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A 4-variable model to predict cardio-kidney events and mortality in chronic kidney disease: The Chronic Renal Insufficiency Cohort (CRIC) Study.

Introduction Current prognostic models for CKD are complex and were designed to predict one single outcome. We aimed to develop and validate a simple and parsimonious prognostic model to predict cardio-kidney events and mortality. Methods Patients from the CRIC Study (n=3718) were randomly divided into derivation (n=2478) and validation (n=1240) cohorts. Twenty-nine candidate variables were pre-selected. Multivariable Cox regression models were developed using stepwise selection for various cardio-kidney endpoints, namely: i) the primary composite outcome of 50% decline in eGFR from baseline, end-stage renal disease or cardiovascular mortality; ii) hospitalization for heart failure (HHF) or cardiovascular mortality; iii) 3-point major CV endpoints (3P-MACE); iv) all-cause death. Results During a median follow-up of 9 years, the primary outcome occurred in 977 patients of the derivation cohort and 501 patients of the validation cohort. Log-transformed NT-proBNP, log-transformed hs-cTnT, log-transformed albuminuria and eGFR were the dominant predictors. The primary outcome risk score discriminated well (c-statistic=0.83) with a proportion of events of 11.4% in the lowest tertile of risk and 91.5% in the highest tertile at 10 years. The risk model presented good discrimination for HHF or cardiovascular mortality, 3P-MACE and all-cause death (c-statistic=0.80, 0.75 and 0.75, respectively). The 4-variable risk model achieved similar c-statistics for all tested outcomes in the validation cohort. The discrimination of the 4-variable risk model was mostly superior to that of published models. Conclusion The combination of NT-proBNP, hs-cTnT, albuminuria and eGFR in a single 4-variable model provides a unique individual prognostic assessment of multiple cardio-kidney outcomes in CKD.

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