Dataset Information

A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records.

ABSTRACT:

Objective

Electronic health records (EHRs) contain information to detect adverse drug reactions (ADRs), as they contain comprehensive clinical information. A major challenge of using comprehensive information involves confounding. We propose a novel data-driven method to identify ADR signals accurately by adjusting for confounders.

Materials and methods

We focused on two serious ADRs, rhabdomyolysis and pancreatitis, and used information in 264,155 unique patient records. We identified an ADR using established criteria, selected potential confounders, and then used penalized logistic regressions to estimate confounder-adjusted ADR associations. A reference standard was created to evaluate and compare the precision of the proposed method and four others.

Results

Precision was 83.3% for rhabdomyolysis and 60.8% for pancreatitis when using the proposed method, and we identified several drug safety signals that are interesting for further clinical review.

Discussion

The proposed method effectively estimated ADR associations after adjusting for confounders. A main cause of error was probably due to the nature of the dataset in that a substantial number of patients had a single visit only and, therefore, it was not possible to determine correctly the appropriate sequence of events for them. It is likely that performance will be improved with use of EHR data that contain more longitudinal records.

Conclusions

This data-driven method is effective in controlling for confounding, resulting in either a higher or similar precision when compared with four comparators, has the unique ability to provide insight into confounders for each specific medication-ADR pair, and can be easily adapted to other EHR systems.

SUBMITTER: Li Y

PROVIDER: S-EPMC3932454 | biostudies-literature | 2014 Mar-Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records.

Li Ying Y Salmasian Hojjat H Vilar Santiago S Chase Herbert H Friedman Carol C Wei Ying Y

Journal of the American Medical Informatics Association : JAMIA 20130801 2

<h4>Objective</h4>Electronic health records (EHRs) contain information to detect adverse drug reactions (ADRs), as they contain comprehensive clinical information. A major challenge of using comprehensive information involves confounding. We propose a novel data-driven method to identify ADR signals accurately by adjusting for confounders.<h4>Materials and methods</h4>We focused on two serious ADRs, rhabdomyolysis and pancreatitis, and used information in 264,155 unique patient records. We ident ...[more]

PMID: 23907285

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Project description:BACKGROUND:Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. OBJECTIVE:The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies' designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard. METHODS:PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. RESULTS:A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0% to 17.9%, with a median of 0.8%. The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8% to 60%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20% to 91%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9% to 83.3%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0% to 60%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4%, 10.5%, 71.1%, and 68.4% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. CONCLUSIONS:We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies.

Dataset Information

A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records.

Objective

Materials and methods

Results

Discussion

Conclusions

Publications

A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records.

Similar Datasets

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