24 hours of hospital arrival (Supplemental Table 6, available online only). Most of the remaining ICU admissions occurred by hospital day 4 (Figure 2). More than four-fifths of patients (n = 1203; 81.3%) required some form of respiratory support during their hospital stay and 583 (39.4%) developed respiratory failure, including 413 (27.9%) who received invasive mechanical ventilation, 129 (8.7%) who received noninvasive positive pressure ventilation, and 254 (17.2%) who were treated with high-flow nasal cannula (Table 2). Respiratory failure occurred in 284 patients (19.2%) within 24 hours of hospital arrival. Among the 567 (38.3%) patients who required advanced organ-support therapies (high-flow nasal cannula, positive pressure ventilation, renal replacement therapy, and/or vasopressors), mechanical ventilation plus vasopressor support was the most common combination (23.1%, Figure 3). The vast majority (85.0%) of patients who received invasive mechanical ventilation were also treated with vasopressors during their hospitalization.

Many patients experienced prolonged hospitalization, with median (IQR) hospital stays of 8 (5-15) days (Supplemental Figure 2, available online only). On day 15 of hospitalization, 355 (24.0%) patients remained admitted; 117 (7.9%) patients remained admitted on hospital day 28. Hospitalizations were longer for patients admitted to an ICU than for patients not admitted to an ICU (median [IQR], 15 [9-24] days vs 6 [4-9] days).

Hospital mortality was 17.7% (n=262, Table 2). Mortality was higher among patients admitted to an ICU (35.5%) than among patients never admitted to an ICU (5.3%). Mortality correlated with the number of organ failures, occurring in 11 of 58 (19.0%) patients who received mechanical ventilation and had isolated respiratory failure, 111 of 254 (43.7%) intubated patients who also required vasopressor support, and 64 of 97 (66.0%) intubated patients who required both vasopressors and renal replacement therapy. Unadjusted hospital mortality was higher in older patients; men; patients with hypertension, diabetes, cancer, or chronic cardiovascular disease; and individuals admitted from a long-term care facility (Table 1; Supplemental Figure 3, available online only).

Penalized regression identified the following risk factors for mortality: older age, shorter reported interval from symptom onset to hospitalization, male sex, comorbidity burden, tachycardia, tachypnea, hypotension, abnormal mental status, hypoxemia, higher first-available creatinine level, and higher first-available white blood cell count (Table 3). Race/ethnicity was among the 4 candidate variables not identified as contributing risk factors for mortality. After multivariable regression, the risk of mortality increased exponentially with age, reaching an adjusted odds ratio of 30.7 (95% CI, 8.8-107.0) in patients 80 years and older compared with patients younger than 40 years. Adjusted odds ratios were similar in the sensitivity analysis reclassifying 12 patients discharged with hospice services as having the mortality outcome.

Fewer risk factors were identified for early respiratory failure. These risk factors included body mass index, dyspnea on presentation, initial respiratory rate, abnormal mental status, higher first-available creatinine level, higher first-available white blood cell count, and elevated first-available aspartate aminotransferase level (Supplemental Table 7, available online only). The adjusted odds of early respiratory failure were more than 4 times higher if the first-available Glasgow Coma Scale score was less than 15 (odds ratio, 4.69; 95% CI, 3.07-7.16). In contrast, risk factors identified for late respiratory failure and the magnitudes of the observed associations were similar to those identified for mortality (Supplemental Table 8, available online only). Chronic pulmonary disease was not identified as a risk factor for either early or late respiratory failure. Sensitivity analyses that excluded patients who died without meeting respiratory failure criteria or excluded face mask oxygen support from the definition of respiratory failure yielded similar results.

Among the 1218 survivors, the 221 (18.1%) patients who were prescribed at least 1 form of new respiratory support were older and experienced a higher incidence of respiratory failure during their hospitalization than did patients who did not receive new respiratory support (Supplemental Table 9, available online only). Discharge with new home-based or facility-based health care services occurred in 259 of the 1153 (22.5%) survivors initially admitted from home. Compared with survivors who were not discharged with new health care services, these patients were older and had more severe illness and longer hospitalizations (Supplemental Table 10, available online only). Ten of the 34 (29%) survivors who required new renal replacement therapy during their admission continued dialysis after discharge.

In this large US cohort of inpatients with COVID-19 followed to hospital discharge, we observed prolonged hospital stays and 17.7% hospital mortality during the first pandemic wave. Most patients developing critical illness were admitted to the ICU by hospital day 4, and mortality increased substantially when respiratory failure was complicated by shock or the need for dialysis. Increasing age had an exponential association with mortality. Male sex, comorbidity burden, hypoxemia, and abnormalities of vital signs and laboratory test results on hospital day 1 were also associated with increased odds of dying. Among survivors, 18.1% were discharged with respiratory support that was new or higher in intensity than at baseline. Nearly 1 in 4 survivors admitted from home received new at-home or facility-based health care services at discharge.

Mortality correlated with the number of organ failures, reaching 66% among intubated patients who also required vasopressors and dialysis.

Hospital mortality in our cohort was lower than in some contemporaneous cohorts in China2  and the United States1,20,21  but higher than the 10% mortality observed in a systematic review of studies performed through April 20, 2020.22  Heterogeneous mortality rates across studies may be due to differing follow-up duration as well as between-hospital differences in admission thresholds, patient mix, management strategies, resource strain, and hospital-level operational modifications.5,6,2325  We captured data through hospital discharge for all cohort patients, including the nearly 10% who remained in the hospital for more than 4 weeks, thus avoiding the potential for misestimation of hospital mortality.20,26  The geographic heterogeneity of this cohort should also mitigate the effects of regional-level and hospital-level variation, yielding a more generalizable estimate of COVID-19 hospital mortality early in the pandemic. However, current mortality rates may differ from our estimates in the face of shifting SARS-CoV-2 infection rates and COVID-19 treatment.27,28 

Our data also highlight the high inpatient morbidity and prolonged hospitalizations experienced by patients with COVID-19. Shock and renal failure were common among nonsurvivors, corroborating reports suggesting that COVID-19–related critical illness and death often result from multiorgan dysfunction rather than isolated respiratory failure.5,29,30  Adding to this prior work, we found that ICU admission, when required, was likely to occur early in the hospitalization. Together, these findings suggest that efforts to reduce the incidence of COVID-19 critical illness should consider the multiorgan effects of SARS-CoV-2 infection and focus on the prehospital and early hospital settings.

The number of patients who received new post-discharge health care services or respiratory support suggests that substantial impairment relative to baseline health status and function is common among survivors of a hospitalization related to COVID-19. However, characterization of the severity, patterns, and duration of symptoms and disability after outpatient SARS-CoV-2 infection and hospitalization for COVID-19 remains preliminary.3134  Future studies are needed to better characterize the posthospital trajectory in COVID-19 survivors as well as predictors, mechanisms, trajectory, management, and prevention of persistent impairment in this population.

We evaluated a comprehensive and generalizable array of demographic and clinical characteristics as potential risk factors for COVID-19 mortality and respiratory failure. Men made up a disproportionately large fraction of hospitalized patients with COVID-19 and experienced poorer outcomes than women, confirming the results of prior studies.3,5,35,36  Drivers of sex-based disparities in clinical outcomes warrant further study and may include differences in angiotensin-converting enzyme 2 receptor expression, immune response, and other mechanisms.37,38  However, as with any other multivariable analysis, the parameters chosen and their estimated effect sizes are conditional on the other variables in the model. This is particularly important when interpreting our data suggesting that, for a given level of demographic and clinical risk at a given hospital, COVID-19 outcomes in our cohort were similar across race/ethnicity categories once patients were hospitalized. This finding is consistent with prior reports39  and suggests that higher COVID-19 mortality among minority patients does not indicate a biological difference in risk but rather reflects underlying health disparities leading to worse baseline health status, higher illness severity on presentation, and delayed hospital presentation combined with disparities in SARS-CoV-2 infection rates,4043  admission thresholds, and potentially differences in care quality at hospitals treating a greater proportion of minority patients.44,45 

Efforts to reduce COVID-19 critical illness should focus on prehospital and early inpatient treatment.

Our study has important strengths and several notable limitations. Leveraging the resources and personnel of an experienced clinical trials network, we collected a rich body of carefully curated data for a cohort representative of the underlying population of all patients with COVID-19 hospitalized early in the pandemic at 57 geographically diverse US hospitals. Follow-up to hospital discharge and identification of new health support needs among survivors yielded a comprehensive portrait of the sickest patients’ hospital trajectories. Our patient cohort was enrolled early in the COVID-19 pandemic, allowing us to contribute to collaborative international efforts46  to describe and study the epidemiology of COVID-19 and substantively inform clinical trial design for national and international efforts such as Operation Warp Speed’s ACTIV-3 studies.47  Pharmacologic and organ support management strategies, however, have continued to evolve with emerging evidence, new therapeutic options, and clinicians’ increasing experience managing this disease. For instance, treatment with repurposed drugs (hydroxychloroquine and azithromycin) shown in subsequent trials to lack efficacy was common in our cohort,4852  although corticosteroids—which now appear beneficial—were rarely used.53,54  Our definition of respiratory failure included receipt of high-flow oxygen therapy as well as invasive or noninvasive positive pressure ventilation, increasing the likelihood that identified risk factors are unaffected by shifting management practices. However, additional studies are needed to validate the risk factors we identified and evaluate how new therapeutic strategies affect the observed associations.

Our study has several additional limitations. First, our data do not include deaths or readmissions occurring after study hospital discharge. Second, although collection of patients’ clinical histories (including symptoms) by manual review of clinical documentation has advantages over claims-based or automated analyses, our findings may be vulnerable to recall bias, underreporting dependent on patients’ illness severity, and incomplete clinician documentation in times of strain. Third, most study sites were academic referral hospitals, so complex, severely ill patients with COVID-19 may be overrepresented relative to a population-based sample. Fourth, our analysis did not directly evaluate potential effects on patient management and outcomes due to variations in patient volumes and resource strain between hospitals and across the enrollment period. Inclusion of a site-level random effect in our risk factor analyses mitigated the effect of such differences. Finally, because of the selection procedure used to identify important risk factors, reported CIs may underestimate the imprecision for the effect size estimates.55  The impact of this selective inference is substantially mitigated by the relatively small collection of candidate variables and the relatively strong prognostic value of the included risk factors.

Among patients hospitalized with COVID-19 early in the pandemic at a geographically diverse network of US hospitals, mortality was 17.7% and was associated with comorbidity burden, male sex, and advancing age. Admission to the ICU, when required, usually occurred within 4 days of hospital arrival. Patients experienced prolonged hospital stays, and a substantial proportion of survivors received new facility-based or home-based health care services or new respiratory support at discharge.

The authors thank Dr Angela Presson for input regarding statistical analyses and Alison Pollock, Xiaoqi Bao, and Dr Bo Zhao for assistance creating data visualizations. See the list of PETAL Network contributors in Supplement 2 (available online only).

1
Lewnard
JA
,
Liu
VX
,
Jackson
ML
, et al
.
Incidence, clinical outcomes, and transmission dynamics of severe coronavirus disease 2019 in California and Washington: prospective cohort study
.
BMJ
.
2020
;
369
:
m1923
.
2
Zhou
F
,
Yu
T
,
Du
R
, et al
.
Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study
.
Lancet
.
2020
;
395
(
10229
):
1054
-
1062
.
3
Anesi
GL
,
Jablonski
J
,
Harhay
MO
, et al
.
Characteristics, outcomes, and trends of patients with COVID-19–related critical illness at a learning health system in the United States
.
Ann Intern Med
.
2021
;
174
(
5
):
613
-
621
.
4
Docherty
AB
,
Harrison
EM
,
Green
CA
, et al;
ISARIC4C investigators
.
Features of 20 133 UK patients in hospital with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study
.
BMJ
.
2020
;
369
:
m1985
.
5
Gupta
S
,
Hayek
SS
,
Wang
W
, et al;
STOP-COVID Investigators
.
Factors associated with death in critically ill patients with coronavirus disease 2019 in the US
.
JAMA Intern Med
.
2020
;
180
(
11
):
1436
-
1447
.
6
Domecq
JP
,
Lal
A
,
Sheldrick
CR
, et al;
Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study (VIRUS): COVID-19 Registry Investigator Group
.
Outcomes of patients with coronavirus disease 2019 receiving organ support therapies: the international Viral Infection and Respiratory Illness Universal Study registry
.
Crit Care Med
.
2021
;
49
(
3
):
437
-
448
.
7
Harris
PA
,
Taylor
R
,
Thielke
R
,
Payne
J
,
Gonzalez
N
,
Conde
JG
.
Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support
.
J Biomed Inform
.
2009
;
42
(
2
):
377
-
381
.
8
WHO Working Group on the Clinical Characterisation and Management of COVID-19 infection
.
A minimal common outcome measure set for COVID-19 clinical research
.
Lancet Infect Dis
.
2020
;
20
(
8
):
e192
-
e197
.
9
Vincent
JL
,
Moreno
R
,
Takala
J
, et al
.
The SOFA (Sepsis-Related Organ Failure Assessment) score to describe organ dysfunction/failure
.
Intensive Care Med
.
1996
;
22
(
7
):
707
-
710
.
10
Brown
SM
,
Grissom
CK
,
Moss
M
, et al;
NIH/NHLBI PETAL Network Collaborators
.
Nonlinear imputation of Spo2/Fio2 from Spo2/Fio2 among patients with acute respiratory distress syndrome
.
Chest
.
2016
;
150
(
2
):
307
-
313
.
11
Efron
B
,
Hastie
T
,
Johnstone
I
,
Tibshirani
R
.
Least angle regression
.
Ann Statistics
.
2004
;
32
(
2
):
407
-
499
.
12
Zou
H
,
Hastie
T
.
Regularization and variable selection via the elastic net
.
J R Stat Soc Series B Stat Methodol
.
2005
;
67
(
2
):
301
-
320
.
13
Rubin
DB
.
Multiple Imputation for Nonresponse in Surveys
.
Wiley
;
1987
.
14
van der Heijden
GJ
,
Donders
AR
,
Stijnen
T
,
Moons
KG
.
Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example
.
J Clin Epidemiol
.
2006
;
59
(
10
):
1102
-
1109
.
15
White
IR
,
Royston
P
,
Wood
AM
.
Multiple imputation using chained equations: issues and guidance for practice
.
Stat Med
.
2010
;
30
(
4
):
377
-
399
.
16
van Buuren
S
,
Groothuis-Oudshoorn
K
.
MICE: multivariate imputation by chained equations in R
.
J Stat Software
.
2011
;
45
(
3
):
1
-
67
.
17
Localio
AR
,
Berlin
JA
,
Ten Have
TR
,
Kimmel
SE
.
Adjustments for center in multicenter studies: an overview
.
Ann Intern Med
.
2001
;
135
(
2
):
112
-
123
.
18
Schelldorfer
J
,
Meier
L
,
Bühlmann
P
.
GLMMLasso: an algorithm for high-dimensional generalized linear mixed models using L1-penalization
.
J Computational Graphical Statistics
.
2014
;
23
(
2
):
460
-
477
.
19
Vergouwe
Y
,
Royston
P
,
Moons
KG
,
Altman
DG
.
Development and validation of a prediction model with missing predictor data: a practical approach
.
J Clin Epidemiol
.
2010
;
63
(
2
):
205
-
214
.
20
Richardson
S
,
Hirsch
JS
,
Narasimhan
M
, et al
.
Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area
.
JAMA
.
2020
;
323
(
20
):
2052
-
2059
.
21
Argenziano
MG
,
Bruce
SL
,
Slater
CL
, et al
.
Characterization and clinical course of 1000 patients with coronavirus disease 2019 in New York: retrospective case series
.
BMJ
.
2020
;
369
:
m1996
.
22
Potere
N
,
Valeriani
E
,
Candeloro
M
, et al
.
Acute complications and mortality in hospitalized patients with coronavirus disease 2019: a systematic review and meta-analysis
.
Crit Care
.
2020
;
24
(
1
):
389
.
23
Wadhera
RK
,
Wadhera
P
,
Gaba
P
, et al
.
Variation in COVID-19 hospitalizations and deaths across New York City boroughs
.
JAMA
.
2020
;
323
(
21
):
2192
-
2195
.
24
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
(
1
):
e2034266
. doi:
25
Mathews
KS
,
Seitz
KP
,
Vranas
KC
, et al;
National Heart, Lung, and Blood Institute Prevention and Early Treatment of Acute Lung Injury (PETAL) Clinical Trials Network
.
Variation in initial U.S. hospital responses to the coronavirus disease 2019 pandemic
.
Crit Care Med
.
2021
;
49
(
7
):
1038
-
1048
.
26
Jung
RG
,
Di Santo
P
,
Clifford
C
, et al
.
Methodological quality of COVID-19 clinical research
.
Nat Commun
.
2021
;
12
(
1
):
943
.
27
Asch
DA
,
Sheils
NE
,
Islam
MN
, et al
.
Variation in US hospital mortality rates for patients admitted with COVID-19 during the first 6 months of the pandemic
.
JAMA Intern Med
.
2021
;
181
(
4
):
471
-
478
.
28
Monod
M
,
Blenkinsop
A
,
Xi
X
, et al;
Imperial College COVID-19 Response Team
.
Age groups that sustain resurging COVID-19 epidemics in the United States
.
Science
.
2021
;
371
(
6536
):
eabe8372
.
29
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
(
11
):
1430
-
1434
.
30
Richards-Belle
A
,
Orzechowska
I
,
Gould
DW
, et al;
ICNARC COVID-19 Team
.
COVID-19 in critical care: epidemiology of the first epidemic wave across England, Wales and Northern Ireland
.
Intensive Care Med
.
2020
;
46
(
11
):
2035
-
2047
.
31
Tenforde
MW
,
Kim
SS
,
Lindsell
CJ
, et al;
IVY Network Investigators
;
CDC COVID-19 Response Team
;
IVY Network Investigators
.
Symptom duration and risk factors for delayed return to usual health among outpatients with COVID-19 in a multistate health care systems network—United States, March-June 2020
.
MMWR Morb Mortal Wkly Rep
.
2020
;
69
(
30
):
993
-
998
.
32
Chopra
V
,
Flanders
SA
,
O’Malley
M
,
Malani
AN
,
Prescott
HC
.
Sixty-day outcomes among patients hospitalized with COVID-19
.
Ann Intern Med
.
2021
:
174
(
4
):
576
-
578
.
33
McCue
C
,
Cowan
R
,
Quasim
T
,
Puxty
K
,
McPeake
J
.
Long term outcomes of critically ill COVID-19 pneumonia patients: early learning
.
Intensive Care Med
.
2021
;
47
(
2
):
240
-
241
.
34
Taboada
M
,
Cariñena
A
,
Moreno
E
, et al
.
Post-COVID-19 functional status six-months after hospitalization
.
J Infect
.
2021
;
82
(
4
):
e31
-
e33
. doi:
35
Myers
LC
,
Parodi
SM
,
Escobar
GJ
,
Liu
VX
.
Characteristics of hospitalized adults with COVID-19 in an integrated health care system in California
.
JAMA
.
2020
;
323
(
21
):
2195
-
2198
.
36
Zheng
Z
,
Peng
F
,
Xu
B
, et al
.
Risk factors of critical & mortal COVID-19 cases: a systematic literature review and meta-analysis
.
J Infect
.
2020
;
81
(
2
):
e16
-
e25
. doi:
37
Takahashi
T
,
Ellingson
MK
,
Wong
P
, et al
.
Sex differences in immune responses that underlie COVID-19 disease outcomes
.
Nature
.
2020
;
588
(
7837
):
315
-
320
.
38
Griffith
DM
,
Sharma
G
,
Holliday
CS
, et al
.
Men and COVID-19: a biopsychosocial approach to understanding sex differences in mortality and recommendations for practice and policy interventions
.
Prev Chronic Dis
.
2020
;
17
:
E63
.
39
Price-Haywood
EG
,
Burton
J
,
Fort
D
,
Seoane
L
.
Hospitalization and mortality among Black patients and White patients with Covid-19
.
N Engl J Med
.
2020
;
382
(
26
):
2534
-
2543
.
40
Introduction to COVID-19 racial and ethnic health disparities
.
Centers for Disease Control and Prevention. Updated December 10, 2020
. Accessed February 22, 2021.
41
Adegunsoye
A
,
Ventura
IB
,
Liarski
VM
.
Association of Black race with outcomes in COVID-19 disease: a retrospective cohort study
.
Ann Am Thorac Soc
.
2020
;
17
(
10
):
1336
-
1339
.
42
Bui
DP
,
McCaffrey
K
,
Friedrichs
M
, et al
.
Racial and ethnic disparities among COVID-19 cases in workplace outbreaks by industry sector—Utah, March 6-June 5, 2020
.
MMWR Morb Mort Wkly Rep
.
2020
;
69
(
33
):
1133
-
1138
.
43
Azar
KMJ
,
Shen
Z
,
Romanelli
RJ
, et al
.
Disparities in outcomes among COVID-19 patients in a large health care system in California
.
Health Aff (Millwood)
.
2020
;
39
(
7
):
1253
-
1262
.
44
Corl
K
,
Levy
M
,
Phillips
G
,
Terry
K
,
Friedrich
M
,
Trivedi
AN
.
Racial and ethnic disparities in care following the New York State Sepsis Initiative
.
Health Aff (Millwood)
.
2019
;
38
(
7
):
1119
-
1126
.
45
Jones
JM
,
Fingar
KR
,
Miller
MA
, et al
.
Racial disparities in sepsis-related in-hospital mortality: using a broad case capture method and multivariate controls for clinical and hospital variables, 2004-2013
.
Crit Care Med
.
2017
;
45
(
12
):
e1209
-
e1217
. doi:
46
COVID-19 clinical research resources
.
ISARIC
. Accessed February 4, 2021.
47
ACTIV-3/TICO LY-CoV555 Study Group
,
Lundgren
JD
,
Grund
B
, et al
.
A neutralizing monoclonal antibody for hospitalized patients with Covid-19
.
N Engl J Med
.
2021
;
384
(
10
):
905
-
914
.
48
Self
WH
,
Semler
MW
,
Leither
LM
, et al
.
Effect of hydroxychloroquine on clinical status at 14 days in hospitalized patients with COVID-19: a randomized clinical trial
.
JAMA
.
2020
;
324
(
21
):
2165
-
2176
.
49
Brown
SM
,
Peltan
I
,
Kumar
N
, et al
.
Hydroxychloroquine vs. azithromycin for hospitalized patients with COVID-19 (HAHPS): results of a randomized, active comparator trial
.
Ann Am Thorac Soc
.
2020
;
18
(
4
):
590
-
597
.
50
WHO Solidarity Trial Consortium
,
Pan
H
,
Peto
R
, et al
.
Repurposed antiviral drugs for Covid-19 — interim WHO Solidarity Trial results
.
N Engl J Med
.
2021
;
384
(
6
):
497
-
511
.
51
Furtado
RHM
,
Berwanger
O
,
Fonseca
HA
, et al;
COALITION COVID-19 Brazil II Investigators
.
Azithromycin in addition to standard of care versus standard of care alone in the treatment of patients admitted to the hospital with severe COVID-19 in Brazil (COALITION II): a randomised clinical trial
.
Lancet
.
2020
;
396
(
10256
):
959
-
967
.
52
RECOVERY Collaborative Group
.
Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial
.
Lancet
.
2021
;
397
(
10274
):
605
-
612
.
53
WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working Group
,
Sterne
JAC
,
Murthy
S
, et al
.
Association between administration of systemic corticosteroids and mortality among critically ill patients with COVID-19: a meta-analysis
.
JAMA
.
2020
;
324
(
13
):
1330
-
1341
.
54
RECOVERY Collaborative Group
,
Horby
P
,
Lim
WS
, et al
.
Dexamethasone in hospitalized patients with Covid-19
.
N Engl J Med
.
2021
;
384
(
8
):
693
-
704
.
55
Taylor
J
,
Tibshirani
RJ
.
Statistical learning and selective inference
.
Proc Natl Acad Sci USA
.
2015
;
112
(
25
):
7629
-
7634
.

Footnotes

FINANCIAL DISCLOSURES

This work was supported by the National Heart, Lung, and Blood Institute (NHLBI)(3U01HL123009-06S2, U01HL123009, U01HL122998, U01HL123018, U01HL123023, U01HL123008, U01HL123031, U01HL123004, U01HL123027, U01HL123010, U01HL123033, U01HL122989, U01HL123022, and U01HL123020) and the National Institutes of Health (UL1RR025758). This work does not necessarily reflect the view of the US Government, National Institutes of Health, or Department of Veterans Affairs.

 

To purchase electronic or print reprints, contact American Association of Critical-Care Nurses, 27071 Aliso Creek Road, Aliso Viejo, CA 92656. Phone, (800) 899-1712 or (949) 362-2050 (ext 532); fax, (949) 362-2049; email, reprints@aacn.org.