Comparison of Electronic Health Data-Based Frailty Assessment Tools for Prediction of Adverse Outcomes of Patients With Chronic Kidney Disease.

Abstract

Aim: To compare four electronic health record (EHR)-based frailty tools-the Hospital Frailty Risk Score (HFRS), Johns Hopkins Adjusted Clinical Groups Frailty Indicator (CFI), Electronic Frailty Index (eFI) and a Laboratory-based Frailty Index (FI-Lab)-in predicting progression and all-cause mortality in hospitalised patients with chronic kidney disease (CKD).

Methods: This retrospective study evaluated the indices in two cohorts: a single-centre cohort (n = 5715) for CKD progression and the Medical Information Mart for Intensive Care (MIMIC) database (n = 2674) for mortality. We used Cox proportional hazards regression for association analyses. The incremental predictive value of adding frailty indices to established risk models was quantified using the area under the curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Results: Correlations between the frailty indices were weak to moderate (Spearman's ρ = 0.205-0.451). In adjusted analyses, CFI, eFI and FI-Lab were associated with a higher risk of CKD progression, whereas HFRS was not. In the MIMIC cohort, all four indices were significantly associated with all-cause mortality. Notably, the CFI association was non-significant in patients < 65 years for both CKD progression and 28-day mortality. For predictive enhancement, adding FI-Lab and eFI to established CKD risk models significantly improved progression prediction (ΔAUC p < 0.05), yielding substantial reclassification (NRI: 0.376-0.498) and discrimination (IDI: 0.032-0.048). For mortality prediction, all indices improved baseline severity scores, with FI-Lab providing the greatest incremental value.

Conclusion: Among the evaluated EHR-based frailty indices, FI-Lab offers the most robust utility for risk stratification in hospitalised patients with CKD, followed closely by eFI.