MacGowan, GuyCharman, Sarah JaneOkwose, Nduka CGroenewegen, AmyDel Franco, AnnamariaTafelmeier, MariaPreveden, AndrejGarcia Sebastian, CristinaFuller, Amy SSinclair, DavidEdwards, DuncanNelissen, Anne PaulineMalitas, PetrosZisaki, AikateriniDarbà, JosepBosnic, ZoranVracar, PetarBarlocco, FaustoFotiadis, DimitrisBanerjee, Prithwish2025-06-262025-06-262025-01-072044-6055https://hdl.handle.net/2445/221805Introduction Heart failure (HF) is a complex clinical syndrome. Accurate risk stratification and early diagnosis of HF are challenging as its signs and symptoms are non-specific. We propose to address this global challenge by developing the STRATIFYHF artificial intelligence-driven decision support system (DSS), which uses novel analytical methods in determining the risk, diagnosis and prognosis of HF. The primary aim of the present study is to collect prospective clinical data to validate the STRATIFYHF DSS (in terms of diagnostic accuracy, sensitivity and specificity) as a tool to predict the risk, diagnosis and progression of HF. The secondary outcomes are the demographic and clinical predictors of risk, diagnosis and progression of HF.9 p.application/pdfengcc-by (c) Charman, S.J. et al., 2025http://creativecommons.org/licenses/by/4.0/DiagnòsticInsuficiència cardíacaMètode longitudinalIntel·ligència artificialPresa de decisions multicriteriFactors de risc en les malaltiesDiagnosisHeart failureLongitudinal methodArtificial intelligenceMultiple criteria decision makingRisk factors in diseasesinfo:eu-repo/semantics/article7532782025-06-26info:eu-repo/semantics/openAccess