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Artificial Intelligence driven prediction of multiple outcomes in older adults with coronary heart disease
Innocent Tesha1,2, Meng Qi1, Wang JiaSi1
1Department of Geriatrics Medicine, First Affiliated Hospital, Jinzhou Medical University, Liaoning, China.
Insights
A new machine learning (ML) model integrates frailty into risk assessment for older adults with coronary heart disease (CHD), improving prediction of mortality and hospital outcomes. This AI tool enhances clinical decision-making in geriatric cardiology.
Area of Science:
- Geriatric Cardiology
- Artificial Intelligence in Medicine
- Digital Health Innovations
Background:
- Frail older adults with coronary heart disease (CHD) have high risks of mortality, prolonged hospitalizations, and readmissions.
- Existing risk stratification tools inadequately address frailty and multi-morbidity in geriatric care.
- A gap exists in effective clinical decision-making tools for this population.
Purpose of the Study:
- To develop and validate the first machine learning (ML) model integrating frailty into a multi-outcome risk assessment framework.
- To enhance clinical decision-making for frail older adults with CHD.
- To improve risk identification and personalized interventions in geriatric cardiology.
Main Methods:
- Developed a multinomial prediction model using electronic health records of hospitalized frail CHD patients.
- Employed advanced ML techniques: principal component analysis, gradient boosting, and random forest.
- Incorporated explainable AI features and prioritized key predictors like biomarkers and comorbidities.
Main Results:
- The ML model showed superior predictive performance: AUC 0.94 for mortality, 0.72 for readmission, and 0.68 for prolonged hospital stay.
- Enabled earlier identification of high-risk patients.
- Facilitated personalized intervention strategies.
Conclusions:
- This AI-driven approach advances geriatric cardiology by providing real-time, patient-centered decision support.
- The model is designed for integration into hospital dashboards to optimize workflow and resource allocation.
- It sets a new standard for digital health innovations and AI-driven precision medicine in aging care.
Background And Objectives:
Frail older adults with coronary heart disease (CHD) face significantly elevated risks of adverse clinical outcomes, including mortality, prolonged hospitalizations, and frequent readmissions. Conventional risk stratification tools, inadequately account for frailty and multimorbidity, limiting their effectiveness in geriatric care. To address this gap, we developed and validated the first machine learning (ML) model that integrates frailty into a multi-outcome risk assessment framework, thereby enhancing clinical decision-making in geriatric cardiology.
Research Design And Methods:
Utilizing electronic health records from hospitalized frail CHD patients, we developed a multinomial prediction model employing advanced ML techniques, including principal component analysis, gradient boosting, and random forest. The model incorporates explainable artificial intelligence (AI) features to enhance interpretability and a clinical applicability, prioritizing key predictors such as biomarkers and comorbidities.
Results:
The ML model demonstrated superior predictive performance with receiver operating characteristic curve analysis (area under the curve 0.94, 95% CI: 0.88-1.00) for mortality, 0.72 (95% CI: 0.55-0.87) readmission, and 0.68 (95% CI: 0.57-0.77) prolonged hospital stay, enabling earlier risk identification and personalized intervention strategies.
Discussion And Implications:
This AI-driven approach represents a significant advancement in geriatric cardiology designed for integration into hospital dashboards, providing real-time patient-centered decision support, optimization of clinical workflow and resource allocation. By advancing digital health solutions and AI driven precision medicine, this model sets a new standard for digital health innovations in aging care.
