Outpatient Acute Kidney Disease Detection by National and Institutional Health Data

Background: The development of diagnostic definitions for acute kidney disease in outpatients (AKDOPT) remains an unattained goal because of consensus gaps and data silos in health-care systems. Methods: Our team developed the Acute Kidney Injury Detection System (AKIDS) to screen for undiagnosed AKDOPT using National Health Insurance MediCloud data and institutional electronic medical records. The criteria for AKDOPT were a >50% change in maximum and minimum serum creatinine levels or a >35% change in corresponding estimated glomerular filtration rate values within the 180 days before an appointed outpatient visit. In this retrospective cohort study, the associations between AKDOPT and composite kidney outcome (CKO; e.g., end-stage kidney disease, a >40% drop in estimated glomerular filtration rate, or a 2-fold or more increase in serum creatinine), all-cause mortality, and de novo non-dialysis chronic kidney disease (CKD-ND) were evaluated using multivariable Cox proportional hazards models. Results: Of 79,838 eligible adult patients screened by the AKIDS, 12,510 (15.7%) have AKDOPT. The adjusted absolute risk reduction for 1-year incident CKO, mortality, and de novo CKD-ND for patients with AKDOPT relative to those without are 79.0 (95% CI 78.9–79.1), 25.3 (95% CI 25.2-25.3), and 54.8 (54.7–54.9) per 1,000 patients, respectively. The adjusted hazard ratios for 1-year CKO, mortality, and de novo CKD-ND are 16.2 (14.2–18.5), 2.6 (2.4–2.9), and 3.5 (3.1–3.9), respectively. Conclusions: By integrating the national-local data using the AKIDS, this study comprehensively characterizes a previously unrecognized phenotype, AKDOPT, illustrating the potential of healthcare big data in transforming global approaches to AKD patterns and prevention.