ArticleClinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Interpretable Machine Learning Models Using Peripheral Immune Cells to Predict 90-Day Readmission or Mortality in Acute Heart Failure Patients.
Article in Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Comparison of Interpretable Machine Learning Models Using Systemic Inflammation Index to Predict Preterm Birth in Gestational Diabetes Mellitus.International journal of women's health · 2026Article
- Artificial intelligence in transitional care: practice, promise, and pitfalls-a scoping review.Frontiers in digital health · 2025Review
- Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model.Frontiers in cardiovascular medicine · 2025Article
- Interpretable machine learning models for predicting short-term prognosis in AChR-Ab+ generalized myasthenia gravis using clinical features and systemic inflammation index.Frontiers in neurology · 2024Article
- Interpretable Machine Learning Models for Predicting Malignant Ventricular Arrhythmia in Patients with Acute ST-Segment Elevation Myocardial Infarction Based on Systemic Inflammation Index.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute heart failure (AHF) carries a grave prognosis, marked by high readmission and mortality rates within 90 days post-discharge. This underscores the urgent need for enhanced care transitions, early monitoring, and precise interventions for at-risk individuals during this critical period.
objectiveOur study aims to develop and validate an interpretable machine learning (ML) model that integrates peripheral immune cell data with conventional clinical markers. Our goal is to accurately predict 90-day readmission or mortality in patients AHF.
methodsIn our study, we conducted a retrospective analysis on 1210 AHF patients, segregating them into training and external validation cohorts. Patients were categorized based on their 90-day outcomes post-discharge into groups of 'with readmission/mortality' and 'without readmission/mortality'. We developed various ML models using data from peripheral immune cells, traditional clinical indicators, or both, which were then internally validated. The feature importance of the most promising model was examined through the Shapley Additive Explanations (SHAP) method, culminating in external validation.
resultsIn our cohort of 1210 patients, 28.4% (344) faced readmission or mortality within 90 days post-discharge. Our study pinpointed 10 significant indicators-spanning peripheral immune cells and traditional clinical metrics-that predict these outcomes, with the support vector machine (SVM) model showing superior performance. SHAP analysis further distilled these predictors to five key determinants, including three clinical indicators and two immune cell types, essential for assessing 90-day readmission or mortality risks.
conclusionOur analysis identified the SVM model, which merges traditional clinical indicators and peripheral immune cells, as the most effective for predicting 90-day readmission or mortality in AHF patients. This innovative approach promises to refine risk assessment and enable more targeted interventions for at-risk individuals through continuous improvement.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.