Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study
Introduction 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...
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2025-01-01
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| author | Prithwish Banerjee Guy A MacGowan Frans H Rutten Iacopo Olivotto Petar M Seferović Jonathan Mant Djordje G Jakovljevic Oscar Fernandez Josep Darbà Nduka C Okwose Jose Zamorano Duncan Edwards David Sinclair Lazar Velicki Amy Groenewegen Lars S Maier Dimitris Fotiadis Sarah Jane Charman Annamaria Del Franco Maria Tafelmeier Andrej Preveden Cristina Garcia Sebastian Amy S Fuller Anne Pauline Nelissen Petros Malitas Aikaterini Zisaki Zoran Bosnic Petar Vracar Fausto Barlocco Marta Jiménez-Blanco Bravo Nenad Filipovic Bano Fatima Williams Kate Fornaro Alessandra Milovancev Aleksandra Ascanio Meritxell Aguera Ainoa Pičulin Matej Flis Borut Boucharas Dimitrios Manousos Dimitris Tsiknakis Manolis Kassiotis Thomas Sustersic Tijana Milicevic Bogdan Hobbs FD Richard Kaagman Onno |
| author_facet | Prithwish Banerjee Guy A MacGowan Frans H Rutten Iacopo Olivotto Petar M Seferović Jonathan Mant Djordje G Jakovljevic Oscar Fernandez Josep Darbà Nduka C Okwose Jose Zamorano Duncan Edwards David Sinclair Lazar Velicki Amy Groenewegen Lars S Maier Dimitris Fotiadis Sarah Jane Charman Annamaria Del Franco Maria Tafelmeier Andrej Preveden Cristina Garcia Sebastian Amy S Fuller Anne Pauline Nelissen Petros Malitas Aikaterini Zisaki Zoran Bosnic Petar Vracar Fausto Barlocco Marta Jiménez-Blanco Bravo Nenad Filipovic Bano Fatima Williams Kate Fornaro Alessandra Milovancev Aleksandra Ascanio Meritxell Aguera Ainoa Pičulin Matej Flis Borut Boucharas Dimitrios Manousos Dimitris Tsiknakis Manolis Kassiotis Thomas Sustersic Tijana Milicevic Bogdan Hobbs FD Richard Kaagman Onno |
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| description | Introduction 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.Methods and analysis STRATIFYHF is a prospective, multicentre, longitudinal study that will recruit up to 1600 individuals (n=800 suspected/at risk of HF and n=800 diagnosed with HF) aged ≥45 years old, with up to 24 months of follow-up observations. Individuals suspected of HF will be divided into two categories based on current definitions and predefined inclusion criteria. All participants will have their medical history recorded, along with data on physical examination (signs and symptoms), blood tests including serum natriuretic peptides levels, ECG and echocardiogram results, as well as demographic, socioeconomic and lifestyle data, and use of complete novel technologies (cardiac output response to stress test and voice recognition biomarkers). All measurements will be recorded at baseline and at 12-month follow-up, with medical history and hospitalisation also recorded at 24-month follow-up. Cardiovascular MRI assessment will be completed in a subset of participants (n=20–40) from eligible clinical centres only at baseline. Each clinical centre will recruit a subset of participants (n=30) who will complete a 6-month home-based monitoring of clinical characteristics and accelerometry (wrist-worn monitor) to determine the feasibility and acceptability of the STRATIFYHF mobile application. Focus groups and semistructured interviews will be conducted with up to 15 healthcare professionals and up to 20 study participants (10 at risk of HF and 10 diagnosed with HF) to explore the needs of patients and healthcare professionals prior to the development of the STRATIFYHF DSS and to evaluate the acceptability of this mobile application.Ethics and dissemination Ethical approval has been granted by the East Midlands - Leicester Central Research Ethics Committee (24/EM/0101). Dissemination activities will include journal publications and presentations at conferences, as well as development of training materials and delivery of focused training on the STRATIFYHF DSS and mobile application. We will develop and propose policy guidelines for integration of the STRATIFYHF DSS and mobile application into the standard of care in the HF care pathway.Trial registration number NCT06377319. |
| format | Article |
| id | doaj-art-41cdee60a10a48ae8cf716fe704f0f7c |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-41cdee60a10a48ae8cf716fe704f0f7c2025-01-09T15:35:09ZengBMJ Publishing GroupBMJ Open2044-60552025-01-0115110.1136/bmjopen-2024-091793Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study Prithwish Banerjee0Guy A MacGowan1Frans H Rutten2Iacopo Olivotto3Petar M Seferović4Jonathan Mant5Djordje G Jakovljevic6Oscar Fernandez7Josep Darbà8Nduka C Okwose9Jose Zamorano10Duncan Edwards11David Sinclair12Lazar Velicki13Amy Groenewegen14Lars S Maier15Dimitris Fotiadis16Sarah Jane Charman17Annamaria Del Franco18Maria Tafelmeier19Andrej Preveden20Cristina Garcia Sebastian21Amy S Fuller22Anne Pauline Nelissen23Petros Malitas24Aikaterini Zisaki25Zoran Bosnic26Petar Vracar27Fausto Barlocco28Marta Jiménez-Blanco Bravo29Nenad Filipovic30Bano FatimaWilliams KateFornaro AlessandraMilovancev AleksandraAscanio MeritxellAguera AinoaPičulin MatejFlis BorutBoucharas DimitriosManousos DimitrisTsiknakis ManolisKassiotis ThomasSustersic TijanaMilicevic BogdanHobbs FD RichardKaagman Onno17 Department of Cardiology, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UKBiosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom5 Department of General Practice, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands20 Experimental and Clinical Medicine, University of Florence, Firenze, Italy21 University of Belgrade Faculty of Medicine, Belgrade, Serbia12 Department of Public Health and Primary Care, Primary Care Unit, University of Cambridge, Cambridge, UKTranslational and Clinical Research Institute, Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, United Kingdom2 Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK14 Department of Economics, University of Barcelona, Barcelona, SpainTranslational and Clinical Research Institute, Newcastle University Faculty of Medical Sciences, Newcastle upon Tyne, United Kingdom10 Hospital Universitario Ramón y Cajal, Madrid, Spain12 Department of Public Health and Primary Care, Primary Care Unit, University of Cambridge, Cambridge, UK11 Newcastle University Population Health Sciences Institute, Newcastle upon Tyne, UK8 University of Novi Sad Faculty of Medicine, Novi Sad, Serbia5 Department of General Practice, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands7 Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany16 Department of Biomedical Research, Foundation for Research and Technology - Hellas, Hellas, Greece1 Newcastle University Translational and Clinical Research Institute, Newcastle upon Tyne, UK6 University Hospital Careggi, Firenze, Italy7 Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany8 University of Novi Sad Faculty of Medicine, Novi Sad, Serbia10 Hospital Universitario Ramón y Cajal, Madrid, Spain3 Centre for Health and Life Sciences, Coventry University - Coventry Campus, Coventry, UK5 Department of General Practice, Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands13 PKNM Solutions Sàrl, Colombier, Vaud, Switzerland13 PKNM Solutions Sàrl, Colombier, Vaud, Switzerland15 University of Ljubljana Faculty of Computer and Information Science, Ljubljana, Slovenia15 University of Ljubljana Faculty of Computer and Information Science, Ljubljana, Slovenia6 University Hospital Careggi, Firenze, Italy10 Hospital Universitario Ramón y Cajal, Madrid, Spain22 BioIRC, Research and Development Center for Bioengineering, Kragujevac, SerbiaIntroduction 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.Methods and analysis STRATIFYHF is a prospective, multicentre, longitudinal study that will recruit up to 1600 individuals (n=800 suspected/at risk of HF and n=800 diagnosed with HF) aged ≥45 years old, with up to 24 months of follow-up observations. Individuals suspected of HF will be divided into two categories based on current definitions and predefined inclusion criteria. All participants will have their medical history recorded, along with data on physical examination (signs and symptoms), blood tests including serum natriuretic peptides levels, ECG and echocardiogram results, as well as demographic, socioeconomic and lifestyle data, and use of complete novel technologies (cardiac output response to stress test and voice recognition biomarkers). All measurements will be recorded at baseline and at 12-month follow-up, with medical history and hospitalisation also recorded at 24-month follow-up. Cardiovascular MRI assessment will be completed in a subset of participants (n=20–40) from eligible clinical centres only at baseline. Each clinical centre will recruit a subset of participants (n=30) who will complete a 6-month home-based monitoring of clinical characteristics and accelerometry (wrist-worn monitor) to determine the feasibility and acceptability of the STRATIFYHF mobile application. Focus groups and semistructured interviews will be conducted with up to 15 healthcare professionals and up to 20 study participants (10 at risk of HF and 10 diagnosed with HF) to explore the needs of patients and healthcare professionals prior to the development of the STRATIFYHF DSS and to evaluate the acceptability of this mobile application.Ethics and dissemination Ethical approval has been granted by the East Midlands - Leicester Central Research Ethics Committee (24/EM/0101). Dissemination activities will include journal publications and presentations at conferences, as well as development of training materials and delivery of focused training on the STRATIFYHF DSS and mobile application. We will develop and propose policy guidelines for integration of the STRATIFYHF DSS and mobile application into the standard of care in the HF care pathway.Trial registration number NCT06377319.https://bmjopen.bmj.com/content/15/1/e091793.full |
| spellingShingle | Prithwish Banerjee Guy A MacGowan Frans H Rutten Iacopo Olivotto Petar M Seferović Jonathan Mant Djordje G Jakovljevic Oscar Fernandez Josep Darbà Nduka C Okwose Jose Zamorano Duncan Edwards David Sinclair Lazar Velicki Amy Groenewegen Lars S Maier Dimitris Fotiadis Sarah Jane Charman Annamaria Del Franco Maria Tafelmeier Andrej Preveden Cristina Garcia Sebastian Amy S Fuller Anne Pauline Nelissen Petros Malitas Aikaterini Zisaki Zoran Bosnic Petar Vracar Fausto Barlocco Marta Jiménez-Blanco Bravo Nenad Filipovic Bano Fatima Williams Kate Fornaro Alessandra Milovancev Aleksandra Ascanio Meritxell Aguera Ainoa Pičulin Matej Flis Borut Boucharas Dimitrios Manousos Dimitris Tsiknakis Manolis Kassiotis Thomas Sustersic Tijana Milicevic Bogdan Hobbs FD Richard Kaagman Onno Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study BMJ Open |
| title | Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study |
| title_full | Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study |
| title_fullStr | Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study |
| title_full_unstemmed | Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study |
| title_short | Clinical validation of an artificial intelligence-based decision support system for diagnosis and risk stratification of heart failure (STRATIFYHF): a protocol for a prospective, multicentre longitudinal study |
| title_sort | clinical validation of an artificial intelligence based decision support system for diagnosis and risk stratification of heart failure stratifyhf a protocol for a prospective multicentre longitudinal study |
| url | https://bmjopen.bmj.com/content/15/1/e091793.full |
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