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. Author manuscript; available in PMC: 2025 Oct 1.
Published in final edited form as: Transpl Infect Dis. 2024 Jul 9;26(5):e14333. doi: 10.1111/tid.14333

Risk factors for severe outcomes of coronavirus disease 2019 through the waves of the pandemic: Comparing patients with and without solid organ transplantation

Stephen B Lee 1, Ran Dai 2, Evan French 3, Jerrod A Anzalone 2, Amy L Olex 3, Jin Ge 4, Makayla Schissel 2, Gaurav Agarwal 5, Amanda Vinson 6, Vithal Madhira 7, Roslyn B Mannon 8, N3C Consortium
PMCID: PMC11502248  NIHMSID: NIHMS2018251  PMID: 38980969

Abstract

Background:

While coronavirus disease 2019 (COVID-19) is no longer a public health emergency, certain patients remain at risk of severe outcomes. To better understand changing risk profiles, we studied the risk factors for patients with and without solid organ transplantation (SOT) through the various waves of the pandemic.

Methods:

Using the National COVID Cohort Collaborative we studied a cohort of adult patients testing positive for COVID-19 between January 1, 2020, and May 2, 2022. We separated the data into waves of COVID-19 as defined by the Centers for Disease Control. In our primary outcome, we used multivariable survival analysis to look at various risk factors for hospitalization in those with and without SOT.

Results:

A total of 3,570,032 patients were captured. We found an overall risk attenuation of adverse COVID-19-associated outcomes over time. In both non-SOT and SOT populations, diabetes, chronic kidney disease, and congestive heart failure were risk factors for hospitalization. For SOT specifically, longer time periods between transplant and COVID-19 were protective and age was a risk factor. Notably, asthma was not a risk factor for major adverse renal cardiovascular events, hospitalization, or mortality in either group.

Conclusions:

Our study provides a longitudinal view of the risks associated with adverse COVID-related outcomes amongst SOT and non-SOT patients, and how these risk factors evolved over time. Our work will help inform providers and policymakers to better target high-risk patients.

Keywords: COVID-19, solid organ transplantation, N3C, SARS-CoV-2, transplantation, COVID

1 |. INTRODUCTION

Since its emergence in late 2019, coronavirus disease 2019 (COVID-19) has caused significant health, societal, and economic disruptions to the world. Work done earlier in the pandemic shows a high risk of severe outcomes in those with solid organ transplantation (SOT).1–4

Mortality and severity of illness in the general population declined during the Omicron period compared to previous waves.5,6 One study found the Omicron death risk was 66% lower than that of the Delta wave,7 and another found Omicron’s inpatient death rate was 7.1% compared to Delta’s 12.2% and Alpha’s 7.6%.8 It is theorized that the observed differences are due to a combination of viral changes which amounted to a decreased ability to penetrate the lower lungs and improved natural and vaccine immunity.5 Although characterized by surges, the overall mortality, hospitalizations, and magnitude of peaks have decreased as per data from the Centers for Disease Control and Prevention (CDC).9 As such, the World Health Organization declared COVID-19 was no longer a public health emergency on May 5, 2023.10 Whether similar changes in mortality and severity of illness are found in the SOT population is less clear, although we believe that the overall risk of severe outcomes would generally decrease over time. There is evidence, albeit mostly single-center, affirming decreased mortality and severity over time in the SOT population.11–13

In this study, we used the National COVID Cohort Collaborative (N3C), a centralized repository containing the largest cohort of US patients with COVID-19 gathered to date, to characterize outcomes through the waves of the pandemic for the SOT versus non-SOT populations. We evaluated specific risk factors associated with hospitalization, death, and major adverse renal and cardiac events (MARCE) within the SOT and non-SOT groups to better characterize the changing risks for SOT patients through the waves of the pandemic.

2 |. METHODS

This study was performed using the National COVID Cohort Collaborative under Data Use Request RP-CA3365. This retrospective cohort study received Institutional Review Board approval from the University of Nebraska Medical Center (0853-21-EP), Johns Hopkins University (IRB00309495), and the University of California, San Francisco (22-37380).

2.1 |. N3C database

N3C aggregates and harmonizes data from electronic health records (EHR) across the United States14 and contains billions of row-level data on millions of patients.15 Incoming EHR data from partner sites were originally structured in four data models, OMOP, PCORnet, TriNetX, and ACT. After appropriate data quality checks, they are ingested into the N3C, and the data is aligned and harmonized to a common OMOP 5.3.1 model. This harmonization allows combined data analysis in the N3C Enclave, a secure cloud-based database with embedded SQL, R, and Python analytical capabilities. More details regarding the methodology and design of the N3C database were described previously.5

2.2 |. Study design and exposures

Using the Enclave, we performed a cohort study of adults (>18 years old) diagnosed with COVID-19 between January 1, 2020, and May 2, 2022. While the global emergency was declared over in 2023,5 a return to normal happened earlier in developed countries. This frameshift in mentality occurred gradually with no set date. Due to the differences in the mentality of healthcare providers, governments, and the public during this period, confounders such as behavioral changes in gathering, masking, social distancing, and efforts to self-isolate and test would have arisen. For instance, individuals who may have purposely chosen a farther but less congested grocery store in 2020 may have felt comfortable going to a closer but busier store in 2022. Many of these changes are not easily captured in data. Due to this, we chose an end date of May 2, 2022, to capture sufficient data from the Omicron period and avoid most of what we deemed a complete return to normal. May 2 represented the last day a diagnosis of COVID-19 was made in our cohort; however, those diagnosed on May 2 were followed for an additional 90 days for survival analysis. Patients who did not have follow-up data for at least 90 days were excluded. The diagnosis of COVID-19 was based on a set of a-priori-defined severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests including a polymerase chain reaction or positive diagnostic codes, the OMOP codes for diagnoses are publicly available via GitHub and have been published previously.16

A series of risk factors were defined, including age (stratified into 18–45, 46–65, and over 65), asthma, coronary artery disease (CAD), cancer, congestive heart failure (CHF), chronic kidney disease (CKD), diabetes, hypertension, liver disease, obesity (defined as body mass index > = 30 kg/m2), peripheral vascular disease, ethnicity (Asian, Black, White, and Hispanic), sex, SOT history (organ type and time since transplant) and vaccination status (defined as un-vaccinated vs. vaccinated). Vaccination was defined as equal to or more than two doses of mRNA vaccine or 1 dose of Johnson & Johnson. No patients were considered vaccinated in the pre-vaccine period and this risk factor was not analyzed in the pre-vaccine groups. Patients with SOT were considered the exposure group and those without, the control group. These co-morbidities were defined by standard concept sets created by the N3C Logic Liaison Team, which correlated to those from the Charlson Comorbidity Index and the CDC’s risk factor definitions and have been used by other publications.2,3,4,16,17 The data was separated into various waves to describe risk factors throughout the pandemic. These were pre-vaccine (mainly original strain of COVID-19), Delta-wave, and Omicron-wave. As our data captured existing health information, no variant identification testing was routinely available (most hospitals did not identify variants). Due to this, we defined the waves using time periods based on vaccine availability and the variant-dominant wave in the US. These were pre-vaccine as before December 10, 2020 (the date when vaccines became available), Delta-dominant as June 16, 2021, to December 22, 2021, and Omicron-dominant wave as after December 22, 2021-May 2, 2022 (patients followed for 90 days after diagnosis).18 Notably, we decided not to analyze the Alpha wave due to multiple circulating variants (Alpha, Beta, and Gamma).19

2.3 |. Outcomes

Our primary outcome was hospitalization; our secondary outcomes were death and MARCE. Data were censored after 90 days.

2.4 |. Data collection and analysis

Descriptive statistics were generated to describe the cohort distribution in terms of demographic and baseline comorbid conditions of the SOT and non-SOT patients. Respectively for the SOT and non-ISC sub-cohorts, for each risk factor, we marginally examined the sample size and proportion of patients developing each outcome within the 90-day time window after the COVID-19 diagnosis index date. To understand the associations between the wave status and the risks of our outcomes (hospitalization and death within the 90-day time window), we conducted multivariate survival analyses using Cox proportional hazard models including the risk factors listed above.

Our primary analysis was performed in the overall population (including SOT and non-SOT). Secondary subgroup analyses were done in the SOT cohort specifically to understand the potential differential impact of the risk factors in this group. Next, we also constructed subgroups according to the wave period in which COVID-19 was diagnosed. We perform multivariable survival analysis to understand the potential comorbidity impacts across different periods of time of the COVID-19 pandemic.

Data was extracted using Python 3.6.10 (PySpark), R 3.6.3, or SQL and analyzed in the secure cloud-based Palantir Enclave. Analysis was completed within the secure enclave as per N3C privacy requirements and once completed, approval was gained from the N3C for data download and publication.

3 |. RESULTS

The study cohort includes 3,570,032 COVID-19 patients diagnosed between January 1, 2020, to May 2, 2022, of whom 24,615 were SOT and 3,545,417 were non-SOT. The demographics of these cohorts are shown in Table 1.

TABLE 1.

Baseline demographics for patient cohort and hospitalized patients.

Total Patients Hospitalizations
Solid organ transplant Non-solid organ transplant Solid organ transplant Non-solid organ transplant
Pre-vaccine Delta Omicron p-Value Pre-vaccine Delta Omicron p-Value Pre-vaccine Delta Omicron p-Value Pre-vaccine Delta Omicron p-Value
n = 7238 n = 6681 n = 10,696 n = 1,431,168 n = 1,005,392 n = 1,108,857 n = 3846 n = 2835 n = 4109 n = 197,947 n = 123,026 n = 109,134
Age 18–45 1681 (23.2%) 1757 (26.3%) 2631 (24.6%) P < 0.001 717,114 (50.1%) 526,477 (52.4%) 575,464 (51.9%) P < 0.001 758 (19.7%) 619 (21.8%) 819 (19.9%) p = 0.012 45,694 (23.1%) 36,934 (30.0%) 35,136 (32.2%) P < 0.001
Age 46–65 3594 (49.7%) 3201 (47.9%) 4928 (46.1%) 461,522 (32.2%) 317,710 (31.6%) 342,760 (30.9%) 1863 (48.4%) 1365 (48.1%) 1909 (46.5%) 67,096 (33.9%) 43,374 (35.3%) 31,801 (29.1%)
Age 65+ 1963 (27.1%) 1723 (25.8%) 3137 (29.3%) 252,532 (17.6%) 161,205 (16%) 190,633 (17.2%) 1225 (31.9%) 851 (30.0%) 1381 (33.6%) 85,157 (43.0%) 42,718 (34.7%) 42,197 (38.7%)
Male 4361 (60.3%) 3938 (58.9%) 6118 (57.2%) P < 0.001 646,830 (45.2%) 444,767 (44.2%) 450,214 (40.6%) P < 0.001 2354 (61.2%) 1715 (60.5%) 2399 (58.4%) p = 0.029 97,515 (49.3%) 58,407 (47.5%) 47,033 (43.1%) P < 0.001
Female 2877 (39.7%) 2743 (41.1%) 4578 (42.8%) 781,744 (54.6%) 559,642 (55.7%) 657,597 (59.3%) 1492 (38.8%) 1120 (39.5%) 1710 (41.6%) 100,398 (50.7%) 64,604 (52.5%) 62,087 (56.9%)
Asthma 708 (9.8%) 657 (9.8%) 1201 (11.2%) p = 0.0014 75,088 (5.2%) 66,508 (6.6%) 98,690 (8.9%) P < 0.001 386 (10.0%) 264 (9.3%) 502 (12.2%) p<0.001 14,382 (7.3%) 10,092 (8.2%) 11,513 (10.5%) P < 0.001
CAD 2101 (29%) 1945 (29.1%) 3360 (31.4%) P < 0.001 68,901 (4.8%) 51,414 (5.1%) 66,911 (6%) P < 0.001 1267 (32.9%) 1012 (35.7%) 1609 (39.2%) p<0.001 30,188 (15.3%) 17,942 (14.6%) 20,883 (19.1%) P < 0.001
Cancer 1379 (19.1%) 1417 (21.2%) 2563 (24%) P < 0.001 65,972 (4.6%) 48,863 (4.9%) 74,191 (6.7%) P < 0.001 754 (19.6%) 630 (22.2%) 1013 (24.7%) p<0.001 21,874 (11.1%) 13,302 (10.8%) 17,177 (15.7%) P < 0.001
CHF 2257 (31.2%) 2048 (30.7%) 3454 (32.3%) p = 0.059 57,674 (4%) 41,214 (4.1%) 54,113 (4.9%) P < 0.001 1423 (37.0%) 1102 (38.9%) 1766 (43.0%) p<0.001 30,635 (15.5%) 18,258 (14.8%) 22,088 (20.2%) P < 0.001
CKD 6139 (84.8%) 5721 (85.6%) 9195 (86%) p = 0.096 70,239 (4.9%) 49,160 (4.9%) 66,925 (6%) P < 0.001 3369 (87.6%) 2529 (89.2%) 3654 (88.9%) p = 0.074 35,011 (17.7 %) 19,107 (15.5%) 22,548 (20.7%) P < 0.001
Diabetes 3924 (54.2%) 3498 (52.4%) 5712 (53.4%) p = 0.090 156,818 (11%) 108,998 (10.8%) 133,261 (12%) P < 0.001 2308 (60.0%) 1708 (60.2%) 2545 (61.9%) p = 0.17 57,026 (28.8%) 32,308 (26.3%) 31,043 (28.4%) P < 0.001
Hypertension 6302 (87.1%) 5880 (88.0%) 9453 (88.4%) p = 0.029 311,702 (21.8%) 235,153 (23.4%) 298,651 (26.9%) p<0.001 3492 (90.8%) 2606 (91.9%) 3762 (91.6%) p = 0.24 96,843 (48.9%) 57,684 (46.9%) 59,000 (54.1%) P < 0.001
Liver Disease 1781 (24.6%) 1711 (25.6%) 2963 (27.7%) P < 0.001 45,621 (3.2%) 39,875 (4%) 56,169 (5.1%) P < 0.001 959 (24.9%) 805 (28.4%) 1235 (30.1%) p<0.001 15,070 (7.6%) 11,223 (9.1%) 12,820 (11.7%) P < 0.001
Obesity 3765 (52%) 3579 (53.6%) 5769 (53.9%) p = 0.035 365,089 (25.5%) 291,886 (29%) 363,973 (32.8%) P < 0.001 2134 (55.5%) 1612 (56.7%) 2337 (56.9%) p = 0.38 81,720 (41.3%) 52,820 (42.9%) 48,322 (44.3%) P < 0.001
PVD 1182 (16.3%) 1092 (16.3%) 1922 (18%) p = 0.0034 30,109 (2.1%) 22,970 (2.3%) 29,416 (2.7%) P < 0.001 736 (19.1%) 579 (20.4%) 966 (23.5%) p<0.001 14,089 (7.1%) 8,908 (7.2%) 10,320 (9.5%) P < 0.001
Asian 201 (2.8%) 129 (1.9%) 317 (3%) P < 0.001 28,669 (2%) 10,273 (1%) 26,449 (2.4%) P < 0.001 103 (2.7%) 56 (2.0%) 98 (2.4%) p<0.001 5355 (2.7%) 1271 (1.0%) 1928 (1.8%) P < 0.001
African American 1800 (24.9%) 1318 (19.7%) 2318 (21.7%) 183,478 (12.8%) 122,320 (12.2%) 148,457 (13.4%) 1055 (27.4%) 685 (24.2%) 1105 (26.9%) 38,161 (19.3%) 19,636 (16.0%) 20,427 (18.7%)
Hispanic 1161 (16%) 611 (9.1%) 1251 (11.7%) 210,717 (14.7%) 74,408 (7.4%) 109,163 (9.8%) 660 (17.2%) 267 (9.4%) 555 (13.5%) 32,894 (16.6%) 9,566 (7.8%) 10,803 (9.9%)
White Non-Hispanic 3431 (47.4%) 4238 (63.4%) 6111 (57.1%) 797,483 (55.7%) 702,620 (69.9%) 706,713 (63.7%) 1659 (43.1%) 1666 (58.8%) 2075 (50.5%) 101,106 (51.1%) 84,506 (68.7%) 67,930 (62.2%)
Unvaccinated - 4826 (72.2%) 7046 (65.9%) P < 0.001 - 820,880 (81.6%) 766,469 (69.1%) P < 0.001 - 2094 (73.9%) 2832 (68.9%) p<0.001 - 106,217 (86.3%) 85,572 (78.4%) P < 0.001
Vaccinated - 1855 (27.8%) 3650 (34.1%) - 184,512 (18.4%) 342,388 (30.9%) - 741 (26.1%) 1277 (31.1%) - 16,809 (13.7%) 23,562 (21.6%)
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There was an attenuation of risk of hospitalization, MARCE, and death over time for SOT (Figure 1). More detailed information is available in Table 1 and SOT-specific demographics are presented in Table S1. A comparison of risks per type of allograft and SOT-specific factor analysis is available in Table 2. Vaccination was protective across all combinations of groups, waves, and outcomes (Figure 2).

FIGURE 1.

FIGURE 1

FIGURE 1

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Change in Outcomes over Time (major adverse renal and cardiac events [MARCE]: Pre-vaccine 57.7% (4179), Delta 48.1% (3216), Omicron 43.1% (4612), p < 0.001; Hospitalization: Pre-vaccine 53% (3846), Delta 42.4% (2835), Omicron 38.4% (4,109), p < 0.001; Death: Pre-vaccine 9.7% (704), Delta 10.1% (678), Omicron 5.7% (610), p < 0.001).

TABLE 2.

Transplant-specific risk factors for severe outcomes of coronavirus disease 2019 (COVID-19) throughout the pandemic.

MARCE Hospitalization Death
Pre-vaccine Delta Omicron Pre-vaccine Delta Omicron Pre-vaccine Delta Omicron
ATG Induction 1.03 (0.9–1.18) 0.94 (0.82–1.06) 0.89 (0.8–0.99) 0.93 (0.83–1.05) 0.9 (0.8–1.01) 0.87 (0.79–0.96) 0.78 (0.56–1.09) 1.11 (0.87–1.42) 1.01 (0.77–1.32)
Basiliximab 0.99 (0.84–1.18) 0.94 (0.8–1.12) 0.89 (0.78–1.02) 0.91 (0.78–1.06) 0.91 (0.78–1.07) 1.02 (0.91–1.15) 0.64 (0.42–0.97) 0.87 (0.62–1.22) 0.78 (0.57–1.07)
Cyclosporine 1.04 (0.91–1.2) 1.13 (0.98–1.31) 1.06 (0.93–1.2) 0.98 (0.86–1.11) 1.04 (0.9–1.2) 1.04 (0.93–1.16) 0.89 (0.68–1.17) 1.26 (0.96–1.65) 0.79 (0.58–1.09)
MMF 0.96 (0.87–1.06) 0.98 (0.87–1.11) 1.09 (0.98–1.21) 1.04 (0.95–1.14) 1.03 (0.93–1.15) 1.09 (0.99–1.2) 1.02 (0.84–1.25) 1.07 (0.85–1.34) 1.04 (0.82–1.33)
Tacrolimus 0.94 (0.84–1.05) 0.9 (0.79–1.02) 0.84 (0.75–0.94) 0.97 (0.88–1.07) 0.87 (0.77–0.97) 0.85 (0.77–0.94) 0.65 (0.53–0.81) 0.85 (0.68–1.08) 0.83 (0.64–1.06)
Heart Transplant 0.75 (0.65–0.85) 0.9 (0.77–1.04) 0.87 (0.77–0.99) 0.85 (0.75–0.95) 0.97 (0.84–1.11) 0.85 (0.76–0.96) 0.62 (0.46–0.84) 0.73 (0.54–0.97) 0.64 (0.47–0.88)
Liver Transplant 1.24 (1.07–1.43) 0.95 (0.8–1.14) 1.07 (0.93–1.23) 1.22 (1.08–1.39) 1.07 (0.92–1.25) 1.12 (0.99–1.27) 1.57 (1.21–2.03) 0.84 (0.6–1.17) 0.79 (0.58–1.08)
Lung Transplant 1.1 (0.93–1.31) 1.2 (1.01–1.42) 1.32 (1.14–1.52) 1.08 (0.93–1.25) 1.29 (1.1–1.5) 1.35 (1.19–1.53) 1.33 (0.97–1.84) 1.1 (0.79–1.51) 1.48 (1.11–1.99)
Multiple Transplant 0.98 (0.85–1.13) 0.97 (0.84–1.11) 1.01 (0.9–1.12) 1.02 (0.9–1.16) 1.07 (0.94–1.22) 1 (0.9–1.11) 1.14 (0.86–1.52) 0.93 (0.71–1.21) 0.83 (0.63–1.09)
6–24months since transplant 0.7 (0.63–0.78) 0.65 (0.57–0.74) 0.71 (0.64–0.79) 0.73 (0.67–0.8) 0.65 (0.57–0.73) 0.7 (0.63–0.76) 0.88 (0.7–1.09) 0.76 (0.58–1) 0.76 (0.59–0.99)
24+months since transplant 0.88 (0.8–0.98) 0.78 (0.69–0.88) 0.67 (0.61–0.74) 0.85 (0.77–0.92) 0.74 (0.66–0.83) 0.66 (0.6–0.72) 1.03 (0.84–1.27) 0.99 (0.77–1.27) 0.86 (0.68–1.09)
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FIGURE 2.

FIGURE 2

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(A) Risk factors for major adverse renal and cardiac events (MARCE) in solid organ transplant and non-solid organ transplant populations. (B) Risk factors for hospitalization in solid organ transplant and non-solid organ transplant populations. (C) Risk factors for death in solid organ transplant and non-solid organ transplant populations.

For mortality in SOT; older age 65+: Omicron hazard ratio (HR) 4.05, 95% confidence Interval (CI) 2.98–5.49; Delta HR 4.86, 95% CI 3.56–6.62; HR Pre-Vaccine 4.50, 95% CI 3.3–6.14, CKD, diabetes, and CHF were risk factors. In the non-SOT population, older age (65+: HR Omicron 12.78 95% CI 11.56–14.13, HR Delta 11.99 95% CI 11.11–12.95; HR Pre-Vaccine 30, 95% CI 27.66–32.53), male sex (HR Omicron 1.35, 95% CI 1.3–1.41; HR Delta 1.38, 95% CI 1.34–1.44; HR Pre-Vaccine 1.39, 95% CI 1.35–1.43), liver disease, hypertension, diabetes, CKD, CHF, and cancer (HR Omicron 1.86, 95% CI 1.78–1.95; HR Delta 1.49, 95% CI 1.42–1.55; HR Pre-Vaccine 1.33, 95% CI 1.29–1.38) were risk factors. Of note is that asthma was not a risk factor in either population or may have been protective. The role of obesity on mortality for non-SOT seems to have changed through the pandemic, with higher risk during the pre-vaccine and Delta waves and reduced risk during the Omicron wave (HR 0.77, 95% CI 0.74–0.80), possibly due to the obesity paradox (Figure 2).

For SOT hospitalization, longer time periods between transplant and COVID-19 (24+ months 0.66, 0.74, and 0.85) was a protective factor in the SOT population, while age (65+ HR Omicron 1.31, 95% CI 1.19–1.44; HR Delta 1.35, 95% CI 1.2–1.51; Pre-Vaccine HR 1.50, 95% CI 1.35–1.66), diabetes, CKD, and CHF were risk factors for hospitalization. In the non-SOT population, PVD (HR Omicron 1.25, 95% CI 1.22–1.28; HR Delta 1.15, 95% CI 1.12–1.18; HR Pre-Vaccine 1.09, 95% CI 1.07–1.12), liver disease, hypertension, diabetes, CKD, CHF, cancer (HR Omicron 1.37, 95% CI 1.35–1.40; HR Delta 1.25, 95% CI 1.22–1.27; HR Pre-Vaccine 1.25, 95% CI 1.23–1.27), and CAD (HR Omicron 1.20, 95% CI 1.18–1.23; HR Delta 1.09, 95% CI 1.06–1.11; HR Pre-Vaccine 1.12, 95% CI 1.10–1.13) were risk factors. Obesity was a risk factor in earlier eras (HR Delta 1.04, 95% CI 1.03–1.06; HR Pre-Vaccine 1.13, 95% CI 1.12–1.14) but not in the Omicron era (HR 0.94, 95% CI 0.92–0.95) for non-SOT patients. Asthma again was not found to be a risk factor but may have been protective (Figure 2).

Finally for SOT MARCE, age (65+: HR Omicron 1.34, 95% CI 1.21–1.49; HR Delta 1.49, 95% CI 1.31–1.69; HR pre-vaccine 1.52, 95% CI 1.35–1.71), hypertension, diabetes, CKD, CHF, and CAD (HR Omicron 1.17, 95% CI 1.08–1.26; HR Delta 1.2, 95% CI 1.09–1.32; HR pre-vaccine 1.18, 95% CI 1.08–1.29) were risk factors. For non-SOT patients, male sex (HR Omicron 1.31, 95% CI 1.28–1.34; HR Delta 1.40, 95% CI 1.37–1.43; HR pre-vaccine 1.43, 95% CI 1.40–1.45), PVD (HR Omicron 1.20, 95% CI 1.16–1.23; HR Delta 1.10, 95% CI 1.07–1.13; HR pre-vaccine 1.07, 95% CI 1.05–1.10), liver disease, hypertension, diabetes, CKD, CHF, cancer (HR Omicron 1.23, 95% CI 1.20–1.26; HR Delta 1.22, 95% CI 1.19–1.26; HR pre-vaccine 1.17, 95% CI 1.15–1.20), and CAD (HR Omicron 1.34, 95% CI 1.31–1.37; HR Delta 1.20, 95% CI 1.17–1.23; HR pre-vaccine 1.20, 95% CI 1.18–1.23) were risk factors. Again, asthma (HR Omicron 0.86, 95% CI 0.83–0.89; HR Delta 0.87, 95% CI 0.85–0.9; HR pre-vaccine 0.89, 95% CI 0.87–0.92) did not seem to be a risk factor. Obesity’s role has again changed with it no longer being a risk factor for non-SOT in the Omicron era (Omicron HR 0.84, 95% CI 0.82–0.86 vs. Delta HR 1.06, 95% CI 1.04–1.08 and pre-vaccine HR 1.05, 95% CI 1.03–1.06). (Figure 2).

4 |. DISCUSSION

The highest mortality was observed in the Delta era with less severe outcomes observed during Omicron than in other eras; however, during each COVID wave, immunosuppressed patients remain at higher risk for hospitalization, MARCE, and death. Continued work into characterizing the heightened risk of severe outcomes through the waves remains important in the pandemic, and identifying the changing risk profile is important for risk-stratifying patients over time.

Our work characterized different risk factors for severe COVID-19 outcomes (MARCE, hospitalization, and death) in different waves of the pandemic for SOT. It also compared these outcomes to those without SOT. This study helps provide an updated description of risk factors in Omicron and shows how it may contrast with earlier periods, which can help inform policymakers and providers for resource prioritization and risk stratification.

We show updated data that vaccination continued to be highly protective across all waves for both SOT and non-SOT, emphasizing the continued importance of vaccines. CHF, CKD, and advanced age were significant risk factors for poor outcomes. Two novel findings that may help modify previous understandings were that asthma was found to not be a risk factor for MARCE, hospitalization, or death and obesity was not a uniform risk factor.

Through the pandemic, there has been controversy as to the threat that asthma poses.20–23 Guidelines from many societies all identify asthma as a risk factor for COVID-19 severity.24–26 To our knowledge, our study utilizes the largest granular database to show that, among individuals diagnosed with COVID-19, asthma is not a risk factor for MARCE, hospitalization, or death in either SOT or non-SOT populations. Interestingly in some of our analyses, it may have been mildly protective (SOT hospitalization and death during the Delta wave; non-SOT MARCE, hospitalization, and death across all waves). Post-mortem biopsies of COVID-19 cases showed high levels of ACE2 receptor expression compared to controls which is the receptor used by SARS-CoV-2.27 Work has also shown that airway ACE2 receptor expression was decreased in asthmatics, which could lead to decreased viral binding.28 Furthermore, asthmatics show an increase in TH-2 lymphocytes, which may protect against SARS-CoV-2.29 Although more study is needed, these factors may help explain our findings.

In SOT specifically, asthma is not a risk factor, but further work is needed to determine if a minor protective effect also exists. It is possible that there is a critical threshold of viral ACE2 receptor binding, beyond which patients inevitably progress toward severe COVID-19 regardless of any additional virus. The co-morbidities that inevitably accompany complex SOT patients may result in an upregulation of ACE2 receptors due to non-asthma disease processes abrogating any protective effects from asthma.30

In our study, obesity seemed to be a risk factor earlier in the pandemic, however, in the Omicron era, it was not a risk factor. In the past, the “obesity paradox” has been described, whereby obese patients have a better prognosis for infection than underweight or normal-weight patients.31 The mechanism of this phenomenon is unclear, but possibilities include increased inflammation that prevents pathogen replication or increased reserves for a period of illness.11

The effect of liver disease was attenuated for SOT in Omicron and pre-vaccine as compared to non-SOT. Diabetes mellitus had similar risk factors on SOT and non-SOT. Hypertension, CKD, and CHF remained risk factors for both SOT and non-SOT, but the effect was more pronounced in the non-SOT. While older age was an increased risk factor for severe outcomes, it was a relatively larger risk in non-SOT compared to SOT. Non-SOT patients with hypertension and CHF had an increase in relative risk during the latter eras of the pandemic.

In SOT-specific factors, timeframes greater than 6 months after transplantation were protective in general. There was a possible trend toward increased protection during the short-term time frame (6–24 months) as compared to over 24 months after transplant. It is possible that there is increased medical care and personal caution for patients in the short-term post-transplant which was protective. In general, lung transplantation had worse outcomes as compared to kidney transplantation which may be explained by COVID-19’s primary site of infection being the lungs.26 Ultimately this study elucidates areas that could be of interest for future study.

However, a series of limitations exist. Our study uses a massive volume of automated data extraction from EHRs. This causes a loss in nuanced descriptors. For example, our definition of obesity was based on the readily accessible data to calculate body mass index (BMI). Adipose tissue, and not muscle, is thought to explain the risks obesity poses.32 BMI does not differentiate between adipose tissue and muscle mass. Therefore, a patient with no adipose tissue but significant muscle mass could be considered obese. Additionally, as we age, adipose tissue replaces muscles thus BMI will underestimate body fat.12 Similar losses in nuance apply to other descriptors as well.

Aside from issues with capturing nuanced information, our study has other limitations. Our database captures information from EHRs capable of participating in the N3C and thus may be biased toward sicker or more complex patients by missing smaller organizations (e.g., independent walk-in clinics). Even when it captures these mild cases it may miss data that is not inherently captured in such a database. For instance, if a healthcare practitioner managed a case with no investigations or objective measures of disease, but instead provided only verbal reassurance, this data would be uncaptured. Further to this issue, given that significant vaccination efforts happened at outpatient pharmacies, there is significant vaccination data missing in our dataset. While this limits our ability to make novel or definitive conclusions on vaccination, our study continues to reinforce well-replicated findings of previous studies on the benefit of vaccines on all endpoints across patient groups33,34 and uses the same definitions as has been extensively published and validated by the N3C group.35

Our study provides a longitudinal view of the various waves of the pandemic and characterizes the evolution of various risk factors in both SOT and non-SOT populations. We specifically report findings contrary to accepted beliefs regarding obesity in the Omicron era and asthma as risk factors for more severe COVID-19 outcomes. Future prospective work may better elucidate the effect of these risk factors on COVID-19 prognosis. Regardless, our work helps provide more detailed information to guide providers and policymakers in targeting high-risk patients.

Supplementary Material

N3C Policies
Supplementary Material
Visual Abstract

ACKNOWLEDGMENTS

We thank Dr. Christos Argyropoulos for his invaluable feedback on the manuscript. This study is funded by KL2TR001870 (National Center for Advancing Translational Sciences, Ge), P30DK026743 (UCSF Liver Center Grant, Ge), National Institute of General Medical Sciences: U54GM104942 and U54GM115458. Dr. Amy Olex and Evan French are funded through the CTSA Grant UL1TR002649 (see Supporting Information for N3C policy).

Funding information

National Center for Advancing Translational Sciences, Grant/Award Number: KL2TR001870; UCSF Liver Center, Grant/Award Number: P30DK026743; National Institute of General Medical Sciences, Grant/Award Numbers: U54GM104942, U54GM115458; CTSA, Grant/Award Number: UL1TR002649

Footnotes

CONFLICT OF INTEREST STATEMENT

Dr. Jin Ge receives research support from Merck and Co.; and consults for Astellas Pharmaceuticals/Iota Biosciences. Dr. Roslyn Mannon receives grant funding from VericiDx and is a consultant for Sanofi, Hibio, CSL Behring, Natera, and Chinook. Dr. Amanda Vinson is a consultant for Paladin Labs Inc. and Taketa Pharmaceuticals.

SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

N3C Policies
NIHMS2018251-supplement-N3C_Policies.docx (16.1KB, docx)
Supplementary Material
NIHMS2018251-supplement-Supplementary_Material.docx (17.7KB, docx)
Visual Abstract
NIHMS2018251-supplement-Visual_Abstract.pdf (199.7KB, pdf)

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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