Routine cardiac biomarkers for the prediction of incident major adverse cardiac events in patients with glomerulonephritis: a real-world analysis using a global federated database

Abstract

Abstract Rationale & objective Glomerulonephritis (GN) is a leading cause of chronic kidney disease (CKD). Major adverse cardiovascular events (MACE) are prolific in CKD. The risk of MACE in GN cohorts is multifactorial. We investigated the prognostic significance of routine cardiac biomarkers, Troponin I and N-terminal pro-BNP (NT-proBNP) in predicting MACE within 5 years of GN diagnosis. Study Design Retrospective cohort study. Setting & participants Data were obtained from TriNetX, a global federated health research network of electronic health records (EHR). Exposure or predictor Biomarker thresholds: Troponin I: 18 ng/L, NT-proBNP: 400 pg/mL. Outcomes Primary outcome: Incidence of major adverse cardiovascular events (MACE). Secondary outcome: was the risk for each individual component of the composite outcome. Analytical Approach 1:1 propensity score matching using logistic regression. Cox proportional hazard models were used to assess the association of cardiac biomarkers with the primary and secondary outcomes, reported as Hazard Ratio (HR) and 95% confidence intervals (CI). Survival analysis was performed which estimates the probability of an outcome over a 5-year follow-up from the index event. Results Following PSM, 34,974 and 18,218 patients were analysed in the Troponin I and NTproBNP cohorts, respectively. In the Troponin I all cause GN cohort, 3,222 (9%) developed composite MACE outcome HR 1.79; (95% CI, 1.70, 1.88, p < 0.0001). In the NTproBNP GN cohort, 1,686 (9%) developed composite MACE outcome HR 1.99; (95% CI, 1.86, 2.14, p < 0.0001). Limitations The data are derived from EHR for administrative purposes; therefore, there is the potential for data errors or missing data. Conclusions In GN, routinely available cardiac biomarkers can predict incident MACE. The results suggest the clinical need for cardiovascular and mortality risk profiling in glomerular disease using a combination of clinical and laboratory variables.