NT-proBNP Qualifies as a Surrogate for Clinical End Points in Heart Failure

N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a well-established biomarker in heart failure (HF) but controversially discussed as a potential surrogate marker in HF trials. We analyzed the NT-proBNP/mortality relationship in real-world data (RWD) of 108,330 HF patients from the IBM Watson Health Explorys database and compared it with the NT-proBNP / clinical event end-point relationship in 20 clinical HF studies. With a hierarchical statistical model, we quantified the functional relationship and interstudy variability. To independently qualify the model, we predicted outcome hazard ratios in five phase III HF studies solely based on NT-proBNP measured early in the respective study. In RWD and clinical studies, the relationship between NT-proBNP and clinical outcome is well described by an E-max model. The NT-proBNP independent baseline risk (R-0, RWD/studies median (interstudy interquartile range): 5.5%/3.0% (1.7-4.9%)) is very low compared with the potential NT-proBNP-associated maximum risk (R-max: 55.2%/79.4% (61.5-89.0%)). The NT-proBNP concentration associated with the half-maximal risk is comparable in RWD and across clinical studies (EC50: 3,880/2,414 pg/mL (1,460-4,355 pg/mL)). Model-based predictions of phase III outcomes, relying on short-term NT-proBNP data only, match final trial results with comparable confidence intervals. Our analysis qualifies NT-proBNP as a surrogate for clinical outcome in HF trials. NT-proBNP levels after short treatment durations of less than 10 weeks quantitatively predict hazard ratios with confidence levels comparable to final trial readout. Early NT-proBNP measurement can therefore enable shorter and smaller but still reliable HF trials.

Automated summary

The study demonstrates that NT-proBNP can serve as a surrogate for clinical outcomes in HF trials, with a well-described relationship between NT-proBNP concentration and clinical events using an E-max model. Quantitative analysis provided insights into potential risks and risks associated with clinical events. Model-based predictions solely based on NT-proBNP measurements match final trial results, and early NT-proBNP measurements enable shorter yet reliable HF trials.