Name
Title | ||
|---|---|---|
A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records. |
Abstract | ||
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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 264155 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1136/amiajnl-2013-001718 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Field | DocType | Volume |
Data mining,Confounding,Confounding Factors (Epidemiology),Drug,Logistic regression,Medicine,Reference standards | Journal | 21 |
Issue | ISSN | Citations |
2 | 1067-5027 | 10 |
PageRank | References | Authors |
0.56 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Li | 1 | 10 | 0.56 |
| Hojjat Salmasian | 2 | 65 | 5.56 |
| Santiago Vilar | 3 | 106 | 9.12 |
| Herbert S Chase | 4 | 171 | 14.04 |
| Carol Friedman | 5 | 1618 | 147.25 |
| Ying Wei | 6 | 54 | 8.93 |