European Heart Journal - Digital Health, 2022
Full text: Unavailable
Abstract Introduction Patients with congestive heart failure (HF) are prone to clinical deterioration leading to hospital admissions, burdening both patients and the healthcare system. Predicting hospital admission in this patient group could enable timely intervention, with subsequent reduction of these admissions. To date, hospital admission prediction remains challenging. Increasing amounts of acquired data and development of artificial intelligence (AI) technology allow for the creation of reliable hospital prediction algorithms for HF patients. Objective This scoping review describes the current literature on strategies and performance of AI-based algorithms for prediction of hospital admission in patients with HF. Methods PubMed, EMBASE and the Web of Science were used to search for articles using machine learning (ML) and deep learning methods to predict hospitalization in patients with HF. Results After eligibility screening, 23 articles were included. Sixteen articles predicted 30-day hospital (re-)admission resulting in an AUC ranging from 0.61 to 0.79. Six studies predicted hospital admission over longer time periods ranging from six months to three years, with AUC’s ranging from 0.65 to 0.78. One study prospectively evaluated performance of a disposable sensory patch at home after hospitalization which resulted in an AUC of 0.89 for unplanned hospital admission prediction. Conclusion AI has the potential to enable prediction of hospital admission in HF patients. Improvement of data management, adding new data sources such as telemonitoring data and ML models and prospective and external validation of current models must be performed before clinical applicability is possible.