Abstract
AbstractPurposeTo establish a nomogram for predicting the overall survival (OS) of patients with ischemic cardiomyopathy and heart failure based on the Surgical Treatment for Ischemic Heart Failure (STICH) trail.MethodsPatients who had valid key variables in the hypothesis 1 were included and randomly divided into the training and validation groups (7:3 ratio). Using Cox proportional hazards model, predictors for the OS in training group were identified and integrated to establish a nomogram for predicting 1-year, 3-year, 5-year, and ten-year survival probability. The nomogram performance was evaluated using Harrell’s concordance index (C-index), time-dependent receiver operating characteristic curve, decision curve analysis, and Kaplan-Meier survival analysis.Results940 of 1212 patients who had valid key variables were included. Seven predictors, including treatment type, gender, estimated glomerular filtration rate, Charlson co-morbidity index, 6-minute walk, end-systolic volume index and mitral regurgitation class were identified to establish the nomogram. The C-indices of the nomogram were 0.641 (95% CI: 0.627-0.655) and 0.649 (95% CI: 0.627-0.671) for training and validation groups, respectively. The calibration curves revealed consistency between predicted and observed survival. The area under 1-year, 3-year, 5-year, and ten-year OS receiver operating characteristic curves were 0.634, 0.616, 0.630 and 0.638 in the training group, respectively. Decision curve analysis showed effective net benefits of the model in clinical decision-making. Divided by the cutoff values of prognostic indices, low-risk patients showed better OS than those with high risk in training and validation groups (both p < 0.0001).ConclusionThe current nomogram can effectively predict the OS of patients with ischemic cardiomyopathy and heart failure, provide information about multidisciplinary therapeutic that may prolong the survival time, and serve as a perfect tool in conjunction with the STS and EuroSCORE II risk models in clinical decision making.
Publisher
Cold Spring Harbor Laboratory