Single center studies are limited by bias, lack of generalizability and variability, and inability to study rare conditions. Multicenter observational research could address many of those concerns, especially in hand ...
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Single center studies are limited by bias, lack of generalizability and variability, and inability to study rare conditions. Multicenter observational research could address many of those concerns, especially in hand surgery where multicenter research is currently quite limited;however, there are numerous barriers including regulatory issues, lack of common terminology, and variable data set structures. The observationalhealthdatasciences and informatics (OHDSI) program aims to surmount these limitations by enabling large-scale, collaborative research across multiple institutions. The OHDSI uses the observational Medical Outcomes Partnership (OMOP) Common data Model (CDM) to standardize health care data into a common language, enabling consistent and reliable analysis. The OMOP CDM has been transformative in converting multiple databases into a standardized code with a single vocabulary, allowing for coherent analysis across multiple data sets. Building upon the OMOP CDM, OHDSI provides an extensive suite of open-source tools for all research stages, from data extraction to statistical modeling. By keeping sensitive data local and only sharing summary statistics, OHDSI ensures compliance with privacy regulations while allowing for large-scale analyses. For hand surgery, OHDSI can enhance research depth, understanding of outcomes, risk factors, complications, and device performance, ultimately leading to better patient care. (J Hand Surg Am. 2025;50(3):363e367. Copyright (c) 2025 by the American Society for Surgery of the Hand. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)
Objective More than one third of appropriately treated patients with epilepsy have continued seizures despite two or more medication trials, meeting criteria for drug-resistant epilepsy (DRE). Accurate and reliable id...
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Objective More than one third of appropriately treated patients with epilepsy have continued seizures despite two or more medication trials, meeting criteria for drug-resistant epilepsy (DRE). Accurate and reliable identification of patients with DRE in observationaldata would enable large-scale, real-world comparative effectiveness research and improve access to specialized epilepsy care. In the present study, we aim to develop and compare the performance of computable phenotypes for DRE using the observational Medical Outcomes Partnership (OMOP) Common data Model. Methods We randomly sampled 600 patients from our academic medical center's electronic health record (EHR)-derived OMOP database meeting previously validated criteria for epilepsy (January 2015-August 2021). Two reviewers manually classified patients as having DRE, drug-responsive epilepsy, undefined drug responsiveness, or no epilepsy as of the last EHR encounter in the study period based on consensus definitions. Demographic characteristics and codes for diagnoses, antiseizure medications (ASMs), and procedures were tested for association with DRE. Algorithms combining permutations of these factors were applied to calculate sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for DRE. The F1 score was used to compare overall performance. Results Among 412 patients with source record-confirmed epilepsy, 62 (15.0%) had DRE, 163 (39.6%) had drug-responsive epilepsy, 124 (30.0%) had undefined drug responsiveness, and 63 (15.3%) had insufficient records. The best performing phenotype for DRE in terms of the F1 score was the presence of >= 1 intractable epilepsy code and >= 2 unique non-gabapentinoid ASM exposures each with >= 90-day drug era (sensitivity = .661, specificity = .937, PPV = .594, NPV = .952, F1 score = .626). Several phenotypes achieved higher sensitivity at the expense of specificity and vice versa. Significance OMOP algorithms can identify DRE in EH
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