ArticleFrontiers in cardiovascular medicine2022
Nomogram for Predicting In-hospital Mortality in Infective Endocarditis Based on Early Clinical Features and Treatment Options.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 4 citations in OpenAlex.
- Nomogram for predicting inadequate treatment response in vertebral osteomyelitis.Annals of medicine · 2026Article
- Incremental Predictive Value of Post-Baseline Surgery and the Impact of Temporal Coding on Mortality Associations in Infective Endocarditis: A Temporally Validated Cohort Study.Diagnostics (Basel, Switzerland) · 2026Article
- Development and validation of a nomogram for predicting mortality in patients with vertebral osteomyelitis.Infection · 2026Article
- Development and validation of a nomogram for predicting 1-year mortality in infective endocarditis patients.Frontiers in cardiovascular medicine · 2026Article
- Nomogram for predicting mortality in hospitalized patients with infective endocarditis.Scientific reports · 2025Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
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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.
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