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Zhang H, Mu R. Refining heart disease prediction accuracy using hybrid machine learning techniques with novel metaheuristic algorithms. Int J Cardiol 2024; 416:132506. [PMID: 39218253 DOI: 10.1016/j.ijcard.2024.132506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 08/06/2024] [Accepted: 08/29/2024] [Indexed: 09/04/2024]
Abstract
Early diagnosis of heart disease is crucial, as it's one of the leading causes of death globally. Machine learning algorithms can be a powerful tool in achieving this goal. Therefore, this article aims to increase the accuracy of predicting heart disease using machine learning algorithms. Five classification models are explored: eXtreme Gradient Boosting (XGBC), Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Nearest Neighbors Classifier (KNNC), and Logistic Regression Classifier (LRC). Additionally, four optimizers are evaluated: Slime mold Optimization Algorithm, Forest Optimization Algorithm, Pathfinder algorithm, and Giant Armadillo Optimization. To ensure robust model selection, a feature selection technique utilizing k-fold cross-validation is employed. This method identifies the most relevant features from the data, potentially improving model performance. The top three performing models are then coupled with the optimization algorithms to potentially enhance their generalizability and accuracy in predicting heart failure. In the final stage, the shortlisted models (XGBC, RFC, and DTC) were assessed using performance metrics like accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). This rigorous evaluation identified the XGGA hybrid model as the top performer, demonstrating its effectiveness in predicting heart failure. XGGA achieved impressive metrics, with an accuracy, precision, recall, and F1-score of 0.972 in the training phase, underscoring its robustness. Notably, the model's predictions deviated by less than 5.5 % for patients classified as alive and by less than 1.2 % for those classified as deceased compared to the actual outcomes, reflecting minimal error and high predictive reliability. In contrast, the DTC base model was the least effective, with an accuracy of 0.840 and a precision of 0.847. Overall, the optimization using the GAO algorithm significantly enhanced the performance of the models, highlighting the benefits of this approach.
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Affiliation(s)
- Haifeng Zhang
- The first people's Hospital of Baiyin, Baiyin, Gansu 730900, China
| | - Rui Mu
- The second people's Hospital of Baiyin, Baiyin, Gansu 730900, China.
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Levinson RT, Paul C, Meid AD, Schultz JH, Wild B. Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study. JMIR Cardio 2024; 8:e54994. [PMID: 39042456 PMCID: PMC11318205 DOI: 10.2196/54994] [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: 11/29/2023] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND Patients with heart failure (HF) are the most commonly readmitted group of adult patients in Germany. Most patients with HF are readmitted for noncardiovascular reasons. Understanding the relevance of HF management outside the hospital setting is critical to understanding HF and factors that lead to readmission. Application of machine learning (ML) on data from statutory health insurance (SHI) allows the evaluation of large longitudinal data sets representative of the general population to support clinical decision-making. OBJECTIVE This study aims to evaluate the ability of ML methods to predict 1-year all-cause and HF-specific readmission after initial HF-related admission of patients with HF in outpatient SHI data and identify important predictors. METHODS We identified individuals with HF using outpatient data from 2012 to 2018 from the AOK Baden-Württemberg SHI in Germany. We then trained and applied regression and ML algorithms to predict the first all-cause and HF-specific readmission in the year after the first admission for HF. We fitted a random forest, an elastic net, a stepwise regression, and a logistic regression to predict readmission by using diagnosis codes, drug exposures, demographics (age, sex, nationality, and type of coverage within SHI), degree of rurality for residence, and participation in disease management programs for common chronic conditions (diabetes mellitus type 1 and 2, breast cancer, chronic obstructive pulmonary disease, and coronary heart disease). We then evaluated the predictors of HF readmission according to their importance and direction to predict readmission. RESULTS Our final data set consisted of 97,529 individuals with HF, and 78,044 (80%) were readmitted within the observation period. Of the tested modeling approaches, the random forest approach best predicted 1-year all-cause and HF-specific readmission with a C-statistic of 0.68 and 0.69, respectively. Important predictors for 1-year all-cause readmission included prescription of pantoprazole, chronic obstructive pulmonary disease, atherosclerosis, sex, rurality, and participation in disease management programs for type 2 diabetes mellitus and coronary heart disease. Relevant features for HF-specific readmission included a large number of canonical HF comorbidities. CONCLUSIONS While many of the predictors we identified were known to be relevant comorbidities for HF, we also uncovered several novel associations. Disease management programs have widely been shown to be effective at managing chronic disease; however, our results indicate that in the short term they may be useful for targeting patients with HF with comorbidity at increased risk of readmission. Our results also show that living in a more rural location increases the risk of readmission. Overall, factors beyond comorbid disease were relevant for risk of HF readmission. This finding may impact how outpatient physicians identify and monitor patients at risk of HF readmission.
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Affiliation(s)
- Rebecca T Levinson
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Cinara Paul
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Andreas D Meid
- Medical Faculty of Heidelberg, Internal Medicine IX - Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Jobst-Hendrik Schultz
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Beate Wild
- Department of General Internal Medicine and Psychosomatics, Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
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Islam MS, Kalmady SV, Hindle A, Sandhu R, Sun W, Sepehrvand N, Greiner R, Kaul P. Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications. Can J Cardiol 2024:S0828-282X(24)00523-3. [PMID: 38992812 DOI: 10.1016/j.cjca.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the prognosis of not only cardiac but, increasingly, noncardiac conditions. In this review, we summarize clinical studies and AI-enhanced ECG-based clinical applications in the early detection, diagnosis, and estimating prognosis of cardiovascular diseases in the past 5 years (2019-2023). With advancements in deep learning and the rapid increased use of ECG technologies, a large number of clinical studies have been published. However, most of these studies are single-centre, retrospective, proof-of-concept studies that lack external validation. Prospective studies that progress from development toward deployment in clinical settings account for < 15% of the studies. Successful implementations of ECG-based AI applications that have received approval from the Food and Drug Administration have been developed through commercial collaborations, with approximately half of them being for mobile or wearable devices. The field is in its early stages, and overcoming several obstacles is essential, such as prospective validation in multicentre large data sets, addressing technical issues, bias, privacy, data security, model generalizability, and global scalability. This review concludes with a discussion of these challenges and potential solutions. By providing a holistic view of the state of AI in ECG analysis, this review aims to set a foundation for future research directions, emphasizing the need for comprehensive, clinically integrated, and globally deployable AI solutions in cardiovascular disease management.
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Affiliation(s)
- Md Saiful Islam
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Sunil Vasu Kalmady
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Abram Hindle
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Roopinder Sandhu
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Smidt Heart Institute, Cedars-Sinai Medical Center Hospital System, Los Angeles, California, USA
| | - Weijie Sun
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Nariman Sepehrvand
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada; Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada
| | - Padma Kaul
- Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada.
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Petmezas G, Papageorgiou VE, Vassilikos V, Pagourelias E, Tsaklidis G, Katsaggelos AK, Maglaveras N. Recent advancements and applications of deep learning in heart failure: Α systematic review. Comput Biol Med 2024; 176:108557. [PMID: 38728995 DOI: 10.1016/j.compbiomed.2024.108557] [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: 03/15/2024] [Revised: 04/12/2024] [Accepted: 05/05/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND Heart failure (HF), a global health challenge, requires innovative diagnostic and management approaches. The rapid evolution of deep learning (DL) in healthcare necessitates a comprehensive review to evaluate these developments and their potential to enhance HF evaluation, aligning clinical practices with technological advancements. OBJECTIVE This review aims to systematically explore the contributions of DL technologies in the assessment of HF, focusing on their potential to improve diagnostic accuracy, personalize treatment strategies, and address the impact of comorbidities. METHODS A thorough literature search was conducted across four major electronic databases: PubMed, Scopus, Web of Science and IEEE Xplore, yielding 137 articles that were subsequently categorized into five primary application areas: cardiovascular disease (CVD) classification, HF detection, image analysis, risk assessment, and other clinical analyses. The selection criteria focused on studies utilizing DL algorithms for HF assessment, not limited to HF detection but extending to any attempt in analyzing and interpreting HF-related data. RESULTS The analysis revealed a notable emphasis on CVD classification and HF detection, with DL algorithms showing significant promise in distinguishing between affected individuals and healthy subjects. Furthermore, the review highlights DL's capacity to identify underlying cardiomyopathies and other comorbidities, underscoring its utility in refining diagnostic processes and tailoring treatment plans to individual patient needs. CONCLUSIONS This review establishes DL as a key innovation in HF management, highlighting its role in advancing diagnostic accuracy and personalized care. The insights provided advocate for the integration of DL in clinical settings and suggest directions for future research to enhance patient outcomes in HF care.
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Affiliation(s)
- Georgios Petmezas
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | | | - Vasileios Vassilikos
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efstathios Pagourelias
- 3rd Department of Cardiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - George Tsaklidis
- Department of Mathematics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
| | - Nicos Maglaveras
- 2nd Department of Obstetrics and Gynecology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Gupta U, Paluru N, Nankani D, Kulkarni K, Awasthi N. A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms. Heliyon 2024; 10:e26787. [PMID: 38562492 PMCID: PMC10982903 DOI: 10.1016/j.heliyon.2024.e26787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
Deep learning has made many advances in data classification using electrocardiogram (ECG) waveforms. Over the past decade, data science research has focused on developing artificial intelligence (AI) based models that can analyze ECG waveforms to identify and classify abnormal cardiac rhythms accurately. However, the primary drawback of the current AI models is that most of these models are heavy, computationally intensive, and inefficient in terms of cost for real-time implementation. In this review, we first discuss the current state-of-the-art AI models utilized for ECG-based cardiac rhythm classification. Next, we present some of the upcoming modeling methodologies which have the potential to perform real-time implementation of AI-based heart rhythm diagnosis. These models hold significant promise in being lightweight and computationally efficient without compromising the accuracy. Contemporary models predominantly utilize 12-lead ECG for cardiac rhythm classification and cardiovascular status prediction, increasing the computational burden and making real-time implementation challenging. We also summarize research studies evaluating the potential of efficient data setups to reduce the number of ECG leads without affecting classification accuracy. Lastly, we present future perspectives on AI's utility in precision medicine by providing opportunities for accurate prediction and diagnostics of cardiovascular status in patients.
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Affiliation(s)
- Utkarsh Gupta
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Naveen Paluru
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, 560012, India
| | - Deepankar Nankani
- Department of Computer Science and Engineering, Indian Institute of Technology, Guwahati, Assam, 781039, India
| | - Kanchan Kulkarni
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, Pessac, Bordeaux, F-33000, France
- University of Bordeaux, INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, F-33000, France
| | - Navchetan Awasthi
- Faculty of Science, Mathematics and Computer Science, Informatics Institute, University of Amsterdam, Amsterdam, 1090 GH, the Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC, Amsterdam, 1081 HV, the Netherlands
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Shi YN, Yuan F, Xu Y. Assessing the prognostic significance of mean pulmonary artery velocity in heart failure with slightly reduced ejection fraction. Curr Probl Cardiol 2024; 49:102238. [PMID: 38040212 DOI: 10.1016/j.cpcardiol.2023.102238] [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: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 12/03/2023]
Abstract
OBJECTIVE In this study, we assessed the prognostic significance of the mean velocity of the pulmonary artery (mvPA) using CMR in patients who have heart failure with mildly reduced ejection fraction (HFmrEF) and pulmonary hypertension, both as a combined condition and individually. METHODS This retrospective study involved 284 consecutive patients diagnosed with HFmrEF who were hospitalized and underwent CMR imaging to assess RV-PA coupling parameters, including mvPA. We collected baseline data clinical profiles, lab test results, and cardiac imaging findings of patients with HFmrEF who had at least two echocardiograms conducted three months apart. The primary endpoint was a composite of all-cause mortality or readmission due to heart failure. RESULTS A total of 139 patients met the primary endpoint during an average follow-up of 49 months. The most effective threshold value for predicting the primary endpoint, determined by a receiver operating curve analysis, was 9. cm/s for mvPA. According to the Kaplan-Meier survival plots, when mvPA ≤ 9.05 cm/s, there was a significantly higher mortality rate (Log-Rank: 71.93, p < 0.001). It is important to highlight that the predictive value of mvPA remained consistent, irrespective of RV function. mvPA ≤ 9.05 cm/s served as an independent prognostic indicator, alongside ischemic cardiomyopathy and hyponatremia. CONCLUSIONS mvPA has affirmed its significance as an initial prognostic indicator by identifying a group of high-risk patients who have sustained RV function. While the results of this study displayed potential in stratifying the extended prognosis of patients with HFmrEF, additional research is required.
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Affiliation(s)
- Ya-Nan Shi
- Department of Heart Failure, Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Heart Center of Henan Provincial People's Hospital, Zhengzhou, Henan, 450003, China
| | - Fang Yuan
- Department of Heart Failure, Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Heart Center of Henan Provincial People's Hospital, Zhengzhou, Henan, 450003, China.
| | - Yu Xu
- Department of Heart Failure, Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Heart Center of Henan Provincial People's Hospital, Zhengzhou, Henan, 450003, China
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Yoon M, Park JJ, Hur T, Hua CH, Hussain M, Lee S, Choi DJ. Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future. INTERNATIONAL JOURNAL OF HEART FAILURE 2024; 6:11-19. [PMID: 38303917 PMCID: PMC10827704 DOI: 10.36628/ijhf.2023.0050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 02/03/2024]
Abstract
The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.
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Affiliation(s)
- Minjae Yoon
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jin Joo Park
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Taeho Hur
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Cam-Hao Hua
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Musarrat Hussain
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Sungyoung Lee
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Dong-Ju Choi
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [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: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Komlósi F, Tóth P, Bohus G, Vámosi P, Tokodi M, Szegedi N, Salló Z, Piros K, Perge P, Osztheimer I, Ábrahám P, Széplaki G, Merkely B, Gellér L, Nagy KV. Machine-Learning-Based Prediction of 1-Year Arrhythmia Recurrence after Ventricular Tachycardia Ablation in Patients with Structural Heart Disease. Bioengineering (Basel) 2023; 10:1386. [PMID: 38135977 PMCID: PMC10740977 DOI: 10.3390/bioengineering10121386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND Ventricular tachycardia (VT) recurrence after catheter ablation remains a concern, emphasizing the need for precise risk assessment. We aimed to use machine learning (ML) to predict 1-month and 1-year VT recurrence following VT ablation. METHODS For 337 patients undergoing VT ablation, we collected 31 parameters including medical history, echocardiography, and procedural data. 17 relevant features were included in the ML-based feature selection, which yielded six and five optimal features for 1-month and 1-year recurrence, respectively. We trained several supervised machine learning models using 10-fold cross-validation for each endpoint. RESULTS We observed 1-month VT recurrence was observed in 60 (18%) cases and accurately predicted using our model with an area under the receiver operating curve (AUC) of 0.73. Input features used were hemodynamic instability, incessant VT, ICD shock, left ventricular ejection fraction, TAPSE, and non-inducibility of the clinical VT at the end of the procedure. A separate model was trained for 1-year VT recurrence (observed in 117 (35%) cases) with a mean AUC of 0.71. Selected features were hemodynamic instability, the number of inducible VT morphologies, left ventricular systolic diameter, mitral regurgitation, and ICD shock. For both endpoints, a random forest model displayed the highest performance. CONCLUSIONS Our ML models effectively predict VT recurrence post-ablation, aiding in identifying high-risk patients and tailoring follow-up strategies.
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Affiliation(s)
- Ferenc Komlósi
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Patrik Tóth
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Gyula Bohus
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Péter Vámosi
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Márton Tokodi
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Nándor Szegedi
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Zoltán Salló
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Katalin Piros
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Péter Perge
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - István Osztheimer
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Pál Ábrahám
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Gábor Széplaki
- Mater Private Hospital, 69 Eccles St., D07 WKW8 Dublin, Ireland;
| | - Béla Merkely
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - László Gellér
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
| | - Klaudia Vivien Nagy
- Heart and Vascular Center, Semmelweis University, Városmajor u. 68, 1122 Budapest, Hungary; (F.K.); (G.B.); (M.T.); (N.S.); (Z.S.); (K.P.); (P.P.); (P.Á.); (B.M.); (L.G.)
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Di Bidino R, Piaggio D, Andellini M, Merino-Barbancho B, Lopez-Perez L, Zhu T, Raza Z, Ni M, Morrison A, Borsci S, Fico G, Pecchia L, Iadanza E. Scoping Meta-Review of Methods Used to Assess Artificial Intelligence-Based Medical Devices for Heart Failure. Bioengineering (Basel) 2023; 10:1109. [PMID: 37892839 PMCID: PMC10604154 DOI: 10.3390/bioengineering10101109] [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: 07/25/2023] [Revised: 09/13/2023] [Accepted: 09/17/2023] [Indexed: 10/29/2023] Open
Abstract
Artificial intelligence and machine learning (AI/ML) are playing increasingly important roles, permeating the field of medical devices (MDs). This rapid progress has not yet been matched by the Health Technology Assessment (HTA) process, which still needs to define a common methodology for assessing AI/ML-based MDs. To collect existing evidence from the literature about the methods used to assess AI-based MDs, with a specific focus on those used for the management of heart failure (HF), the International Federation of Medical and Biological Engineering (IFMBE) conducted a scoping meta-review. This manuscript presents the results of this search, which covered the period from January 1974 to October 2022. After careful independent screening, 21 reviews, mainly conducted in North America and Europe, were retained and included. Among the findings were that deep learning is the most commonly utilised method and that electronic health records and registries are among the most prevalent sources of data for AI/ML algorithms. Out of the 21 included reviews, 19 focused on risk prediction and/or the early diagnosis of HF. Furthermore, 10 reviews provided evidence of the impact on the incidence/progression of HF, and 13 on the length of stay. From an HTA perspective, the main areas requiring improvement are the quality assessment of studies on AI/ML (included in 11 out of 21 reviews) and their data sources, as well as the definition of the criteria used to assess the selection of the most appropriate AI/ML algorithm.
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Affiliation(s)
- Rossella Di Bidino
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS—The Graduate School of Health Economics and Management (ALTEMS), 00168 Rome, Italy
| | - Davide Piaggio
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
| | - Martina Andellini
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
| | - Beatriz Merino-Barbancho
- Life Supporting Technologies, Photonics Technology and Bioengineering Department, School of Telecommunication Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain (L.L.-P.); (G.F.)
| | - Laura Lopez-Perez
- Life Supporting Technologies, Photonics Technology and Bioengineering Department, School of Telecommunication Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain (L.L.-P.); (G.F.)
| | - Tianhui Zhu
- NIHR London In-Vitro Diagnostics Cooperative, Imperial College of London, London W2 1NY, UK
| | - Zeeshan Raza
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
| | - Melody Ni
- NIHR London In-Vitro Diagnostics Cooperative, Imperial College of London, London W2 1NY, UK
| | - Andra Morrison
- Canadian Agency for Drugs and Technologies in Health, Ottawa, ON K1S 5S8, Canada;
| | - Simone Borsci
- NIHR London In-Vitro Diagnostics Cooperative, Imperial College of London, London W2 1NY, UK
- Department of Learning, Data Analysis, and Technology, Cognition, Data and Education (CODE) Group, Faculty of Behavioural Management and Social Sciences, University of Twente, 7522 Enschede, The Netherlands
| | - Giuseppe Fico
- Life Supporting Technologies, Photonics Technology and Bioengineering Department, School of Telecommunication Engineering, Universidad Politécnica de Madrid, 28040 Madrid, Spain (L.L.-P.); (G.F.)
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; (D.P.); (M.A.); (Z.R.); (L.P.)
- School of Engineering, University Campus Bio-Medico, 00128 Rome, Italy
- International Federation of Medical and Biological Engineering, B-1090 Brussels, Belgium
| | - Ernesto Iadanza
- International Federation of Medical and Biological Engineering, B-1090 Brussels, Belgium
- Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
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11
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Bazoukis G, Bollepalli SC, Chung CT, Li X, Tse G, Bartley BL, Batool-Anwar S, Quan SF, Armoundas AA. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med 2023; 19:1337-1363. [PMID: 36856067 PMCID: PMC10315608 DOI: 10.5664/jcsm.10532] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 03/02/2023]
Abstract
STUDY OBJECTIVES Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.
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Affiliation(s)
- George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | | | - Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
| | - Xinmu Li
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China
| | - Gary Tse
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
- Kent and Medway Medical School, Canterbury, Kent, United Kingdom
| | - Bethany L. Bartley
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Salma Batool-Anwar
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Stuart F. Quan
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
- Asthma and Airway Disease Research Center, University of Arizona College of Medicine, Tucson, Arizona
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
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12
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Kresoja KP, Unterhuber M, Wachter R, Thiele H, Lurz P. A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction. Basic Res Cardiol 2023; 118:10. [PMID: 36939941 PMCID: PMC10027799 DOI: 10.1007/s00395-023-00982-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/21/2023] [Accepted: 02/26/2023] [Indexed: 03/21/2023]
Abstract
A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.
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Affiliation(s)
- Karl-Patrik Kresoja
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Matthias Unterhuber
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany
| | - Rolf Wachter
- Department of Cardiology, University Hospital Leipzig, Leipzig, Germany
- Clinic for Cardiology and Pneumology, University Medicine Göttingen, Göttingen, Germany
- German Cardiovascular Research Center (DZHK), Partner Site Göttingen, Göttingen, Germany
| | - Holger Thiele
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
| | - Philipp Lurz
- Department of Internal Medicine/Cardiology, Heart Center Leipzig at University of Leipzig, Struempellstr. 39, 04289, Leipzig, Germany.
- Leipzig Heart Institute, Leipzig Heart Science at Heart Center Leipzig, Leipzig, Germany.
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13
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Kamio T, Ikegami M, Machida Y, Uemura T, Chino N, Iwagami M. Machine learning-based prognostic modeling of patients with acute heart failure receiving furosemide in intensive care units. Digit Health 2023; 9:20552076231194933. [PMID: 37576718 PMCID: PMC10422900 DOI: 10.1177/20552076231194933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/28/2023] [Indexed: 08/15/2023] Open
Abstract
Purpose This study developed machine learning models to predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation in patients with acute heart failure receiving furosemide in intensive care units. Method An extensive database comprising static and dynamic features obtained from a Japanese hospital chain was used to construct and train the machine learning models. Results The results revealed that the proposed machine learning models predict in-hospital mortality, initiation of acute renal replacement therapy, and mechanical ventilation with good accuracy. However, the optimal models vary depending on the predicted outcomes. The linear support vector machine classification models exhibited the highest in-hospital mortality and mechanical ventilation prediction accuracy, with the area under the receiver operating characteristic curve of 0.73 and 0.73, respectively, whereas the multi-layer neural network exhibited the highest accuracy for acute renal replacement therapy initiation prediction with an area under the receiver operating characteristic curve of 0.70. Conclusions In conclusion, this study demonstrated that machine learning models could help predict the clinical outcomes of patients with acute heart failure receiving furosemide. However, the optimal models may differ depending on the outcome of interest.
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Affiliation(s)
- Tadashi Kamio
- Division of Critical Care, Shonan Kamakura General Hospital, Kanagawa, Japan
| | - Masaru Ikegami
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Yoshihito Machida
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Tomoko Uemura
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Naotaka Chino
- Terumo Corporation R and D Center, Shonan Center, Ashigarakami-gun, Kanagawa, Japan
| | - Masao Iwagami
- Department of Health Services Research, University of Tsukuba, Ibaraki, Japan
- Health Services Research and Development Center, University of Tsukuba, Ibaraki, Japan
- Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, UK
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14
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LI XM, GAO XY, Tse G, HONG SD, CHEN KY, LI GP, LIU T. Electrocardiogram-based artificial intelligence for the diagnosis of heart failure: a systematic review and meta-analysis. J Geriatr Cardiol 2022; 19:970-980. [PMID: 36632204 PMCID: PMC9807402 DOI: 10.11909/j.issn.1671-5411.2022.12.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND The electrocardiogram (ECG) is an inexpensive and easily accessible investigation for the diagnosis of cardiovascular diseases including heart failure (HF). The application of artificial intelligence (AI) has contributed to clinical practice in terms of aiding diagnosis, prognosis, risk stratification and guiding clinical management. The aim of this study is to systematically review and perform a meta-analysis of published studies on the application of AI for HF detection based on the ECG. METHODS We searched Embase, PubMed and Web of Science databases to identify literature using AI for HF detection based on ECG data. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria. Random-effects models were used for calculating the effect estimates and hierarchical receiver operating characteristic curves were plotted. Subgroup analysis was performed. Heterogeneity and the risk of bias were also assessed. RESULTS A total of 11 studies including 104,737 subjects were included. The area under the curve for HF diagnosis was 0.986, with a corresponding pooled sensitivity of 0.95 (95% CI: 0.86-0.98), specificity of 0.98 (95% CI: 0.95-0.99) and diagnostic odds ratio of 831.51 (95% CI: 127.85-5407.74). In the patient selection domain of QUADAS-2, eight studies were designated as high risk. CONCLUSIONS According to the available evidence, the incorporation of AI can aid the diagnosis of HF. However, there is heterogeneity among machine learning algorithms and improvements are required in terms of quality and study design.
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Affiliation(s)
- Xin-Mu LI
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xin-Yi GAO
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Gary Tse
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
- Kent and Medway Medical School, Canterbury, United Kingdom
| | - Shen-Da HONG
- National Institute of Health Data Science at Peking University, Peking University, Beijing, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, China
| | - Kang-Yin CHEN
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Guang-Ping LI
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - Tong LIU
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
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15
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Nakamura K, Zhou X, Sahara N, Toyoda Y, Enomoto Y, Hara H, Noro M, Sugi K, Huang M, Moroi M, Nakamura M, Zhu X. Risk of Mortality Prediction Involving Time-Varying Covariates for Patients with Heart Failure Using Deep Learning. Diagnostics (Basel) 2022; 12:2947. [PMID: 36552953 PMCID: PMC9777280 DOI: 10.3390/diagnostics12122947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 11/17/2022] [Accepted: 11/18/2022] [Indexed: 11/26/2022] Open
Abstract
Heart failure (HF) is challenging public medical and healthcare systems. This study aimed to develop and validate a novel deep learning-based prognostic model to predict the risk of all-cause mortality for patients with HF. We also compared the performance of the proposed model with those of classical deep learning- and traditional statistical-based models. The present study enrolled 730 patients with HF hospitalized at Toho University Ohashi Medical Center between April 2016 and March 2020. A recurrent neural network-based model (RNNSurv) involving time-varying covariates was developed and validated. The proposed RNNSurv showed better prediction performance than those of a deep feed-forward neural network-based model (referred as "DeepSurv") and a multivariate Cox proportional hazard model in view of discrimination (C-index: 0.839 vs. 0.755 vs. 0.762, respectively), calibration (better fit with a 45-degree line), and ability of risk stratification, especially identifying patients with high risk of mortality. The proposed RNNSurv demonstrated an improved prediction performance in consideration of temporal information from time-varying covariates that could assist clinical decision-making. Additionally, this study found that significant risk and protective factors of mortality were specific to risk levels, highlighting the demand for an individual-specific clinical strategy instead of a uniform one for all patients.
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Affiliation(s)
- Keijiro Nakamura
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan
| | - Xue Zhou
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
| | - Naohiko Sahara
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan
| | - Yasutake Toyoda
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan
| | - Yoshinari Enomoto
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan
| | - Hidehiko Hara
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan
| | - Mahito Noro
- Division of Cardiovascular Medicine, Odawara Cardiovascular Hospital, Odawara 250-0873, Japan
| | - Kaoru Sugi
- Division of Cardiovascular Medicine, Odawara Cardiovascular Hospital, Odawara 250-0873, Japan
| | - Ming Huang
- Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma 630-0192, Japan
| | - Masao Moroi
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan
| | - Masato Nakamura
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan
| | - Xin Zhu
- Graduate Department of Computer and Information Systems, The University of Aizu, Aizuwakamatsu 965-8580, Japan
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16
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Karatzia L, Aung N, Aksentijevic D. Artificial intelligence in cardiology: Hope for the future and power for the present. Front Cardiovasc Med 2022; 9:945726. [PMID: 36312266 PMCID: PMC9608631 DOI: 10.3389/fcvm.2022.945726] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022] Open
Abstract
Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.
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Affiliation(s)
- Loucia Karatzia
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,National Institute for Health and Care Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Dunja Aksentijevic
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,*Correspondence: Dunja Aksentijevic,
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17
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Chung CT, Lee S, King E, Liu T, Armoundas AA, Bazoukis G, Tse G. Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis. INTERNATIONAL JOURNAL OF ARRHYTHMIA 2022; 23:24. [PMID: 36212507 PMCID: PMC9525157 DOI: 10.1186/s42444-022-00075-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 07/13/2022] [Indexed: 11/07/2022] Open
Abstract
Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.
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Affiliation(s)
- Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, China
| | - Sharen Lee
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, China
| | - Emma King
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, China
| | - Tong Liu
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, 300211 China
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA USA
- Broad Institute, Massachusetts Institute of Technology, Cambridge, MA USA
| | - George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Inomenon Polition Amerikis, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, 2414 Nicosia, Cyprus
| | - Gary Tse
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, 300211 China
- Kent and Medway Medical School, Canterbury, UK
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18
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Błaziak M, Urban S, Wietrzyk W, Jura M, Iwanek G, Stańczykiewicz B, Kuliczkowski W, Zymliński R, Pondel M, Berka P, Danel D, Biegus J, Siennicka A. An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review. Biomedicines 2022; 10:biomedicines10092188. [PMID: 36140289 PMCID: PMC9496386 DOI: 10.3390/biomedicines10092188] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/19/2022] [Accepted: 08/27/2022] [Indexed: 11/23/2022] Open
Abstract
Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.
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Affiliation(s)
- Mikołaj Błaziak
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
- Correspondence: (M.B.); (W.K.); Tel.: +48-71-733-11-12 (M.B.)
| | - Szymon Urban
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Weronika Wietrzyk
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Maksym Jura
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, Poland
| | - Gracjan Iwanek
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Bartłomiej Stańczykiewicz
- Department of Psychiatry, Division of Consultation Psychiatry and Neuroscience, Wroclaw Medical University, 50-367 Wroclaw, Poland
| | - Wiktor Kuliczkowski
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
- Correspondence: (M.B.); (W.K.); Tel.: +48-71-733-11-12 (M.B.)
| | - Robert Zymliński
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Maciej Pondel
- Institute of Information Systems in Economics, Wroclaw University of Economics and Business, 53-345 Wroclaw, Poland
| | - Petr Berka
- Department of Information and Knowledge Engineering, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 130 67 Prague, Czech Republic
| | - Dariusz Danel
- Department of Anthropology, Ludwik Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wroclaw, Poland
| | - Jan Biegus
- Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, Poland
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, Poland
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Chopannejad S, Sadoughi F, Bagherzadeh R, Shekarchi S. Predicting major adverse cardiovascular events in acute coronary syndrome: A scoping review of machine learning approaches. Appl Clin Inform 2022; 13:720-740. [PMID: 35617971 PMCID: PMC9329142 DOI: 10.1055/a-1863-1589] [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: 11/02/2022] Open
Abstract
BACKGROUND Acute coronary syndrome is the topmost cause of death worldwide; therefore, it is necessary to predict major adverse cardiovascular events and cardiovascular deaths in patients with acute coronary syndrome to make correct and timely clinical decisions. OBJECTIVE The current review aimed to highlight algorithms and important predictor variables through examining those studies which used machine learning algorithms for predicting major adverse cardiovascular events in patients with acute coronary syndrome. METHODS In order to predict major adverse cardiovascular events in patients with acute coronary syndrome, the preferred reporting items for scoping reviews guidelines were used. PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases were searched for articles published between 2005 and 2021. The findings of the studies are presented in the form of a narrative synthesis of evidence. RESULTS According to the results, 14 (63.64%) studies did not perform external validation and only used registry data. The algorithms used in this study comprised, inter alia, Regression Logistic, Random Forest, Boosting Ensemble, Non-Boosting Ensemble, Decision Trees, and Naive Bayes. Multiple studies (N=20) achieved a high Area under the ROC Curve between 0.8 to 0.99 in predicting mortality and major adverse cardiovascular events. The predictor variables used in these studies were divided into demographic, clinical, and therapeutic features. However, no study reported the integration of machine learning model into clinical practice. CONCLUSION Machine learning algorithms rendered acceptable results to predict major adverse cardiovascular events and mortality outcomes in patients with acute coronary syndrome. However, these approaches have never been integrated into clinical practice. Further research is required to develop feasible and effective machine learning prediction models to measure their potentially important implications for optimizing the quality of care in patients with acute coronary syndrome.
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Affiliation(s)
- Sara Chopannejad
- Student Research Committee, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Farahnaz Sadoughi
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Rafat Bagherzadeh
- English Language Department, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
| | - Sakineh Shekarchi
- School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran (the Islamic Republic of)
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20
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Sun Z, Dong W, Shi H, Ma H, Cheng L, Huang Z. Comparing Machine Learning Models and Statistical Models for Predicting Heart Failure Events: A Systematic Review and Meta-Analysis. Front Cardiovasc Med 2022; 9:812276. [PMID: 35463786 PMCID: PMC9020815 DOI: 10.3389/fcvm.2022.812276] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 02/28/2022] [Indexed: 01/16/2023] Open
Abstract
Objective To compare the performance, clinical feasibility, and reliability of statistical and machine learning (ML) models in predicting heart failure (HF) events. Background Although ML models have been proposed to revolutionize medicine, their promise in predicting HF events has not been investigated in detail. Methods A systematic search was performed on Medline, Web of Science, and IEEE Xplore for studies published between January 1, 2011 to July 14, 2021 that developed or validated at least one statistical or ML model that could predict all-cause mortality or all-cause readmission of HF patients. Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias, and random effect model was used to evaluate the pooled c-statistics of included models. Result Two-hundred and two statistical model studies and 78 ML model studies were included from the retrieved papers. The pooled c-index of statistical models in predicting all-cause mortality, ML models in predicting all-cause mortality, statistical models in predicting all-cause readmission, ML models in predicting all-cause readmission were 0.733 (95% confidence interval 0.724–0.742), 0.777 (0.752–0.803), 0.678 (0.651–0.706), and 0.660 (0.633–0.686), respectively, indicating that ML models did not show consistent superiority compared to statistical models. The head-to-head comparison revealed similar results. Meanwhile, the immoderate use of predictors limited the feasibility of ML models. The risk of bias analysis indicated that ML models' technical pitfalls were more serious than statistical models'. Furthermore, the efficacy of ML models among different HF subgroups is still unclear. Conclusions ML models did not achieve a significant advantage in predicting events, and their clinical feasibility and reliability were worse.
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Affiliation(s)
- Zhoujian Sun
- Zhejiang Lab, Hangzhou, China
- Zhejiang University, Hangzhou, China
| | - Wei Dong
- Department of Cardiology, The First Medical Center of Chinese PLA General Hospital, Beijing, China
| | | | - Hong Ma
- Department of Cardiology, The Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, China
| | | | - Zhengxing Huang
- Zhejiang University, Hangzhou, China
- *Correspondence: Zhengxing Huang
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21
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Chung CT, Bazoukis G, Lee S, Liu Y, Liu T, Letsas KP, Armoundas AA, Tse G. Machine learning techniques for arrhythmic risk stratification: a review of the literature. INTERNATIONAL JOURNAL OF ARRHYTHMIA 2022; 23. [PMID: 35449883 PMCID: PMC9020640 DOI: 10.1186/s42444-022-00062-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.
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22
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An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit. Diagnostics (Basel) 2022; 12:diagnostics12020241. [PMID: 35204333 PMCID: PMC8871182 DOI: 10.3390/diagnostics12020241] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/14/2022] [Accepted: 01/14/2022] [Indexed: 11/21/2022] Open
Abstract
Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963–0.972), heart failure AUC of 0.838 (CI: 0.825–0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821–0.842), pulmonary embolism AUC of 0.802 (CI: 0.764–0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499–2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.
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23
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Jasinska-Piadlo A, Bond R, Biglarbeigi P, Brisk R, Campbell P, McEneaneny D. What can machines learn about heart failure? A systematic literature review. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2021. [DOI: 10.1007/s41060-021-00300-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
AbstractThis paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.
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24
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Bollepalli SC, Sevakula RK, Au-Yeung WTM, Kassab MB, Merchant FM, Bazoukis G, Boyer R, Isselbacher EM, Armoundas AA. Real-Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks. J Am Heart Assoc 2021; 10:e023222. [PMID: 34854319 PMCID: PMC9075394 DOI: 10.1161/jaha.121.023222] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount significance in providing timely care. However, traditional ICU monitors generate a high rate of false alarms causing alarm fatigue. In this work, we develop an algorithm to improve life threatening arrhythmia detection in the ICUs using a deep learning approach. Methods and Results This study involves a total of 953 independent life-threatening arrhythmia alarms generated from the ICU bedside monitors of 410 patients. Specifically, we used the ECG (4 channels), arterial blood pressure, and photoplethysmograph signals to accurately detect the onset and offset of various arrhythmias, without prior knowledge of the alarm type. We used a hybrid convolutional neural network based classifier that fuses traditional handcrafted features with features automatically learned using convolutional neural networks. Further, the proposed architecture remains flexible to be adapted to various arrhythmic conditions as well as multiple physiological signals. Our hybrid- convolutional neural network approach achieved superior performance compared with methods which only used convolutional neural network. We evaluated our algorithm using 5-fold cross-validation for 5 times and obtained an accuracy of 87.5%±0.5%, and a score of 81%±0.9%. Independent evaluation of our algorithm on the publicly available PhysioNet 2015 Challenge database resulted in overall classification accuracy and score of 93.9% and 84.3%, respectively, indicating its efficacy and generalizability. Conclusions Our method accurately detects multiple arrhythmic conditions. Suitable translation of our algorithm may significantly improve the quality of care in ICUs by reducing the burden of false alarms.
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Affiliation(s)
| | - Rahul K Sevakula
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | | | - Mohamad B Kassab
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | | | - George Bazoukis
- Second Department of Cardiology Evangelismos General Hospital of Athens Athens Greece
| | - Richard Boyer
- Anesthesia Department Massachusetts General Hospital Boston MA
| | | | - Antonis A Armoundas
- Cardiovascular Research Center Massachusetts General Hospital Boston MA.,Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA
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25
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Patel MH, Sampath S, Kapoor A, Damani DN, Chellapuram N, Challa AB, Kaur MP, Walton RD, Stavrakis S, Arunachalam SP, Kulkarni K. Advances in Cardiac Pacing: Arrhythmia Prediction, Prevention and Control Strategies. Front Physiol 2021; 12:783241. [PMID: 34925071 PMCID: PMC8674736 DOI: 10.3389/fphys.2021.783241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 11/08/2021] [Indexed: 02/01/2023] Open
Abstract
Cardiac arrhythmias constitute a tremendous burden on healthcare and are the leading cause of mortality worldwide. An alarming number of people have been reported to manifest sudden cardiac death as the first symptom of cardiac arrhythmias, accounting for about 20% of all deaths annually. Furthermore, patients prone to atrial tachyarrhythmias such as atrial flutter and fibrillation often have associated comorbidities including hypertension, ischemic heart disease, valvular cardiomyopathy and increased risk of stroke. Technological advances in electrical stimulation and sensing modalities have led to the proliferation of medical devices including pacemakers and implantable defibrillators, aiming to restore normal cardiac rhythm. However, given the complex spatiotemporal dynamics and non-linearity of the human heart, predicting the onset of arrhythmias and preventing the transition from steady state to unstable rhythms has been an extremely challenging task. Defibrillatory shocks still remain the primary clinical intervention for lethal ventricular arrhythmias, yet patients with implantable cardioverter defibrillators often suffer from inappropriate shocks due to false positives and reduced quality of life. Here, we aim to present a comprehensive review of the current advances in cardiac arrhythmia prediction, prevention and control strategies. We provide an overview of traditional clinical arrhythmia management methods and describe promising potential pacing techniques for predicting the onset of abnormal rhythms and effectively suppressing cardiac arrhythmias. We also offer a clinical perspective on bridging the gap between basic and clinical science that would aid in the assimilation of promising anti-arrhythmic pacing strategies.
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Affiliation(s)
- Mehrie Harshad Patel
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, United States
| | - Shrikanth Sampath
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, United States
| | - Anoushka Kapoor
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, United States
| | | | - Nikitha Chellapuram
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | | | - Manmeet Pal Kaur
- Department of Medicine, GAIL, Mayo Clinic, Rochester, MN, United States
| | - Richard D. Walton
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux, University of Bordeaux, Bordeaux, France
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
| | - Stavros Stavrakis
- Heart Rhythm Institute, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Shivaram P. Arunachalam
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, United States
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
- Department of Medicine, GAIL, Mayo Clinic, Rochester, MN, United States
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Kanchan Kulkarni
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France
- Centre de Recherche Cardio-Thoracique de Bordeaux, University of Bordeaux, Bordeaux, France
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, Bordeaux, France
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26
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Alkhodari M, Jelinek HF, Karlas A, Soulaidopoulos S, Arsenos P, Doundoulakis I, Gatzoulis KA, Tsioufis K, Hadjileontiadis LJ, Khandoker AH. Deep Learning Predicts Heart Failure With Preserved, Mid-Range, and Reduced Left Ventricular Ejection Fraction From Patient Clinical Profiles. Front Cardiovasc Med 2021; 8:755968. [PMID: 34881307 PMCID: PMC8645593 DOI: 10.3389/fcvm.2021.755968] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/19/2021] [Indexed: 02/03/2023] Open
Abstract
Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF. Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges. Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories. Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant (p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98. Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.
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Affiliation(s)
- Mohanad Alkhodari
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
| | - Herbert F Jelinek
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
- Department of Biomedical Engineering, Biotechnology Center (BTC), Khalifa University, Abu Dhabi, United Arab Emirates
| | - Angelos Karlas
- Chair of Biological Imaging, Center for Translational Cancer Research (TranslaTUM), Technical University of Munich, Munich, Germany
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Neuherberg, Germany
- Department for Vascular and Endovascular Surgery, Rechts der Isar University Hospital, Technical University of Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Stergios Soulaidopoulos
- First Cardiology Department, School of Medicine, "Hippokration" General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Petros Arsenos
- First Cardiology Department, School of Medicine, "Hippokration" General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis Doundoulakis
- First Cardiology Department, School of Medicine, "Hippokration" General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantinos A Gatzoulis
- First Cardiology Department, School of Medicine, "Hippokration" General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Konstantinos Tsioufis
- First Cardiology Department, School of Medicine, "Hippokration" General Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Healthcare Engineering Innovation Center (HEIC), Khalifa University, Abu Dhabi, United Arab Emirates
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27
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Sun Y, Si J, Li J, Dai M, King E, Zhang X, Zhang Y, Xia Y, Tse G, Liu Y. Predictive Value of HFA-PEFF Score in Patients With Heart Failure With Preserved Ejection Fraction. Front Cardiovasc Med 2021; 8:656536. [PMID: 34778384 PMCID: PMC8585787 DOI: 10.3389/fcvm.2021.656536] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 10/04/2021] [Indexed: 12/17/2022] Open
Abstract
Aims: HFA-PEFF score has been proposed for diagnosing heart failure with preserved ejection fraction (HFpEF). Currently, there are only a limited number of tools for predicting the prognosis. In this study, we evaluated whether the HFA-PEFF score can predict mortality in patients with HFpEF. Methods: This single-center, retrospective observational study enrolled patients diagnosed with HFpEF at the First Affiliated Hospital of Dalian Medical University between January 1, 2015, and April 30, 2018. The subjects were divided according to their HFA-PEFF score into low (0–2 points), intermediate (3–4 points), and high (5–6 points) score groups. The primary outcome was all-cause mortality. Results: A total of 358 patients (mean age: 70.21 ± 8.64 years, 58.1% female) were included. Of these, 63 (17.6%), 156 (43.6%), and 139 (38.8%) were classified into the low, intermediate, and high score groups, respectively. Over a mean follow-up of 26.9 months, 46 patients (12.8%) died. The percentage of patients who died in the low, intermediate, and high score groups were 1 (1.6%), 18 (11.5%), and 27 (19.4%), respectively. A multivariate Cox regression identified HFA-PEFF score as an independent predictor of all-cause mortality [hazard ratio (HR):1.314, 95% CI: 1.013–1.705, P = 0.039]. A Cox analysis demonstrated a significantly higher rate of mortality in the intermediate (HR: 4.912, 95% CI 1.154–20.907, P = 0.031) and high score groups (HR: 5.291, 95% CI: 1.239–22.593, P = 0.024) than the low score group. A receiver operating characteristic (ROC) analysis indicated that the HFA-PEFF score can effectively predict all-cause mortality after adjusting for age and New York Heart Association (NYHA) class [area under the curve (AUC) 0.726, 95% CI 0.651–0.800, P = 0.000]. With an HFA-PEFF score cut-off value of 3.5, the sensitivity and specificity were 78.3 and 54.8%, respectively. The AUC on ROC analysis for the biomarker component of the score was similar to that of the total score. Conclusions: The HFA-PEFF score can be used both to diagnose HFpEF and predict the prognosis. The higher scores are associated with higher all-cause mortality.
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Affiliation(s)
- Yuxi Sun
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jinping Si
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jiaxin Li
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Mengyuan Dai
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Emma King
- Cardiovascular Analytics Group, Hong Kong SAR, China
| | - Xinxin Zhang
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yanli Zhang
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yunlong Xia
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Gary Tse
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, China.,Cardiovascular Analytics Group, Hong Kong SAR, China.,Kent and Medway Medical School, Canterbury, United Kingdom
| | - Ying Liu
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, China
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28
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Sun Y, Song S, Zhang Y, Mo W, Zhang X, Wang N, Xia Y, Tse G, Liu Y. Effect of angiotensin receptor neprilysin inhibitors on left atrial remodeling and prognosis in heart failure. ESC Heart Fail 2021; 9:667-675. [PMID: 34779134 PMCID: PMC8787963 DOI: 10.1002/ehf2.13691] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 09/25/2021] [Accepted: 10/25/2021] [Indexed: 01/19/2023] Open
Abstract
Aims The angiotensin receptor–neprilysin inhibitor (ARNI), sacubitril/valsartan, confers additional protective effects compared with angiotensin‐converting enzyme inhibitors/angiotensin II receptor blockers (ACEIs/ARBs) in terms of reversed left ventricular (LV) remodelling and improves the prognosis of patients with heart failure (HF). However, few studies have examined the effects of ARNI on the left atrium. Accordingly, this study compared the effects of ARNI and ACEI/ARB on left atrial (LA) remodelling in heart failure with reduced ejection fraction (HFrEF). Methods and results This was a single‐centre retrospective study of patients with HFrEF hospitalized at the First Affiliated Hospital of Dalian Medical University between 26 February 2016 and 8 July 2020. Patients were classified into ARNI and ACEI/ARB groups and further subgroups based on the left atrial volume index (LAVI): mildly abnormal (29 mL/m2 ≤ LAVI < 34 mL/m2), moderately abnormal (34 mL/m2 ≤ LAVI < 40 mL/m2), and severely abnormal (LAVI ≥ 40 mL/m2). The primary endpoint was changes in LA parameters by echocardiography. The secondary endpoint was all‐cause mortality. A total of 336 patients (mean age: 64.11 ± 12.86, 30.06% female) were included. Except those lost to follow‐up, 274 HFrEF patients remained, with 144 cases in the ARNI group and 130 cases in the ACEI/ARB group. Greater reductions from baseline were seen with ARNI in LA diameter (LAD) (P = 0.013, t‐test), superior and LA superior–inferior diameter (LASID) (P < 0.0001), LA transverse diameter (LATD) (P < 0.0001), LA volume (LAV) (P < 0.0001), LAVI (P < 0.0001), and LA sphericity index (LASI) (P < 0.0001). Over a mean follow‐up of 19.40 months, 97 patients (67.3%) in the ARNI group and 29 patients (22.3%) in the ACEI/ARB group showed LA reverse remodelling (LARR). Kaplan–Meier analysis showed significantly lower overall mortality in the ARNI group compared with the ACEI/ARB group (P = 0.048, log‐rank test). The mildly abnormal LAVI group of ARNI patients showed a reduction in mortality compared with ACEI/ARB patients (P = 0.044). However, no significant difference was observed for the moderately abnormal (P = 0.571) or severely abnormal LAVI groups (P = 0.609), suggesting that early initiation of ARNI was associated with a better prognosis. Conclusions In this proof‐of‐concept study, ARNI use showed greater effects on LARR and was associated with a better prognosis compared with ACEI/ARB use in HFrEF. Early initiation of ARNI in the HF disease process may produce greater benefit, but this needs to be confirmed in future studies.
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Affiliation(s)
- Yuxi Sun
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Shuang Song
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Yanli Zhang
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Wenqiong Mo
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Xinxin Zhang
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Ning Wang
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Yunlong Xia
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
| | - Gary Tse
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China.,Kent and Medway Medical School, Canterbury, Kent, UK
| | - Ying Liu
- Heart Failure and Structural Cardiology Ward, First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, China
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29
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Maurya MR, Riyaz NUSS, Reddy MSB, Yalcin HC, Ouakad HM, Bahadur I, Al-Maadeed S, Sadasivuni KK. A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring. Med Biol Eng Comput 2021; 59:2185-2203. [PMID: 34611787 DOI: 10.1007/s11517-021-02447-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 09/01/2021] [Indexed: 02/07/2023]
Abstract
Over the last decade, there has been a huge demand for health care technologies such as sensors-based prediction using digital health. With the continuous rise in the human population, these technologies showed to be potentially effective solutions to life-threatening diseases such as heart failure (HF). Besides being a potential for early death, HF has a significantly reduced quality of life (QoL). Heart failure has no cure. However, treatment can help you live a longer and more active life with fewer symptoms. Thus, it is essential to develop technological aid solutions allowing early diagnosis and consequently, effective treatment with possibly delayed mortality. Commonly, forecasts of HF are based on the generation of vast volumes of data usually collected from an individual patient by different components of the family history, physical examination, basic laboratory results, and other medical records. Though, these data are not effectively useful for predicting this failure, nevertheless, with the aid of advanced medical technology such as interconnected multi-sensory-based devices, and based on several medical history characteristics, the broad data provided machine learning algorithms to predict risk factors for heart disease of an individual is beneficial. There will be many challenges for the next decade of advancements in HF care: exploiting an increasingly growing repertoire of interconnected internal and external sensors for the benefit of patients and processing large, multimodal datasets with new Artificial Intelligence (AI) software. Various methods for predicting heart failure and, primarily the significance of invasive and non-invasive sensors along with different strategies for machine learning to predict heart failure are presented and summarized in the present study.
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Affiliation(s)
- Muni Raj Maurya
- Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar
- Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | | | - M Sai Bhargava Reddy
- Center for Nanoscience and Technology, Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana State, 500085, India
| | | | - Hassen M Ouakad
- Mechanical and Industrial Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khoudh, 123, PO-BOX 33, Muscat, Oman.
| | - Issam Bahadur
- Mechanical and Industrial Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khoudh, 123, PO-BOX 33, Muscat, Oman
| | - Somaya Al-Maadeed
- Department of Computer Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
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30
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Lee AWC, Razeghi O, Solis-Lemus JA, Strocchi M, Sidhu B, Gould J, Behar JM, Elliott M, Mehta V, Plank G, Rinaldi CA, Niederer SA. Non-invasive simulated electrical and measured mechanical indices predict response to cardiac resynchronization therapy. Comput Biol Med 2021; 138:104872. [PMID: 34598070 DOI: 10.1016/j.compbiomed.2021.104872] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/09/2021] [Accepted: 09/09/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Cardiac Resynchronization Therapy (CRT) in dyssynchronous heart failure patients is ineffective in 20-30% of cases. Sub-optimal left ventricular (LV) pacing location can lead to non-response, thus there is interest in LV lead location optimization. Invasive acute haemodynamic response (AHR) measurements have been used to optimize the LV pacing location during CRT implantation. In this manuscript, we aim to predict the optimal lead location (AHR>10%) with non-invasive computed tomography (CT) based measures of cardiac anatomical and mechanical properties, and simulated electrical activation times. METHODS Non-invasive measurements from CT images and ECG were acquired from 34 patients indicated for CRT upgrade. The LV lead was implanted and AHR was measured at different pacing sites. Computer models of the ventricles were used to simulate the electrical activation of the heart, track the mechanical motion throughout the cardiac cycle and measure the wall thickness of the LV on a patient specific basis. RESULTS We tested the ability of electrical, mechanical and anatomical indices to predict the optimal LV location. Electrical (RV-LV delay) and mechanical (time to peak contraction) indices were correlated with an improved AHR, while wall thickness was not predictive. A logistic regression model combining RV-LV delay and time to peak contraction was able to predict positive response with 70 ± 11% accuracy and AUROC curve of 0.73. CONCLUSION Non-invasive electrical and mechanical indices can predict optimal epicardial lead location. Prospective analysis of these indices could allow clinicians to test the AHR at fewer pacing sites and reduce time, costs and risks to patients.
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Affiliation(s)
- Angela W C Lee
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
| | - Orod Razeghi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jose Alonso Solis-Lemus
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Marina Strocchi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Baldeep Sidhu
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Justin Gould
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan M Behar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Royal Brompton Hospital, London, United Kingdom
| | - Mark Elliott
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Vishal Mehta
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Gernot Plank
- Department of Biophysics, Medical University of Graz, Graz, Austria
| | - Christopher A Rinaldi
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Steven A Niederer
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.
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31
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Zhang X, Sun Y, Zhang Y, Chen F, Zhang S, He H, Song S, Tse G, Liu Y. Heart Failure With Midrange Ejection Fraction: Prior Left Ventricular Ejection Fraction and Prognosis. Front Cardiovasc Med 2021; 8:697221. [PMID: 34409076 PMCID: PMC8364975 DOI: 10.3389/fcvm.2021.697221] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/29/2021] [Indexed: 01/21/2023] Open
Abstract
Aims: Evidence-based guidelines for heart failure management depend mainly on current left ventricular ejection fraction (LVEF). However, fewer studies have examined the impact of prior LVEF. Patients may enter the heart failure with midrange ejection fraction (HFmrEF) category when heart failure with preserved ejection fraction (HFpEF) deteriorates or heart failure with reduced ejection fraction (HFrEF) improves. In this study, we examined the association between change in LVEF and adverse outcomes. Methods: HFmrEF patients with at least two or more echocardiograms 3 months apart at the First Affiliated Hospital of Dalian Medical University between September 1, 2015 and November 30, 2019 were identified. According to the prior LVEF, the subjects were divided into improved group (prior LVEF < 40%), stable group (prior LVEF between 40 and 50%), and deteriorated group (prior LVEF ≥ 50%). The primary outcomes were cardiovascular death, all-cause mortality, hospitalization for worsening heart failure, and composite event of all-cause mortality or all-cause hospitalization. Results: A total of 1,168 HFmrEF patients (67.04% male, mean age 63.60 ± 12.18 years) were included. The percentages of improved, stable, and deteriorated group were 310 (26.54%), 334 (28.60%), and 524 (44.86%), respectively. After a period of follow-up, 208 patients (17.81%) died and 500 patients met the composite endpoint. The rates of all-cause mortality were 35 (11.29%), 55 (16.47%), and 118 (22.52%), and the composite outcome was 102 (32.90%), 145 (43.41%), and 253 (48.28%) for the improved, stable, and deteriorated groups, respectively. Cox regression analysis showed that the deterioration group had higher risk of cardiovascular death (HR: 1.707, 95% CI: 1.064–2.739, P = 0.027), all-cause death (HR 1.948, 95% CI 1.335–2.840, P = 0.001), and composite outcome (HR 1.379, 95% CI 1.096–1.736, P = 0.006) compared to the improvement group. The association still remained significant after fully adjusted for both all-cause mortality (HR = 1.899, 95% CI 1.247–2.893, P = 0.003) and composite outcome (HR: 1.324, 95% CI: 1.020–1.718, P = 0.035). Conclusion: HFmrEF patients are heterogeneous with three different subsets identified, each with different outcomes. Strategies for managing HFmrEF should include previously measured LVEF to allow stratification based on direction changes in LVEF to better optimize treatment.
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Affiliation(s)
- Xinxin Zhang
- Heart Failure and Structural Cardiology Ward, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yuxi Sun
- Heart Failure and Structural Cardiology Ward, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yanli Zhang
- Heart Failure and Structural Cardiology Ward, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Feifei Chen
- Heart Failure and Structural Cardiology Ward, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shuyuan Zhang
- Heart Failure and Structural Cardiology Ward, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Hongyan He
- Heart Failure and Structural Cardiology Ward, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shuang Song
- Heart Failure and Structural Cardiology Ward, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Gary Tse
- Heart Failure and Structural Cardiology Ward, The First Affiliated Hospital of Dalian Medical University, Dalian, China.,Kent and Medway Medical School, Canterbury, United Kingdom
| | - Ying Liu
- Heart Failure and Structural Cardiology Ward, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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32
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A CNN-based novel solution for determining the survival status of heart failure patients with clinical record data: numeric to image. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102716] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Au-Yeung WTM, Sevakula RK, Sahani AK, Kassab M, Boyer R, Isselbacher EM, Armoundas AA. Real-time machine learning-based intensive care unit alarm classification without prior knowledge of the underlying rhythm. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:437-445. [PMID: 34604758 PMCID: PMC8482048 DOI: 10.1093/ehjdh/ztab058] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 06/13/2021] [Accepted: 06/20/2021] [Indexed: 01/29/2023]
Abstract
AIMS This work attempts to develop a standalone heart rhythm alerting system for the intensive care unit (ICU), where life-threatening arrhythmias have to be identified/alerted more precisely and more instantaneously (i.e. with lower latency) than existing bedside monitors. METHODS AND RESULTS We use the dataset from the PhysioNet 2015 Challenge, which contains records that led to true and false arrhythmic alarms in the ICU. These records have been re-annotated as one of eight classes, namely (i) asystole, (ii) extreme bradycardia, (iii) extreme tachycardia, (iv) ventricular fibrillation (VF), (v) ventricular tachycardia (VT), (vi) normal sinus rhythm, (vii) sinus tachycardia, and (viii) noise/artefacts. Arrhythmia-specific features and features that measure the signal quality were extracted from all the records. To improve VF detection, an improved, over an existing, single-lead R-wave detection was developed that takes into account the R-waves detected in all electrocardiographic (ECG) leads. To avoid false R-wave detection due to pacing spikes, ECG signals were filtered with a low pass filter prior to R-wave detection, while the raw signals were used for feature extraction. Random forest was used as the classifier, and 10-time five-fold cross-validation, resulted in a macro-average sensitivity of 81.54%. CONCLUSIONS In conclusion, comparing with the bedside monitors used in the PhysioNet 2015 competition, we find that our method achieves higher positive predictive values for asystole, extreme bradycardia, VT, and VF; furthermore, our method is able to alert the presence of arrhythmia instantaneously, i.e. up to 4 s earlier.
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Affiliation(s)
- Wan-Tai M Au-Yeung
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129, USA
| | - Rahul K Sevakula
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129, USA
| | - Ashish K Sahani
- Center for Biomedical Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab 14001, India
| | - Mohamad Kassab
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129, USA
| | - Richard Boyer
- Anesthesia Department, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Eric M Isselbacher
- Healthcare Transformation Lab, Massachusetts General Hospital, 50 Staniford St, Boston, MA 02114, USA
| | - Antonis A Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129, USA,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 45 Carleton St, Cambridge, MA 02142, USA,Corresponding author. Tel: +1 617-726-0930,
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34
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Ju C, Zhou J, Lee S, Tan MS, Liu T, Bazoukis G, Jeevaratnam K, Chan EWY, Wong ICK, Wei L, Zhang Q, Tse G. Derivation of an electronic frailty index for predicting short-term mortality in heart failure: a machine learning approach. ESC Heart Fail 2021; 8:2837-2845. [PMID: 34080784 PMCID: PMC8318426 DOI: 10.1002/ehf2.13358] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 02/15/2021] [Accepted: 03/29/2021] [Indexed: 12/12/2022] Open
Abstract
Aims Frailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time‐consuming, and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short‐term mortality prediction in patients with heart failure. Methods and results This was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson co‐morbidity index (≥2), neutrophil‐to‐lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A total of 8893 patients (median: age 81, Q1–Q3: 71–87 years old) were included, in whom 9% had 30 day mortality and 17% had 90 day mortality. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five‐fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively. Conclusions The electronic frailty index based on co‐morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques.
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Affiliation(s)
- Chengsheng Ju
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
| | - Jiandong Zhou
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Sharen Lee
- Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, LKS Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong SAR, China
| | | | - Tong Liu
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China
| | - George Bazoukis
- Second Department of Cardiology, Evangelismos General Hospital, Athens, Greece
| | | | - Esther W Y Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong SAR, China
| | - Ian Chi Kei Wong
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK.,Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong SAR, China
| | - Li Wei
- Research Department of Practice and Policy, School of Pharmacy, University College London, London, UK
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong SAR, China
| | - Gary Tse
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, China.,Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
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Luo G, Stone BL, Sheng X, He S, Koebnick C, Nkoy FL. Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis. JMIR Res Protoc 2021; 10:e27065. [PMID: 34003134 PMCID: PMC8170556 DOI: 10.2196/27065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 04/12/2021] [Accepted: 04/19/2021] [Indexed: 12/05/2022] Open
Abstract
Background Asthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well. Objective To enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions. Methods We will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians’ decisions to use integrated disease management to prevent proneness to exacerbation. Results We have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years. Conclusions Our results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources. International Registered Report Identifier (IRRID) PRR1-10.2196/27065
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Affiliation(s)
- Gang Luo
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Bryan L Stone
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
| | - Xiaoming Sheng
- College of Nursing, University of Utah, Salt Lake City, UT, United States
| | - Shan He
- Care Transformation and Information Systems, Intermountain Healthcare, West Valley City, UT, United States
| | - Corinna Koebnick
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States
| | - Flory L Nkoy
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
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Sun Y, Wang N, Li X, Zhang Y, Yang J, Tse G, Liu Y. Predictive value of H 2 FPEF score in patients with heart failure with preserved ejection fraction. ESC Heart Fail 2021; 8:1244-1252. [PMID: 33403825 PMCID: PMC8006728 DOI: 10.1002/ehf2.13187] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Revised: 12/05/2020] [Accepted: 12/11/2020] [Indexed: 12/17/2022] Open
Abstract
Aims The H2FPEF score is a convenient risk stratification tool for diagnosing heart failure with preserved ejection fraction (HFpEF). This study examined the value of the H2FPEF score for predicting all‐cause mortality and rehospitalization in HFpEF patients. Methods and results This was a retrospective cohort study of patients diagnosed with HFpEF by echocardiography at a single tertiary centre between 1 January 2015 and 30 April 2018. According to the H2FPEF score, the subjects were divided into low (0–1 points), intermediate (2–5 points), and high (6–9 points) score groups. The primary outcomes were all‐cause mortality and rehospitalization. A total of 476 patients (mean age: 70.5 ± 8.4 years, 60.7% female) were included. Of these, 47 (9.9%), 262 (55.0%), and 167 (35.1%) were classified into the low, intermediate, and high score groups, respectively. Over a mean follow‐up of 27.5 months, 63 patients (13.2%) died, and 311 patients (65.3%) were rehospitalized. The mortality rates were 3 (6.4%), 29 (11.1%), and 31 (18.6%), and the number of patients with rehospitalization was 28 (59.6%), 159 (60.7%), and 124 (74.3%) for the low, intermediate, and high score groups, respectively. Multivariate Cox regression identified H2FPEF score as an independent predictor of all‐cause mortality (hazard ratio [HR]: 1.46, 95% CI: 1.23–1.73, P < 0.0001) and rehospitalization (HR: 1.15, 95% CI: 1.08–1.22, P < 0.0001). Receiver operating characteristic (ROC) analysis demonstrated the H2FPEF score can effectively predict all‐cause mortality (AUC 0.67, 95% CI: 0.60–0.73, P < 0.0001) and rehospitalization (AUC 0.59, 95% CI: 0.54–0.65, P = 0.001) after adjusting for age and NYHA class. With a cut‐off value of 5.5, the sensitivity and specificity were 68.3% and 55.4% for all‐cause mortality and 50.5% and 66.7% for rehospitalization. Conclusions The H2FPEF score can be used to predict prognosis in HFpEF patients. Higher scores are associated with higher all‐cause mortality and rehospitalization.
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Affiliation(s)
- Yuxi Sun
- Heart Failure and Structural Cardiology Division, Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, 193 United Road, Dalian, Liaoning, 116021, China
| | - Niuniu Wang
- Department of Cardiology, The Second Affiliated Hospital of Xuzhou Medical University, NO. 32 Meijian Road, Xuzhou, Jiangsu, 221000, China
| | - Xiao Li
- Department of Cardiology, Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, Hunan, 410011, China
| | - Yanli Zhang
- Heart Failure and Structural Cardiology Division, Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, 193 United Road, Dalian, Liaoning, 116021, China
| | - Jie Yang
- School of Public Health, Dalian Medical University, No. 9 W. Lvshun South Road, Dalian, Liaoning, 116044, China
| | - Gary Tse
- Heart Failure and Structural Cardiology Division, Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, 193 United Road, Dalian, Liaoning, 116021, China
| | - Ying Liu
- Heart Failure and Structural Cardiology Division, Department of Cardiology, The First Affiliated Hospital of Dalian Medical University, 193 United Road, Dalian, Liaoning, 116021, China
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