ArticleJournal of the American Heart Association2022
Machine Learning-Based Risk Model for Predicting Early Mortality After Surgery for Infective Endocarditis.
Article in Journal of the American Heart Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Development and validation of a machine learning model for predicting 6-month mortality in patients with infective endocarditis.Biomedical engineering online · 2026Article
- Development and validation of a long-term survival prediction model for older adults with asthma.Archives of public health = Archives belges de sante publique · 2026Article
- Application of artificial intelligence in predicting the results of open-heart surgery: a scoping review.BMC medical informatics and decision making · 2025Article
- Shaping the future of cardiac interventions and cardiac surgeries: The impact of virtual reality and artificial intelligence.Global cardiology science & practice · 2025Review
- Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis.PLOS digital health · 2025Article
- Predicting high-risk factors for postoperative inadequate analgesia and adverse reactions in cesarean delivery surgery: a prospective study.International journal of surgery (London, England) · 2025Article
- Anemia and Transfusion in Infective Endocarditis.Reviews in cardiovascular medicine · 2025Review
- Infective endocarditis risk scores: a narrative review.Journal of thoracic disease · 2025Review
- Change of Heart: Can Artificial Intelligence Transform Infective Endocarditis Management?Pathogens (Basel, Switzerland) · 2025Review
- Construction and validation of a machine learning model integrating ultrasound features and inflammatory markers (OVART-ML) for predicting ovarian torsion and ischemic necrosis risk in children.Frontiers in pediatrics · 2025Article
- Enhanced Risk Stratification in Infective Endocarditis Surgery: A Comprehensive External Validation of All Available Mortality Prediction Scores.Clinical epidemiology · 2025Article
- Pathogenic spectrum of infective endocarditis and analysis of prognostic risk factors following surgical treatment in a tertiary hospital in China.BMC infectious diseases · 2024Article
- Perioperative risk stratification scores in infective endocarditis and its usefulness.Indian journal of thoracic and cardiovascular surgery · 2024Review
- Identification of independent risk factors for hypoalbuminemia in patients with CKD stages 3 and 4: the construction of a nomogram.Frontiers in nutrition · 2024Article
- Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions.Annals of surgery · 2023Article
- Contemporary risk models for infective endocarditis surgery: a narrative review.Therapeutic advances in cardiovascular diseaseReview
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background The early mortality after surgery for infective endocarditis is high. Although risk models help identify patients at high risk, most current scoring systems are inaccurate or inconvenient. The objective of this study was to construct an accurate and easy-to-use prediction model to identify patients at high risk of early mortality after surgery for infective endocarditis. Methods and Results A total of 476 consecutive patients with infective endocarditis who underwent surgery at 2 centers were included. The development cohort consisted of 276 patients. Eight variables were selected from 89 potential predictors as input of the XGBoost model to train the prediction model, including platelet count, serum albumin, current heart failure, urine occult blood ≥(++), diastolic dysfunction, multiple valve involvement, tricuspid valve involvement, and vegetation >10 mm. The completed prediction model was tested in 2 separate cohorts for internal and external validation. The internal test cohort consisted of 125 patients independent of the development cohort, and the external test cohort consisted of 75 patients from another center. In the internal test cohort, the area under the curve was 0.813 (95% CI, 0.670-0.933) and in the external test cohort the area under the curve was 0.812 (95% CI, 0.606-0.956). The area under the curve was significantly higher than that of other ensemble learning models, logistic regression model, and European System for Cardiac Operative Risk Evaluation II (all,
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.