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J Am Med Inform Assoc. 2007 Jul-Aug;14(4):451-8. Epub 2007 Apr 25.

A systematic review of the performance characteristics of clinical event monitor signals used to detect adverse drug events in the hospital setting.

Author information

1
Department of Medicine, Division of Geriatric Medicine, University of Pittsburgh, 3471 Fifth Ave, Suite 500, Pittsburgh, PA 15213, USA. handlersm@upmc.edu

Erratum in

  • J Am Med Inform Assoc. 2007 Sep-Oct;14(5):686.

Abstract

OBJECTIVE:

Despite demonstrated benefits, few healthcare organizations have implemented clinical event monitors to detect adverse drug events (ADEs). The objective of this study was to conduct a systematic review of pharmacy and laboratory signals used by clinical event monitors to detect ADEs in hospitalized adults.

DESIGN:

We performed a comprehensive search of MEDLINE, CINHAL and EMBASE to identify studies published between 1985 through 2006. Studies were included if they: described a clinical event monitor to detect ADEs in an adult hospital setting; described laboratory or pharmacy ADE signals; and, provided positive predictive values (PPVs) or information to allow the calculation of PPVs for individual ADE signals.

MEASUREMENTS:

We calculated overall estimates of PPVs and 95% confidence intervals (CIs) for signals reported in 2 or more studies and contained no evidence heterogeneity. Results were examined by signal category: medication levels, laboratory tests, or antidotes.

RESULTS:

We identified 12 observational studies describing 36 unique ADE signals. Fifteen signals (3 antidotes, 4 medication levels, and 8 laboratory values) contained no evidence of heterogeneity. The pooled PPVs for these individual signals ranged from 0.03 [CI=0.03-0.03] for hypokalemia, to 0.50 [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 (0.03-0.27), and medication levels (0.03-0.50).

CONCLUSION:

Results from this study should help clinical information system and computerized decision support producers develop or improve existing clinical event monitors to detect ADEs in their own hospitals by prioritizing those signals with the highest PPVs [corrected]

PMID:
17460130
PMCID:
PMC2244905
DOI:
10.1197/jamia.M2369
[Indexed for MEDLINE]
Free PMC Article
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