Nomogram for Predicting In-hospital Mortality in Infective Endocarditis Based on Early Clinical Features and Treatment Options.

ABSTRACT

Aim: The aim of this study was to develop a nomogram based on early clinical features and treatment options for predicting in-hospital mortality in infective endocarditis (IE). Methods: We retrospectively analyzed the data of 294 patients diagnosed with IE in our hospital from June 01, 2012 to November 24, 2021, determined independent risk factors for in-hospital mortality by univariate and multivariate logistic regression analysis, and established a Nomogram prediction model based on these factors. Finally, the prediction performance of nomogram is evaluated by C-index, bootstrapped-concordance index, and calibration plots. Results: Age, abnormal leukocyte count, left-sided IE, right-sided IE, and no surgical treatment were independent risk factors for in-hospital mortality in patients with IE, and we used these independent risk factors to construct a nomogram prediction model to predict in-hospital mortality in IE. The C-index of the model was 0.878 (95% CI 0.824-0.931), and the internal validation of the model by bootstrap validation method showed a prediction accuracy of 0.852 and a bootstrapped-concordance index of 0.53. Conclusion: Our nomogram can accurately predict in-hospital mortality in IE patients and can be used for early identification of high-risk IE patients.