Long-term prognostic value of inflammatory biomarkers for patients with acute heart failure: Construction of an inflammatory prognostic scoring system - Zhu, Xu; Cheang, Iokfai; Xu, Fang; Gao, Rongrong; Liao, Shengen; Yao, Wenming; Zhou, Yanli; Zhang, Haifeng; Li, Xinli (Frontiers in Immunology, 2022)
Objective Systemic inflammation is associated with a poor prognosis in acute heart failure (AHF). This study was to assess the long-term prognostic value of combining the accessible inflammatory markers in relation to all-cause mortality in patients with AHF. Methods Consecutive patients with AHF who were hospitalized between March 2012 and April 2016 at the Department of Cardiology of the First Affiliated Hospital of Nanjing Medical University were enrolled in this prospective study. The LASSO regression model was used to select the most valuable inflammatory biomarkers to develop an inflammatory prognostic scoring (IPS) system. Kaplan-Meier method, multivariate COX regression and time-dependent ROC analysis were used to assess the relationship between inflammatory markers and AHF prognosis. A randomized survival forest model was used to estimate the relative importance of each inflammatory marker in the prognostic risks of AHF. Results A total of 538 patients with AHF were included in the analysis (mean age, 61.1 ± 16.0 years; 357 [66.4%] men). During a median follow-up of 34 months, there were 227 all-cause deaths (42.2%). C-reactive protein (CRP), red blood cell distribution width (RDW) and neutrophil-to-lymphocyte ratio (NLR) were incorporated into the IPS system (IPS = 0.301×CRP + 0.263×RDW + 0.091×NLR). A higher IPS meant a significantly worse long-term prognosis in Kaplan-Meier analysis, with 0.301 points as the optimal cut-off value (P log-rank <0.001). IPS remained an independent prognostic factor associated with an increased risk of all-cause mortality among patients with AHF in multivariate Cox regression models with a full adjustment of the other significant covariables. Random forest variable importance and minimal depth analysis further validated that the IPS system was the most predictive for all-cause mortality in patients with AHF. Conclusions Inflammatory biomarkers were associated with the risk of all-cause mortality in patients with AHF, while IPS significantly improved the predictive power of the model and could be used as a practical tool for individualized risk stratification of patients with AHF.