Skip to main content
Erschienen in: BMC Infectious Diseases 1/2022

Open Access 30.04.2022 | COVID-19 | Research

Predictors of critical care, mechanical ventilation, and mortality among hospitalized patients with COVID-19 in an electronic health record database

verfasst von: Andrea K. Chomistek, Caihua Liang, Michael C. Doherty, C. Robin Clifford, Rachel P. Ogilvie, Robert V. Gately, Jennifer N. Song, Cheryl Enger, Nancy D. Lin, Florence T. Wang, John D. Seeger

Erschienen in: BMC Infectious Diseases | Ausgabe 1/2022

Abstract

Background

There are limited data on risk factors for serious outcomes and death from COVID-19 among patients representative of the U.S. population. The objective of this study was to determine risk factors for critical care, ventilation, and death among hospitalized patients with COVID-19.

Methods

This was a cohort study using data from Optum’s longitudinal COVID-19 electronic health record database derived from a network of healthcare provider organizations across the US. The study included patients with confirmed COVID-19 (presence of ICD-10-CM code U07.1 and/or positive SARS-CoV-2 test) between January 2020 and November 2020. Patient characteristics and clinical variables at start of hospitalization were evaluated for their association with subsequent serious outcomes (critical care, mechanical ventilation, and death) using odds ratios (OR) and 95% confidence intervals (CI) from logistic regression, adjusted for demographic variables.

Results

Among 56,996 hospitalized COVID-19 patients (49.5% male and 72.4% ≥ 50 years), 11,967 received critical care, 9136 received mechanical ventilation, and 8526 died. The median duration of hospitalization was 6 days (IQR: 4, 11), and this was longer among patients that experienced an outcome: 11 days (IQR: 6, 19) for critical care, 15 days (IQR: 8, 24) for mechanical ventilation, and 10 days (IQR: 5, 17) for death. Dyspnea and hypoxemia were the most prevalent symptoms and both were associated with serious outcomes in adjusted models. Additionally, temperature, C-reactive protein, ferritin, lactate dehydrogenase, D-dimer, and oxygen saturation measured during hospitalization were predictors of serious outcomes as were several in-hospital diagnoses. The strongest associations were observed for acute respiratory failure (critical care: OR, 6.30; 95% CI, 5.99–6.63; ventilation: OR, 8.55; 95% CI, 8.02–9.11; death: OR, 3.36; 95% CI, 3.17–3.55) and sepsis (critical care: OR, 4.59; 95% CI, 4.39–4.81; ventilation: OR, 5.26; 95% CI, 5.00–5.53; death: OR, 4.14; 95% CI, 3.92–4.38). Treatment with angiotensin-converting enzyme inhibitors/angiotensin receptor blockers during hospitalization were inversely associated with death (OR, 0.57; 95% CI, 0.54–0.61).

Conclusions

We identified several clinical characteristics associated with receipt of critical care, mechanical ventilation, and death among COVID-19 patients. Future studies into the mechanisms that lead to severe COVID-19 disease are warranted.
Hinweise

Supplementary Information

The online version contains supplementary material available at https://​doi.​org/​10.​1186/​s12879-022-07383-6.
Andrea K. Chomistek and Caihua Liang contributed equally as first authors

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
ACE
Angiotensin-converting enzyme inhibitor
ARB
Angiotensin receptor blockers
ATC
Anatomical therapeutic chemical
CI
Confidence interval
COVID-19
Coronavirus disease 2019
CPT-4
Current Procedural Terminology, 4th Edition
CRP
C-reactive protein
ECMO
Extracorporeal membrane oxygenation
EHR
Electronic health record
EMR
Electronic medical record
ICD-10-CM
International Classification of Diseases, 10th Revision, Clinical Modification
ICD-10-PCS
International Classification of Diseases, 10th Revision, Procedural Coding System
IQR
Interquartile range
LDH
Lactate dehydrogenase
NSAID
Nonsteroidal anti-inflammatory drug
OR
Odds ratio
SARS-CoV-2
Severe acute respiratory syndrome coronavirus 2
US
United States

Introduction

Since coronavirus disease 2019 (COVID-19) first emerged in China in December 2019, there have been over 270 million confirmed cases and 5.3 million deaths worldwide due to COVID-19 as of 15 December 2021, according to the World Health Organization [1]. The United States (US) leads the world in cases (49.8 million) and deaths (792,371) from COVID-19 and these numbers are expected to continue to rise through the start of 2022.
There is significant heterogeneity in the clinical presentation of COVID-19 infection, ranging from patients who are asymptomatic to those with severe disease [24]. It is important to determine predictors of serious outcomes as patients may decline rapidly after initially presenting with mild symptoms [5]. Identifying predictors of serious outcomes may enable clinicians to deliver appropriate care to patients early as well as inform interventions to reduce risk of death [6].
The serious outcomes of COVID-19 (e.g., intensive care unit admission, receipt of mechanical ventilation, death) and their preceding risk factors have been identified previously [710]. Common factors associated with progression to serious disease include age, male sex, obesity, and comorbid diseases, including diabetes and renal disease. Additionally, it has been recognized that biomarkers, such as C-reactive protein (CRP) and D-dimer, may be associated with serious outcomes. However, studies from early in the pandemic were small and sought to identify the strongest predictors of serious disease and death from a broad set of variables. Further, some of these studies were hospital-based case series and may not be representative of all patients hospitalized with COVID-19 in the United States.
The purpose of this study was to apply an exploratory, data-driven approach to the identification of potential risk factors for serious outcomes among patients with COVID-19 in order to inform clinicians and researchers of characteristics that may be integral to identifying high risk patients. Thus, the objective was to determine demographic and clinical predictors associated with critical care, mechanical ventilation, and death among hospitalized COVID-19 patients in a large electronic health record (EHR) database that is representative of a geographically diverse U.S. population.

Methods

Study design and study population

This was a retrospective cohort study that included patients confirmed with COVID-19 infection between January 2020 and November 2020. Among these patients, a subset of patients hospitalized with COVID-19 was identified from those with an inpatient health care encounter. Confirmation of COVID-19 infection was based on presence of a specific International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis code (U07.1) and/or a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral test. The date of confirmed infection was the earlier of the date of diagnosis or the date of a positive test. The cohort entry date was the earliest date that the patient met both of the following criteria: confirmed COVID-19 infection and admission to the hospital. For patients who were hospitalized prior to contracting COVID-19, the date of cohort entry was the date of confirmed infection. For patients who were confirmed to have COVID-19 before they were admitted to the hospital, the date of cohort entry was date of hospital admission. This approach for assigning cohort entry date allows for the description of clinical characteristics at the time patients were first hospitalized with COVID-19. For clinical characteristics other than death, patients were followed from cohort entry to discharge or 30 days after cohort entry, whichever came first. Deaths were identified in all follow-up available, including during and after hospitalization.

Data source

Patients were identified from Optum’s longitudinal COVID-19 EHR database derived from a network of healthcare provider organizations across the U.S. The COVID-19 EHR database consists of a subset of patients from Optum’s EHR database, which represents a geographically diverse U.S. patient population with more than 85 million patients from 2007 through 2019. Optum’s EHR database includes data collected from tens of thousands of providers and hundreds of hospitals representing more than 60 electronic medical record (EMR)-based provider/hospital networks across the U.S. This database incorporates clinical and medical administrative data from both inpatient and ambulatory EMRs, practice management systems, and numerous other internal systems. Information is processed from across the continuum of care, including acute inpatient stays and outpatient visits. The COVID-19 data captures diagnostics specific to the COVID-19 patient during initial presentation at hospital admission, acute illness, and convalescence with over 500 mapped labs and bedside observations, including COVID-19 specific testing. The data are incorporated into the underlying database on a biweekly basis, allowing for near real-time analysis and assessment of the COVID-19 clinical landscape. The database is certified as de-identified by an independent statistical expert following Health Insurance Portability and Accountability Act statistical de-identification rules.

Ascertainment of covariates

Demographic characteristics were assessed on the date of cohort entry. Comorbidities and medication use were assessed in the 21 days prior to cohort entry. Comorbidities were identified by ICD-10-CM diagnosis code with a diagnosis status of “history of” and medications were mapped according to the Anatomical Therapeutic Chemical (ATC) classification scheme.
Additionally, vital signs, laboratory results, symptoms, diagnoses, and treatments during hospitalization were assessed. For vital signs and laboratory results, the first measurement on or after the date of cohort entry was selected if the patient had multiple measurements. Symptoms and diagnoses were identified by ICD-10-CM diagnosis codes.

Identification of outcomes

The primary outcomes of interest were receipt of critical care, mechanical ventilation, and death. Patients were classified according to the presence or absence of an outcome, and outcome groups were not mutually exclusive.
Receipt of critical care was identified using Current Procedural Terminology, 4th Edition (CPT-4) codes. Receipt of mechanical ventilation was identified by CPT-4 and International Classification of Diseases, 10th Revision, Procedural Coding System (ICD-10-PCS) codes and included intubation, mechanical ventilation, and extracorporeal membrane oxygenation (ECMO). Receipt of critical care and mechanical ventilation were ascertained during hospitalization.
Death was ascertained via linkage to the Social Security Administration’s Death Master File and/or the presence of a death indicator in the structured EHR data within all data available during and after hospitalization.

Statistical analysis

All analyses were conducted using SAS version 9.4 (SAS Institute Inc, Cary, NC). Baseline characteristics were examined overall and according to outcome (critical care, mechanical ventilation, and death). Categorical variables were summarized using frequency and percent while continuous variables were summarized using median and interquartile range (IQR).
For the association analyses, vital signs and laboratory values were transformed into categorical variables. Dichotomous variables were created based on clinically-relevant cutpoints. Additionally, categories based on quintiles were generated to examine the shape of the dose–response relationship of vital signs and laboratory values with each outcome. Quintiles were determined based on the distribution of each biomarker among hospitalized patients overall. Tests for linear trend were computed by using the medians of the quintiles modeled as a continuous variable.
Logistic regression models were used to estimate unadjusted and adjusted odds ratios (OR) and corresponding 95% confidence intervals (CI) for associations between the covariates and each outcome. Adjusted models included age, gender, region, race, and week of cohort entry. Week of cohort entry was included as a covariate to adjust for any potential changes in patient characteristics or treatments over time.

Results

Descriptive analyses

A total of 56,996 hospitalized patients with COVID-19 between January 2020 and November 2020 were identified (Fig. 1). Of these, 11,967 (21.0%) received critical care, 9136 (16.0%) received mechanical ventilation, and 8526 (15.0%) died. Table 1 shows the demographics, comorbidities, and patient-reported medications at baseline overall and according to outcome. The majority of hospitalized patients were aged 50 years and older (72.4%), Caucasian (58.8%), and from the Midwest or Northeast (38.0% and 29.0%, respectively); this was also observed for each outcome. Females comprised 50.5% of hospitalized patients, but males comprised the majority of patients experiencing each outcome (58.8% for critical care, 60.4% for mechanical ventilation, and 57.4% for death). Prior to the cohort entry date, 76.7% of patients received care in the emergency department, while fewer patients received care in the outpatient or inpatient settings. Confirmation of COVID-19 infection was based on both a positive SARS-CoV-2 viral test and presence of a U07.1 diagnosis code for 70.2% of hospitalized patients, a U07.1 diagnosis code only for 26.0% of patients, and a positive SARS-CoV-2 viral test only for 3.8% of patients. The largest proportion of patients were admitted to the hospital in April 2020 (18.3%), followed by November 2020 (17.8%).
Table 1
Baseline characteristics among hospitalized COVID-19 patients, overall and by outcome
 
Overall
Critical Care
Mechanical Ventilation
Death
N
%
N
%
N
%
N
%
Total patients
56,996
100.0
11,967
100.0
9136
100.0
8526
100.0
Age
 < 10
396
0.7
82
0.7
29
0.3
7
0.1
 10–19
671
1.2
135
1.1
46
0.5
1
0.0
 20–29
3610
6.3
293
2.4
175
1.9
37
0.4
 30–39
5123
9.0
615
5.1
380
4.2
110
1.3
 40–49
5889
10.3
1,113
9.3
794
8.7
285
3.3
 50–59
9486
16.6
2,125
17.8
1605
17.6
770
9.0
 60–69
11,871
20.8
2,926
24.5
2552
27.9
1658
19.4
 70–79
10,396
18.2
2,621
21.9
2137
23.4
2257
26.5
 80 + 
9554
16.8
2,057
17.2
1418
15.5
3401
39.9
Gender
 Female
28,782
50.5
4925
41.2
3620
39.6
3636
42.6
 Male
28,214
49.5
7042
58.8
5516
60.4
4890
57.4
Race
 African American
11,694
20.5
2282
19.1
1873
20.5
1529
17.9
 Asian
1336
2.3
329
2.7
250
2.7
206
2.4
 Caucasian
33,524
58.8
7125
59.5
5227
57.2
5536
64.9
 Other/Unknown
10,442
18.3
2231
18.6
1786
19.5
1255
14.7
Ethnicity
 Hispanic
8497
14.9
1711
14.3
1271
13.9
814
9.5
 Not Hispanic
42,881
75.2
9079
75.9
6910
75.6
6908
81.0
 Unknown
5618
9.9
1177
9.8
955
10.5
804
9.4
Region
 Midwest
21,647
38.0
4979
41.6
3851
42.2
2995
35.1
 Northeast
16,548
29.0
3621
30.3
2516
27.5
2616
30.7
 South
12,654
22.2
2094
17.5
1583
17.3
2114
24.8
 West
4378
7.7
947
7.9
949
10.4
560
6.6
 Other/Unknown
1769
3.1
326
2.7
237
2.6
241
2.8
Care settings prior to admission date
 Inpatient
4684
8.2
747
6.2
736
8.1
945
11.1
 Outpatient
10,479
18.4
2121
17.7
1554
17.0
1253
14.7
 Emergency department
43,724
76.7
9819
82.1
7615
83.4
7194
84.4
Insurance type on admission date
 Multiple
16,266
28.5
3790
31.7
2831
31.0
3274
38.4
 Commercial
15,775
27.7
2916
24.4
2193
24.0
1134
13.3
 Medicare
12,364
21.7
2854
23.8
2258
24.7
2888
33.9
 Medicaid
5111
9.0
1107
9.3
801
8.8
354
4.2
 Other payer type
2936
5.2
483
4.0
364
4.0
275
3.2
 Uninsured
973
1.7
156
1.3
109
1.2
68
0.8
 Unknown
3571
6.3
661
5.5
580
6.3
533
6.3
Confirmatory event
 Positive SARS-CoV-2 viral test only
2166
3.8
258
2.2
215
2.4
318
3.7
 U07.1 diagnosis code only
14,822
26.0
3027
25.3
2367
25.9
1989
23.3
 Positive SARS-CoV-2 viral test and U07.1 diagnosis code
40,008
70.2
8682
72.5
6554
71.7
6219
72.9
Month of cohort entry
 January 2020 through March 2020
4497
7.9
1389
11.6
1397
15.3
1040
12.2
 April 2020
10,422
18.3
2924
24.4
2205
24.1
2360
27.7
 May 2020
5,859
10.3
1472
12.3
994
10.9
1028
12.1
 June 2020
4611
8.1
908
7.6
594
6.5
593
7.0
 July 2020
6082
10.7
1136
9.5
840
9.2
916
10.7
 August 2020
5013
8.8
856
7.2
644
7.0
721
8.5
 September 2020
4318
7.6
824
6.9
575
6.3
623
7.3
 October 2020
6028
10.6
1102
9.2
858
9.4
776
9.1
 November 2020
10,166
17.8
1356
11.3
1029
11.3
469
5.5
Comorbidities
 Diabetes
16,826
29.5
4851
40.5
3918
42.9
3375
39.6
 Obesity
13,544
23.8
3861
32.3
3183
34.8
1893
22.2
 Pulmonary disease
  COPD
5830
10.2
1,812
15.1
1582
17.3
1498
17.6
  Asthma
4948
8.7
1,121
9.4
916
10.0
510
6.0
 Cardiovascular disease
  Hypertension
23,229
40.8
5855
48.9
4495
49.2
4029
47.3
  Coronary artery disease
8407
14.8
2477
20.7
1986
21.7
2228
26.1
  Congestive heart failure
7242
12.7
2425
20.3
2058
22.5
2246
26.3
 Kidney disease*
17,694
31.0
6103
51.0
5110
55.9
5257
61.7
 Liver disease
2671
4.7
985
8.2
830
9.1
681
8.0
 Cancer
4369
7.7
1080
9.0
780
8.5
1029
12.1
Patient-reported medications
 Statins
14,933
26.2
3631
30.3
2975
32.6
2733
32.1
 ACEs/ARBs
11,517
20.2
2695
22.5
2256
24.7
1879
22.0
 NSAIDS
5908
10.4
1092
9.1
873
9.6
618
7.2
 Steroids
6827
12.0
1556
13.0
1144
12.5
882
10.3
 PPIs
9966
17.5
2269
19.0
1878
20.6
1749
20.5
COPD chronic obstructive pulmonary disease, ACE angiotensin-converting enzyme, ARB angiotensin II receptor blocker, NSAIDS non-steroidal anti-inflammatory drugs, PPIs proton-pump inhibitors, SARS-CoV-2 severe acute respiratory syndrome coronavirus 2
*Includes acute and chronic kidney disease
The most common comorbidities among the patients hospitalized with COVID-19 overall were hypertension (40.8%), kidney disease (31.0%), diabetes (29.5%), and obesity (23.8%) (Table 1). In general, the prevalence of these comorbidities was higher among patients who experienced one of the outcomes of interest, compared to the broader hospitalized population. Among patients who received critical care, 48.9% had hypertension, 51.0% had kidney disease, 40.5% had diabetes, and 32.3% were obese. Among patients who received mechanical ventilation, 49.2% had hypertension, 55.9% had kidney disease, 42.9% had diabetes, and 34.8% were obese. Among patients who died, 47.3% had hypertension, 61.7% had kidney disease, and 39.6% had diabetes; 22.2% were obese, slightly less than hospitalized patients overall. Statins and angiotensin-converting enzyme inhibitors (ACEs)/angiotensin receptor blockers (ARBs) were the most prevalent patient-reported medications (26.2% and 20.2%, respectively, overall).
Table 2 presents the distributions of vital signs, laboratory values, symptoms, diagnoses, and treatments received during hospitalization among COVID-19 patients. The median duration of hospitalization overall was 6 days (IQR: 4, 11). The duration was longer among patients that experienced an outcome: 11 days (IQR: 6, 19) for critical care, 15 days (IQR: 8, 24) for mechanical ventilation, and 10 days (IQR: 5, 17) for death. Among hospitalized patients overall, 6.6% had a temperature > 38 degrees Celsius and 10.1% had an oxygen saturation < 90%. Markers of inflammation and coagulation were elevated for many patients, including 85.7% of patients with C-reactive protein (CRP) > 10 mg/L and 76.8% of patients with D-dimer > 250 ng/mL (DDU).
Table 2
Laboratory results, symptoms, diagnoses, and interventions during hospitalization among hospitalized COVID-19 patients, overall and by outcome
 
Overall
Critical Care
Mechanical ventilation
Death
N
%
N
%
N
%
N
%
Duration of Hospitalization (median, IQR)
6.0
(4, 11)
11.0
(6, 19)
15.0
(8, 24)
10.0
(5, 17)
Observations (median, IQR)
 Temperature, °C
36.8
(36.5, 37.1)
36.8
(36.5, 37.2)
36.8
(36.5, 37.3)
36.8
(36.4, 37.4)
  > 38
3,609
6.6
971
8.3
929
10.4
981
11.9
 Oxygen saturation (SpO2), %
95.0
(93.0, 97.3)
95.0
(92.0, 97.2)
95.0
(92.0, 97.4)
95.0
(91.0, 97.0)
  < 90
2457
10.1
1193
14.9
1107
14.7
1051
17.6
 Platelet count × 109 per L
224.0
(169.0, 298.0)
228.0
(165.0, 309.0)
225.0
(161.0, 306.0)
200.0
(143.0, 271.0)
  < 150
9355
17.0
2324
19.7
1,879
20.8
2334
28.1
 C-reactive protein, mg/L
60.5
(21.1, 124.0)
86.3
(34.1, 163.0)
97.7
(40.3, 178.0)
107.9
(51.0, 181.3)
  > 10
30,979
85.7
8353
90.5
6469
92.1
5773
94.1
 Ferritin, ng/mL
478.0
(219.1, 942.0)
632.0
(309.0, 1175.5)
673.1
(338.0, 1284.7)
689.0
(339.1, 1309.9)
  > 300
22,216
66.3
6371
75.6
5141
78.0
4394
78.3
 Lactate dehydrogenase, U/L
310.0
(232.0, 428.0)
377.0
(277.0, 526.0)
406.0
(294.0, 566.0)
405.0
(285.0, 571.0)
  > 280
19,726
58.9
6235
73.9
5266
78.4
4384
75.8
 D-Dimer, ng/mL
465.0
(264.0, 880.0)
680.0
(376.5, 1345.0)
773.8
(429.5, 1616.0)
855.0
(480.0, 1695.0)
  > 250
19,021
76.8
5024
87.2
4006
89.7
3587
92.5
 Fibrinogen, mg/dL
529.0
(403.0, 667.0)
546.0
(400.0, 700.0)
557.0
(403.0, 700.0)
537.0
(394.0, 693.0)
  > 400
12,116
75.5
3750
74.9
3309
75.2
2501
74.2
Symptoms
 Hypoxemia
14,718
25.8
3968
33.2
3231
35.4
2719
31.9
 Fever
9158
16.1
2617
21.9
2035
22.3
1641
19.2
 Cough
7182
12.6
1656
13.8
1266
13.9
1032
12.1
 Nausea/Vomiting
3711
6.5
814
6.8
547
6.0
345
4.0
 Malaise and fatigue
7483
13.1
2242
18.7
1723
18.9
1484
17.4
 Dyspnea or shortness of breath
15,979
28.0
4472
37.4
3781
41.4
2971
34.8
Diagnoses
 Acute respiratory failure
25,525
44.8
9472
79.2
7825
85.7
6187
72.6
 Pneumonia
33,946
59.6
9771
81.6
7738
84.7
6565
77.0
 Sepsis
12,646
22.2
5812
48.6
4995
54.7
4168
48.9
 Coagulation defects or hemorrhagic conditions
3490
6.1
1715
14.3
1456
15.9
1101
12.9
 Arrhythmia
4799
8.4
2112
17.6
1772
19.4
1439
16.9
 Heart failure
7934
13.9
2775
23.2
2380
26.1
2441
28.6
 MI
3437
6.0
1535
12.8
1293
14.2
1251
14.7
 Kidney disease*
17,694
31.0
6103
51.0
5110
55.9
5257
61.7
 Liver disease
2671
4.7
985
8.2
830
9.1
681
8.0
Treatments
 Chloroquine/Hydroxychloroquine
8852
15.5
2805
23.4
2632
28.8
2084
24.4
 Lopinavir/Ritonavir
427
0.7
205
1.7
181
2.0
166
1.9
 Remdesivir
12,990
22.8
3274
27.4
2829
31.0
1901
22.3
 Dexamethasone
16,698
29.3
3968
33.2
3582
39.2
2520
29.6
 ACEs/ARBs
12,313
21.6
2492
20.8
2112
23.1
1546
18.1
 Anticoagulants
45,509
79.8
10,569
88.3
8286
90.7
7362
86.3
 Immunosuppressants
3009
5.3
1409
11.8
1344
14.7
799
9.4
 Antibacterials for systemic use
38,050
66.8
9373
78.3
7927
86.8
6967
81.7
 Antivirals for systemic use
1847
3.2
633
5.3
555
6.1
434
5.1
 Corticosteroids for systemic use
26,615
46.7
6897
57.6
6320
69.2
4699
55.1
MI myocardial infarction, ACE angiotensin-converting enzyme, ARB angiotensin II receptor blocker, ICU intensive care unit
*Includes acute and chronic kidney disease
The most common symptoms during hospitalization among COVID-19 patients were dyspnea (28.0%) and hypoxemia (25.8%) (Table 2). Prevalence of these symptoms was higher among patients who received critical care (37.4% and 33.2%, respectively), those who received ventilation (41.4% and 35.4%, respectively), and those who died (34.8% and 31.9%, respectively). Relatedly, the most prevalent diagnoses during hospitalization among patients overall were pneumonia (59.6%) and acute respiratory failure (44.8%). These diagnoses were even more common among those with an outcome, with the highest prevalence observed among patients that received mechanical ventilation (acute respiratory failure, 85.7%; pneumonia, 84.7%). Anticoagulants were the most prevalent treatment, received by 79.8% of hospitalized patients with COVID-19 overall during their hospitalization. Among other treatments, 15.5% of patients received chloroquine or hydroxychloroquine, 29.3% received dexamethasone, and 22.8% received remdesivir.

Associations between covariates and serious outcomes

Baseline demographics and comorbidities

Figure 2 presents the associations between baseline characteristics and receipt of critical care, ventilation, and death adjusted for age, gender, region, race, and week of cohort entry. Unadjusted ORs for the associations between covariates and outcomes are provided in Additional file 1: Tables S1–S3. Age was associated with all 3 outcomes, particularly death; the OR for patients ≥ 80 years of age compared to those 50–59 years of age was 7.61 (95% CI, 6.96–8.32) (Fig. 2A). Female patients were less likely to experience any of the 3 outcomes compared to males. Regarding race, hospitalized patients that received critical care, ventilation, or died were less likely to be African American compared to Caucasian (critical care: OR, 0.82; 95% CI, 0.77–0.87; mechanical ventilation: OR, 0.94; 95% CI, 0.89–1.00; death: OR, 0.83; 95% CI, 0.77–0.88) (Fig. 2B).
After adjusting for demographic variables, several comorbidities at baseline were associated with serious outcomes (Fig. 2C). Comorbidities associated with higher odds of all 3 outcomes included diabetes, obesity, chronic obstructive pulmonary disease, coronary artery disease, congestive heart failure, kidney disease, and liver disease. Asthma, hypertension, and cancer showed varying associations with each outcome. In the unadjusted model, hypertension was positively associated with death (OR, 1.37; 95% CI, 1.30–1.43; Additional file 1: Table S3). However, once adjusted for demographic variables, there was an inverse association between hypertension and death (OR, 0.85; 95% CI, 0.81–0.89). Asthma was associated with higher odds of critical care (OR, 1.21; 95% CI, 1.12–1.30) and ventilation (OR, 1.33; 95% CI, 1.23–1.44), but lower odds of death (OR, 0.87; 95% CI, 0.79–0.97).
Among select patient-reported medications at baseline, use of statins, corticosteroids, and PPIs was positively associated with receipt of critical care and ventilation, but not death (Fig. 2D). Statins, ACEs/ARBs, and NSAIDs showed inverse associations with death.

Vital signs and laboratory values

Figure 3 presents the adjusted ORs for the associations between clinical characteristics during hospitalization and receipt of critical care, ventilation, and death among hospitalized patients with COVID-19. Measurements of temperature, CRP, ferritin, lactate dehydrogenase (LDH), and D-dimer that exceeded the clinically-relevant cutpoint were significantly associated with an increased risk of all 3 serious outcomes in adjusted models (Fig. 3A). Likewise, measurements of oxygen saturation and platelets that were less than the clinically-relevant cutpoint were also significantly associated with an increased risk of all 3 serious outcomes. D-dimer and LDH measured during hospitalization had the highest adjusted ORs for the association with death: OR, 2.95 (95% CI, 2.59–3.35) for D-dimer > 250 ng/mL (DDU) and OR, 2.81 (95% CI, 2.61–3.02) for LDH > 280 U/L. Fibrinogen > 400 mg/dL was associated with a lower risk of all 3 outcomes.
The associations between each biomarker in quintiles and serious outcomes are shown in Fig. 4. For CRP, D-dimer, ferritin, and LDH, the relationships appeared linear for all 3 outcomes (p values for linear trend < 0.0001, Additional file 1: Tables S1–S3). For temperature, oxygen saturation, and platelets, the relationships were less linear, although many of the p values for linear trend were < 0.0001. For fibrinogen, the associations with all 3 outcomes were non-linear, with p values of 0.44 for critical care, 0.92 for mechanical ventilation, and 0.09 for death. The biomarkers that showed the strongest associations with outcomes were LDH and D-dimer. The ORs for the 5th (> 466 U/L) versus 1st (< 215 U/L) quintiles of LDH were 4.75 (95% CI, 4.35–5.19) for critical care, 6.84 (95% CI, 6.16–7.58) for ventilation, and 6.97 (95% CI, 6.23–7.79) for death. Likewise, for D-dimer, the ORs for the 5th (> 1030 ng/mL) versus 1st quintile (< 230 ng/mL) were 4.34 (95% CI, 3.89–4.84) for critical care, 5.90 (95% CI, 5.19–6.71) for ventilation, and 6.07 (95% CI, 5.21–7.08) for death (Additional file 1: Tables S1–S3).

Symptoms and diagnoses during hospitalization

Dyspnea and hypoxemia were positively associated with all 3 serious outcomes in adjusted models, with both showing the strongest association with receipt of ventilation (dyspnea: OR, 1.68; 95% CI, 1.59–1.76; hypoxemia: OR, 1.44; 95% CI, 1.37–1.52) (Fig. 3B). In contrast, cough was associated with lower odds of all 3 outcomes (critical care: OR, 0.87; 95% CI, 0.81–0.92; mechanical ventilation: OR, 0.80; 95% CI, 0.74–0.85; death: OR, 0.70; 95% CI, 0.65–0.76). Patients with nausea and vomiting as well as malaise also had lower odds of death.
All selected in-hospital diagnoses showed positive associations with each of the 3 serious outcomes (Fig. 3C). The strongest associations were observed for acute respiratory failure (critical care: OR, 6.30; 95% CI, 5.99–6.63; ventilation: OR, 8.55; 95% CI, 8.02–9.11; death: OR, 3.36; 95% CI, 3.17–3.55) and sepsis (critical care: OR, 4.59; 95% CI, 4.38–4.81; ventilation: OR, 5.26; 95% CI, 5.00–5.53; death: OR, 4.14; 95% CI, 3.92–4.38).

Treatments received during hospitalization

With the exception ACEs/ARBs, all of the selected treatments were associated with higher odds of serious outcomes (Fig. 3D). The highest ORs were observed for the associations between treatments and receipt of ventilation, ranging from 1.87 (95% CI, 1.68–2.08) for antivirals to 4.02 (95% CI, 3.71–4.36) for immunosuppressants. Treatment with ACEs/ARBs was inversely associated with receipt of critical care (OR, 0.84; 95% CI, 0.80–0.89) and death (OR, 0.57; 95% CI, 0.54–0.61).

Discussion

In this study, we identified and described patients who experienced a serious outcome (critical care, mechanical ventilation, and death) among 56,996 hospitalized patients with COVID-19 within a large, EHR database. We conducted an evaluation of the association of many demographic and clinical characteristics with these outcomes in order to identify potential signals for experiencing a serious outcome. As was observed in prior studies [710], older age and male gender were associated with higher risk of serious outcomes, along with comorbidities such as diabetes and kidney disease. We also observed associations with clinical characteristics measured during hospitalization, including several laboratory markers, symptoms, and diagnoses.
In this study of hospitalized patients with COVID-19, we found that African-Americans and women were at lower risk of experiencing a serious outcome compared to Caucasians and men, respectively. Since the start of the pandemic, African-Americans have been more likely than Caucasians to contract and be hospitalized with COVID-19 [11]. Nonetheless, evidence suggests that once hospitalized, they do not have a higher risk of adverse outcomes [10, 1214]. In contrast, there does not appear to be sex difference in number of confirmed cases of COVID-19, but the death rate has been higher in men than women [15]. The reason for the sex difference in rate of mortality among patients with COVID-19 remains unknown, but it has been suggested that the mechanism involves a combination of biological and psychosocial factors [16].
Hypertension was the most prevalent comorbidity among hospitalized COVID-19 patients in this study and its prevalence was even higher among patients with a serious outcome. Hypertension was positively associated with receipt of critical care and mechanical ventilation, but inversely associated with mortality after adjusting for age and other demographics. Findings in this study are consistent with previous studies that have found hypertension to be common among adults diagnosed with COVID-19, but not associated with mortality after adjusting for covariates [14, 17]. Thus, while there may be overrepresentation of hypertension among adults with COVID-19, it appears this association may be confounded by age and other covariates, and possibly affected by treatment.
In the current study, treatment with ACEs/ARBs during hospitalization was associated with lower odds of critical care and mortality. It is recommended that patients who are prescribed ACEs/ARBs for cardiovascular disease continue taking these medications if hospitalized with COVID-19 [18, 19]. With the exception of ACEs/ARBs, we observed that many treatments received during hospitalization were associated with higher odds of receipt of critical care, mechanical ventilation, or death. An explanation for this finding may be that most medications, particularly those that were investigational, were only recommended for use among patients with severe disease. For example, remdesivir and dexamethasone are recommended for hospitalized COVID-19 patients that require supplemental oxygen [19]. As of 15 December 2021, remdesivir is the only FDA-approved drug for the treatment of COVID-19, although emergency use authorizations have been issued for multiple anti-SARS-CoV-2 monoclonal antibody products (i.e., bamlanivimab plus etesevimab, casirivimab plus imdevimab, and sotrovimab) [19]. Other medications are under investigation.
Several vital signs and laboratory results were associated with serious outcomes in COVID-19 patients in this study, with the strongest associations observed for LDH, D-dimer, and CRP. LDH is an enzyme found within cells in almost all organ systems [20]. Its levels rise when the body’s tissues are damaged. Recent studies have found that high levels of LDH may be predictive of COVID-19 severity and death [17, 20]. Similarly, increased D-dimer, an indicator of coagulopathy, has been linked to higher risk of mortality in COVID-19 patients [8, 21]. CRP is an inflammatory marker and has been shown to be elevated in patients with severe COVID-19 disease [9, 12, 22]. The findings in the current study are consistent with these smaller studies and, taken together, suggest these laboratory markers, if measured soon after admission, may help clinicians triage patients who may be at higher risk of progression to severe disease.
The current study was based on an analysis of EHR data, which are valuable for the examination of clinical health outcomes and treatment patterns. Nonetheless, all EHR databases have inherent limitations because the data are collected for the purpose of clinical patient management, not research. Unlike in clinical trials, where the collection of clinical and laboratory measures is standardized, the Optum EHR includes real-world clinical data obtained from multiple medical and laboratory settings used for patient care. Because data are not collected in a systematic way, clinical measurements (e.g., vital signs and laboratory results) were not available for all patients.
Additionally, the presence of a diagnosis code in EHR data may not represent the actual presence of disease, as the diagnosis code may be incorrectly coded, or included as rule-out criteria rather than actual disease. We assumed the absence of a diagnosis code meant the patient did not have the disease. This assumption may be a reason why we observed an inverse association between cough and serious outcomes; in a patient who is very ill, severe symptoms like dyspnea and hypoxemia may be more likely to be recorded than minor symptoms such as cough. Furthermore, it is possible that some comorbidities and medications may not have been captured as health care encounters with medical providers who do not contract with Optum would not be observed.
The median duration of hospitalization among patients who died was 10 days. However, deaths were identified within all data available, not only during hospitalization. As such, it is possible that duration of hospitalization may have been shorter among patients that died during hospitalization. An additional limitation of the EHR database is the data lag at the time of data extraction, which likely resulted in an underestimation of the number of deaths. Finally, residual confounding is a concern as we only adjusted for demographic covariates.

Conclusion

In summary, we utilized an exploratory, data-driven approach to identify many clinical characteristics that were associated with receipt of critical care, mechanical ventilation, and death among patients hospitalized with COVID-19. As more continues to be learned about COVID-19 by clinicians and researchers, future studies should move toward causal inference and focus on identifying the etiologic factors and mechanisms responsible for some patients experiencing more severe COVID-19 disease.

Acknowledgements

The authors would like to acknowledge Katherine Reed for her technical assistance in preparing the manuscript.

Declarations

Not applicable because this study utilized a commercially available, de-identified database. No administrative permissions were required for this study as this database has been certified as de-identified by an independent statistical expert following Health Insurance Portability and Accountability Act statistical de-identification rules and managed according to Optum® customer data use agreements. As no study team members had access to patient identifiers linked to the data, review by an ethics committee or institutional review board was not required, nor was consent to participate.
Not applicable.

Competing interests

AKC, CL, MCD, CRC, RPO, RVG, JNS, CE, FTW, and JDS are employees of Optum receiving stock and/or stock options in UnitedHealth Group. NDL was formerly an employee of Optum and is currently an employee of IQVIA.
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://​creativecommons.​org/​licenses/​by/​4.​0/​. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Anhänge
Literatur
2.
Zurück zum Zitat Chen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507–13.CrossRef Chen N, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507–13.CrossRef
3.
Zurück zum Zitat Guan WJ, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708–20.CrossRef Guan WJ, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. 2020;382(18):1708–20.CrossRef
4.
Zurück zum Zitat Richardson S, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA. 2020;323(20):2052–9.CrossRef Richardson S, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City Area. JAMA. 2020;323(20):2052–9.CrossRef
5.
Zurück zum Zitat Wang D, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. 2020;323(11):1061–9.CrossRef Wang D, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. 2020;323(11):1061–9.CrossRef
6.
Zurück zum Zitat Traversi D, et al. Precision medicine and public health: new challenges for effective and sustainable health. J Pers Med. 2021;11(2):135.CrossRef Traversi D, et al. Precision medicine and public health: new challenges for effective and sustainable health. J Pers Med. 2021;11(2):135.CrossRef
7.
Zurück zum Zitat Liang W, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med. 2020;180(8):1081–9.CrossRef Liang W, et al. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Intern Med. 2020;180(8):1081–9.CrossRef
8.
Zurück zum Zitat Zhou F, 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):1054–62.CrossRef Zhou F, 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):1054–62.CrossRef
9.
Zurück zum Zitat Berenguer J, et al. Characteristics and predictors of death among 4035 consecutively hospitalized patients with COVID-19 in Spain. Clin Microbiol Infect. 2020. Berenguer J, et al. Characteristics and predictors of death among 4035 consecutively hospitalized patients with COVID-19 in Spain. Clin Microbiol Infect. 2020.
10.
Zurück zum Zitat Kim L, et al. Risk factors for intensive care unit admission and in-hospital mortality among hospitalized adults identified through the U.S. coronavirus disease 2019 (COVID-19)-associated hospitalization surveillance network (COVID-NET). Clin Infect Dis. 2020;72:e206.CrossRef Kim L, et al. Risk factors for intensive care unit admission and in-hospital mortality among hospitalized adults identified through the U.S. coronavirus disease 2019 (COVID-19)-associated hospitalization surveillance network (COVID-NET). Clin Infect Dis. 2020;72:e206.CrossRef
12.
Zurück zum Zitat Garibaldi BT et al. Patient trajectories among persons hospitalized for COVID-19: a cohort study. Ann Intern Med. 2020. Garibaldi BT et al. Patient trajectories among persons hospitalized for COVID-19: a cohort study. Ann Intern Med. 2020.
13.
Zurück zum Zitat Price-Haywood EG, et al. Hospitalization and mortality among black patients and white patients with Covid-19. N Engl J Med. 2020;382(26):2534–43.CrossRef Price-Haywood EG, et al. Hospitalization and mortality among black patients and white patients with Covid-19. N Engl J Med. 2020;382(26):2534–43.CrossRef
14.
Zurück zum Zitat Suleyman G, et al. Clinical characteristics and morbidity associated with coronavirus disease 2019 in a series of patients in metropolitan detroit. JAMA Netw Open. 2020;3(6): e2012270.CrossRef Suleyman G, et al. Clinical characteristics and morbidity associated with coronavirus disease 2019 in a series of patients in metropolitan detroit. JAMA Netw Open. 2020;3(6): e2012270.CrossRef
16.
Zurück zum Zitat Griffith DM, 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.CrossRef Griffith DM, 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.CrossRef
17.
Zurück zum Zitat Zhao Z, et al. Prediction model and risk scores of ICU admission and mortality in COVID-19. PLoS ONE. 2020;15(7): e0236618.CrossRef Zhao Z, et al. Prediction model and risk scores of ICU admission and mortality in COVID-19. PLoS ONE. 2020;15(7): e0236618.CrossRef
18.
Zurück zum Zitat Lopes R, o.b.o.t.B.C.I. Continuing versus suspending ACE inhibitors and ARBs: impact of adverse outcomes in hospitalized patients with COVID-19—the BRACE CORONA Trial. in European Society of Cardiology 2020 Congress. 2020. Lopes R, o.b.o.t.B.C.I. Continuing versus suspending ACE inhibitors and ARBs: impact of adverse outcomes in hospitalized patients with COVID-19—the BRACE CORONA Trial. in European Society of Cardiology 2020 Congress. 2020.
20.
Zurück zum Zitat Henry BM, et al. Lactate dehydrogenase levels predict coronavirus disease 2019 (COVID-19) severity and mortality: a pooled analysis. Am J Emerg Med. 2020;38(9):1722–6.CrossRef Henry BM, et al. Lactate dehydrogenase levels predict coronavirus disease 2019 (COVID-19) severity and mortality: a pooled analysis. Am J Emerg Med. 2020;38(9):1722–6.CrossRef
21.
Zurück zum Zitat Tang N, et al. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18(4):844–7.CrossRef Tang N, et al. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18(4):844–7.CrossRef
22.
Zurück zum Zitat Manson JJ, et al. COVID-19-associated hyperinflammation and escalation of patient care: a retrospective longitudinal cohort study. Lancet Rheumatol. 2020;2(10):e594–602.CrossRef Manson JJ, et al. COVID-19-associated hyperinflammation and escalation of patient care: a retrospective longitudinal cohort study. Lancet Rheumatol. 2020;2(10):e594–602.CrossRef
Metadaten
Titel
Predictors of critical care, mechanical ventilation, and mortality among hospitalized patients with COVID-19 in an electronic health record database
verfasst von
Andrea K. Chomistek
Caihua Liang
Michael C. Doherty
C. Robin Clifford
Rachel P. Ogilvie
Robert V. Gately
Jennifer N. Song
Cheryl Enger
Nancy D. Lin
Florence T. Wang
John D. Seeger
Publikationsdatum
30.04.2022
Verlag
BioMed Central
Schlagwort
COVID-19
Erschienen in
BMC Infectious Diseases / Ausgabe 1/2022
Elektronische ISSN: 1471-2334
DOI
https://doi.org/10.1186/s12879-022-07383-6

Weitere Artikel der Ausgabe 1/2022

BMC Infectious Diseases 1/2022 Zur Ausgabe

Leitlinien kompakt für die Innere Medizin

Mit medbee Pocketcards sicher entscheiden.

Seit 2022 gehört die medbee GmbH zum Springer Medizin Verlag

Update Innere Medizin

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.