Observational multicentre registry to update key interventions and new approaches with artificial intelligence for heart failure (OKINAWA-HF): a retrospective medical record-derived registry study protocol
Yoshino Kinjo, Haruno Nagata, Yuichiro Toma, Chikashi Nago, Mamoru Manita, Masanori Kakazu, Minoru Wake, Moriichi Sugama, Osahiko Sunagawa, Shinichiro Ueda, Kenya KusunoseIntroduction
The efficacy of guideline-directed medical therapy (GDMT) for heart failure has been established in clinical trials, but trial populations differ substantially from real-world clinical practice. Consequently, the real-world status of GDMT implementation, the patient-level factors that determine whether guideline-recommended targets are achieved and the clinical circumstances that limit optimisation remain poorly characterised. We designed the Observational Multicenter Registry to Update Key Interventions and New Approaches with Artificial Intelligence for Heart Failure registry, a retrospective multicentre study based on medical records from seven teaching hospitals in Okinawa, Japan, to address a central scientific question: how GDMT is implemented at discharge in routine care for patients hospitalised with heart failure and which patient-level and treatment-limiting factors explain incomplete implementation. Analyses of postdischarge outcomes and artificial intelligence (AI)-derived imaging variables are explicitly positioned as secondary and exploratory components.
Methods and analysis
Consecutive patients hospitalised for new-onset or worsening heart failure will be enrolled. Clinical characteristics, laboratory data, medication use, chest X-ray and echocardiographic images will be collected. The primary analyses will describe patterns of GDMT implementation, target-dose achievement and documented reasons for non-implementation and examine patient-level clinical characteristics and treatment-limiting factors associated with incomplete GDMT implementation and target-dose non-achievement. As a secondary objective, associations between guideline-based GDMT optimisation and postdischarge clinical outcomes will be examined descriptively and as hypothesis-generating analyses. Exploratory analyses will evaluate AI-derived imaging variables obtained from routinely acquired chest X-ray and echocardiography as standardised imaging phenotypes and assess their potential incremental prognostic value beyond conventional clinical and imaging variables.
Ethics and dissemination
The study is being conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained through a centralised ethics review system at the University of the Ryukyus Hospital Ethics Committee, which served as the central reviewing body for this multicentre study. Under this centralised review framework, all participating institutions conducted the study based on this single ethical approval. On completion of data collection and analyses, the findings of this study will be disseminated through academic and clinical conferences, reports to funding agencies and publications in peer-reviewed journals. We will also conduct dissemination events to report the study findings and support their application in regional clinical practices.