Published online Feb 27, 2026. doi: 10.4254/wjh.v18.i2.114481
Revised: October 13, 2025
Accepted: December 16, 2025
Published online: February 27, 2026
Processing time: 145 Days and 8.4 Hours
Due to immunosuppression (IS) use, patients with autoimmune hepatitis (AIH) may be at high risk for poor coronavirus disease 2019 (COVID-19) outcomes.
To investigate the associations between IS type and COVID-19 severity in AIH patients using the National Clinical Cohort Collaborative (N3C) COVID enclave.
We identified all AIH patients with COVID-19 in the N3C COVID enclave. We used adjusted logistic regressions to determine associations between IS type and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We used Cox models to determine associations between IS type and all-cause mortality in the subset of AIH patients infected with SARS-CoV-2.
Of 15187 AIH patients, 5106 (33%) had IS exposure, and 1604 (11%) tested positive for SARS-CoV-2 during the study period from March 2020 through March 2025. There was an association with prednisone [odds ratio (OR): 0.81, 95%CI: 0.65-0.99, P = 0.04] exposure and SARS-CoV-2 test positivity. For interactions between different IS combinations and SARS-CoV-2 test positivity in the overall cohort, budesonide and azathioprine (OR: 1.88, 95%CI: 1.02-3.44, P = 0.04) and pre
Prednisone exposure was negatively associated, and statistically significant IS interactions were positively associated with testing positive for SARS-CoV-2. Further work involves determining the impact of vaccinations and advances in COVID-19 treatment on outcomes in this population.
Core Tip: The effect of immunosuppressive medications on coronavirus disease 2019 (COVID-19) outcomes, including for patients with autoimmune hepatitis (AIH), has been variable across research studies. To address these uncertainties, we utilized data from the National Clinical Cohort Collaborative COVID enclave, a national COVID-19 multi-center electronic health record database to determine the influence of immunosuppressive therapy on testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and clinical outcomes in AIH patients. In this study of 15187 patients with AIH, we found a negative association between prednisone use and SARS-CoV-2 infection, while interactions between prednisone and tacrolimus and budesonide and azathioprine had a positive association with SARS-CoV-2 infection.
- Citation: Rai K, Lindquist KJ, Kornak J, Ge J. Assessing the associations between immunosuppressant use and COVID-19 severity in patients with autoimmune hepatitis. World J Hepatol 2026; 18(2): 114481
- URL: https://www.wjgnet.com/1948-5182/full/v18/i2/114481.htm
- DOI: https://dx.doi.org/10.4254/wjh.v18.i2.114481
Autoimmune hepatitis (AIH) is a chronic, inflammatory liver disease characterized by autoimmune-mediated attack on hepatocytes, leading to progressive liver damage, fibrosis, and eventually cirrhosis and portal hypertension. Although the precise etiology of AIH remains poorly understood, it affects individuals across age, gender, and race demographics[1]. Management of AIH frequently involves immunosuppressive (IS) therapies aimed at controlling inflammation and preventing disease progression. Corticosteroids such as prednisone or budesonide are typically employed as first-line treatments, either alone or in combination with other IS agents such as azathioprine[1]. Alternative or second-line agents, such as mycophenolate mofetil, are also utilized, particularly in cases demonstrating inadequate response or intolerance to standard therapies, reflecting the clinical heterogeneity observed in patients[2].
The impact of IS therapy on susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the clinical outcomes of coronavirus disease 2019 (COVID-19) disease have demonstrated varied results. Some studies report no significant association between IS therapy and severe COVID-19 outcomes, such as mechanical ventilation or in-hospital mortality[3]. Other studies have reported some favorable COVID-19 outcomes amongst the immunosuppressed population, specifically limiting the hyperinflammatory phase of the disease from steroid use[4]. Conversely, other studies have shown an increased risk of mortality among patients receiving IS with hazard ratios (HR) as high as 2.07 (95%CI: 1.20-3.57) compared to controls who do not receive IS therapy[5]. In addition, one study found glucocorticoid use to be associated with elevated risks of hospitalization and death in SARS-CoV-2 positive patients[6]. The relationship between IS therapies and susceptibility to SARS-CoV-2 infection and severity of COVID-19 is, therefore, mixed and more complex than initially thought.
Consequently, patients with chronic liver disease who are on IS therapy, specifically AIH patients, may experience increased susceptibility to infection and severe disease manifestations. One study with 254 patients found baseline treatment of systemic glucocorticoids associated with worse COVID-19 severity at onset of the infection [adjusted odds ratio (aOR) = 4.73, 95%CI: 1.12-25.9] at onset of COVID-19 in AIH patients[7]. Other studies of AIH patients under IS treatment, however, have failed to corroborate these results, finding a similar susceptibility to SARS-CoV-2 infection and COVID-19 disease course in AIH patients with IS compared to those not on IS and not recommending IS treatment be stopped in these patients[8-10]. Unlike certain IS medications, corticosteroids, have been used to treat severe COVID-19 symptoms as well, therefore, creating uncertainty in patients with AIH[6]. To address these uncertainties, it is crucial to evaluate how different types of IS therapies impact both the susceptibility to SARS-CoV-2 infection and the clinical outcomes in patients with AIH.
In this study, we queried the National Clinical Cohort Collaborative (N3C) COVID enclave, which is an extensive repository of centralized electronic health record (EHR) data from 98 sites[11], to address the following research question: In patients diagnosed with AIH, does the type of IS therapy influence the risk of being diagnosed with SARS-CoV-2 infection (defined as testing positive for SARS-CoV-2) and the clinical outcomes of COVID-19 disease among those infected?
The N3C is a secure, centralized, harmonized clinical data resource with embedded analytical capabilities[11-14]. EHR data in the N3C COVID enclave are harmonized in the Observational Medical Outcomes Partnership (OMOP) common data model, version 5.3.1[15]. It is the largest COVID-19 resource for EHR data in the United States, including approximately 9 million patients infected with SARS-CoV-2 and approximately 23 million persons in total[11,16]. The data on this platform spans from March 2020, the onset of the COVID-19 pandemic, to March 2025. For all analyses, we used the N3C COVID data enclave level 3 limited data set, version 193, dated May 16, 2025, and accessed on June 12, 2025[11].
SARS-CoV-2 testing/positivity status, AIH and other chronic liver disease (CLD) etiologies, and decompensated cirrhosis status were defined based on OMOP concept identifiers derived from a search of the Observational Health Data Sciences and Informatics (OHDSI) program’s ATHENA and ATLAS platforms and previous work in the N3C COVID enclave[12,17,18]. SARS-CoV-2 testing status was determined primarily from nucleic acid amplification. The OMOP concept identifiers selected for nucleic acid amplification were (586517, 586518, 586519, 586520, 586523, 586526, 706154, 706155, 706156, 706157, 706158, 706159, 706160, 706161, 706163, 706165, 706166, 706167, 706168, 706169, 706170, 706171, 706172, 706173, 706174, 706175, 715260, 715261, 715262, 757677, 757678)[12]. Culture testing had one identifier: 586516[12]. Antibody testing was not used, as this could signify vaccination or remote infection rather than active infection with SARS-CoV-2[12]. For SARS-CoV-2 testing status, we took the earliest SARS-CoV-2 testing date after the start date of one of six IS medications commonly used in the management of AIH (defined further below). Repeat SARS-CoV-2 infections were not included. We included only adults (documented age ≥ 18 years). The outcome of death was defined from N3C shared logic[11-13].
The six immunosuppressant medications included were prednisone, azathioprine, budesonide, tacrolimus, cyclosporine, and mycophenolate mofetil as these are the most common medications used in AIH. We defined active treatment with IS medications to be an active drug exposure (as defined by the OMOP data model) at most 90 days prior to SARS-CoV-2 testing[15,19]. To prevent repeat measurements, the first SARS-CoV-2 testing date and the corresponding closest (but after) IS starting date were isolated. Additionally, multiple IS usage was defined by at least 2 or more IS medications used at most 90 days prior to the SARS-CoV-2 testing date.
For the question of whether IS type was associated with SARS-CoV-2 test positivity, our outcome of interest was SARS-CoV-2 test status (positive or negative) amongst patients with AIH with and without IS use. For the question of severe COVID-19 outcomes for AIH patients with IS use, we focused on the outcome of all-cause mortality from the date of SARS-CoV-2 test positivity. These two outcomes were defined based on prior work amongst CLD patients utilizing the N3C COVID enclave[12,17,18].
We extracted the following baseline characteristics: Age, gender, race/ethnicity, region (defined as Northeast, Midwest, South, and West by the Centers for Disease Control and used in prior work)[12,20], height, weight, and body mass index. Patients without a defined region were classified as “other/unknown” region. For all categorical variables, we labeled them as “unknown/other” as a category if OMOP identifiers were not found. Alongside comorbidities, a modified Charlson comorbidity index score was calculated, excluding “mild liver disease” and “severe liver disease”[12,17,18]. We utilized N3C shared logic sets, OHDSI’s ATHENA and ATLAS platforms, and OMOP identifiers from literature[12]. We calculated the model for end stage liver disease (MELD) 3.0 score using corresponding laboratory test measurements and gender at birth, where data were available (19%). For all MELD 3.0 components, we selected the laboratory test results closest to the date of SARS-CoV-2 testing.
Clinical characteristics and laboratory data were summarized by means for continuous variables or numbers and percentages for categorical variables. Where appropriate, χ2 tests were performed for comparisons between groups. Two-sided P values < 0.05 were considered statistically significant in all analyses[12]. We used a logistic regression model to associate IS type, defined as the six types above, with SARS-CoV-2 test positivity. This model was adjusted for age, sex, race, region, CLD etiology, and the presence of decompensated cirrhosis.
To account for possible synergistic effects between multiple IS, we included interactions between common medication combinations used in the management of AIH: Prednisone with budesonide, prednisone with azathioprine, prednisone with tacrolimus, prednisone with mycophenolate mofetil, budesonide and azathioprine, budesonide and tacrolimus, budesonide and mycophenolate mofetil[21-23]. We used a Cox proportional hazards model to associate all-cause mortality with IS type amongst the patients who had tested positive for SARS-CoV-2. This model was adjusted to utilize the same variables used to adjust the prior logistic regression. We verified the proportional hazards assumption un
All data queries, transformations, extractions, and statistical analyses in the N3C COVID data enclave were conducted using the Palantir Foundry implementation of Spark-SQL 3.4.1, Spark-R 4.3.0, and Spark-Python 3.9.22[12,25].
Given that only 19% of the cohort had full MELD 3.0 laboratory studies available, and that this population likely reflected patients who had cirrhosis, we conducted sensitivity analyses on this population. These sensitivity analyses consisted of the same analyses as above (logistic regression and Cox modeling) but with the subpopulation of patients with full MELD labs available.
Submissions of data from individual centers to N3C are governed by a central institutional review board (IRB) protocol #IRB00249128 hosted at Johns Hopkins University School of Medicine via the SMART IRB40 Master Common Reciprocal reliance agreement. This central IRB covers data contributions and transfers to N3C but does not cover research using N3C data. If elected, individual sites may choose to exercise their own local IRB agreements instead of utilizing the central IRB. As the National Institutes of Health (NIH) National Center for Advancing Translational Sciences (NCATS) is the steward of the repository, data received and hosted by NCATS on the N3C COVID data enclave, its maintenance, and its storage are covered under a central NIH IRB protocol to make EHR-derived data available for the clinical and research community to use for studying COVID-19. Our institution has an active data transfer agreement with N3C. This specific analysis of the N3C COVID data enclave was approved by N3C under data use agreements titled (RP-E77B79) COVID-19 outcomes in vaccinated patients with liver diseases. The use of N3C data for this study was authorized by the IRB at the University of California, San Francisco under study, No. 21-35861[12].
Table 1 contains full baseline characteristics for the AIH cohort. We isolated 15187 AIH patients, of whom 5106 (33%) had IS exposure and 10081 (66%) did not have IS exposure. In the cohort, 1604 (11%) tested positive for SARS-CoV-2 during the study period of March 2020 through March 2025. Between the IS exposure and non-IS exposure groups, there were significant differences in age, race/ethnicity, region, and weight. There was a similar distribution of women in the IS exposure group (71%) compared to the non-IS exposure group (71%). Amongst the 10081 patients who were exposed to IS, the IS types were prednisone (58%), azathioprine (41%), tacrolimus (18%), budesonide (14%), mycophenolate mofetil (4%), and cyclosporine (4%). There were 2942 (19%) individuals with all components for MELD 3.0 score available.
| Characteristic | Total (n = 15187) | |
| Individuals not on IS (n = 10081) | Individuals on IS (n = 5106) | |
| Age | ||
| 18-29 | 780 (7.7) | 572 (11.2) |
| 30-39 | 1003 (9.9) | 620 (12.1) |
| 40-49 | 1392 (13.8) | 685 (13.4) |
| 50-59 | 1922 (19.1) | 869 (17.0) |
| 60 + | 4976 (49.4) | 2358 (46.2) |
| Sex | ||
| Female | 7176 (71.2) | 3609 (70.7) |
| Male | 2681 (26.6) | 1275 (25.0) |
| Unknown | 224 (2.2) | 222 (4.3) |
| Race/ethnicity | ||
| White | 6898 (68.4) | 3344 (65.5) |
| Black | 1479 (14.7) | 921 (18.0) |
| Hispanic | 640 (6.3) | 396 (7.8) |
| Asian | 453 (4.5) | 142 (2.8) |
| Other/unknown | 611 (6.1) | 303 (5.9) |
| Height (in.) | 65.5 | 65.8 |
| Weight (lbs.) | 180.51 | 177.4 |
| BMI | 30.24 | 29.5 |
| Region | ||
| Northeast | 432 (4.3) | 252 (4.9) |
| Midwest | 1558 (15.5) | 1034 (20.3) |
| South | 2026 (20.1) | 1248 (24.4) |
| West | 1049 (10.4) | 779 (15.3) |
| Other/unlisted | 5016 (49.8) | 1793 (35.1) |
| Comorbid conditions | ||
| CCI | 5.16 | 5.17 |
| Diabetes | 1072 (10.6) | 635 (12.4) |
| MI | 369 (3.7) | 181 (3.5) |
| CHF | 533 (5.3) | 307 (6.0) |
| PVD | 568 (5.6) | 302 (5.9) |
| Chronic renal disease | 512 (5.1) | 461 (9.0) |
| Chronic pulmonary disease | 1406 (13.9) | 883 (17.3) |
| Cirrhosis | ||
| Compensated cirrhosis | 284 (2.8) | 159 (3.1) |
| Decompensated cirrhosis | 143 (1.4) | 74 (1.4) |
| Complications of cirrhosis | ||
| Hepatic encephalopathy | < 201 | < 201 |
| Esophageal varices | 33 (0.3) | < 201 (0.5) |
| Ascites/pleural effusion | 76 (0.8) | 27 (0.5) |
| Hepatorenal syndrome | < 201 | < 201 |
| Hepatocellular carcinoma | 361 (3.6) | 312 (6.1) |
| IS used | ||
| Azathioprine | 2112 (41.36) | |
| Prednisone | 2977 (58.3) | |
| Budesonide | 720 (14.1) | |
| Mycophenolate mofetil | 208 (4.1) | |
| Tacrolimus | 940 (18.4) | |
| Cyclosporine | 200 (3.9) | |
| Laboratory tests | ||
| MELD 3.0 score | 13.8 | 13.8 |
| Sodium (mmol/L) | 135.8 | 135.8 |
| Creatinine (mg/dL) | 1.2 | 1.2 |
| Bilirubin (mg/dL) | 12 | 2.2 |
| INR | 1.3 | 1.3 |
| Albumin (g/dL) | 3.2 | 3.3 |
| AST (U/L) | 112.7 | 95.8 |
| ALT (U/L) | 109.9 | 87.0 |
| Alkaline phosphatase (U/L) | 139.3 | 157.6 |
| IgG (mg/dL) | 1662.3 | 1595.2 |
| WBC (1000/μL)2 | 4.7 | 6.2 |
| Neutrophils (1000/μL) | 8.8 | 6.9 |
| Hemoglobin (g/dL) | 12.6 | 12.1 |
| Platelet (1000/ μL) | 214.1 | 195.7 |
| Number of IS | ||
| 0 | 10081 (100.0) | |
| 1 | 3326 (65.1) | |
| 2 | 1526 (29.9) | |
| 3+ | 254 (5.0) | |
In our adjusted logistic regressions assessing associations with SARS-CoV-2 test positivity, we found prednisone [odds ratio (OR): 0.81, 95%CI: 0.65-0.99), P = 0.04] to be associated with decreased odds of COVID-19 diagnosis. While tacrolimus use (OR: 0.66, 95%CI: 0.41-0.99, P = 0.06) and budesonide use (OR: 0.77, 95%CI: 0.50-1.15, P = 0.22) showed lower odds ratios, these were not statistically significant. Azathioprine (OR: 0.98, 95%CI: 0.78-1.21, P = 0.84), my
| Immunosuppressant | Odds ratio | P value | 95%CI |
| Prednisone | 0.81 | 0.04 | 0.65-0.99 |
| Azathioprine | 0.98 | 0.84 | 0.78-1.21 |
| Budesonide | 0.77 | 0.22 | 0.50-1.15 |
| Tacrolimus | 0.66 | 0.06 | 0.41-0.99 |
| Mycophenolate mofetil | 1.32 | 0.47 | 0.59-2.61 |
| Cyclosporine | 0.81 | 0.44 | 0.44-1.35 |
| Azathioprine × budesonide | 1.88 | 0.04 | 1.02-3.44 |
| Prednisone × azathioprine | 1.06 | 0.78 | 0.72-1.54 |
| Prednisone × budesonide | 1.25 | 0.50 | 0.65-2.32 |
| Budesonide × mycophenolate mofetil | 0.84 | 0.87 | 0.04-5.23 |
| Prednisone × mycophenolate mofetil | 0.64 | 0.42 | 0.21-1.88 |
| Budesonide × tacrolimus | 0.80 | 0.73 | 0.18-2.47 |
| Prednisone × tacrolimus | 1.99 | 0.02 | 1.14-3.53 |
Amongst AIH patients who tested positive for SARS-CoV-2, we found no significant associations between IS type and all-cause mortality. Of note, prednisone (HR: 0.77, 95%CI: 0.42-1.41, P = 0.40) and azathioprine (HR: 0.66, 95%CI: 0.24-1.78, P = 0.41) exposure showed a lower hazard of mortality vs respective no IS use, but these were not statistically significant associations. In addition, budesonide exposure (HR: 2.03, 95%CI: 0.67-6.14, P = 0.21) was not statistically significant but showed a higher hazard of mortality. The results of the Cox regression model are presented in Table 3.
| Immunosuppressant | Odds ratio | P value | 95%CI |
| Prednisone | 0.93 | 0.74 | 0.58-1.43 |
| Azathioprine | 1.07 | 0.79 | 0.63-1.74 |
| Budesonide | 0.60 | 0.36 | 0.18-1.57 |
| Tacrolimus | 0.76 | 0.57 | 0.25-1.81 |
| Mycophenolate mofetil | 1.56 | 0.68 | 0.08-8.97 |
| Cyclosporine | 0.93 | 0.89 | 0.27-2.38 |
| Azathioprine × budesonide | 4.44 | 0.02 | 1.29-16.72 |
| Prednisone × azathioprine | 0.93 | 0.86 | 0.41-2.10 |
| Prednisone × budesonide | 1.46 | 0.56 | 0.39-5.16 |
| Budesonide × mycophenolate mofetil | 1.92e-06 | 0.99 | NA, infinity |
| Prednisone × mycophenolate mofetil | 0.29 | 0.41 | 0.01-8.19 |
| Budesonide × tacrolimus | 3.21 | 0.23 | 0.38-20.00 |
| Prednisone × tacrolimus | 1.85 | 0.32 | 0.58-6.69 |
Amongst the patients with all MELD 3.0 data available, we found no significant associations in our logistic regression model for individual IS type and SARS-CoV-2 test positivity. The interaction between azathioprine and budesonide (OR: 4.44, 95%CI: 1.29-16.7, P = 0.02), however, was associated with an increased odds of SARS-CoV-2 infection compared to no interaction. None of the other IS interactions had significant associations. These results are presented in Table 4. Amongst the AIH patients who tested positive for SARS-CoV-2, there were no significant associations between IS type and all-cause mortality. Prednisone showed a lower hazard of mortality (HR: 0.25, 95%CI: 0.05-1.35, P = 0.11), but this was not statistically significant. These results are presented in Table 5.
| Immunosuppressant | Hazard ratio | P value | 95%CI |
| Prednisone | 0.77 | 0.40 | 0.42-1.41 |
| Azathioprine | 0.66 | 0.41 | 0.24-1.78 |
| Budesonide | 2.03 | 0.21 | 0.67-6.14 |
| Tacrolimus | 3.46 | 0.10 | 0.80-14.92 |
| Mycophenolate mofetil | 3.47 | 0.24 | 0.44-27.59 |
| Cyclosporine | 1.18 | 0.83 | 0.25-5.53 |
| Immunosuppressant | Hazard ratio | P value | 95%CI |
| Prednisone | 0.25 | 0.11 | 0.05-1.35 |
| Azathioprine | 0.06 | 0.25 | 0.00-7.24 |
| Budesonide | 12.2 | 0.30 | 0.11-1321.74 |
| Tacrolimus | 1.51 | 0.74 | 0.14-16.50 |
| Mycophenolate mofetil | NA | NA | NA, NA |
| Cyclosporine | 1.75e-09 | 1.00 | 0.00 to infinity |
In our cohort of 15187 patients with AIH from the N3C COVID enclave, we found 33% of patients with IS exposure and 11% with SARS-CoV-2 positivity. In our analyses, we found that one IS type (prednisone with OR 0.81) was associated with lower odds of having SARS-CoV-2 positivity or diagnosed with SARS-CoV-2. This result was not clearly consistent with previous studies demonstrating that IS exposure was associated with increased risk for being diagnosed with viral infections, including amongst AIH patients using IS[10,26,27]. These studies, however, also did not find an association between IS and adverse outcomes related to AIH patients diagnosed with SARS-CoV-2[10,27].
In addition, other studies outside of AIH patients have previously suggested that immunosuppressants may have a protective effect against COVID-19[28]. Similarly, in our analyses, we found that prednisone exposure had a lower hazard (HR = 0.77) of all-cause mortality in our Cox regressions of all-cause mortality amongst patients with COVID-19, but this was not statistically significant. Of note, corticosteroids have been a part of the standard set of treatments recommended for COVID treatment for anti-inflammatory effects[29,30]. Corticosteroid therapy has shown benefits in the treatment of severe COVID-19 infections, but this has not been conclusively shown for milder cases of COVID-19[31]. In another study, low-dose, prolonged glucocorticoid use was associated with high efficacy in treating severe COVID-19 infection[32]. These results indicate the paradoxical finding that certain classes of IS, despite previous findings of an association with increased viral infections, may have protection against SARS-CoV-2 and its adverse outcomes.
As AIH patients are often on multiple IS, we conducted interaction analyses to account for this. The interactions between budesonide and azathioprine (OR: 1.88) and prednisone and tacrolimus (OR: 1.99) were associated with in
There were other limitations to this study. In the N3C COVID enclave, there is an overrepresentation of tertiary academic centers, missingness of data in certain variables, as well as selection bias within the SARS-CoV-2 positive and negative patient populations[12,18]. The number of patients with complete MELD 3.0 scores was only 2942 (19% of cohort), which indicated significant missingness and reflected its being a real-world database of EHR data. To overcome this limitation, we conducted sensitivity analyses amongst patients with full MELD 3.0 data only. The presence of complete set of labs to calculate the MELD 3.0 score, however, may be indicative of a sub-population with more severe liver disease that warranted closer laboratory monitoring. For this reason, this subgroup is likely biased towards AIH patients with cirrhosis. Despite this limitation, the information from this sensitivity analysis is important for understanding the impact of IS medications for a more clinically ill sub-population. Since prednisone is a first line of treatment for AIH, the vast majority of patients were prescribed prednisone compared to other IS types[27]. Within the SARS-CoV-2 positive subgroup (n = 1604) and Cox regression sensitivity analysis (n = 242), the small sample size for IS including mycophenolate mofetil and cyclosporine was a significant limitation in the analysis. The small sample sizes in both the overall and sensitivity analyses likely affected results, leading to undetermined odds ratios, hazards ratios, and associated confidence intervals for certain medications and interactions.
Within the 15187 patients, some may have been studied in previous studies related to liver disease. Consistent with these previous studies, culture testing was also included to assess COVID-19 infection status[12,18]. An additional sensitivity analysis removing the culture testing identifier 586516 did not affect the analysis and minimally changed the number of patients. However, this study contains the most recent data for all patients. Additionally, despite these limitations, this study is one of the largest studies addressing SARS-CoV-2 infection and COVID-19 outcomes in AIH patients with and without IS. The number of clinical sites in this study (harmonized data from 98 sites as of May 16, 2025) is a major strength in terms of number of individual patients, national generalizability, and demographic representation[12]. In addition, the cohort selected was diverse, sex-balanced, and larger than multiple previous studies regarding these research questions[18].
Our results indicate prednisone use was associated with decreased odds of SARS-CoV-2 infection, but the interaction between prednisone and tacrolimus was associated with increased odds of infection. The interaction between budesonide and azathioprine was associated with a similar increased effect. Further work includes determining the impact of vaccinations and advances in COVID-19 treatment on clinical outcomes in this population.
This study was conducted by the authors on behalf of the National Clinical Cohort Collaborative (N3C) COVID Enclave.
The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C COVID data enclave: https://covid.cd2h.org and N3C attribution & publication policy v 1.2-2020-08-25b supported by NCATS contract No. 75N95023D00001, axle informatics subcontract: NCATS-P00438-B. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource (https://doi.org/10.1093/jamia/ocaa196).
Disclaimer the N3C publication committee confirmed that this manuscript (MSID: 2549.844) is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program.
We gratefully acknowledge the following core contributors to N3C: Adam B Wilcox, Adam M Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E Williams, Andrew M Southerland, Andrew T Girvin, Anita Walden, Anjali Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, G Caleb Alexander, Carolyn T Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher G Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A Eichmann, Diego Mazzotti, Donald E Brown, Eilis Boudreau, Elaine L Hill, Emily Carlson Marti, Emily R Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold P Lehmann, Heidi Spratt, Hemalkumar B Mehta, JW Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Yasmine Islam, Jin Ge, Joel Gagnier, Johanna J Loomba, John B Buse, Jomol Mathew, Joni L Rutter, Julie A McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M Walters, Ken Wilkins, Kenneth R Gersing, Kenrick Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Pyles, Lesley Cottrell, Lili M Portilla, Mariam Deacy, Mark M Bissell, Marshall Clark, Mary Emmett, Matvey B Palchuk, Melissa A Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G Kurilla, Michele Morris, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A Francis, Penny Wung Burgoon, Philip RO Payne, Randeep Jawa, Rebecca Erwin-Cohen, Rena C Patel, Richard A Moffitt, Richard L Zhu, Rishikesan Kamaleswaran, Robert Hurley, Robert T Miller, Saiju Pyarajan, Sam G Michael, Samuel Bozzette, Sandeep K Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T O'Neil, Soko Setoguchi, Stephanie S Hong, Steven G Johnson, Tellen D Bennett, Tiffany J Callahan, Umit Topaloglu, Valery Gordon, Vignesh Subbian, Warren A Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors.
Available: Advocate Health Care Network - UL1TR002389: The Institute for Translational Medicine (ITM); Aurora Health Care Inc - UL1TR002373: Wisconsin Network For Health Research; Boston University Medical Campus - UL1TR001430: Boston University Clinical and Translational Science Institute; Brown University - U54GM115677: Advance Clinical Translational Research (Advance-CTR); Carilion Clinic - UL1TR003015: ITHRIV Integrated Translational health Research Institute of Virginia; Case Western Reserve University - UL1TR002548: The Clinical & Translational Science Collaborative of Cleveland (CTSC); Charleston Area Medical Center - U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI); Children’s Hospital Colorado - UL1TR002535: Colorado Clinical and Translational Sciences Institute; Columbia University Irving Medical Center - UL1TR001873: Irving Institute for Clinical and Translational Research; Dartmouth College - none (voluntary) Duke University - UL1TR002553: Duke Clinical and Translational Science Institute; George Washington Children’s Research Institute - UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN); George Washington University - UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN); Harvard Medical School - UL1TR002541: Harvard Catalyst; Indiana University School of Medicine - UL1TR002529: Indiana Clinical and Translational Science Institute; Johns Hopkins University - UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research; Louisiana Public Health Institute - none (voluntary); Loyola Medicine - Loyola University Medical Center; Loyola University Medical Center - UL1TR002389: The Institute for Translational Medicine (ITM); Maine Medical Center - U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network; Mary Hitchcock Memorial Hospital & Dartmouth Hitchcock Clinic - none (voluntary); Massachusetts General Brigham - UL1TR002541: Harvard Catalyst; Mayo Clinic Rochester - UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS); Medical University of South Carolina - UL1TR001450: South Carolina Clinical & Translational Research Institute (SCTR); MITRE Corporation - none (voluntary); Montefiore Medical Center - UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore; Nemours - U54GM104941: Delaware CTR ACCEL Program; NorthShore University HealthSystem - UL1TR002389: The Institute for Translational Medicine (ITM); Northwestern University at Chicago - UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS); OCHIN - INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks; Oregon Health & Science University - UL1TR002369: Oregon Clinical and Translational Research Institute; Penn State Health Milton S Hershey Medical Center - UL1TR002014: Penn State Clinical and Translational Science Institute; Rush University Medical Center - UL1TR002389: The Institute for Translational Medicine (ITM); Rutgers, The State University of New Jersey - UL1TR003017: New Jersey Alliance for Clinical and Translational Science; Stony Brook University - U24TR002306; The Alliance at the University of Puerto Rico, Medical Sciences Campus - U54GM133807: Hispanic Alliance for Clinical and Translational Research (The Alliance); The Ohio State University - UL1TR002733: Center for Clinical and Translational Science; The State University of New York at Buffalo - UL1TR001412: Clinical and Translational Science Institute; The University of Chicago - UL1TR002389: The Institute for Translational Medicine (ITM); The University of Iowa - UL1TR002537: Institute for Clinical and Translational Science; The University of Miami Leonard M Miller School of Medicine - UL1TR002736: University of Miami Clinical and Translational Science Institute; The University of Michigan at Ann Arbor - UL1TR002240: Michigan Institute for Clinical and Health Research; The University of Texas Health Science Center at Houston - UL1TR003167: Center for Clinical and Translational Sciences (CCTS); The University of Texas Medical Branch at Galveston - UL1TR001439: The Institute for Translational Sciences; The University of Utah - UL1TR002538: Uhealth Center for Clinical and Translational Science; Tufts Medical Center - UL1TR002544: Tufts Clinical and Translational Science Institute; Tulane University - UL1TR003096: Center for Clinical and Translational Science; The Queens Medical Center - none (voluntary); University Medical Center New Orleans - U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center; University of Alabama at Birmingham - UL1TR003096: Center for Clinical and Translational Science; University of Arkansas for Medical Sciences - UL1TR003107: UAMS Translational Research Institute; University of Cincinnati - UL1TR001425: Center for Clinical and Translational Science and Training; University of Colorado Denver, Anschutz Medical Campus - UL1TR002535: Colorado Clinical and Translational Sciences Institute; University of Illinois at Chicago - UL1TR002003: UIC Center for Clinical and Translational Science; University of Kansas Medical Center - UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute; University of Kentucky - UL1TR001998: UK Center for Clinical and Translational Science; University of Massachusetts Medical School Worcester - UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS); University Medical Center of Southern Nevada - none (voluntary); University of Minnesota - UL1TR002494: Clinical and Translational Science Institute; University of Mississippi Medical Center - U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR); University of Nebraska Medical Center - U54GM115458: Great Plains IDeA-Clinical & Translational Research; University of North Carolina at Chapel Hill - UL1TR002489: North Carolina Translational and Clinical Science Institute; University of Oklahoma Health Sciences Center - U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI); University of Pittsburgh - UL1TR001857: The Clinical and Translational Science Institute (CTSI); University of Pennsylvania - UL1TR001878: Institute for Translational Medicine and Therapeutics; University of Rochester - UL1TR002001: UR Clinical & Translational Science Institute; University of Southern California - UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI); University of Vermont - U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network; University of Virginia - UL1TR003015: ITHRIV Integrated Translational health Research Institute of Virginia; University of Washington - UL1TR002319: Institute of Translational Health Sciences; University of Wisconsin-Madison - UL1TR002373: UW Institute for Clinical and Translational Research; Vanderbilt University Medical Center - UL1TR002243: Vanderbilt Institute for Clinical and Translational Research; Virginia Commonwealth University - UL1TR002649: C Kenneth and Dianne Wright Center for Clinical and Translational Research; Wake Forest University Health Sciences - UL1TR001420: Wake Forest Clinical and Translational Science Institute; Washington University in St. Louis - UL1TR002345: Institute of Clinical and Translational Sciences; Weill Medical College of Cornell University - UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center; West Virginia University - U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI).
Submitted: Icahn School of Medicine at Mount Sinai - UL1TR001433: ConduITS Institute for Translational Sciences; The University of Texas Health Science Center at Tyler - UL1TR003167: Center for Clinical and Translational Sciences (CCTS); University of California, Davis - UL1TR001860: UCDavis Health Clinical and Translational Science Center; University of California, Irvine - UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS); University of California, Los Angeles - UL1TR001881: UCLA Clinical Translational Science Institute; University of California, San Diego - UL1TR001442: Altman Clinical and Translational Research Institute; University of California, San Francisco - UL1TR001872: UCSF Clinical and Translational Science Institute; NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository Core - OTA-21-015A: Post-Acute Sequelae of SARS-CoV-2 Infection Initiative (RECOVER).
Pending: Arkansas Children’s Hospital - UL1TR003107: UAMS Translational Research Institute; Baylor College of Medicine - none (voluntary); Children’s Hospital of Philadelphia - UL1TR001878: Institute for Translational Medicine and Therapeutics; Cincinnati Children’s Hospital Medical Center - UL1TR001425: Center for Clinical and Translational Science and Training; Emory University - UL1TR002378: Georgia Clinical and Translational Science Alliance; HonorHealth - none (voluntary); Loyola University Chicago - UL1TR002389: The Institute for Translational Medicine (ITM); Medical College of Wisconsin - UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin; MedStar Health Research Institute - none (voluntary); Georgetown University - UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS); MetroHealth - none (voluntary); Montana State University - U54GM115371: American Indian/Alaska Native CTR; NYU Langone Medical Center - UL1TR001445: Langone Health’s Clinical and Translational Science Institute; Ochsner Medical Center - U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center; Regenstrief Institute - UL1TR002529: Indiana Clinical and Translational Science Institute; Sanford Research - none (voluntary); Stanford University - UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education; The Rockefeller University - UL1TR001866: Center for Clinical and Translational Science; The Scripps Research Institute - UL1TR002550: Scripps Research Translational Institute; University of Florida - UL1TR001427: UF Clinical and Translational Science Institute; University of New Mexico Health Sciences Center - UL1TR001449: University of New Mexico Clinical and Translational Science Center; University of Texas Health Science Center at San Antonio - UL1TR002645: Institute for Integration of Medicine and Science; Yale New Haven Hospital - UL1TR001863: Yale Center for Clinical Investigation.
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