Drug-Drug Interaction Signals for Major Bleeding in Nursing Home Residents: High-Throughput Screening of Clopidogrel and Oral Anticoagulants.

PURPOSE: Nursing home (NH) residents are at high risk for major bleeding and are frequently exposed to complex medication regimens that may increase the likelihood of clinically important drug-drug interactions (DDIs). However, DDI evidence from community-dwelling populations may not generalize to NH residents, and real-world signals involving clopidogrel and oral anticoagulants remain incompletely characterized in this setting. We aimed to identify and interpret candidate DDI signals for major bleeding involving clopidogrel and oral anticoagulants among NH residents.

METHODS

Using linked Minimum Data Set assessments and Medicare fee-for-service claims (2013-2020), we conducted high-throughput, self-controlled case series (SCCS) screening analyses for four object drugs (clopidogrel, apixaban, rivaroxaban, and warfarin) paired with candidate precipitant medications. We estimated rate ratios (RRs) and 95% confidence intervals (CIs) comparing periods of concomitant use versus object drug use alone, applying semi-Bayes shrinkage to reduce false-positive signals. To aid interpretation, we incorporated two negative controls: a negative-control object drug (pravastatin) and a negative-control outcome (fall-related injury). A panel of experts evaluated signals for biological plausibility and potential bias.

RESULTS

After semi-Bayes adjustment, 5/40 clopidogrel, 1/29 apixaban, 2/15 rivaroxaban, and 6/22 warfarin signals remained significant, and none did for pravastatin. Signals were also observed for fall-related injury. Experts identified only clopidogrel/sertraline and warfarin with cephalexin, sulfamethoxazole-trimethoprim, and furosemide as biologically plausible signals.

CONCLUSION

SCCS screening for DDIs in NH residents identified bleeding signals for clopidogrel and oral anticoagulants, but few were mechanistically coherent and fall-related injury signals suggested residual bias.