Abstract 14276: Using Artificial Intelligence and Natural Language Processing on Electronic Health Records of Heart Failure Patients to Examine the Impact of Estimated Glomerular Filtration Rate Trend on Mortality

Abstract

Introduction and Hypothesis: Heart failure (HF) often leads to an imbalance between heart and kidney function, with a poor prognosis in HF patients linked to dynamic changes in renal function as well as chronic kidney disease (CKD). This study aimed to determine the incidence of reduced renal function upon HF diagnosis and to explore the potential for dynamic changes in kidney function to predict short- and long-term outcomes in a real-world cohort of HF patients. Methods: Clinical data of patients with a diagnosis of HF (index event) were gathered retrospectively using natural language processing. Patients were classified as having CKD based on their eGFR (<60 mL/min/1.73m2) and a history of CKD and/or dialysis. eGFR trajectories were analyzed using linear mixed effects models. Cox proportional hazard models were used to analyze the relationship between baseline variables and mortality. Joint modeling was employed to assess the relationship between the eGFR trend and mortality. Results: There were 1986 patients in the study population, with a mean (SD) age of 74.8 ± 11.7 years and 58% of males. At the index event, 58% (n = 1156) had CKD, and 17% (n = 339) had HF with preserved ejection fraction (HFpEF). Median follow-up was 3.16 years. After 2 years, 10% (n = 198) of the patients had passed away. The proportion of CKD at index was considerably lower in the 2-year survivors group (55% vs 76%, p < 0.001) in this group. The calculated mean annual eGFR reduction was 4.2 mL/min/1.73m 2 . Age, CVA, diastolic blood pressure, hemoglobin, HDL, potassium, and NT-proBNP levels in blood serum were found to be the primary predictors of mortality in multivariate Cox PH model. Joint modeling revealed that a current eGFR value that was 10 mL/min/1.73m 2 lower between patients corresponded with a higher mortality hazard of 1.22 ± 0.05 (p < 0.001). In addition, a drop in eGFR of 10 mL/min/1.73m 2 over the previous year showed an the elevated mortality hazard of 1.97 ± 0.16 (p < 0.001). Conclusions: We show that the prevalence of CKD is significant in a real-world population of HF patients, and CKD is a standalone predictor of mortality. Current eGFR value and the eGFR slope from the previous year have a potential to be used for individual mortality hazard assessment in the clinical follow-up of HF patients.