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Background and Objective: The use of electronic health record (EHR) data can facilitate efficient research and quality initiatives. The imprecision of ICD-10 codes for kidney diagnoses has been an obstacle to discrete data-defined diagnoses in the EHR. This manuscript describes the Kidney Research Network (KRN) registry and database that provide an example of a prospective, real-world data glomerular disease registry for research and quality initiatives. Methods: KRN is a multicenter collaboration of patients, physicians, and scientists across diverse health-care settings with a focus on improving treatment options and outcomes for patients with glomerular disease. The registry and data warehouse amasses retrospective and prospective data including EHR, active research study, completed clinical trials, patient reported outcomes, and other relevant data. Following consent, participating sites enter the patient into KRN and provide a physician-confirmed primary kidney diagnosis. Kidney biopsy reports are redacted and uploaded. Site programmers extract local EHR data including demographics, insurance type, zip code, diagnoses, encounters, laboratories, procedures, medications, dialysis/transplant status, vitals, and vital status monthly. Participating sites transform data to conform to a common data model prior to submitting to the Data Analysis and Coordinating Center (DACC). The DACC stores and reviews each site's EHR data for quality before loading into the KRN database. Results: As of January 2021, 1,192 patients have enrolled in the registry. The database has been utilized for research, clinical trial design, clinical trial end point validation, and supported quality initiatives. The data also support a dashboard allowing enrolling sites to assist with clinical trial enrollment and population health initiatives. Conclusion: A multicenter registry using EHR data, following physician- and biopsy-confirmed glomerular disease diagnosis, can be established and used effectively for research and quality initiatives. This design provides an example which may be readily replicated for other rare or common disease endeavors.
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Introduction: Patients with chronic health conditions, particularly chronic kidney disease, are at heightened risk for psychiatric disorders; yet, there are limited data on those with primary glomerular disease. Methods: This study included patients with glomerular disease enrolled in the kidney research network multisite patient registry. Registry data include encounter, diagnoses, medication, laboratory, and vital signs data extracted from participants' electronic health records. ICD-9/10 diagnosis codes were used to identify a subset of psychiatric disorders focused on anxiety, mood, and behavioral disorders. Time-varying Cox proportional hazard models were used to analyze time from the onset of kidney disease to diagnosis of psychiatric disorder. Adjusted models retained significant covariates from the full list of potential confounders, including age, sex, race, ethnicity, time-varying treatment, the estimated glomerular filtration rate, and proteinuria (urine protein-to-creatinine ratio [UPCR]). Analogous models examined diagnosis of psychiatric disorder as a predictor of time to end-stage kidney disease (ESKD). Results: Data were available for 950 participants, with a median of 58 months of follow-up. 110 (12%) participants were diagnosed with psychiatric disorder during the follow-up. The estimated rate of psychiatric diagnosis after kidney disease was 14.7 cases per 1,000 person-years and was highest among those of adolescent age at the time of kidney disease diagnosis. Adjusted analyses found adolescent age (vs. adult, hazard ratio [HR] = 3.11, 95% confidence interval [CI] 1.87-5.17) and Asian race (vs. white, HR = 0.34, 95% CI 0.16-0.71) were associated with psychiatric diagnosis. A higher UPCR per 1 log unit (HR 1.13, 95% CI 1.01-1.27) and a higher total number of oral medications were associated with psychiatric disorder (p < 0.001). Psychiatric diagnosis was also associated with progression to ESKD (HR = 2.45, 95% CI 1.53-3.92) in adjusted models. Discussion/Conclusion: Psychiatric disorders were documented in approximately one-eighth of patients with glomerular disease and correlated with clinical disease characteristics such as age, race, proteinuria, and oral medication burden. These findings suggest mental health screening is warranted in patients of all ages with glomerular disease.
