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.
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.
Authors: Gianluca Trifiro; Annie Fourrier-Reglat; Miriam C J M Sturkenboom; Carlos Díaz Acedo; Johan Van Der Lei Journal: Stud Health Technol Inform Date: 2009
Authors: Rave Harpaz; Santiago Vilar; William Dumouchel; Hojjat Salmasian; Krystl Haerian; Nigam H Shah; Herbert S Chase; Carol Friedman Journal: J Am Med Inform Assoc Date: 2012-10-31 Impact factor: 4.497
Authors: N P Tatonetti; J C Denny; S N Murphy; G H Fernald; G Krishnan; V Castro; P Yue; P S Tsao; P S Tsau; I Kohane; D M Roden; R B Altman Journal: Clin Pharmacol Ther Date: 2011-05-25 Impact factor: 6.875
Authors: Rave Harpaz; Alison Callahan; Suzanne Tamang; Yen Low; David Odgers; Sam Finlayson; Kenneth Jung; Paea LePendu; Nigam H Shah Journal: Drug Saf Date: 2014-10 Impact factor: 5.606
Authors: Suehyun Lee; Jongsoo Han; Rae Woong Park; Grace Juyun Kim; John Hoon Rim; Jooyoung Cho; Kye Hwa Lee; Jisan Lee; Sujeong Kim; Ju Han Kim Journal: Drug Saf Date: 2019-05 Impact factor: 5.606
Authors: Po-Yen Wu; Chih-Wen Cheng; Chanchala D Kaddi; Janani Venugopalan; Ryan Hoffman; May D Wang Journal: IEEE Trans Biomed Eng Date: 2016-10-10 Impact factor: 4.538