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Pingitore A, Zhang C, Vassalle C, Ferragina P, Landi P, Mastorci F, Sicari R, Tommasi A, Zavattari C, Prencipe G, Sîrbu A. Machine learning to identify a composite indicator to predict cardiac death in ischemic heart disease. Int J Cardiol 2024; 404:131981. [PMID: 38527629 DOI: 10.1016/j.ijcard.2024.131981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/13/2024] [Accepted: 03/17/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Machine learning (ML) employs algorithms that learn from data, building models with the potential to predict events by aggregating a large number of variables and assessing their complex interactions. The aim of this study is to assess ML potential in identifying patients with ischemic heart disease (IHD) at high risk of cardiac death (CD). METHODS 3987 (mean age 68 ± 11) hospitalized IHD patients were enrolled. We implemented and compared various ML models and their combination into ensembles. Model output constitutes a new ML indicator to be employed for stratification. Primary variable importance was assessed with ablation tests. RESULTS An ensemble classifier combining three ML models achieved the best performance to predict CD (AUROC of 0.830, F1-macro of 0.726). ML indicator use through Cox survival analysis outperformed the 18 variables individually, producing a better stratification compared to standard multivariate analysis (improvement of ∼20%). Patients in the low risk group defined through ML indicator had a significantly higher survival (88.8% versus 29.1%). The main variables identified were Dyslipidemia, LVEF, Previous CABG, Diabetes, Previous Myocardial Infarction, Smoke, Documented resting or exertional ischemia, with an AUROC of 0.791 and an F1-score of 0.674, lower than that of 18 variables. Both code and clinical data are freely available with this article. CONCLUSION ML may allow a faster, low-cost and reliable evaluation of IHD patient prognosis by inclusion of more predictors and identification of those more significant, improving outcome prediction towards the development of precision medicine in this clinical field.
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Affiliation(s)
| | - Chenxiang Zhang
- Computer Science Department, University of Pisa, Pisa, Italy
| | | | - Paolo Ferragina
- Computer Science Department, University of Pisa, Pisa, Italy
| | | | | | - Rosa Sicari
- Clinical Physiology Institute, CNR, Pisa, Italy
| | | | | | | | - Alina Sîrbu
- Computer Science Department, University of Pisa, Pisa, Italy
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Medhi D, Kamidi SR, Mamatha Sree KP, Shaikh S, Rasheed S, Thengu Murichathil AH, Nazir Z. Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review. Cureus 2024; 16:e59661. [PMID: 38836155 PMCID: PMC11148729 DOI: 10.7759/cureus.59661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/04/2024] [Indexed: 06/06/2024] Open
Abstract
Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.
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Affiliation(s)
- Diptiman Medhi
- Internal Medicine, Gauhati Medical College and Hospital, Guwahati, Guwahati, IND
| | | | | | - Shifa Shaikh
- Cardiology, SMBT Institute of Medical Sciences and Research Centre, Igatpuri, IND
| | - Shanida Rasheed
- Emergency Medicine, East Sussex Healthcare NHS Trust, Eastbourne, GBR
| | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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Tedeschi A, Palazzini M, Trimarchi G, Conti N, Di Spigno F, Gentile P, D’Angelo L, Garascia A, Ammirati E, Morici N, Aschieri D. Heart Failure Management through Telehealth: Expanding Care and Connecting Hearts. J Clin Med 2024; 13:2592. [PMID: 38731120 PMCID: PMC11084728 DOI: 10.3390/jcm13092592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 04/21/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
Heart failure (HF) is a leading cause of morbidity worldwide, imposing a significant burden on deaths, hospitalizations, and health costs. Anticipating patients' deterioration is a cornerstone of HF treatment: preventing congestion and end organ damage while titrating HF therapies is the aim of the majority of clinical trials. Anyway, real-life medicine struggles with resource optimization, often reducing the chances of providing a patient-tailored follow-up. Telehealth holds the potential to drive substantial qualitative improvement in clinical practice through the development of patient-centered care, facilitating resource optimization, leading to decreased outpatient visits, hospitalizations, and lengths of hospital stays. Different technologies are rising to offer the best possible care to many subsets of patients, facing any stage of HF, and challenging extreme scenarios such as heart transplantation and ventricular assist devices. This article aims to thoroughly examine the potential advantages and obstacles presented by both existing and emerging telehealth technologies, including artificial intelligence.
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Affiliation(s)
- Andrea Tedeschi
- Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy; (F.D.S.); (D.A.)
| | - Matteo Palazzini
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Giancarlo Trimarchi
- Department of Clinical and Experimental Medicine, University of Messina, 98100 Messina, Italy;
| | - Nicolina Conti
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Francesco Di Spigno
- Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy; (F.D.S.); (D.A.)
| | - Piero Gentile
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Luciana D’Angelo
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Andrea Garascia
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Enrico Ammirati
- “De Gasperis” Cardio Center, Niguarda Hospital, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy; (M.P.); (N.C.); (P.G.); (L.D.); (A.G.); (E.A.)
| | - Nuccia Morici
- IRCCS Fondazione Don Carlo Gnocchi, 20148 Milan, Italy;
| | - Daniela Aschieri
- Cardiology Unit of Emergency Department, Guglielmo da Saliceto Hospital, 29121 Piacenza, Italy; (F.D.S.); (D.A.)
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Fedorchenko Y, Mahmudov K, Abenov Z, Zimba O, Yessirkepov M. Rehabilitation of patients with inflammatory rheumatic diseases and comorbidities: unmet needs. Rheumatol Int 2024; 44:583-591. [PMID: 38296848 DOI: 10.1007/s00296-023-05529-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 12/25/2023] [Indexed: 02/02/2024]
Abstract
Comorbidities may contribute to inadequate response to therapy and accelerate disability in various rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and psoriatic arthritis (PsA). Cardiovascular, oncological, and infectious comorbidities are common in rheumatic patients. The rehabilitation of patients with inflammatory rheumatic diseases (IRDs) with comorbidities requires a multidisciplinary approach to improving patients' functional mobility, slowing down the disease progression and minimizing the risks of complications. The evidence suggests that cardiac rehabilitation can be implemented in daily practice in patients with IRDs to reduce mortality for those with established risk factors. Physical exercises reduce the severity, improve the clinical course, and reduce hospitalization rates in patients with rheumatic diseases. A rehabilitation program with focused physical therapy can lead to functional improvements and reduction of disease activity in patients with lowered quality of life (QoL). Health professionals should provide evidence-based recommendations for patients with rheumatic diseases and comorbidities to initiate the self-management of their diseases and prevent complications.
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Affiliation(s)
- Yuliya Fedorchenko
- Department of Pathophysiology, Ivano-Frankivsk National Medical University, Halytska Str. 2, Ivano-Frankivsk, 76018, Ukraine.
| | - Khaiyom Mahmudov
- Department of Propaedeutics of Internal Diseases, Avicenna Tajik State Medical University, Dushanbe, Tajikistan
| | - Zhumabek Abenov
- Student Polyclinic, Shymkent, Kazakhstan
- South Kazakhstan Medical Academy, Shymkent, Kazakhstan
| | - Olena Zimba
- Department of Clinical Rheumatology and Immunology, University Hospital in Krakow, Krakow, Poland
- National Institute of Geriatrics, Rheumatology and Rehabilitation, Warsaw, Poland
- Department of Internal Medicine N2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
| | - Marlen Yessirkepov
- Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan
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Muzammil MA, Javid S, Afridi AK, Siddineni R, Shahabi M, Haseeb M, Fariha FNU, Kumar S, Zaveri S, Nashwan AJ. Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. J Electrocardiol 2024; 83:30-40. [PMID: 38301492 DOI: 10.1016/j.jelectrocard.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/28/2023] [Accepted: 01/22/2024] [Indexed: 02/03/2024]
Abstract
Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.
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Affiliation(s)
| | - Saman Javid
- CMH Kharian Medical College, Gujrat, Pakistan
| | | | | | | | | | - F N U Fariha
- Dow University of Health Sciences, Karachi, Pakistan
| | - Satesh Kumar
- Shaheed Mohtarma Benazir Bhutto Medical College, Karachi, Pakistan
| | - Sahil Zaveri
- Department of Medicine, SUNY Downstate Health Sciences University, New York, USA; Cardiovascular Research Program, VA New York Harbor Healthcare System, New York, USA
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Vidal-Perez R, Grapsa J, Bouzas-Mosquera A, Fontes-Carvalho R, Vazquez-Rodriguez JM. Current role and future perspectives of artificial intelligence in echocardiography. World J Cardiol 2023; 15:284-292. [PMID: 37397831 PMCID: PMC10308270 DOI: 10.4330/wjc.v15.i6.284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/02/2023] [Accepted: 06/21/2023] [Indexed: 06/26/2023] Open
Abstract
Echocardiography is an essential tool in diagnostic cardiology and is fundamental to clinical care. Artificial intelligence (AI) can help health care providers serving as a valuable diagnostic tool for physicians in the field of echocardiography specially on the automation of measurements and interpretation of results. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management specially on prognostication. In this review article, we describe the current role and future perspectives of AI in echocardiography.
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Affiliation(s)
- Rafael Vidal-Perez
- Servicio de Cardiología, Unidad de Imagen y Función Cardíaca, Complexo Hospitalario Universitario A Coruña Centro de Investigación Biomédica en Red-Instituto de Salud Carlos III, A Coruña 15006, Spain
| | - Julia Grapsa
- Department of Cardiology, Guys and St Thomas NHS Trust, London SE1 7EH, United Kingdom
| | - Alberto Bouzas-Mosquera
- Servicio de Cardiología, Unidad de Imagen y Función Cardíaca, Complexo Hospitalario Universitario A Coruña Centro de Investigación Biomédica en Red-Instituto de Salud Carlos III, A Coruña 15006, Spain
| | - Ricardo Fontes-Carvalho
- Cardiology Department, Centro Hospitalar de Vila Nova de Gaia/Espinho, Vilanova de Gaia 4434-502, Portugal
- Cardiovascular R&D Centre - UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto 4200-319, Portugal
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7
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Vidal-Perez R, Vazquez-Rodriguez JM. Role of artificial intelligence in cardiology. World J Cardiol 2023; 15:116-118. [PMID: 37124979 PMCID: PMC10130891 DOI: 10.4330/wjc.v15.i4.116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/19/2023] [Accepted: 04/10/2023] [Indexed: 04/20/2023] Open
Abstract
Artificial intelligence (AI) is the process of having a computational program that can perform tasks of human intelligence by mimicking human thought processes. AI is a rapidly evolving transdisciplinary field which integrates many elements to develop algorithms that aim to simulate human intuition, decision-making, and object recognition. The overarching aims of AI in cardiovascular medicine are threefold: To optimize patient care, improve efficiency, and improve clinical outcomes. In cardiology, there has been a growth in the potential sources of new patient data, as well as advances in investigations and therapies, which position the field well to uniquely benefit from AI. In this editorial, we highlight some of the main research priorities currently and where the next steps are heading us.
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Affiliation(s)
- Rafael Vidal-Perez
- Servicio de Cardiología, Unidad de Imagen y Función Cardíaca, Complexo Hospitalario Universitario A Coruña Centro de Investigación Biomédica en Red-Instituto de Salud Carlos III, A Coruña 15006, A Coruña, Spain
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8
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Swamy AK, Rajagopal V, Krishnan D, Ghorai PA, Palani SR, Narayan P. Machine learning algorithms for population-specific risk score in coronary artery bypass grafting. Asian Cardiovasc Thorac Ann 2023:2184923231171493. [PMID: 37122283 DOI: 10.1177/02184923231171493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
BACKGROUND The aim of this study was to develop a new risk prediction score (NH Score) for patients undergoing coronary artery bypass grafting (CABG) specific to the Indian population and compare it to the Society of Thoracic Surgeon (STS) Score and the EuroSCORE II. METHOD The baseline features of adult patients who underwent CABG between the years 2015 and 2021 (n = 6703) were taken and split into training data (2015-2020; n = 5561) and validation data (2020-2021; n = 1142). The CatBoost algorithm was trained to predict risk scores (NH score), and the performance was tested on the validation set by Precision-Recall Curve and F1 Score. Model calibration was measured by the Brier Score, Expected Calibration Error and Maximum Calibration Error. RESULTS The NH score outperformed both the STS and EuroSCORE II for all outcomes. For mortality, the PR AUC for NH Score was (0.463 [95% confidence interval [CI], 0.28-0.64]) compared to 0.113 [95% CI, 0.04-0.22] for the STS score and 0.146 [95% CI, 0.06-0.31] for the EuroSCORE II (p ≪ 0.0001). With respect to morbidity NH Score was superior to the STS score (0.43 [95% CI, 0.33-0.50]) vs. (0.229 [95% CI, 0.18-0.3, p < 0.0001). The observed to the predicted ratio for NH score was superior to the STS Score and similar to EuroSCORE II. NH Score was also more accurate at predicting the risk of prolonged ventilation compared to the STS Score. CONCLUSION NH score shows an excellent improvement over the performance of STS score and EuroSCORE II for modelling risk predictions for patients undergoing CABG in Indian population. It warrants further validation for larger datasets.
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Affiliation(s)
| | - Vivek Rajagopal
- Medha Analytics - Advanced analytics & AI, Narayana Health, Bengaluru, India
| | - Deepak Krishnan
- Medha Analytics - Advanced analytics & AI, Narayana Health, Bengaluru, India
| | | | | | - Pradeep Narayan
- Department of Cardiothoracic Surgery, Narayana Health, India
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Clua-Espuny JL, Molto-Balado P, Lucas-Noll J, Panisello-Tafalla A, Muria-Subirats E, Clua-Queralt J, Queralt-Tomas L, Reverté-Villarroya S. Early Diagnosis of Atrial Fibrillation and Stroke Incidence in Primary Care: Translating Measurements into Actions-A Retrospective Cohort Study. Biomedicines 2023; 11:biomedicines11041116. [PMID: 37189734 DOI: 10.3390/biomedicines11041116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/08/2023] [Accepted: 03/27/2023] [Indexed: 05/17/2023] Open
Abstract
(1) Background: AF-related strokes will triple by 2060, are associated with an increased risk of cognitive decline, and alone or in combination, will be one of the main health and economic burdens on the European population. The main goal of this paper is to describe the incidence of new AF associated with stroke, cognitive decline and mortality among people at high risk for AF. (2) Methods: Multicenter, observational, retrospective, community-based studies were conducted from 1 January 2015 to 31 December 2021. The setting was primary care centers. A total of 40,297 people aged ≥65 years without previous AF or stroke were stratified by AFrisk at 5 years. The main measurements were the overall incidence density/1000 person-years (CI95%) of AF and stroke, prevalence of cognitive decline, and Kaplan-Meier curve. (3) Results: In total, 46.4% women, 77.65 ± 8.46 years old on average showed anAF incidence of 9.9/103/year (CI95% 9.5-10.3), associated with a four-fold higher risk of stroke (CI95% 3.4-4.7), cognitive impairment(OR 1.34 (CI95% 1.1-1.5)), and all-cause mortality (OR 1.14 (CI95% 1.0-1.2)), but there was no significant difference in ischemic heart disease, chronic kidney disease, or peripheral arteriopathy. Unknown AF was diagnosed in 9.4% and of these patients, 21.1% were diagnosed with new stroke. (4) Conclusions: The patients at high AF risk (Q4th) already had an increased cardiovascular risk before they were diagnosed with AF.
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Affiliation(s)
- Josep-Lluis Clua-Espuny
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP), EAP Tortosa-Est, Plaça Carrilet s/núm, 43500 Tortosa, Spain
- Research Support Unit Terres de l'Ebre, Institut Universitarid'Investigació en Atenció Primària Jordi Gol (IDIAP JGol), USR Terres de l'Ebre, 43500 Tortosa, Spain
| | - Pedro Molto-Balado
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP) Terres de l'Ebre, UUDDTortosa-Terres de l'Ebre, 43500 Tortosa, Spain
| | - Jorgina Lucas-Noll
- Health Department, Management CatSalut Terres de l'Ebre, 43500 Tortosa, Spain
| | - Anna Panisello-Tafalla
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP), EAP Tortosa-Est, Plaça Carrilet s/núm, 43500 Tortosa, Spain
| | - Eulalia Muria-Subirats
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP) Terres de l'Ebre, EAP Amposta, C/Sebastià Juan Arbó, 139, 43870 Amposta, Spain
| | - Josep Clua-Queralt
- Research Support Unit Terres de l'Ebre, Institut Universitarid'Investigació en Atenció Primària Jordi Gol (IDIAP JGol), USR Terres de l'Ebre, 43500 Tortosa, Spain
| | - Lluïsa Queralt-Tomas
- Primary Health-Care Centre, Institut Català de la Salut, Primary Care Service (SAP), EAP Tortosa-Oest, Avda Cristobal Colon, 16, 43500 Tortosa, Spain
| | - Silvia Reverté-Villarroya
- Nursing Department, Campus Terres de l'Ebre, University Rovira i Virgili, Av Remolins, 13, 43500 Tortosa, Spain
- Advanced Nursing Research Group, Medicine and Health Sciences, University Rovira i Virgili, 43002 Tarragona, Spain
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Ma M, Hao X, Zhao J, Luo S, Liu Y, Li D. Predicting heart failure in-hospital mortality by integrating longitudinal and category data in electronic health records. Med Biol Eng Comput 2023:10.1007/s11517-023-02816-z. [PMID: 36959414 DOI: 10.1007/s11517-023-02816-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 03/02/2023] [Indexed: 03/25/2023]
Abstract
Heart failure is a life-threatening syndrome that is diagnosed in 3.6 million people worldwide each year. We propose a deep fusion learning model (DFL-IMP) that uses time series and category data from electronic health records to predict in-hospital mortality in patients with heart failure. We considered 41 time series features (platelets, white blood cells, urea nitrogen, etc.) and 17 category features (gender, insurance, marital status, etc.) as predictors, all of which were available within the time of the patient's last hospitalization, and a total of 7696 patients participated in the observational study. Our model was evaluated against different time windows. The best performance was achieved with an AUC of 0.914 when the observation window was 5 days and the prediction window was 30 days. Outperformed other baseline models including LR (0.708), RF (0.717), SVM (0.675), LSTM (0.757), GRU (0.759), GRU-U (0.766) and MTSSP (0.770). This tool allows us to predict the expected pathway of heart failure patients and intervene early in the treatment process, which has significant implications for improving the life expectancy of heart failure patients.
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Affiliation(s)
- Meikun Ma
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, 030024, China
| | - Xiaoyan Hao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jumin Zhao
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China
- Intelligent Perception Engineering Technology Center of Shanxi, Taiyuan, 030024, China
| | - Shijie Luo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Yi Liu
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, 030024, China
- College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Dengao Li
- Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province, Taiyuan, 030024, China.
- Technology Research Center of Spatial Information Network Engineering of Shanxi, Taiyuan, 030024, China.
- College of Data Science, Taiyuan University of Technology, Taiyuan, 030024, China.
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