Expert panel assessment of acute liver injury identification in observational data.

BACKGROUND: Observational data are useful for studying drug safety; however, to be effective, accurate outcome measurement is paramount. OBJECTIVES: This study compared alternative outcome definitions for acute liver injury (ALI) and explored opportunities for improving ALI identification in observational data. METHODS: The Truven MarketScan® Lab Database (MSLR) was used to identify patients meeting at least 1 of 4 ALI definitions, including definitions based on diagnosis codes, laboratory measures, or combinations of diagnoses, procedures, and/or laboratory measures. Expert panelists reviewed patient data using a Web dashboard. Panelists determined whether they believed the patient had ALI and identified factors influencing their decision. Logistic regression models explored which factors were influential in case determination. RESULTS: Overall, only 37 of 208 reviewed patients (17.8%) were classified as cases. The diagnosis-based definition yielded no positive cases and the laboratory-based definition yielded the most positive cases (31 of 60). The most influential factors in case classification were occurrence of procedures after the index date (OR = 13.2, 95% CI = 5.3-32.9), no occurrence of drug treatments before the index date (OR = 4.6; 95% CI = 1.6-13.2), occurrence of drug treatments before the index date (OR = 0.3; 95% CI = 0.1-0.6), and no drug treatments after the index date (OR = 0.2; 95% CI = 0.0-0.5). CONCLUSIONS: Comparing ALI definitions illustrated tradeoffs between the number of plausible cases identified and the likelihood of cases being classified as positive. Future research should refine ALI case definitions, considering the import of laboratory results, procedures, and drugs in defining a case.

Developing an expert panel process to refine health outcome definitions in observational data.

OBJECTIVES: Drug safety surveillance using observational data requires valid adverse event, or health outcome of interest (HOI) measurement. The objectives of this study were to develop a method to review HOI definitions in claims databases using (1) web-based digital tools to present de-identified patient data, (2) a systematic expert panel review process, and (3) a data collection process enabling analysis of concepts-of-interest that influence panelists' determination of HOI. METHODS: De-identified patient data were presented via an interactive web-based dashboard to enable case review and determine if specific HOIs were present or absent. Criteria for determining HOIs and their severity were provided to each panelist. Using a modified Delphi method, six panelist pairs independently reviewed approximately 200 cases across each of three HOIs (acute liver injury, acute kidney injury, and acute myocardial infarction) such that panelist pairs independently reviewed the same cases. Panelists completed an assessment within the dashboard for each case that included their assessment of the presence or absence of the HOI, HOI severity (if present), and data contributing to their decision. Discrepancies within panelist pairs were resolved during a consensus process. RESULTS: Dashboard development was iterative, focusing on data presentation and recording panelists' assessments. Panelists reported quickly learning how to use the dashboard. The assessment module was used consistently. The dashboard was reliable, enabling an efficient review process for panelists. Modifications were made to the dashboard and review process when necessary to facilitate case review. Our methods should be applied to other health outcomes of interest to further refine the dashboard and case review process. CONCLUSION: The expert review process was effective and was supported by the web-based dashboard. Our methods for case review and classification can be applied to future methods for case identification in observational data sources.