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Risk of Severe Coronavirus Disease 2019 in Patients With Inflammatory Bowel Disease in the United States: A Multicenter Research Network Study

  • Author Footnotes
    ∗ Authors share co-first authorship.
    Shailendra Singh
    Correspondence
    Correspondence Address correspondence to: Shailendra Singh, MD, Charleston Gastroenterology Associates, 3100 MacCorkle Ave SE #509, Charleston, West Virginia 25304. fax: (304) 345-6679.
    Footnotes
    ∗ Authors share co-first authorship.
    Affiliations
    West Virginia University Health Sciences Center Charleston Division, Charleston, West Virginia

    Charleston Area Medical Center Health System, Charleston, West Virginia
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  • Author Footnotes
    ∗ Authors share co-first authorship.
    Ahmad Khan
    Footnotes
    ∗ Authors share co-first authorship.
    Affiliations
    Department of Medicine, West Virginia University Health Sciences Center Charleston Division, Charleston, West Virginia
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  • Monica Chowdhry
    Affiliations
    Department of Medicine, West Virginia University Health Sciences Center Charleston Division, Charleston, West Virginia
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  • Mohammad Bilal
    Affiliations
    Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
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  • Gursimran S. Kochhar
    Affiliations
    Division of Gastroenterology, Hepatology and Nutrition, Allegheny Health Network, Pittsburgh, Pennsylvania
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  • Kofi Clarke
    Affiliations
    Division of Gastroenterology and Hepatology, Pennsylvania State University College of Medicine, Hershey, Pennsylvania
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  • Author Footnotes
    ∗ Authors share co-first authorship.

      Keywords

      Abbreviations used in this paper:

      CI (confidence interval), COVID-19 (coronavirus disease 2019), CD (Crohn’s disease), HCO (health care organization), IBD (inflammatory bowel disease), SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), UC (ulcerative colitis)
      Patients with inflammatory bowel disease (IBD), both Crohn’s disease (CD) and ulcerative colitis (UC), may be at an increased risk for severe coronavirus disease 2019 (COVID-19) owing to their immunosuppressant medications or the chronic inflammatory disease state. Recently, a worldwide registry Surveillance Epidemiology of Coronavirus Under Research Exclusion (SECURE-IBD) consisting of physician-reported patients with IBD with COVID-19 reported the clinical course of COVID-19 among patients with IBD and the factors associated with severe COVID-19.
      • Brenner E.J.
      • et al.
      However, there are limited data regarding the comparison of clinical characteristics and outcomes among patients with IBD with COVID-19 and other patients. Moreover, the outcomes of patients with IBD with COVID-19 predominantly in the United States remain unexplored. Our study aimed to evaluate the characteristics and outcomes of patients with IBD with COVID-19 in the United States and compare them to a large cohort of patients without IBD with COVID-19.

      Methods

      This was a population-based retrospective cohort study conducted using TriNetX (Cambridge, MA), a federated health research network data set. We performed a real-time search and analysis of electronic health records of more than 40 million patients from multiple health care organizations (HCOs) globally to identify patients with IBD diagnosed with COVID-19 between January 20, 2020, and May 26, 2020, based on a positive laboratory test result or assignment of COVID-19–specific ICD code. During the same time period, patients diagnosed with COVID-19 and who had no history of or documentation of a diagnosis of IBD ever were included in the non-IBD control group. The outcome of interest was the risk of severe COVID-19 disease, defined as a composite outcome of hospitalization and/or 30-day mortality postdiagnosis of COVID-19. Outcomes were compared in patients with IBD with COVID-19 and patients without IBD with COVID-19 after 1:1 propensity score matching for demographics and comorbid conditions (listed in Table 1) using logistic regression and greedy nearest-neighbor matching algorithm with a caliper of 0.1 pooled standard deviations. Details of data source, quality checks, codes used for patient selection and medications, and statistical analysis have been described previously
      • Singh S.
      • Khan A.
      and are discussed in the Supplementary Materials.
      Table 1Comparison of Patient Demographics, Clinical Presentation, Laboratory Findings Among Patients With IBD With COVID-19 and Patients Without IBD With COVID-19
      Demographics and comorbidities are compared before and after propensity matching of cohorts.
      Clinical Presentation, Laboratory findings and OutcomesDemographics and comorbidities
      Before propensity score matchingAfter propensity score matching
      IBD (n = 232)Non-IBD (n = 19776)P valueIBD (n = 232)Non-IBD (n = 232)P value
      Age, y, mean ± SD51.2 ± 18.149.5 ± 19.1.1851.2 ± 18.151.2 ± 18.9.89
       Female, n (%)147 (63.36)10,937 (55.30).01147 (63.36)149 (64.22).84
      Race, n (%)
       White177 (76.29)10,110 (51.12)<.0001177 (76.29)183 (78.87).51
       Black or African American29 (12.5)4082 (20.64)<.000129 (12.5)30 (12.93).89
       Unknown Race23 (9.91)4957 (25.06)<.000123 (9.91)17 (10.42).32
      Body Mass Index (BMI), kg/m2, mean ± SD29.5 ± 7.4130.5 ± 8.02.0929.5 ± 7.4130.4 ± 8.21.32
      Comorbid conditions, n (%)
       Essential hypertension121 (52.12)5861 (29.64)<.0001121 (52.12)118 (50.86).78
       Chronic lower respiratory diseases (asthma and COPD)91 (39.22)3583 (18.11)<.000191 (39.22)92 (39.65).92
       Diabetes mellitus62 (26.72)3113 (15.74)<.000162 (26.72)55 (23.71).45
       Ischemic heart diseases49 (21.12)1892 (9.56)<.000149 (21.12)45 (19.39).64
       Chronic kidney disease38 (16.38)1377 (6.96)<.000138 (16.38)35 (15.08).70
       Heart failure37 (15.95)1251 (6.33)<.000137 (15.95)35 (15.08).80
       Cerebrovascular diseases30 (12.93)1164 (5.88)<.000130 (12.93)27 (11.63).67
       Nicotine dependence35 (15.09)1597 (8.08)<.000135 (15.09)30 (12.93).50
       Alcohol-related disorders11 (4.74)618 (3.12).1611 (4.74)12 (5.17).83
      Clinical presentation
      IBD (n = 232), n (%)Non-IBD (n = 19,776), n (%)P value
      Cough56 (24.14)4716 (23.84).91
      Fever38 (16.37)3395 (17.16).75
      Dyspnea30 (12.93)2827 (14.29).55
      Nausea and vomiting25 (10.77)813 (4.11)<.0001
      Malaise and fatigue20 (8.62)1167 (5.90).08
      Diarrhea19 (8.19)1018 (5.14).03
      Abdominal pain18 (7.75)535 (2.70)<.0001
      Sore throat14 (6.03)1040 (5.25).59
      Hypoxemia12 (5.17)1444 (7.30).21
      Laboratory findings after COVID-19 diagnosis
      IBD (n = 232), mean ± SD (n)Non-IBD (n = 19,776), mean ± SD (n)P value
      Leukocytes, 1000/μL7.53 ± 3.60 (67)7.54 ± 5.66 (5572).98
      Lymphocytes, 1000/μL1.35 ± 0.87 (86)1.45 ±5.08 (6437).84
      Creatinine, mg/dL1.15 ± 1.14 (94)1.12 ± 1.29 (7418).79
      Alanine aminotransferase, U/L28.55 ± 21.48 (85)45.01 ± 116.13 (6276).19
      Aspartate aminotransferase, U/L32.27 ± 25.23 (85)54.16 ± 288.14 (6304).48
      Alkaline phosphatase, U/L95.95 ± 101.84 (85)89.40 ± 65.26 (6275).36
      Gamma glutamyl transferase, U/L174.6 ± 138.50 (10
      Numbers rounded off to 10 to protect Protected Health Information (PHI).
      )
      186.24 ± 310.69 (159).93
      Total bilirubin, mg/dL0.43 ± 0.23 (84)0.61 ± 1.06 (6239).13
      Albumin, g/dL3.54 ± 0.71 (83)3.4 ± 0.70 (6265).05
      Prothrombin time, s14.74 ± 5.53 (56)14.28 ± 5.80 (3689).55
      Activated partial thromboplastin time, s31.16 ± 5.93 (49)32.88 ± 14.69 (3020).41
      Ferritin, ng/mL682.52 ± 804.27 (52)882.19 ± 2015.49 (4401).47
      C-reactive protein, mg/L46.49 ± 74.79 (66)50.00 ± 69.21 (4870).68
      Erythrocyte sedimentation rate, mm/h33.42 ± 19.03 (19)41.8 ± 27.42 (1407).11
      Lactate dehydrogenase, mmol/L296.45 ± 210.79 (53)374.57 ± 350.33 (4438).11
      Interleukin 6, pg/mL53.36 ± 45.52 (12)314.63 ± 1839.22 (1152).62
      Procalcitonin, ng/mL0.32 ± 0.58 (15)1.75 ± 7.8 (1313).48
      Outcomes
      Before propensity matchingAfter propensity matching
      OutcomesOverall risk n/total (%)Risk ratio (95% CI)P valueOverall risk n/total (%)Risk ratio (95% CI)P value
      Severe COVID-19IBD

      56/232 (24.14)
      1.15 (0.92–1.45).23IBD

      56/232 (24.14)
      0.93 (0.68–1.27).66
      Non-IBD

      4139/19,776 (20.92)
      Non-IBD

      60/232 (25.86)
      HospitalizationsIBD

      56/232 (24.14)
      1.20 (0.96–1.51).11IBD

      56/232 (24.14)
      1.10 (0.74–1.40).91
      Non-IBD

      3960/19,776 (20.02)
      Non-IBD

      55/232 (23.70)
      COPD, chronic obstructive pulmonary disease; SD, standard deviation.
      a Demographics and comorbidities are compared before and after propensity matching of cohorts.
      b Numbers rounded off to 10 to protect Protected Health Information (PHI).

      Results

      Of 196,403 patients with IBD from 31 HCOs, 1901 patients underwent testing for COVID-19, and a total of 232 patients with IBD (CD, 101; UC, 93; indeterminate, 38) were diagnosed with COVID-19. During the same time period, 19,776 patients without IBD were also diagnosed with COVID-19 from the same HCOs. The mean age was similar between the groups, and there were more female patients and more prevalent comorbidities in the IBD group (Table 1). A higher proportion of patients in the IBD group presented with nausea and vomiting (10.77% vs 4.31%, P < .01), diarrhea (8.19% vs 5.14%, P < .01), and abdominal pain (7.75% vs 2.70%, P < .01) (Table 1). In a crude, unadjusted analysis, there was no difference in the risk of severe COVID-19 between the IBD and non-IBD groups (risk ratio [RR], 1.15; 95% confidence interval [CI], 0.92–1.45; P = .23). After propensity score matching, both groups were well balanced, and the risk of severe COVID-19 was similar (RR, 0.93; 95% CI, 0.68–1.27; P = .66) (Table 1). Overall, patients with IBD with severe COVID-19 were older and had a higher proportion of multiple comorbidities (Supplementary Table 1).
      Medication data were collected up to 1 year preceding the diagnosis of COVID-19 and were available for 166 patients in the IBD group. Sixty-two patients were on immune-mediated therapy (biologics, 37 and/or immunomodulators, 34), 32 patients were on aminosalicylate therapy, and 111 patients had received corticosteroids. Subgroup analysis based on the use of immune-mediated therapy in the preceding 1 year was not associated with a higher risk of severe COVID-19 compared to patients with IBD not on immune-mediated therapy (RR, 1.01; 95% CI, 0.62–1.65; P = .97). The risk of severe COVID-19 was higher in an unadjusted analysis of 71 patients with IBD who received corticosteroids up to 3 months before the diagnosis of COVID-19 (30.98%) compared to patients who did not receive corticosteroids (19.25%) (RR, 1.60; 95% CI, 1.01–2.57; P = .04) (Supplementary Table 2).

      Discussion

      The composite outcome of hospitalization or mortality after COVID-19 in patients with IBD is similar to patients without IBD. In addition, patients with IBD with COVID-19 on long-term biologics or nonsteroid immunomodulatory therapies did not have a higher risk of poor COVID-19 outcomes. However, recent corticosteroid use that may as well imply poor disease control may be related to worse outcomes. The risk for severe COVID-19 in patients with IBD is also similar to the widely recognized risk factors for COVID-19 outcomes, such as advanced age and comorbidities,
      • Zhou F.
      • et al.
      and such patients should be closely monitored.
      There are concerns that patients with IBD may be at increased risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–induced infection and poor outcomes. SARS-CoV-2 has been detected in stool samples of patients with COVID-19,
      • Xiao F.
      • et al.
      and high concentrations of angiotensin-converting enzyme 2 (ACE2), the binding site for SARS-CoV-2, are found in the terminal ileum and colon
      • Harmer D.
      • et al.
      and can increase in the inflamed gut of patients with IBD.
      • Garg M.
      • et al.
      However, there is no evidence that these factors can influence the course, infectivity, or severity of COVID-19. Another concern in patients with IBD with COVID-19 relates to the use of immune-mediated therapies. Generally, these therapies can be associated with an increased risk of infections. However, these medications are key in inducing and maintaining remission of IBD with subsequent prevention of disease flare-up that may require hospitalizations and corticosteroids, which can increase the risk of severe COVID-19. Furthermore, the use of these therapies could be advantageous in suppressing the inflammatory response or cytokine storm described in patients with severe COVID-19.
      • Chen C.
      • et al.
      Our study is limited by the inherent limitations of an electronic health records based database. A composite primary outcome of hospitalization or death was chosen because the number of individual events was small to evaluate separate endpoints. Despite limitations, this is the first attempt to compare characteristics and estimate the risk of severe COVID-19 in patients with IBD compared to other patient populations while adjusting for confounding variables. IBD patients in remission and on immunomodulators and biologics should stay on their medications and should exercise social distancing principles like the general population. Patients with IBD with advanced age, multiple comorbidities, or with a poorly controlled disease requiring corticosteroids who develop COVID-19 infection should be aggressively managed, given the increased risk of worse outcomes.

      Acknowledgments

      We acknowledge Charleston Area Medical Center Health System and West Virginia Clinical and Translational Science Institute, which provided us access to and training on the TriNetX global health care network. We also acknowledge the TriNetX (Cambridge, MA) health care network for design assistance to complete this project.

      CRediT Authorship Contributions

      Shailendra Singh, MD (Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Investigation: Lead; Methodology: Lead; Writing – original draft: Lead); Ahmad Khan, MD (Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Investigation: Lead; Methodology: Lead; Writing – original draft: Equal); Monica Chowdhry, MD, (Conceptualization: Supporting; Writing – review & editing: Equal); Mohammad Bilal, MD, (Conceptualization: Supporting; Writing – review & editing: Equal); Gursimran S Kochhar, MD, (Conceptualization: Equal; Methodology: Supporting; Supervision: Supporting; Writing – review & editing: Equal); Kofi Clarke, MD, FACP, FRCP (Lond), AGAF (Conceptualization: Equal; Investigation: Lead; Methodology: Equal; Supervision: Lead; Writing – review & editing: Lead).

      Supplementary Methods

       Data Source

      TriNetX (Cambridge, MA) uses electronic health record data collected from member HCOs. A typical HCO is a large academic health center with data coming from the majority of its affiliates. A single HCO frequently has more than 1 facility, including main and satellite hospitals and outpatient clinics. In the majority of cases, the data originate from the primary electronic health record system. A typical organization has a complex enterprise architecture where the data flow through several different databases, such as a data warehouse and a research data repository, on its way to TriNetX. In addition to electronic health record data, which are usually available in a structured fashion (eg, demographics, diagnoses, procedures, medications, laboratory test results, and vital signs), TriNetX has also the ability to extract facts of interest from the narrative text of clinical documents using natural language processing. TriNetX maps the data to a standard and controlled set of clinical terminologies. The data are then transformed into a proprietary data schema. This transformation process includes an extensive data quality assessment that includes data cleaning, which rejects records that do not meet the TriNetX quality standards.

       Data Quality Checks

      The TriNetX software checks the basic formatting to ensure, for example, that dates are properly represented. It enforces a list of fields that are required (eg, patient identifier) and rejects those records where the required information is missing. Referential integrity checking is done to ensure that data spanning multiple database tables can be successfully joined together. As the data are refreshed, the software monitors changes in volumes of data over time to ensure data validity. TriNetX requires at least 1 nondemographic fact for a patient to be counted in a given data set. Patient records with only demographic information are not included in data sets.
      Tabled 1Coding System
      Clinical factCoding system
      DemographicsHealth Level Seven (HL7), version 3 (administrative standards)
      DiagnosesThe International Classification of Diseases, Ninth and 10th Revisions, Clinical Modification (ICD-9-CM and ICD-10-CM)
      If an HCO provides data in ICD-9-CM, a 9–to–10-CM mapping based on general equivalence mappings (GEM) plus custom algorithms and curation to transform data from ICD-9-CM to ICD-10-CM.
      AND Chronic Condition Indicator
      ProceduresThe International Classification of Diseases, Procedural Classification System, Ninth and 10th Revision, OR Healthcare Common Procedure Coding System
      MedicationsRxNorm
      Laboratory test results, vital signs, and findingsLogical observation identifiers names and codes (LOINC)
      To ease finding and using common laboratory test values, LOINC codes are combined up to the clinically significant level for most frequent laboratory values and coded as TNX: LAB.
      a If an HCO provides data in ICD-9-CM, a 9–to–10-CM mapping based on general equivalence mappings (GEM) plus custom algorithms and curation to transform data from ICD-9-CM to ICD-10-CM.
      b To ease finding and using common laboratory test values, LOINC codes are combined up to the clinically significant level for most frequent laboratory values and coded as TNX: LAB.
      Tabled 1Diagnosis Codes Used to Identify Patient Cohorts
      Coding SystemCodeDescription
      ICD-10B34.2Coronavirus infection unspecified
      ICD-10B97.29Other coronavirus as the cause of diseases classified elsewhere
      ICD-10J12.81Pneumonia due to SARS-associated coronavirus
      ICD-10U07.12019-nCoV acute respiratory disease (WHO)
      ICD-10K50Crohn’s disease [regional enteritis]
      ICD-10K51Ulcerative colitis
      COVID-19–related diagnostic tests
      CPT87635Infectious agent detection by nucleic acid (DNA or RNA); severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (coronavirus disease [COVID-19]), amplified probe technique
      HCPCSU00012019 novel coronavirus real-time RT-PCR diagnostic test panel–CDC
      HCPCSU00022019 novel coronavirus real-time RT-PCR diagnostic test panel–non-CDC
      LOINC94307-6SARS coronavirus 2 N gene [presence] in unspecified specimen by nucleic acid amplification using CDC primer-probe set N1
      LOINC94307-6SARS coronavirus 2 N gene [presence] in unspecified specimen by nucleic acid amplification using CDC primer-probe set N2
      LOINC94309-2SARS coronavirus 2 RNA [presence] in unspecified specimen by NAA with probe detection
      LOINC94310-0SARS-like coronavirus N gene [presence] in unspecified specimen by NAA with probe detection
      LOINC94314-2SARS coronavirus 2 RdRp gene [presence] in unspecified specimen by NAA with probe detection
      LOINC94315-9SARS coronavirus 2 E gene [presence] in unspecified specimen by NAA with probe detection
      LOINC94316-7SARS coronavirus 2 N gene [presence] in unspecified specimen by NAA with probe detection
      LOINC94500-6SARS coronavirus 2 RNA [Presence] in respiratory specimen by NAA with probe detection
      LOINC94501-4Middle East respiratory syndrome coronavirus (MERS-CoV) RNA [presence] in respiratory specimen by NAA with probe detection
      LOINC94502-2SARS-related coronavirus RNA [presence] in respiratory specimen by NAA with probe detection
      LOINC94532-9SARS-related coronavirus + MERS coronavirus RNA [presence] in respiratory specimen by NAA with probe detection
      LOINC94533-7SARS coronavirus 2 N gene [presence] in respiratory specimen by NAA with probe detection
      LOINC94534-5SARS coronavirus 2 RdRp gene [presence] in respiratory specimen by NAA with probe detection
      LOINC94559-2SARS coronavirus 2 ORF1ab region [presence] in respiratory specimen by NAA with probe detection
      LOINC94565-9SARS coronavirus 2 RNA [presence] in nasopharynx by NAA with nonprobe detection
      LOINC94639-2SARS coronavirus 2 ORF1ab region [presence] in unspecified specimen by NAA with probe detection
      LOINC94640-0SARS coronavirus 2 S gene [presence] in respiratory specimen by NAA with probe detection
      LOINC94641-8SARS coronavirus 2 S gene [presence] in unspecified specimen by NAA with probe detection
      LOINC94647-5SARS-related coronavirus RNA [presence] in unspecified specimen by NAA with probe detection
      LOINC94660-8SARS coronavirus 2 RNA [presence] in serum or plasma by NAA with probe detection
      LOINC94756-4SARS coronavirus 2 N gene [presence] in respiratory specimen by nucleic acid amplification using CDC primer-probe set N1
      LOINC94757-2SARS coronavirus 2 N gene [presence] in respiratory specimen by nucleic acid amplification using CDC primer-probe set N2
      LOINC94758-0SARS coronavirus 2 E gene [presence] in respiratory specimen by NAA with probe detection
      LOINC94759-8SARS coronavirus 2 RNA [presence] in nasopharynx by NAA with probe detection
      LOINC94765-5SARS coronavirus 2 E gene [presence] in serum or plasma by NAA with probe detection
      LOINC94766-3SARS coronavirus 2 N gene [presence] in serum or plasma by NAA with probe detection
      LOINC94767-1SARS coronavirus 2 S gene [presence] in serum or plasma by NAA with probe detection
      CDC, Centers for Disease Control and Prevention; NAA, nucleic acid amplification; nCoV, novel coronavirus; ORF, open reading frame; RT-PCR, reverse-transcription polymerase chain reaction; WHO, World Health Organization.
      Tabled 1Codes Used to Identify Medications
      Coding SystemCodeDescriptionClassification
      RXNORM327361AdalimumabBiological therapy
      RXNORM819300GolimumabBiological therapy
      RXNORM709271CertolizumabBiological therapy
      RXNORM191831InfliximabBiological therapy
      RXNORM1538097VedolizumabBiological therapy
      RXNORM354770NatalizumabBiological therapy
      RXNORM847083UstekinumabBiological therapy
      RXNORM6851MethotrexateImmunomodulators
      RXNORM1256AzathioprineImmunomodulators
      RXNORM52582MesalamineAmino salicylates
      RXNORM32385OlsalazineAmino salicylates
      RXNORM9524SulfasalazineAmino salicylates
      RXNORM8640PrednisoneCorticosteroids
      RXNORM19831BudesonideCorticosteroids

       Statistical Analysis

      All statistical analyses were performed in real time using TriNetX. The TriNetX uses a custom-built platform developed from Java 1.8.0_171, R 3.4.4 (R Core Team, Vienna, Austria), and Python 3.6.5 with their software language packages to ensure the accuracy and validity of results. The means, standard deviations, and proportions were used to describe and compare patient characteristics. Categorical variables were compared by using the Pearson chi-square test and continuous variables by using an independent-samples t test. Logistic regression on our input covariates was used to obtain propensity scores for each patient in both cohorts. Logistic regression was performed in Python using standard libraries numpy and sklearn. The same analyses were also performed in R software to ensure that the outputs match. After the calculation of propensity scores, matching was performed using a greedy nearest-neighbor matching algorithm with a caliper of 0.1 pooled standard deviations. The order of the rows in the covariate matrix can affect the nearest neighbor matching; therefore, the order of the rows in the matrix was randomized to eliminate this bias. For each outcome, the risk ratio (RR) with a 95% CI was calculated to compare the association of obesity with the outcome. An a priori defined 2-sided alpha of less than .05 was used for statistical significance. TriNetX obfuscates patient counts to safeguard protected health information by rounding patient counts in analyses up to the nearest 10.
      Supplementary Table 1Characteristics of Patients With IBD With and Without the Composite Outcome of Hospitalization or 30-Day Mortality
      CharacteristicsPatients with composite outcomesPatients without composite outcomesP value
      Number of patients56176
      Age, y, mean ± SD62.6 ± 18.647.6 ± 16.3<.0001
       Female, n (%)32 (57.14)115 (65.34).17
       Male, n (%)24 (42.85)61 (34.65).17
      Race, n (%)
       White41 (73.21)41 (73.21).46
       Black or African American10
      Numbers rounded off to 10 to protect HPI.
      (17.85)
      21 (11.93)
       Unknown race10
      Numbers rounded off to 10 to protect HPI.
      (17.85)
      16 (9.09)
      Body Mass Index (BMI) kg/m228.3 ± 6.8530 ± 7.59.09
      Comorbid conditions, n (%)
       Essential hypertension42 (75)79 (44.88)<.0001
       Diabetes mellitus22 (39.28)44 (22.72).01
       Chronic lower respiratory diseases26 (46.43)65 (36.38).2
       Ischemic heart diseases24 (42.85)25 (14.20)<.0001
       Heart failure23 (41.07)14 (7.95)<.0001
       Cerebrovascular diseases17 (30.36)13 (7.38)<.0001
       Chronic kidney disease17 (30.36)21 (11.93)<.0001
      a Numbers rounded off to 10 to protect HPI.
      Supplementary Table 2Characteristics and Outcomes of Patients With IBD Who Received Corticosteroid Therapy 3 Months Preceding the Diagnosis of COVID-19 Compared to Patients With IBD Who Did Not
      DemographicsBefore propensity score matchingAfter propensity score matching
      SteroidsNonsteroidsP valueSteroidsNonsteroidsP value
      Number of patients711616262
      Age, y, mean ± SD51.3 ± 14.151.1 ± 19.6.9350.2 ± 14.146.9 ± 20.7.31
       Female, n (%)44 (61.97)10,937 (55.30).7740 (64.51)42 (67.74).71
       Male, n (%)27 (38.02)58 (36.02).7722 (35.48)20 (32.25).71
      Race, n (%)
       White51 (71.83)126 (78.26).2845 (72.58)46 (74.19).83
       Black or African American10
      Numbers rounded off to 10 to protect HPI.
      (14.08)
      21 (13.04)10
      Numbers rounded off to 10 to protect HPI.
      (16.12)
      10
      Numbers rounded off to 10 to protect HPI.
      (16.12)
       Unknown race11 (15.49)12 (7.45).0510
      Numbers rounded off to 10 to protect HPI.
      (16.12)
      10
      Numbers rounded off to 10 to protect HPI.
      (16.12)
      Body Mass Index (BMI) kg/m230.7 ± 7.8129 ± 7.2.0930.3 ± 8.0529.6 ± 7.58.63
      Comorbid conditions, n (%)
       Essential hypertension43 (60.56)78 (48.44).0834 (54.84)31 (50).58
       Chronic lower respiratory diseases (asthma and COPD)42 (59.15)49 (30.43)<.000134 (54.83)36 (58.06).71
       Diabetes mellitus24 (33.80)38 (23.60).1118 (29.03)16 (25.80).68
       Heart failure20 (28.17)17 (10.55)<.000114 (22.58)11 (17.74).5
       Ischemic heart diseases20 (28.17)29 (18.01).0814 (22.58)13 (20.96).83
       Chronic kidney disease19 (26.76)20 (12.42)<.000112 (19.35)10 (16.13).63
      OutcomesBefore propensity matchingAfter propensity matching
      Overall risk, n (%)Risk ratio (95% confidence interval)P valueOverall risk, n (%)Risk ratio (95% confidence interval)P value
      Severe COVID-19Steroids: 22 (30.98)1.60 (1.01–2.57)0.04Steroids: 18 (29.03)
      Risk ratio cannot be estimated because of outcomes of ≤10 in the nonsteroid group.
      Nonsteroids: 31 (19.25)Nonsteroids ≤10
      Numbers rounded off to 10 to protect HPI.
      (≤16.13)
      a Numbers rounded off to 10 to protect HPI.
      b Risk ratio cannot be estimated because of outcomes of ≤10 in the nonsteroid group.

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