Clinical utility of N-terminal pro-B-type natriuretic peptide for risk stratification of patients with acute decompensated heart failure. Derivation and validation of the ADHF/NT-proBNP risk score

Background

NT-proBNP has been associated with prognosis in acute decompensated heart failure (ADHF). Whether NT-proBNP provides additional prognostic information beyond that obtained from standard clinical variables is uncertain. We sought to assess whether N-terminal pro-B-type natriuretic peptide (NT-proBNP) determination improves risk reclassification of patients with ADHF and to develop and validate a point-based NT-proBNP risk score.

Methods

This study included 824 patients with ADHF (453 in the derivation cohort, 371 in the validation cohort). We compared two multivariable models predicting 1-year all-cause mortality, including clinical variables and clinical variables plus NT-proBNP. We calculated the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). Then, we developed and externally validated the NT-proBNP risk score.

Results

One-year mortalities for the derivation and validation cohorts were 28.3% and 23.4%, respectively. Multivariable predictors of mortality included chronic obstructive pulmonary disease, estimated glomerular filtration rate, sodium, hemoglobin, left ventricular ejection fraction, and moderate to severe tricuspid regurgitation. Adding NT-proBNP to the clinical variables only model significantly improved the NRI (0.129; p = 0.0027) and the IDI (0.037; p = 0.0005). In the derivation cohort, the NT-proBNP risk score had a C index of 0.839 (95% CI: 0.798–0.880) and the Hosmer–Lemeshow statistic was 1.23 (p = 0.542), indicating good calibration. In the validation cohort, the risk score had a C index of 0.768 (95% CI: 0.711–0.817); the Hosmer–Lemeshow statistic was 2.76 (p = 0.251), after recalibration.

Conclusions

The NT-proBNP risk score provides clinicians with a contemporary, accurate, easy-to-use, and validated predictive tool. Further validation in other datasets is advisable.