Empirical assessment of case‐based methods for identification of drugs associated with upper gastrointestinal bleeding in the French National Healthcare System database (SNDS). Issue 8 (10th June 2020)

• Record Type: Journal Article
• Authors: Thurin, Nicolas H.; Lassalle, Régis; Schuemie, Martijn; Pénichon, Marine; Gagne, Joshua J.; Rassen, Jeremy A.; Benichou, Jacques; Weill, Alain; Blin, Patrick; Moore, Nicholas; Droz‐Perroteau, Cécile
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 29:Issue 8(2020)
• Page Start: 890
• Page End: 903
• Publication Date: 2020-06-10
• Subjects: calibration -- case‐control -- case‐population -- claims database -- pharmacoepidemiology -- self‐controlled case series -- upper gastrointestinal bleeding; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.5038
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: Purpose: Upper gastrointestinal bleeding (UGIB) is a severe and frequent drug‐related event. In order to enable efficient drug safety alert generation in the French National Healthcare System database (SNDS), we assessed and calibrated empirically case‐based designs to identify drug associated with UGIB risk. Methods: All cases of UGIB were extracted from SNDS (2009‐2014) using two definitions. Positive and negative drug controls were used to compare 196 self‐controlled case series (SCCS), case‐control (CC) and case‐population (CP) design variants. Each variant was evaluated in a 1/10 th population sample using area under the receiver operating curve (AUC) and mean square error (MSE). Parameters that had major impacts on results were identified through logistic regression. Optimal designs were replicated in the unsampled population. Results: Using a specific UGIB definition, AUCs ranged from 0.64 to 0.80, 0.44 to 0.61 and 0.50 to 0.67, for SCCS, CC and CP, respectively. MSE ranged from 0.07 to 0.39, 0.83 to 1.33 and 1.96 to 4.6, respectively. Univariate regressions showed that high AUCs were achieved with SCCS with multiple drug adjustment and a 30‐day risk window starting at exposure. The top‐performing SCCS variant in the unsampled population yielded an AUC = 0.84 and MSE = 0.14, with 10/36 negative controls presenting significant estimates. Conclusions: SCCS adjusting for multiple drugs and using a 30‐day risk window has the potential to generate UGIB‐related alerts in the SNDS and hypotheses on its potential population impact. Negative control implementation highlighted that low systematic error was generated but that protopathic bias and confounding by indication remained unaddressed issues.