Coronavirus disease 2019 (COVID-19), caused by the novel betacoronavirus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is an unprecedented global pandemic.1 Susceptibility to COVID-19 is a concern among patients with inflammatory bowel disease (IBD) who are at increased risk of infection due to immunosuppressive therapy. The receptor angiotensin-converting enzyme (ACE) 2, which mediates SARS-CoV-2 entry into cells, is upregulated in IBD2 and may therefore increase host susceptibility. International cohorts have reported no increased risk of COVID-19 in patients with IBD3 , 4; however, these studies do not report the prevalence of SARS-CoV-2 testing and COVID-19 in patients with IBD. Our institution was among the first to initiate large-scale SARS-CoV-2 RNA testing in northern California. We characterized the prevalence and clinical predictors of COVID-19 in patients with IBD.
Methods
We performed a retrospective analysis of consecutive patients whose SARS-CoV-2 testing was performed at Stanford between March 04, 2020, and April 14, 2020. California counties tested, institutional testing eligibility, and performance are described in our Supplementary Methods. Our study was approved by the Stanford Institutional Review Board (Protocol 55975). We included all patients with a diagnosis of Crohn’s disease (K50.xx), ulcerative colitis (K51.xx), and indeterminate colitis (K52.3) who underwent testing. We collected data including demographics, IBD characteristics (subtype, location, phenotype, disease activity), comorbid conditions, reasons for testing, symptoms, medications, and outcomes. We calculated prevalence of IBD among all patients tested and the prevalence of COVID-19 among patients with IBD. We performed univariate and multivariate logistic regression using the firthlogit method to determine predictors of COVID-19 in patients with IBD.5 Our statistical analysis was performed with Statistics/Data Analysis (Stata/IC 15.1 for Windows; StataCorp, College Station, TX) and described in detail in our Supplementary Methods.
Results
Prevalence and Characteristics of Patients With IBD Undergoing SARS-CoV-2 Testing
From March 4, 2020, to April 14, 2020, 14,235 individuals were tested for SARS-CoV-2 at our institution with 8.2% (1160 of 14,235) testing positive. Among the tested patients, the prevalence of IBD was 1.2% (168 of 14,235). Table 1 summarizes the baseline characteristics of patients with IBD who underwent testing; 51.2% had ulcerative colitis, 39.3% had Crohn’s disease, and 9.8% had indeterminate colitis. Of patients with IBD, 16.7% had active disease; 91.7% were symptomatic suggestive of COVID-19, 3.6% were asymptomatic but had a positive travel history, and 4.8% were asymptomatic but had direct exposure to a patient with COVID-19. Common presenting symptoms included cough (63.1%), sore throat (41.1%), dyspnea (37.5%), fever (35.7%), and body pain (32.1%). Gastrointestinal symptoms were present in 19.1% of patients with IBD; diarrhea (15.5%), abdominal pain (13.1%), and nausea and vomiting (8.9%) were most common.
Table 1.
Baseline Clinical Characteristics of Patients With IBD Undergoing SARS-CoV-2 Testing
| Clinical variables | All patients with IBD | SARS-CoV-2 RNA | SARS-CoV-RNA | P |
|---|---|---|---|---|
| (N = 168) | Negative (n = 163) | positive (n = 5) | ||
| Age, y (SD) | 47.7 (±16.3) | 47.0 (±16.0) | 70.6 (± 4.2) | <.001 |
| Age >66, n (%) | 23 (13.7) | 19 (11.7) | 4 (80.0) | <.001 |
| Gender, n (%) | ||||
| Male | 80 (47.6) | 78 (47.9) | 2 (40.0) | .810 |
| Female | 88 (52.4) | 85 (52.1) | 3 (60.0) | |
| Ethnicity, n (%) | ||||
| White | 103 (61.3) | 99 (60.7) | 4 (80.0) | .344 |
| Hispanic | 14 (8.3) | 14 (8.3) | 0 (0.0) | .501 |
| Black | 13 (7.7) | 12 (7.4) | 1 (20.0) | .283 |
| Asian | 29 (17.3) | 29 (17.8) | 0 (0.0) | .309 |
| Pacific Islander | 1 (0.6) | 1 (0.6) | 0 (0.0) | .863 |
| Unknown | 13 (7.7) | 13 (8.0) | 0 (0.0) | .518 |
| Reason for SARS-CoV-2 testing, n (%) | ||||
| Symptomatic | 154 (91.7) | 149 (91.4) | 5 (100) | .501 |
| Asymptomatic, travel history | 6 (3.6) | 6 (3.7) | 0 (0.0) | .667 |
| Asymptomatic, exposure | 8 (4.8) | 8 (4.9) | 0 (0.0) | .405 |
| Clinical features, n (%) | ||||
| Fever | 60 (35.7) | 57 (35.0) | 3 (60.0) | .230 |
| Cough | 106 (63.1) | 102 (62.6) | 4 (80.0) | .380 |
| Nasal congestion | 58 (34.5) | 55 (33.7) | 3 (60.0) | .200 |
| Sore throat | 69 (41.1) | 67 (41.1) | 2 (40.0) | .996 |
| Dyspnea | 63 (37.5) | 61 (37.4) | 2 (40.0) | .874 |
| Fatigue | 43 (25.6) | 40 (24.5) | 3 (60.0) | .067 |
| Body pain | 54 (32.1) | 51 (31.3) | 3 (60.0) | .159 |
| Pneumonia | 10 (6.0) | 8 (4.9) | 2 (40.0) | .131 |
| Gastrointestinal symptoms, n (%) | 32 (19.0) | 31 (19.0) | 1 (20.0) | .935 |
| Abdominal pain | 22 (13.1) | 21 (12.9) | 1 (20.0) | .589 |
| Nausea/Vomiting | 15 (8.9) | 14 (8.6) | 1 (20.0) | .364 |
| Diarrhea | 26 (15.5) | 26 (16.0) | 0 (0.0) | .338 |
| Melena | 1 (0.6) | 1 (0.6) | 0 (0.0) | .862 |
| Hematochezia | 2 (1.2) | 2 (1.2) | 0 (0.0) | .762 |
| Hematemesis | 1 (0.6) | 1 (0.6) | 0 (0.0) | .862 |
| Weight loss | 5 (3.0) | 5 (3.0) | 0 (0.0) | .695 |
| Dysphagia | 3 (1.8) | 3 (0.9) | 0 (0.0) | .762 |
| COVID-19 testing setting, n (%) | ||||
| Outpatient | 105 (62.5) | 101 (62.0) | 4 (80.0) | .352 |
| Emergency department | 43 (25.6) | 40 (24.5) | 3 (60.0) | .068 |
| Inpatient | 23 (13.7) | 22 (13.5) | 1 (20.0) | .893 |
| Ulcerative colitis, n (%) | ||||
| Total | 86 (51.2) | 83 (50.1) | 3 (60.0) | .641 |
| E1 | 24 (27.9) | 23 (27.7) | 1 (33.3) | |
| E2 | 19 (22.1) | 18 (21.7) | 1 (33.3) | |
| E3 | 32 (47.1) | 31 (37.3) | 1 (33.3) | |
| Unknown | 3 (1.8) | 3 (3.6) | 0 (0.0) | |
| Crohn's disease, n (%) | ||||
| Total | 66 (39.3) | 64 (39.3) | 2 (40.0) | .931 |
| L1 | 13 (19.7) | 13 (20.3) | 0 (0.0) | |
| L2 | 14 (21.2) | 14 (21.9) | 0 (0.0) | |
| L3 | 32 (48.5) | 30 (46.9) | 2 (100.0) | |
| L4 | 0 (0.0) | 0 (0.0) | 0 (0.0) | |
| Unknown | 3 (4.7) | 3 (4.7) | 0 (0.0) | |
| Perianal disease, n (%) | 12 (18.8) | 12 (18.8) | 0 (0.0) | |
| B1 | 43 (25.6) | 42 (65.6) | 1 (50.0) | |
| B2 | 10 (6.0) | 9 (14.1) | 1 (50.0) | |
| B3 | 8 (12.5) | 8 (12.5) | 0 (0.0) | |
| Unknown | 1 (1.6) | 1 (1.6) | 0 (0.0) | |
| Indeterminate IBD, n (%) | ||||
| Total | 16 (9.8) | 16 (9.8) | 0 (0.0) | .634 |
| BMI, kg/m2, n (%) | ||||
| <25.0 (normal or underweight) | 90 (53.6) | 89 (54.6) | 1 (20.0) | .146 |
| 25.0–29.9 (overweight) | 50 (29.8) | 49 (30.0) | 1 (20.0) | .656 |
| ≥30.0 (obese) | 30 (17.9) | 27 (16.6) | 3 (60.0) | .011 |
| Smoking, n (%) | ||||
| Current | 10 (6.0) | 10 (6.1) | 0 (0.0) | .988 |
| Former | 26 (15.5) | 26 (16.0) | 1 (20.0) | |
| Never | 131 (80.0) | 127 (77.9) | 4 (80.0) | |
| Alcohol use, n (%) | ||||
| Yes | 72 (42.9) | 69 (42.3) | 3 (60.0) | .701 |
| No | 100 (57.1) | 98 (60.1) | 2 (40.0) | |
| Hypertension, n (%) | ||||
| Yes | 42 (25.0) | 38 (23.3) | 4 (80.0) | <.001 |
| No | 126 (75.0) | 125 (76.7) | 1 (20.0) | |
| Diabetes mellitus, n (%) | ||||
| Yes | 18 (10.7) | 16 (9.8) | 2 (40.0) | .029 |
| No | 150 (89.3) | 147 (90.2) | 3 (60.0) | |
| Medications, n (%) | ||||
| ACE inhibitor | 13 (7.7) | 10 (6.1) | 3 (60.0) | <.001 |
| ARB | 10 (6.0) | 10 (6.1) | 0 (0.0) | .574 |
| PPI | 33 (19.6) | 33 (20.2) | 0 (0.0) | .271 |
| H2 Blocker | 19 (11.3) | 18 (11.0) | 1 (20.0) | .513 |
| Steroids | 34 (20.2) | 33 (20.2) | 1 (20.0) | .984 |
| 5-ASA | 58 (34.5) | 54 (33.1) | 4 (80.0) | .025 |
| 6MP/Azathioprine | 9 (5.4) | 8 (4.9) | 1 (20.0) | .131 |
| Methotrexate | 6 (3.6) | 6 (3.7) | 0 (0.0) | .667 |
| Anti-TNF agent, no. (%) | 34 (20.2) | 33 (20.2) | 1 (20.0) | .984 |
| Vedolizumab | 10 (6.0) | 10 (6.1) | 0 (0.0) | .574 |
| Ustekinumab | 4 (2.4) | 4 (2.5) | 0 (0.0) | .727 |
| Tofacitinib, no (%) | 0 (0.0) | 0 (0.0) | 0 (0.0) | N/A |
| Antiplatelets | 11 (6.5) | 10 (6.1) | 1 (20.0) | .205 |
| Anticoagulant | 11 (6.5) | 10 (6.1) | 1 (20.0) | .205 |
| NSAIDs | 20 (11.9) | 20 (12.2) | 0 (0.0) | .412 |
5-ASA, mesalamine; ARB, angiotensin receptor blocker; B1, nonstricturing, nonpenetrating CD; B2, stricturing CD; B3, penetrating CD; BMI, body mass index; CD, Crohn's disease; E1, distal UC; E2, left-sided UC; E3, extensive UC; L1, ileal CD, L2, colonic CD, L3, ileocolonic CD; NSAID, nonsteroidal anti-inflammatory drug; PPI, proton pump inhibitor; TNF, tumor necrosis factor; UC, ulcerative colitis.
Prevalence, Predictors, and Outcomes of COVID-19 in Patients With IBD
Among 168 patients with IBD tested, the prevalence of COVID-19 was 3.0% (5 of 168). Patients with IBD with COVID-19 were older (70.6 years vs 47 years, P < .001), more obese (60.0% vs 16.6%, P = .011), and more likely to have hypertension (80.0% vs 23.3%, P < .001) and diabetes mellitus (40.0% vs 9.8%, P = .029). Patients with IBD with COVID-19 were more likely to use ACE inhibitors (60.0% vs 6.1%, P < .001) and mesalamine (80.0% vs 33.1%, P = .025). In univariate analysis (Supplementary Table 1), age >66 years (odds ratio [OR] 31.37, P = .003), obesity (BMI ≥30) (OR 7.83, P = .011), hypertension (OR 13.58, P = .021), and ACE inhibitor use (OR 23.70, P = .001) were associated with increased risk of COVID-19 among patients with IBD. Our multivariate logistic regression model, which included age >66 years, obesity, hypertension, and ACE inhibitor use as covariates, showed that age >66 years was independently associated with increased risk (OR 21.30, P = .022) of COVID-19. Clinical outcomes of patients with IBD with COVID-19 are summarized in Supplementary Table 2. Four patients with IBD had a mild course, whereas 1 patient (Patient 3) developed pneumonia and acute respiratory distress syndrome and died despite aggressive interventions.
Discussion
To our knowledge, this is the first study to evaluate the prevalence of SARS-CoV-2 testing and COVID-19 in patients with IBD in a US cohort. The prevalence of IBD among patients undergoing SARS-CoV-2 testing is 1.2%, which is comparable to the prevalence of IBD (1.3%) in the US adult population.6 Our COVID-19 positivity rate of 3% in patients with IBD is comparable to the population-weighted prevalence of SARS-CoV-2–positive serology in Santa Clara county at 2.8%.7 Our data suggest that patients with IBD are not disproportionately being tested more, nor do they have a higher rate of SARS-CoV-2 positivity compared with the background population in northern California. One explanation is that increased ACE 2 expression may not mediate SARS-CoV-2 susceptibility in patients with IBD. Another possibility is that immunosuppressive medications in patients with IBD may attenuate viral-induced respiratory inflammation leading to an asymptomatic or mild COVID-19 course in patients with IBD who subsequently do not seek testing. Our study also demonstrates that patients older than 66 years are at increased risk of COVID-19. Our results are consistent with a prior retrospective study from China that demonstrated that older age is an independent predictor of COVID-19.8 The exact mechanisms underlying susceptibility to COVID-19 in elderly patients are unclear and warrant further investigation.
Our study has several strengths. First, our study provides novel epidemiological data that can inform patients with IBD and clinicians. Currently, there are no published reports estimating the prevalence of COVID-19 among patients with IBD in the United States. Second, we identified predictors of COVID-19 among patients with IBD, highlighting the increased susceptibility of COVID-19 with older age. Third, our study included patients from a large geographic area encompassing a diverse patient population. Our study has several limitations. First, our study was observational and cannot establish causation or account for unmeasured confounders. Second, we were unable to assess the predictors of COVID-19 morbidity and mortality with our small sample size and low event rate. A significantly larger sample size is needed to further clarify predictors of COVID-19 outcomes. Third, our study reflects testing performed by a single center and may not be generalizable to other institutions.
In summary, our results provide much needed epidemiological data and reassurance that COVID-19 rates in patients with IBD may be comparable to the general population. Age older than 66 years was a strong independent predictor of COVID-19 among patients with IBD.
Acknowledgments
We are extremely grateful to our Stanford IBD clinical and research team members Gayathri Swaminathan, PhD, David Limsui, MD, Sidhartha Sinha, MD, Kian Keyashian, MD, Sarah E. Streett, MD, and Chris Cartwright, MD for their critiques and helpful feedback in drafting the manuscript.
CRediT Authorship Contributions
John Gubatan, MD (Conceptualization: Lead; Data curation: Lead; Formal analysis: Lead; Investigation: Lead; Methodology: Lead; Software: Lead; Writing – original draft: Lead; Writing – review & editing: Lead). Steven Levitte, MD, PhD (Data curation: Supporting; Writing – original draft: Supporting; Writing – review & editing: Supporting). Tatiana Balabanis, BA (Data curation: Supporting). Akshar Patel, BA (Data curation: Supporting). Arpita Sharma, PhD (Data curation: Supporting). Aida Habtezion, MD, MSc (Conceptualization: Equal; Formal analysis: Equal; Supervision: Equal; Writing – review & editing: Supporting).
Footnotes
Conflict of interest The authors disclose no conflicts.
Funding This work was in part supported by the Ann and Bill Swindells Charitable Trust as well as Leslie and Douglas Ballinger.
Note: To access the supplementary material accompanying this article, visit the online version of Gastroenterology at www.gastrojournal.org, and at https://doi.org/10.1053/j.gastro.2020.05.009.
Supplementary Methods
Patients resided in several northern California counties, including Santa Clara, San Mateo, Santa Cruz, San Francisco, and Alameda. Patients were offered SARS-CoV-2 RNA testing at our institution if they had symptoms or findings suggestive of COVID-19 (fevers, cough, dyspnea, pneumonia), had a recent travel history with high COVID-19 cases, or had direct exposure to a patient with COVID-19. All SARS-CoV-2 RNA testing was performed using samples from a nasopharyngeal swab. The clinical sensitivity of the COVID-19 test at our institution is 96% (using repeat testing within 48 hours as a surrogate gold standard and assuming all negatives are false negatives) and clinical specificity approaches 100%.
The rate of SARS-CoV-2–positive tests, predictive value of clinical variables on the primary outcome, OR with its 95% confidence interval, and P values were assessed using Statistics/Data Analysis (Stata/IC 15.1 for Windows, StataCorp, College Station, TX). We calculated the prevalence of IBD among patients undergoing SARS-CoV-2 testing by dividing the number of patients with IBD tested by total number of patients tested in our population. The prevalence of SARS-CoV-2 positivity was calculated by dividing the number of patients with IBD with positive SARS-CoV-2 tests over the number of total patients with IBD tested. Dichotomous variables were analyzed for outcomes using the χ2 test or the Fisher exact test where appropriate, and continuous variables were analyzed using t tests if normally distributed, or the Wilcoxon test for non-normal data. Correction for multiple testing was included. All variables were analyzed initially in a univariate fashion to determine their association with COVID-19. P values of factors that showed evidence of an association on COVID-19 (P < .05) then were analyzed on multivariate regression analysis. Rare events may lead to complete separation and problems with convergence in conventional logistic regression models. The Firth method is a general approach to reducing rare event and small-sample bias in maximum likelihood estimation. Because of the small sample size and low event rate for outcome of patients with COVID-19 with IBD, we used the firthlogit penalized maximum likelihood logistic regression in our analysis.
Supplementary Table 1.
Univariate and Multivariate Predictors of COVID-19 Among Patients With IBD
| Clinical variables | Univariate predictors |
Multivariate predictors |
||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P | OR | 95% CI | P | |
| Age >66 y | 31.37 | 3.33–295.46 | .003 | 21.30 | 1.56–291.00 | .022 |
| Obesity (BMI ≥30) | 7.83 | 1.25–49.12 | .011 | 1.35 | 0.09–21.54 | .830 |
| Hypertension | 13.58 | 1.47–125.15 | .021 | 3.65 | 0.30–45.11 | .313 |
| Diabetes mellitus | 6.29 | 0.98–40.49 | .053 | |||
| ACE inhibitor use | 23.70 | 3.55–158.44 | .001 | 10.61 | 0.67–168.09 | .094 |
| Mesalamine (5-ASA) | 8.44 | 0.92–77.37 | .059 | |||
BMI, body mass index; CI, confidence interval.
Supplementary Table 2.
Clinical Characteristics and Outcomes in Patients With IBD With COVID-19
| Patient | Demographics | Montreal | Disease | IBD | COVID-19 | Mild COVID-19 | Severe COVID-19 | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| number | ethnicity | classification | activity | medications | symptoms | Outpta | EDb | Hospc | ICUd | MVe | Death |
| 1 | 68 F | CD | A | Prednisone | Fever, Cough | Yes | Yes | No | No | No | No |
| White | L3, B2 | IFX | Fatigue | ||||||||
| 2 | 74 M | CD | R | 5-ASA | Cough | Yes | Yes | No | No | No | No |
| White | L3, B1 | ||||||||||
| 3 | 76 M | UC | R | 5-ASA | Fever | No | No | Yes | Yes | Yes | Yesa |
| Black | Dyspnea | ||||||||||
| 4 | 69 F | UC | R | 5-ASA | Fever, Cough | Yes | No | No | No | No | No |
| White | E3 | Fatigue | |||||||||
| 5 | 66 F | UC | R | 5-ASA | Cough | Yes | No | No | No | No | No |
| White | E2 | AZA | Dyspnea | ||||||||
5-ASA, mesalamine; A, active; AZA, Azathioprine; B1, nonstricturing, nonpenetrating CD; B2, stricturing CD; B3, penetrating CD; CD, Crohn's disease; E1, distal UC; E2, left-sided UC; E3, extensive UC; ED, emergency department; F, female; Hosp, hospitalization; IFX, Infliximab; ICU, intensive care unit; L1, ileal CD, L2, colonic CD, L3, ileocolonic CD; M, male; MV, mechanical ventilation; Outpt, outpatient; R, remission; UC, ulcerative colitis.
Patient died of acute respiratory distress syndrome.
ED = Emergency Department.
Hosp = Inpatient Hospitalization.
ICU = Intensive Care Unit Admission.
MV = Mechanical Ventilation.
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