Comparative Effectiveness of Famotidine in Hospitalized COVID-19 Patients

Abstract

Background: Famotidine has been posited as a potential treatment for COVID-19. We compared the incidence of COVID-19 outcomes (i.e., death; and death or intensive services use) among hospitalized famotidine users vs. proton pump inhibitors (PPIs) users, hydroxychloroquine users or famotidine non-users separately. Methods: We constructed a retrospective cohort study using data from COVID-19 Premier Hospital electronic health records. Study population were COVID-19 hospitalized patients aged 18 years or older. Famotidine, PPI and hydroxychloroquine exposure groups were defined as patients dispensed any medication containing one of the three drugs on the day of admission. The famotidine non-user group was derived from the same source population with no history of exposure to any drug with famotidine as an active ingredient prior to or on the day of admission. Time-at-risk was defined based on the intention-to-treat principle starting 1 day after admission to 30 days after admission. For each study comparison group, we fit a propensity score (PS) model through large-scale regularized logistic regression. The outcome was modeled using a survival model. Results: We identified 2193 users of PPI, 5950 users of the hydroxychloroquine, 1816 users of famotidine and 26,820 non-famotidine users. After PS stratification, the hazard ratios for death were as follows: famotidine vs no famotidine HR 1.03 (0.89-1.18); vs PPIs: HR 1.14 (0.94-1.39); vs hydroxychloroquine:1.03 (0.85-1.24). Similar results were observed for the risk of death or intensive services use. Conclusion: We found no evidence of a reduced risk of COVID-19 outcomes among hospitalized COVID-19 patients who used famotidine compared to those who did not or compared to PPI or hydroxychloroquine users.

Real‐World Evidence for Assessing Pharmaceutical Treatments in the Context of COVID‐19

“…Using PHD, the authors identified 1,816 users of famotidine, 2,193 users of PPIs, 5,950 users of the hydroxychloroquine, and 26,820 nonfamotidine users. 38 Although it is substantially larger than other EHR-based studies on the same topic, 39 PHD does not contain laboratory results to confirm COVID-19 status, and the study cohort was defined as "condition, measurement or observation indicative of COVID-19." 38 In an inpatient setting, medication information can be generated at the following stages.…”

“…38 Although it is substantially larger than other EHR-based studies on the same topic, 39 PHD does not contain laboratory results to confirm COVID-19 status, and the study cohort was defined as "condition, measurement or observation indicative of COVID-19." 38 In an inpatient setting, medication information can be generated at the following stages. (i) Electronic ordering data are produced when physicians prescribe a medication in the EHR system.…”

“…For example, Geleris et al used electronic medication administration data from the EHR for their hydroxychloroquine study 35 and Shoaibi et al used data generated by billed medication (corresponding to "administered medication") in PHD for the study of famotidine vs. PPI. 38 Medication information based on inpatient administration data has little potential for drug exposure misclassification.…”

“…compared adverse outcomes, including death and receiving intensive services, among patients hospitalized for COVID‐19 taking famotidine, proton pump inhibitors (PPIs), or hydroxychloroquine vs. famotidine nonusers. Using PHD, the authors identified 1,816 users of famotidine, 2,193 users of PPIs, 5,950 users of the hydroxychloroquine, and 26,820 non‐famotidine users 38 . Although it is substantially larger than other EHR‐based studies on the same topic, 39 PHD does not contain laboratory results to confirm COVID‐19 status, and the study cohort was defined as “condition, measurement or observation indicative of COVID‐19.” 38 …”

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

“…Observational Health Data Sciences and Informatics (OHDSI) is an international, multi-stakeholder collaboration that has developed open-source solutions for large-scale analytics [4]. The OHDSI community has used these open-source solutions to generate observational evidence for COVID-19 and has impacted international clinical guidelines [5][6][7][8][9][10][11][12][13][14][15][16].…”