ArticleCardiovascular digital health journal2022
Machine learning predicting mortality in sarcoidosis patients admitted for acute heart failure.
Article in Cardiovascular digital health journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.BMC cardiovascular disorders · 2025Pooled it
- Predictive Value of Machine Learning for the Risk of In-Hospital Death in Patients With Heart Failure: A Systematic Review and Meta-Analysis.Clinical cardiology · 2025Pooled it
- Machine Learning Prediction of Tongue Pressure in Elderly Patients with Head and Neck Tumor: A Cross-Sectional Study.Journal of clinical medicine · 2024Article
- Trends and Disparities Around Cardiovascular Mortality in Sarcoidosis: Does Big Data Have the Answers?Journal of the American Heart Association · 2024Article
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Corrections and comments
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Authors and funding
6 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Sarcoidosis with cardiac involvement, although rare, has a worse prognosis than sarcoidosis involving other organ systems. Objective: We used a large dataset to train machine learning models to predict in-hospital mortality among sarcoidosis patients admitted with heart failure (HF). Method: Utilizing the National Inpatient Sample, we identified 4659 patients hospitalized with a primary diagnosis of HF. In this cohort, we identified patients with a secondary diagnosis of sarcoidosis using Results: A total of 2.3% of sarcoidosis patients died in HF admission. Our machine learning model analysis found the RF model to have the highest AUC score and sensitivity. Feature analysis found that comorbid arrhythmias and fluid electrolyte disorders were the strongest factors in predicting in-hospital mortality. Conclusion: Machine learning methods can be useful in identifying predictors of in-hospital mortality in a given dataset.
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Registered trials
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