Construction and internal validation of a novel nomogram for predicting prognosis of infective endocarditis.

To develop a nomogram prediction model capable of early identification of high-risk infective endocarditis (IE) patients. We retrospectively analyzed the clinical data of 383 patients with IE and divided them into survival and non-survival groups according to different hospitalization outcomes. Univariate and multivariate logistic regression methods were used to screen independent risk factors affecting the survival outcome of IE, and a Nomogram prediction model was constructed by these factors. The Hosmer-Lemeshow goodness-of-fit test was applied to assess the model fit, the discrimination and calibration of the model were evaluated by plotting ROC curves and calibration curves. Advanced age, embolic symptoms, abnormal leukocyte count, low hemoglobin level and double-sided IE were associated with higher in-hospital mortality in patients with IE (P < 0.05). The Hosmer-Lemeshow goodness-of-fit test for the model was χ2 = 7.107, P = 0.311. The AUC of the ROC curve of the model was 0.738 (95% CI 0.677-0.800). The bootstrap method was used to validate the prediction model. The results showed that the prediction accuracy of the model in the validation cohort was 0.842. The nomogram prediction model can accurately predict the in-hospital mortality risk of IE and can help clinicians identify high-risk IE patients early.

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

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.