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

Area of Science:

• Pediatric pharmacotherapy
• Drug safety research
• Health informatics

Background:

• Adverse drug events (ADEs) in outpatient pediatric pharmacotherapy can lead to serious health consequences, including hospitalization.
• Existing research primarily focuses on ADE identification during inpatient stays, neglecting outpatient-onset events.
• There is a need for effective tools to identify pediatric drug-related hospital admissions originating from outpatient settings.

Purpose of the Study:

• To develop and validate an algorithm for identifying drug-related hospital admissions in pediatric patients.
• To create a pediatric trigger tool specifically designed for detecting ADEs that lead to inpatient care.
• To improve the identification of outpatient-acquired ADEs in children.

Main Methods:

• Conducted a systematic literature search to inform the development of a pediatric trigger tool.
• Tested an initial version of the tool on 292 pediatric patients at a German university children's hospital.
• Refined the tool through a novel approach involving the combination of different modules.

Main Results:

• The algorithm, with 39 triggers across 5 modules, initially achieved 95.5% sensitivity and 16.5% specificity for identifying drug-related admissions.
• After modifications requiring combined module activation, specificity increased to 56.9% while maintaining high sensitivity (81.8%).
• The refined tool identified 36 out of 44 ADEs leading to admission, with a positive predictive value of 25.2%.

Conclusions:

• This algorithm represents the first trigger tool designed to identify outpatient-acquired ADEs leading to pediatric hospital admission.
• The study highlights the potential of this tool for enhancing drug safety monitoring in pediatric populations.
• Further refinement using a larger patient cohort is recommended to improve specificity and reduce the number of triggers.