A Systematic Review of the Performance Characteristics of Clinical Event Monitor Signals Used to Detect Adverse Drug Events in the Hospital Setting
- Steven M Handler,
- Richard L Altman,
- Subashan Perera,
- Joseph T Hanlon,
- Stephanie A Studenski,
- James E Bost,
- Melissa I Saul,
- Douglas B Fridsma
- Affiliations of the authors: Division of Geriatric Medicine, Department of Medicine, School of Medicine, Pittsburgh, PA; Department of Biomedical Informatics, School of Medicine, Pittsburgh, PA; School of Medicine, Pittsburgh, PA; Department of Biostatistics, Graduate School of Public Health, Pittsburgh, PA; Department of Pharmacy and Therapeutics, School of Pharmacy, Pittsburgh, PA; Center for Research on Health Care, Department of Medicine, Pittsburgh, PA; Geriatric Research Education and Clinical Center VAPHS, Pittsburgh, PA; Center for Health Equity Research, Veterans Affairs Pittsburgh Healthcare System (VAPHS), Pittsburgh, PA
- Correspondence and reprints: S. M. Handler, MD, MS, Department of Medicine, Division of Geriatric Medicine, University of Pittsburgh, 3471 Fifth Ave, Suite 500, Pittsburgh, PA 15213; e-mail: <handlersm{at}upmc.edu>
- Received 6 January 2007
- Accepted 10 April 2007
Abstract
Objective We conducted a systematic review of pharmacy and laboratory signals used by clinical event monitor systems to detect adverse drug events (ADEs) in adult hospitals.
Design and Measurements We searched the MEDLINE, CINHAL, and EMBASE databases for the years 1985–2006, and found 12 studies describing 36 unique ADE signals (10 medication levels, 19 laboratory values, and 7 antidotes). We were able to calculate positive predictive values (PPVs) and 95% confidence intervals (CIs) for 15 signals.
Results We found that PPVs ranged from 0.03 (95% CI, 0.03–0.03) for hypokalemia, to 0.50 (95% CI, 0.39–0.61) for supratherapeutic quinidine level. In general, antidotes (range = 0.09–0.11) had the lowest PPVs, followed by laboratory values (range = 0.03–0.27) and medication levels (range = 0.03–0.50).
Conclusion Data from this study should help clinical information system and computerized decision support producers develop or improve existing clinical event monitor systems to detect ADEs in their own hospitals by prioritizing those signals with the highest PPVs.
Footnotes
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This study was supported in part by NIH grants K12 HD049109 (NIH Roadmap Multidisciplinary Clinical Research Career Development Award Grant), 5T32AG021885, P30AG024827, R01AG027017, P30AG024827 and a Merck/AFAR Junior Investigator Award in Geriatric Clinical Pharmacology.
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The authors thank Alice B. Kuller, MLS, for her help in conducting the literature search for this systematic review.
