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

Autores da FMUP

• Luís Carlos Ferreira Mendonça

  Autor

• João Sérgio De Lima Soares Neves

  Autor

• Paulo Manuel Barreiros De Castro Chaves

  Autor

• João Pedro Melo Marques Pinho Ferreira

  Autor

Participantes de fora da FMUP

• Vieira, MB

Unidades de investigação

• Cirurgia e Fisiologia

• Laboratório Associado RISE- Rede de Investigação em Saúde

  

  Investigador

  Fernando Carlos De Landér Schmitt

• RISE-Health

  

  Investigador

  Fernando Carlos De Landér Schmitt

Abstract

IntroductionCurrent 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.MethodsPatients 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.ResultsDuring 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.ConclusionThe 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.

Keywords

• Albuminuria; Biomarkers; Heart Failure; Humans; Kidney; Kidney Failure, Chronic; Natriuretic Peptide, Brain; Peptide Fragments; Prognosis; Renal Insufficiency, Chronic; albumin; aldosterone; amino terminal pro brain natriuretic peptide; bilirubin; low density lipoprotein cholesterol; troponin T; uric acid; biological marker; brain natriuretic peptide; peptide fragment; adult; albuminuria; Article; atrial fibrillation; cardiovascular mortality; cerebrovascular accident; chronic kidney failure; clinical article; cohort analysis; congestive heart failure; creatinine blood level; diabetes mellitus; end stage renal disease; enzyme linked immunosorbent assay; estimated glomerular filtration rate; female; follow up; heart failure; heart infarction; heart rate; hospitalization; human; kidney function; middle aged; mortality; predictive value; regression model; revascularization; risk assessment; risk model; systolic blood pressure; validation process; complication; controlled study; heart fail