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Nomogram for Predicting In-hospital Mortality in Infective Endocarditis Based on Early Clinical Features and
Zhao-Jun Yu1, Zhi Dou1, Jing Li1
1Department of Cardiovascular Surgery, Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, China.
Frontiers in Cardiovascular Medicine
|May 16, 2022
Summary
This study developed a nomogram to predict in-hospital mortality in infective endocarditis (IE) patients using clinical factors. The model accurately identifies high-risk individuals for early intervention.
Area of Science:
- Cardiology
- Infectious Diseases
- Medical Informatics
Background:
- Infective endocarditis (IE) poses a significant risk of in-hospital mortality.
- Early identification of high-risk patients is crucial for timely and effective management.
Purpose of the Study:
- To develop and validate a predictive nomogram for in-hospital mortality in infective endocarditis patients.
- To utilize early clinical features and treatment options for risk stratification.
Main Methods:
- Retrospective analysis of 294 IE patients' data.
- Identification of independent risk factors using univariate and multivariate logistic regression.
- Construction and validation of a nomogram prediction model (C-index: 0.878).
Main Results:
- Key predictors of in-hospital mortality included age, abnormal leukocyte count, left-sided IE, right-sided IE, and lack of surgical treatment.
- The nomogram demonstrated strong predictive performance with a C-index of 0.878.
- Internal validation confirmed the model's accuracy (0.852) and bootstrapped-concordance index (0.53).
Conclusions:
- The developed nomogram accurately predicts in-hospital mortality in IE patients.
- This tool facilitates early identification of high-risk IE patients, enabling prompt clinical intervention.
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