Variation in detected adverse events using trigger tools: A systematic review and meta-analysis

Luisa C Eggenschwiler, Anne W S Rutjes, Sarah N Musy, Dietmar Ausserhofer, Natascha M Nielen, René Schwendimann, Maria Unbeck and Michael Simon

PLOS ONE, 2022, vol. 17, issue 9, 1-24

Abstract: Background: Adverse event (AE) detection is a major patient safety priority. However, despite extensive research on AEs, reported incidence rates vary widely. Objective: This study aimed: (1) to synthesize available evidence on AE incidence in acute care inpatient settings using Trigger Tool methodology; and (2) to explore whether study characteristics and study quality explain variations in reported AE incidence. Design: Systematic review and meta-analysis. Methods: To identify relevant studies, we queried PubMed, EMBASE, CINAHL, Cochrane Library and three journals in the patient safety field (last update search 25.05.2022). Eligible publications fulfilled the following criteria: adult inpatient samples; acute care hospital settings; Trigger Tool methodology; focus on specialty of internal medicine, surgery or oncology; published in English, French, German, Italian or Spanish. Systematic reviews and studies addressing adverse drug events or exclusively deceased patients were excluded. Risk of bias was assessed using an adapted version of the Quality Assessment Tool for Diagnostic Accuracy Studies 2. Our main outcome of interest was AEs per 100 admissions. We assessed nine study characteristics plus study quality as potential sources of variation using random regression models. We received no funding and did not register this review. Results: Screening 6,685 publications yielded 54 eligible studies covering 194,470 admissions. The cumulative AE incidence was 30.0 per 100 admissions (95% CI 23.9–37.5; I2 = 99.7%) and between study heterogeneity was high with a prediction interval of 5.4–164.7. Overall studies’ risk of bias and applicability-related concerns were rated as low. Eight out of nine methodological study characteristics did explain some variation of reported AE rates, such as patient age and type of hospital. Also, study quality did explain variation. Conclusion: Estimates of AE studies using trigger tool methodology vary while explaining variation is seriously hampered by the low standards of reporting such as the timeframe of AE detection. Specific reporting guidelines for studies using retrospective medical record review methodology are necessary to strengthen the current evidence base and to help explain between study variation.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0273800

DOI: 10.1371/journal.pone.0273800

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