ArticleClinical cardiology2021
Predicting 90 day acute heart failure readmission and death using machine learning-supported decision analysis.
Article in Clinical cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- A systematic review of machine learning algorithms for mortality risk, readmission and phenotype prediction in patients with heart failure: exploring key data sources, input variables and outcomes.BMC medical informatics and decision making · 2026Pooled it
- Thyroid feedback quantile-based index predicts 90-day all-cause mortality or readmission in hospitalized heart failure patients: a retrospective cohort study.Frontiers in endocrinology · 2026Article
- Dual doppler dynamics: integrating femoral venous doppler and VExUS for predicting organ dysfunction in acute heart failure.Journal of anesthesia, analgesia and critical care · 2025Article
- Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.European heart journal · 2025Review
- Predictive Performance of Machine Learning Models for Heart Failure Readmission: A Systematic Review.Biomedicines · 2025Review
- Deep Convolutional Generative Adversarial Network for Improved Cardiac Image Classification in Heart Disease Diagnosis.Journal of imaging informatics in medicine · 2025Article
- Time-Adaptive Machine Learning Models for Predicting the Severity of Heart Failure with Reduced Ejection Fraction.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of a prognostic risk model for pediatric patients with left-to-right shunt congenital heart disease and heart failure.Frontiers in public health · 2025Article
- Predictive Analytics in Heart Failure Risk, Readmission, and Mortality Prediction: A Review.Cureus · 2024Review
- Machine Learning For Risk Prediction After Heart Failure Emergency Department Visit or Hospital Admission Using Administrative Health Data.PLOS digital health · 2024Article
- Explainable machine learning for predicting 30-day readmission in acute heart failure patients.iScience · 2024Article
- A machine learning model to predict heart failure readmission: toward optimal feature set.Frontiers in artificial intelligence · 2024Article
- Machine learning based readmission and mortality prediction in heart failure patients.Scientific reports · 2023Article
- Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach.Scientific reports · 2023Article
- Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study.JMIR formative research · 2023Article
- Reduction in 90 day readmission rates utilizing ambulatory pulmonary pressure monitoring.ESC heart failure · 2023Article
- A hybrid machine learning framework to improve prediction of all-cause rehospitalization among elderly patients in Hong Kong.BMC medical research methodology · 2023Article
- Management, clinical outcomes, and its predictors among heart failure patients admitted to tertiary care hospitals in Ethiopia: prospective observational study.BMC cardiovascular disorders · 2023Observational
- Predictive modeling for COVID-19 readmission risk using machine learning algorithms.BMC medical informatics and decision making · 2022Article
- Tailored risk assessment of 90-day acute heart failure readmission or all-cause death to heart failure with preserved versus reduced ejection fraction.Clinical cardiology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Readmission or death soon after heart failure (HF) admission is a significant problem. Traditional analyses for predicting such events often fail to consider the gamut of characteristics that may contribute- tending to focus on 30-day outcomes even though the window of increased vulnerability may last up to 90 days. Risk assessments incorporating machine learning (ML) methods may be better suited than traditional statistical analyses alone to sort through multitude of data in the electronic health record (EHR) and identify patients at higher risk. HYPOTHESIS: ML-based decision analysis may better identify patients at increased risk for 90-day acute HF readmission or death after incident HF admission. METHODS AND
resultsAmong 3189 patients who underwent index HF hospitalization, 15.2% experienced primary or acute HF readmission and 11.5% died within 90 days. For risk assessment models, 98 variables were considered across nine data categories. ML techniques were used to help select variables for a final logistic regression (LR) model. The final model's AUC was 0.760 (95% CI 0.752 to 0.767), with sensitivity of 83%. This proved superior to an LR model alone [AUC 0.744 (95% CI 0.732 to 0.755)]. Eighteen variables were identified as risk factors including dilated inferior vena cava, elevated blood pressure, elevated BUN, reduced albumin, abnormal sodium or bicarbonate, and NT pro-BNP elevation. A risk prediction ML-based model developed from comprehensive characteristics within the EHR can efficiently identify patients at elevated risk of 90-day acute HF readmission or death for whom closer follow-up or further interventions may be considered.
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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 OpenQuestion graph.