Key result
AI-based STRATIFYHF system achieves ~0.94 AUC for early heart failure diagnosis in primary care.
Why the study?
Heart failure is a complex syndrome with high prevalence, poor patient quality of life, and high healthcare costs, necessitating improved risk stratification and early detection methods.
Does an AI-based Decision Support System integrating multimodal patient data accurately predict early diagnosis, risk stratification, and disease progression in patients with or at risk of heart failure?
Does an AI-based Decision Support System integrating multimodal patient data accurately predict early diagnosis, risk stratification, and disease progression in patients with or at risk of heart failure?
The STRATIFYHF project demonstrated high accuracy of AI-driven predictive models (AUC up to 0.94) for the early detection, risk stratification, and progression of heart failure, supported by a scalable clinical decision support system and mobile application.
May aid early HF detection in primary care; leaves open real-world impact pending prospective validation.
STRATIFYHF: AI-based risk stratification for early heart failure detection The STRATIFYHF project aims to develop an AI-based decision support system for the risk stratification, early detection, and management of heart failure, leveraging extensive patient data and innovative technologies for improved clinical outcomes. Heart failure (HF) is a global pandemic currently affecting up to 15 million people in Europe. It is a complex clinical syndrome associated with impaired heart function, poor quality of life for patients and high healthcare costs. The STRATIFYHF project addresses this global challenge with an AI-based Decision Support System (DSS) for HF. STRATIFYHF aims to develop, validate and implement the first artificial intelligence (AI)-based DSS for assessing and predicting the risk of heart failure, its early diagnosis and its progression. The project integrates (1) patient-specific data – demographic, clinical, genetic, lifestyle and socio-economic; (2) an AI-based digital patient library and algorithms for risk stratification, early diagnosis and disease progression; and (3) a highly innovative, multifunctional AI-based and computational-modelling DSS and mobile app for informing patient-centred, personalised prevention and treatment strategies (Fig. 1). The project advanced measurably towards its clinical, economic, scientific, societal and regulatory objectives.
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Nenad Filipović (2026) studied Heart failure. STRATIFYHF AI-based decision support system was evaluated on Model performance for early diagnosis of heart failure in primary care (AUC ≈ 0.94). The STRATIFYHF AI-based decision support system demonstrated high accuracy for the early diagnosis of heart failure in primary care with an AUC of approximately 0.94.
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