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Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital
Moman A Mohammad1, Kevin K W Olesen2, Sasha Koul1
1Department of Cardiology, Clinical Sciences, Lund University, Skane University Hospital, Lund, Sweden.
Insights
An artificial neural network (ANN) accurately predicts 1-year mortality and heart failure hospitalizations after myocardial infarction. This tool helps identify high-risk patients for tailored interventions and resource allocation.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Predictive Analytics
Background:
- Myocardial infarction (MI) survivors face significant risks of mortality (15-20% within 1 year) and heart failure development (25%).
- Reduced quality of life and increased long-term mortality are common post-MI complications.
- Accurate risk stratification is crucial for effective patient management and resource allocation.
Purpose of the Study:
- To develop and validate an artificial neural network (ANN) algorithm for predicting 1-year all-cause mortality and hospital admission for heart failure post-MI.
- To assess the accuracy of the ANN model in large, independent patient cohorts.
Main Methods:
- A nationwide population-based study utilized data from the SWEDEHEART registry (n=139,288) for training (80%) and testing (20%) datasets.
- An ANN was developed using 21 variables, including demographics, medical history, and medications.
- The ANN model underwent external validation using data from the Western Denmark Heart Registry (n=30,971).
Main Results:
- The ANN demonstrated strong predictive performance with an AUROC of 0.85 for 1-year mortality and 0.82 for heart failure admission in the testing dataset.
- External validation confirmed good accuracy, with AUROCs of 0.84 for mortality and 0.78 for heart failure admission.
- The algorithm correctly classified 73.6% of patients for mortality and 61.5% for heart failure in the external cohort, with high probabilities of ruling out adverse outcomes.
Conclusions:
- The developed ANN algorithm is a highly accurate tool for predicting 1-year mortality and heart failure hospitalizations after myocardial infarction.
- This predictive model can aid clinicians in identifying high-risk individuals, enabling personalized treatment strategies and optimized resource allocation.
- The findings support the integration of advanced AI algorithms into routine clinical practice for improved cardiovascular patient care.
Background:
Patients have an estimated mortality of 15-20% within the first year following myocardial infarction and one in four patients who survive myocardial infarction will develop heart failure, severely reducing quality of life and increasing the risk of long-term mortality. We aimed to establish the accuracy of an artificial neural network (ANN) algorithm in predicting 1-year mortality and admission to hospital for heart failure after myocardial infarction.
Methods:
In this nationwide population-based study, we used data for all patients admitted to hospital for myocardial infarction and discharged alive from a coronary care unit in Sweden (n=139 288) between Jan 1, 2008, and April 1, 2017, from the Swedish Web system for Enhancement and Development of Evidence-based care in Heart disease Evaluated According to Recommended Therapies (SWEDEHEART) nationwide registry; these patients were randomly divided into training (80%) and testing (20%) datasets. We developed an ANN using 21 variables (including age, sex, medical history, previous medications, in-hospital characteristics, and discharge medications) associated with the outcomes of interest with a back-propagation algorithm in the training dataset and tested it in the testing dataset. The ANN algorithm was then validated in patients with incident myocardial infarction enrolled in the Western Denmark Heart Registry (external validation cohort) between Jan 1, 2008, and Dec 31, 2016. The predictive ability of the model was evaluated using area under the receiver operating characteristic curve (AUROC) and Youden's index was established as a means of identifying an empirical dichotomous cutoff, allowing further evaluation of model performance.
Findings:
139 288 patients who were admitted to hospital for myocardial infarction in the SWEDEHEART registry were randomly divided into a training dataset of 111 558 (80%) patients and a testing dataset of 27 730 (20%) patients. 30 971 patients with myocardial infarction who were enrolled in the Western Denmark Heart Registry were included in the external validation cohort. A first event, either all-cause mortality or admission to hospital for heart failure 1 year after myocardial infarction, occurred in 32 308 (23·2%) patients in the testing and training cohorts only. For 1-year all-cause mortality, the ANN had an AUROC of 0·85 (95% CI 0·84-0·85) in the testing dataset and 0·84 (0·83-0·84) in the external validation cohort. The AUROC for admission to hospital for heart failure within 1 year was 0·82 (0·81-0·82) in the testing dataset and 0·78 (0·77-0·79) in the external validation dataset. With an empirical cutoff the ANN algorithm correctly classified 73·6% of patients with regard to all-cause mortality and 61·5% of patients with regard to admission to hospital for heart failure in the external validation cohort, ruling out adverse outcomes with 97·1-98·7% probability in the external validation cohort.
Interpretation:
Identifying patients at a high risk of developing heart failure or death after myocardial infarction could result in tailored therapies and monitoring by the allocation of resources to those at greatest risk.
Funding:
The Swedish Heart and Lung Foundation, Swedish Scientific Research Council, Swedish Foundation for Strategic Research, Knut and Alice Wallenberg Foundation, ALF Agreement on Medical Education and Research, Skane University Hospital, The Bundy Academy, the Märta Winkler Foundation, the Anna-Lisa and Sven-Eric Lundgren Foundation for Medical Research.
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