ArticleScientific reports2022
Construction and internal validation of a novel nomogram for predicting prognosis of infective endocarditis.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Interpretable machine learning for predicting low-dose methylprednisolone effectiveness in long COVID.iScience · 2026Article
- The impact of thrombocytopenia on mortality in infective endocarditis - aInternational journal of cardiology. Heart & vasculature · 2025Review
- Nomogram for predicting mortality in hospitalized patients with infective endocarditis.Scientific reports · 2025Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 χ
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