Diagnosis-based cohort augmentation using laboratory results data: The case of chronic kidney disease.

Purpose: In this report, we use data from FDA's Sentinel System to focus on how augmenting a diagnosis-based chronic kidney disease cohort with patients identified through laboratory results impacts cohort characteristics and outcomes.

Methods: We used data from 2 Data Partners. Patients were eligible if they were health plan members on January 1, 2012. We classified chronic kidney disease patients into mutually exclusive categories according to the hierarchy of (1) ICD-9-CM diagnosis (DXGroup), or (2) two estimated glomerular filtration rates <60 mL/min/1.73m , separated by at least 90 days (2-LabGroup), or (3) a single estimated glomerular filtration rates <60 mL/min/1.73m (1-LabGroup). We compared the groups on demographic, clinical, and health care utilization characteristics using pairwise standardized differences. We used Cox regression to compare the groups on mortality, adjusting for baseline covariates.

Results: We identified 209 864 patients: 107 607 in DxGroup (51%) and 102 257 (49%) from laboratory data alone. For every characteristic, the DxGroup was the sickest, followed by the 2-LabGroup and then the 1-LabGroup. The DxGroup was more likely to die than 2-LabGroup (hazard ratio [HR], 1.47; 95% CI, 1.22-1.77) at Site 1; that effect was observed, but attenuated, at Site 2 (HR, 1.16; 95% CI, 1.07-1.25). The DxGroup was more likely to die than the 1-LabGroup at Site 1 (HR, 1.36; 95% CI, 1.20-1.55), but not at Site 2 (HR, 0.94; 95% CI, 0.89-1.00).

Conclusions: We suggest that drug safety researchers consider whether the method of cohort identification contributes to generalizability of safety findings.