Development and Adjustment of an Algorithm for Identifying Drug-Related Hospital Admissions in Pediatrics

Abstract

Objective: Adverse drug events (ADEs) in the outpatient pediatric pharmacotherapy can be serious and lead to inpatient admissions. Recent research only focused on ADE identification during hospitalization. The aim of the present study was to develop an algorithm to identify drug-related hospital admissions in pediatrics.

Methods: A systematic literature research was performed, and a pediatric trigger tool for identifying drug-related inpatient admissions was built. The initial version was tested in a sample of 292 patients admitted to a German university children's hospital. Subsequently, the tool was further improved by combining different modules as a novel approach.

Results: The obtained algorithm with 39 triggers in 5 modules identified drug-related inpatient admissions at a sensitivity of 95.5% (95% confidence interval [CI], 89.3%-100%) and a specificity of 16.5% (95% CI, 11.9%-21.2%), respectively. After modifications including trigger activation requiring a combination of different modules, specificity increased to 56.9% (95% CI, 50.7%-63.0%). Identifying 36 of 44 ADEs leading to admission, sensitivity remained high (81.8% [95% CI, 70.4%-93.2%]). The overall positive predictive value was 25.2% (95% CI, 18.1%-32.3%).

Conclusions: The algorithm is the first trigger tool to identify ambulant acquired ADEs leading to hospital admission in pediatrics. However, the underlying patient sample is small.Using a larger population for refinement will allow further specifications and reduction in the total amount of triggers and thus signals.