Heart failure risk assessment using biomarkers in patients with atrial fibrillation: analysis of 32,041 patients from the COMBINE-AF study

Abstract Introduction Heart failure (HF) is one of the most common comorbidities in patients with AF, resulting in a clinical need for robust risk assessment. Purpose To investigate the incremental value of adding NTproBNP, high-sensitivity cardiac troponin T (hs-cTnT), and growth-differentiation factor-15 (GDF-15) to clinical predictors for HF risk stratification in patients with AF, and the individual contribution of each biomarker accounting for inter-biomarker correlation. Methods We used pooled individual patient data from 3 large RCTs investigating DOACs versus VKAs (ARISTOTLE, ENGAGE AF-TIMI 48, RE-LY) from the COMBINE-AF cohort and included all patients with available biomarkers at baseline. The composite primary endpoint was hospitalization for HF (HHF) or CV death (CVD), with HHF as secondary endpoint. We employed analyses of absolute risk and Cox-regression adjusting for clinical factors to assess the association of the individual biomarkers with both endpoints, testing incremental discrimination with the likelihood ratio test from nested models. To address the inter-biomarker correlation, weighted quantile sum (WQS) regression analysis summarizing the adjusted risk of all biomarkers (per quartile) in one index was applied, thereby exploring the individual and additive contribution of each biomarker to risk assessment. Results Data were available in 32,041 patients (median age 71 [IQR 64-77] years, 36.7% female, 23.0% with paroxysmal AF, 41.6% with established HF). Higher biomarker values were associated with a graded increase in absolute risk for HHF/CVD and HHF (Figure 1). In a model adjusting for clinical variables and all biomarkers, hs-cTnT (HR per 1-SD 1.39 [95% CI 1.33-1.44]), NT-proBNP (HR 1.67 [95% CI 1.58-1.76]), and GDF-15 (HR 1.20 [95% CI 1.15-1.25]) were associated with HHF/CVD. The c-index increased with addition of biomarkers (0.70 [0.69, 0.70] to 0.77 [0.76, 0.78]; Likelihood ratio test p<0.001). Results were similar for HHF alone. NTproBNP was significantly correlated with hs-cTnT (r=0.40, p<0.0001) and GDF-15 (r=0.36, p<0.001). To address this correlation, WQS regression analysis was conducted (Figure 2). NTproBNP and hs-cTnT contributed nearly equally to the risk assessment for HHF/CVD and HHF, with GDF-15 providing statistically significant but less contribution to risk assessment (Figure 2). Conclusion Hs-cTnT, NT-proBNP and GDF-15 contribute individually and additively to the risk assessment for HHF/CVD and HHF in patients with AF. Our analysis suggests that hs-cTnT is as important as NTproBNP for HF risk assessment, with significant but less contribution of GDF-15. Our findings support the possible future routine use of biomarkers to distinguish patients with AF at low or high risk for HF and could guide the introduction of therapies to mitigate the risk of HF events in this growing population.Event incidence by biomarker quartilesWeighted quantil sum regression analysis