1
|
Jain H, Marsool MDM, Odat RM, Noori H, Jain J, Shakhatreh Z, Patel N, Goyal A, Gole S, Passey S. Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support. Cardiol Rev 2024:00045415-990000000-00260. [PMID: 38836621 DOI: 10.1097/crd.0000000000000708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.
Collapse
Affiliation(s)
- Hritvik Jain
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | | | - Ramez M Odat
- Department of Internal Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Hamid Noori
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jyoti Jain
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | - Zaid Shakhatreh
- Department of Internal Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Nandan Patel
- From the Department of Internal Medicine, All India Institte of Medical Sciences (AIIMS), Jodhpur, India
| | - Aman Goyal
- Department of Internal Medicine, Seth GS Medical College and KEM Hospital, Mumbai, India
| | - Shrey Gole
- Department of Immunology and Rheumatology, Stanford University, CA; and
| | - Siddhant Passey
- Department of Internal Medicine, University of Connecticut Health Center, CT
| |
Collapse
|
2
|
Sigfstead S, Jiang R, Avram R, Davies B, Krahn AD, Cheung CC. Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes. Can J Cardiol 2024:S0828-282X(24)00335-0. [PMID: 38670456 DOI: 10.1016/j.cjca.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 04/08/2024] [Accepted: 04/21/2024] [Indexed: 04/28/2024] Open
Abstract
Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing, and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time- and resource-intensive. To date, AI models have shown immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have shown the ability to improve testing protocols; through model identification of disease and genotype, specific clinical testing (eg, drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of AI, particularly regarding the development and implementation of clinically applicable screening tools. In this review we summarize key developments in AI, including studies in long QT syndrome, Brugada syndrome, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathies, and provide direction for effective future AI implementation in clinical practice.
Collapse
Affiliation(s)
- Sophie Sigfstead
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - River Jiang
- Division of Cardiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Robert Avram
- Heartwise (heartwise.ai), Montreal Heart Institute, Montreal, Quebec, Canada; Department of Medicine, Montreal Heart Institute, Université de Montréal, Montreal, Quebec, Canada
| | - Brianna Davies
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrew D Krahn
- Center for Cardiovascular Innovation, Division of Cardiology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Christopher C Cheung
- Division of Cardiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
3
|
Li Y, Liu Z, Liu T, Li J, Mei Z, Fan H, Cao C. Risk Prediction for Sudden Cardiac Death in the General Population: A Systematic Review and Meta-Analysis. Int J Public Health 2024; 69:1606913. [PMID: 38572495 PMCID: PMC10988292 DOI: 10.3389/ijph.2024.1606913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/01/2024] [Indexed: 04/05/2024] Open
Abstract
Objective: Identification of SCD risk is important in the general population from a public health perspective. The objective is to summarize and appraise the available prediction models for the risk of SCD among the general population. Methods: Data were obtained searching six electronic databases and reporting prediction models of SCD risk in the general population. Studies with duplicate cohorts and missing information were excluded from the meta-analysis. Results: Out of 8,407 studies identified, fifteen studies were included in the systematic review, while five studies were included in the meta-analysis. The Cox proportional hazards model was used in thirteen studies (96.67%). Study locations were limited to Europe and the United States. Our pooled meta-analyses included four predictors: diabetes mellitus (ES = 2.69, 95%CI: 1.93, 3.76), QRS duration (ES = 1.16, 95%CI: 1.06, 1.26), spatial QRS-T angle (ES = 1.46, 95%CI: 1.27, 1.69) and factional shortening (ES = 1.37, 95%CI: 1.15, 1.64). Conclusion: Risk prediction model may be useful as an adjunct for risk stratification strategies for SCD in the general population. Further studies among people except for white participants and more accessible factors are necessary to explore.
Collapse
Affiliation(s)
- Yue Li
- College of Management and Economics, Tianjin University, Tianjin, China
| | - Zhengkun Liu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China
| | - Tao Liu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China
| | - Ji Li
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China
| | - Zihan Mei
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China
| | - Haojun Fan
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China
| | - Chunxia Cao
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China
| |
Collapse
|
4
|
Memon S, Qureshi K. Comment on "Comparing the Performance of Published Risk Scores in Brugada Syndrome: A Multi-center Cohort Study". Curr Probl Cardiol 2024; 49:102113. [PMID: 37802170 DOI: 10.1016/j.cpcardiol.2023.102113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 09/30/2023] [Indexed: 10/08/2023]
Affiliation(s)
- Siraj Memon
- Liaquat University of Medical and Health Sciences, Jamshoro, Sindh, Pakistan.
| | - Kashifa Qureshi
- Liaquat University of Medical and Health Sciences, Jamshoro, Sindh, Pakistan
| |
Collapse
|
5
|
Tse G, Lee Q, Chou OHI, Chung CT, Lee S, Chan JSK, Li G, Kaur N, Roever L, Liu H, Liu T, Zhou J. Healthcare Big Data in Hong Kong: Development and Implementation of Artificial Intelligence-Enhanced Predictive Models for Risk Stratification. Curr Probl Cardiol 2024; 49:102168. [PMID: 37871712 DOI: 10.1016/j.cpcardiol.2023.102168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 10/25/2023]
Abstract
Routinely collected electronic health records (EHRs) data contain a vast amount of valuable information for conducting epidemiological studies. With the right tools, we can gain insights into disease processes and development, identify the best treatment and develop accurate models for predicting outcomes. Our recent systematic review has found that the number of big data studies from Hong Kong has rapidly increased since 2015, with an increasingly common application of artificial intelligence (AI). The advantages of big data are that i) the models developed are highly generalisable to the population, ii) multiple outcomes can be determined simultaneously, iii) ease of cross-validation by for model training, development and calibration, iv) huge numbers of useful variables can be analyzed, v) static and dynamic variables can be analyzed, vi) non-linear and latent interactions between variables can be captured, vii) artificial intelligence approaches can enhance the performance of prediction models. In this paper, we will provide several examples (cardiovascular disease, diabetes mellitus, Brugada syndrome, long QT syndrome) to illustrate efforts from a multi-disciplinary team to identify data from different modalities to develop models using territory-wide datasets, with the possibility of real-time risk updates by using new data captured from patients. The benefit is that only routinely collected data are required for developing highly accurate and high-performance models. AI-driven models outperform traditional models in terms of sensitivity, specificity, accuracy, area under the receiver operating characteristic and precision-recall curve, and F1 score. Web and/or mobile versions of the risk models allow clinicians to risk stratify patients quickly in clinical settings, thereby enabling clinical decision-making. Efforts are required to identify the best ways of implementing AI algorithms on the web and mobile apps.
Collapse
Affiliation(s)
- Gary Tse
- School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China; 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.
| | - Quinncy Lee
- Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China
| | - Oscar Hou In Chou
- Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China; Division of Clinical Pharmacology and Therapeutics, Department of Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Cheuk To Chung
- Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China
| | - Sharen Lee
- Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China
| | - Jeffrey Shi Kai Chan
- Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China
| | - Guoliang Li
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Narinder Kaur
- Family Medicine Research Unit, Cardiovascular Analytics Group, PowerHealth Research Institute, Hong Kong, China; School of Cardiovascular Science & Metabolic Health, University of Glasgow, UK
| | - Leonardo Roever
- Department of Clinical Research, Federal University of Uberlândia, Uberlândia, MG 38400384, Brazil
| | - Haipeng Liu
- Research Centre for Intelligent Healthcare, Faculty of Health and Life Sciences, Coventry University, Coventry, UK
| | - 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
| | - Jiandong Zhou
- Division of Health Science, Warwick Medical School, University of Warwick, Coventry, United Kingdom
| |
Collapse
|
6
|
Lee S, Chung CTS, Radford D, Chou OHI, Lee TTL, Ng ZMW, Roever L, Rajan R, Bazoukis G, Letsas KP, Zeng S, Liu FZ, Wong WT, Liu T, Tse G. Secular trends of health care resource utilization and costs between Brugada syndrome and congenital long QT syndrome: A territory-wide study. Clin Cardiol 2023; 46:1194-1201. [PMID: 37489866 PMCID: PMC10577540 DOI: 10.1002/clc.24102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Health care resource utilization (HCRU) and costs are important metrics of health care burden, but they have rarely been explored in the setting of cardiac ion channelopathies. HYPOTHESIS This study tested the hypothesis that attendance-related HCRUs and costs differed between patients with Brugada syndrome (BrS) and congenital long QT syndrome (LQTS). METHODS This was a retrospective cohort study of consecutive BrS and LQTS patients at public hospitals or clinics in Hong Kong, China. HCRUs and costs (in USD) for Accident and Emergency (A&E), inpatient, general outpatient and specialist outpatient attendances were analyzed between 2001 and 2019 at the cohort level. Comparisons were made using incidence rate ratios (IRRs [95% confidence intervals]). RESULTS Over the 19-year period, 516 BrS (median age of initial presentation: 51 [interquartile range: 38-61] years, 92% male) and 134 LQTS (median age of initial presentation: 21 [9-44] years, 32% male) patients were included. Compared to LQTS patients, BrS patients had lower total costs (2 008 126 [2 007 622-2 008 629] vs. 2 343 864 [2 342 828-2 344 900]; IRR: 0.857 [0.855-0.858]), higher costs for A&E attendances (83 113 [83 048-83 177] vs. 70 604 [70 487-70 721]; IRR: 1.177 [1.165-1.189]) and general outpatient services (2,176 [2,166-2,187] vs. 921 [908-935]; IRR: 2.363 [2.187-2.552]), but lower costs for inpatient stay (1 391 624 [1 391 359-1 391 889] vs. 1 713 742 [1 713 166-1 714 319]; IRR: 0.812 [0.810-0.814]) and lower costs for specialist outpatient services (531 213 [531 049-531 376] vs. 558 597 [558268-558926]; IRR: 0.951 [0.947-0.9550]). CONCLUSIONS Overall, BrS patients consume 14% less health care resources compared to LQTS patients in terms of attendance costs. BrS patients require more A&E and general outpatient services, but less inpatient and specialist outpatient services than LQTS patients.
Collapse
Affiliation(s)
- Sharen Lee
- Cardiac Electrophysiology Unit, Cardiovascular Analytics GroupPowerHealth LimitedHong KongChina
| | - Cheuk To Skylar Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics GroupPowerHealth LimitedHong KongChina
| | - Danny Radford
- Kent and Medway Medical SchoolUniversity of Kent and Canterbury Christ Church UniversityCanterburyKentUK
| | - Oscar Hou In Chou
- Cardiac Electrophysiology Unit, Cardiovascular Analytics GroupPowerHealth LimitedHong KongChina
| | - Teddy Tai Loy Lee
- Cardiac Electrophysiology Unit, Cardiovascular Analytics GroupPowerHealth LimitedHong KongChina
| | - Zita Man Wai Ng
- Cardiac Electrophysiology Unit, Cardiovascular Analytics GroupPowerHealth LimitedHong KongChina
| | - Leonardo Roever
- Department of Clinical ResearchFederal University of UberlandiaUberlandiaBrazil
| | - Rajesh Rajan
- Department of CardiologySabah Al Ahmed Cardiac CentreKuwait CityKuwait
| | - George Bazoukis
- Second Department of CardiologyEvangelismos General Hospital of AthensAthensGreece
| | | | - Shaoying Zeng
- Guangdong Cardiovascular InstituteGuangdong Provincial People's HospitalGuangzhouChina
| | - Fang Zhou Liu
- Department of Cardiology, Atrial Fibrillation Center, Guangdong Provincial Cardiovascular Institute, Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina
| | - Wing Tak Wong
- State Key Laboratory of Agrobiotechnology (CUHK), School of Life SciencesChinese University of Hong KongHong KongChina
| | - Tong Liu
- Tianjin Key Laboratory of Ionic‐Molecular Function of Cardiovascular Disease, Department of CardiologySecond Hospital of Tianjin Medical UniversityTianjinChina
| | - Gary Tse
- Tianjin Key Laboratory of Ionic‐Molecular Function of Cardiovascular Disease, Department of CardiologySecond Hospital of Tianjin Medical UniversityTianjinChina
- Division of Natural Sciences, Kent and Medway Medical SchoolUniversity of KentCanterburyKentUK
| |
Collapse
|
7
|
Zaveri S, Qu YS, Chahine M, Boutjdir M. Ethnic and racial differences in Asian populations with ion channelopathies associated with sudden cardiac death. Front Cardiovasc Med 2023; 10:1253479. [PMID: 37600027 PMCID: PMC10436680 DOI: 10.3389/fcvm.2023.1253479] [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: 07/05/2023] [Accepted: 07/21/2023] [Indexed: 08/22/2023] Open
Abstract
Cardiovascular diseases are associated with several morbidities and are the most common cause of worldwide disease-related fatalities. Studies show that treatment and outcome-related differences for cardiovascular diseases disproportionately affect minorities in the United States. The emergence of ethnic and racial differences in sudden cardiac death (SCD) and related ion channelopathies complicates cardiovascular disease prevention, diagnosis, management, prognosis, and treatment objectives for patients and physicians alike. This review compiles and synthesizes current research in cardiac ion channelopathies and genetic disorders in Asian populations, an underrepresented population in cardiovascular literature. We first present a brief introduction to SCD, noting relevant observations and statistics from around the world, including Asian populations. We then examined existing differences between Asian and White populations in research, treatment, and outcomes related to cardiac ion channelopathies and SCD, showing progression in thought and research over time for each ion channelopathy. The review also identifies research that explored phenotypic abnormalities, device usage, and risk of death in Asian patients. We touch upon the unique genetic risk factors in Asian populations that lead to cardiac ion channelopathies and SCD while comparing them to White and Western populations, particularly in the United States, where Asians comprise approximately 7% of the total population. We also propose potential solutions such as improving early genetic screening, addressing barriers affecting access to medical care and device utilization, physician training, and patient education on risks.
Collapse
Affiliation(s)
- Sahil Zaveri
- Department of Medicine, Cell Biology, and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
- Cardiovascular Research Program, VA New York Harbor Healthcare System, New York, NY, United States
| | - Yongxia Sarah Qu
- Department of Medicine, Cell Biology, and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
- Cardiovascular Research Program, VA New York Harbor Healthcare System, New York, NY, United States
- Department of Cardiology, New York Presbyterian Brooklyn Methodist Hospital, New York, NY, United States
| | - Mohamed Chahine
- CERVO Brain Research Center, Institut Universitaire en Santé Mentale de Québec, Québec, QC, Canada
- Department of Medicine, Faculté de Médecine, Université Laval, Quebec, QC, Canada
| | - Mohamed Boutjdir
- Department of Medicine, Cell Biology, and Pharmacology, State University of New York Downstate Health Sciences University, Brooklyn, NY, United States
- Cardiovascular Research Program, VA New York Harbor Healthcare System, New York, NY, United States
- Division of Cardiology, Department of Medicine, NYU Grossman School of Medicine, New York, NY, United States
| |
Collapse
|
8
|
Moturu A, Bhuchakra HP, Bodar YP, Gandhi SK, Patel P, Gutlapalli SD, Arulthasan V, Otterbeck P. Unmasking a Silent Killer and Understanding Sudden Cardiac Death in Brugada Syndrome: A Traditional Review. Cureus 2023; 15:e41076. [PMID: 37519561 PMCID: PMC10375830 DOI: 10.7759/cureus.41076] [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: 05/21/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
Brugada syndrome (BrS) is an intricate and heterogeneous genetic disorder that engenders a formidable risk of life-threatening ventricular arrhythmias (VAs). While initially regarded as an electrophysiological aberration, emergent studies have illuminated the presence of underlying structural anomalies in select BrS cases. Although mutations in the SCN5A gene encoding the α-subunit of the cardiac sodium channel were originally identified as a primary causative factor; they account for only a fraction of the syndrome's multifaceted complexity pointing at genetic heterogeneity as a contributing factor. Remarkably, BrS has been linked to a higher incidence of fatal arrhythmic incidents and sudden cardiac death (SCD) with about 4% of SCD cases thought to be caused by BrS. Patients who spontaneously exhibit type one Brugada ECGs are more likely to experience cardiac events, emphasizing the importance of early risk stratification. To aid in risk stratification, the Shanghai score; a multifactorial risk stratification scoring system that incorporates ECG, clinical history, family history, and genetic test results; is utilized to identify those most susceptible to SCD. Beyond single ECGs, evaluation of arrhythmic findings from 24-hour Holter monitoring, ECG variables, electrophysiologic study (EPS) status in the temporal domain, and EPS data collected over time are all critical factors in risk classification. Among management options avoidance of triggers, early risk stratification, and implantation of an Implantable Cardioverter-Defibrillator (ICD) are recommended for asymptomatic patients. For symptomatic patients, pharmacotherapy and ICD implantation are available, with the latter being a highly effective choice for treating and preventing lethal arrhythmias in BrS.
Collapse
Affiliation(s)
- Aadya Moturu
- Department of Internal Medicine, Sri Ramaswamy Memorial Medical College Hospital and Research Centre, Chennai, IND
| | - Hamsa Priya Bhuchakra
- Department of Internal Medicine, Apollo Institute of Medical Sciences and Research, Hyderabad, IND
| | - Yashvant P Bodar
- Department of Internal Medicine, Orenburg State Medical University, Orenburg, RUS
| | | | - Priyansh Patel
- Department of Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
- Department of Internal Medicine, Medical College Baroda, Vadodara, IND
| | - Sai Dheeraj Gutlapalli
- Department of Internal Medicine, Richmond University Medical Center, New York City, USA
- Department of Internal Medicine, California Institute of Behavioral Neurosciences & Psychology, Fairfield, USA
| | | | - Philip Otterbeck
- Department of Internal Medicine, Richmond University Medical Center, New York City, USA
| |
Collapse
|
9
|
Holmström L, Zhang FZ, Ouyang D, Dey D, Slomka PJ, Chugh SS. Artificial Intelligence in Ventricular Arrhythmias and Sudden Death. Arrhythm Electrophysiol Rev 2023; 12:e17. [PMID: 37457439 PMCID: PMC10345967 DOI: 10.15420/aer.2022.42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/16/2023] [Indexed: 07/18/2023] Open
Abstract
Sudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due to the inadequacy of current risk stratification tools. Artificial intelligence tools are increasingly being used to solve complex problems and are poised to help with this major unmet need in the field of clinical electrophysiology. By leveraging large and detailed datasets, artificial intelligence-based prediction models have the potential to enhance the risk stratification of lethal ventricular arrhythmias. This review presents a synthesis of the published literature and a discussion of future directions in this field.
Collapse
Affiliation(s)
- Lauri Holmström
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, CA, US
| | - Frank Zijun Zhang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
| | - David Ouyang
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
| | - Damini Dey
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
| | - Piotr J Slomka
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
| | - Sumeet S Chugh
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Health System, Los Angeles, CA, US
- Center for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, CA, US
| |
Collapse
|
10
|
Liu Y, Zheng Y, Tse G, Bazoukis G, Letsas K, Goudis C, Korantzopoulos P, Li G, Liu T. Association between sick sinus syndrome and atrial fibrillation: A systematic review and meta-analysis. Int J Cardiol 2023; 381:20-36. [PMID: 37023861 DOI: 10.1016/j.ijcard.2023.03.066] [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: 12/28/2022] [Revised: 03/17/2023] [Accepted: 03/31/2023] [Indexed: 04/08/2023]
Abstract
AIMS Sick sinus syndrome (SSS) and atrial fibrillation (AF) frequently coexist and show a bidirectional relationship. This systematic review and meta-analysis aimed to decipher the precise relationship between SSS and AF, further exploring and comparing different therapy strategies on the occurrence or progression of AF in patients with SSS. METHODS AND RESULTS A systematic literature search was conducted until November 2022. A total of 35 articles with 37,550 patients were included. Patients with SSS were associated with new-onset AF compared to those without SSS. Catheter ablation was associated with a lower risk of AF recurrence, AF progression, all-cause mortality, stroke and hospitalization of heart failure compared to pacemaker therapy. Regarding the different pacing strategies for SSS, VVI/VVIR has higher risk of new-onset AF than DDD/DDDR. No significant difference was found between AAI/AAIR and DDD/DDDR, as well as between DDD/DDDR and minimal ventricular pacing (MVP) for AF recurrence. AAI/AAIR was associated with higher risk of all-cause mortality when compared to DDD/DDDR, but lower risk of cardiac death when compared to DDD/DDDR. Right atrial septum pacing was associated with a similar risk of new-onset AF or AF recurrence compared to right atrial appendage pacing. CONCLUSION SSS is associated with a higher risk of AF. For patients with both SSS and AF, catheter ablation should be considered. This meta-analysis re-emphasizes that high percentage of ventricular pacing should be avoided in patients with SSS in order to decrease AF burden and mortality.
Collapse
Affiliation(s)
- Ying 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
| | - Yi Zheng
- 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
| | - 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, University of Kent and Canterbury Christ Church University, Canterbury, Kent, UK; School of Nursing and Health Studies, Hong Kong, Metropolitan University, Hong Kong, China
| | - 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
| | - Konstantinos Letsas
- Laboratory of Cardiac Electrophysiology, Onassis Cardiac Surgery Center, Athens, Greece
| | - Christos Goudis
- Department of Cardiology, Serres General Hospital, 45110 Serres, Greece
| | | | - Guangping 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 300211, 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.
| |
Collapse
|
11
|
Cavalcante CHL, Primo PEO, Sales CAF, Caldas WL, Silva JHM, Souza AH, Marinho ES, Pedrosa RC, Marques JAL, Santos HS, Madeiro JPV. Sudden cardiac death multiparametric classification system for Chagas heart disease's patients based on clinical data and 24-hours ECG monitoring. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:9159-9178. [PMID: 37161238 DOI: 10.3934/mbe.2023402] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.
Collapse
Affiliation(s)
- Carlos H L Cavalcante
- Federal Institute of Education and Technology of Ceara, Maracanau, Ceara, Brazil
- State University of Ceara - Center for Science and Technology, Fortaleza, Ceara, Brazil
| | - Pedro E O Primo
- Computer Science Department - Federal University of Ceara, Fortaleza, Ceara, Brazil
| | - Carlos A F Sales
- Federal Institute of Education and Technology of Ceara, Maracanau, Ceara, Brazil
| | - Weslley L Caldas
- Computer Science Department - Federal University of Ceara, Fortaleza, Ceara, Brazil
| | - João H M Silva
- Oswaldo Cruz Foundation (Fiocruz), Eusebio, Ceara, Brazil
| | - Amauri H Souza
- Federal Institute of Education and Technology of Ceara, Maracanau, Ceara, Brazil
| | - Emmanuel S Marinho
- State University of Ceara - Center for Science and Technology, Fortaleza, Ceara, Brazil
| | - Roberto C Pedrosa
- Edson Saad Heart Institute - Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - João A L Marques
- Laboratory of Applied Neurosciences -University of Saint Joseph, Macau SAR, China
| | - Hélcio S Santos
- State University of Ceara - Center for Science and Technology, Fortaleza, Ceara, Brazil
| | - João P V Madeiro
- Computer Science Department - Federal University of Ceara, Fortaleza, Ceara, Brazil
| |
Collapse
|
12
|
Lee S, Chung CT, Chou OHI, Lee TTL, Radford D, Jeevaratnam K, Wong WT, Cheng SH, Mok NS, Liu T, Tse G. Attendance-related Healthcare Resource Utilisation and Costs in Patients With Brugada Syndrome in Hong Kong: A Retrospective Cohort Study. Curr Probl Cardiol 2023; 48:101513. [PMID: 36414041 DOI: 10.1016/j.cpcardiol.2022.101513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 11/13/2022] [Indexed: 11/21/2022]
Abstract
Understanding health care resource utilisation and its associated costs are important for identifying areas of improvement regarding resource allocations. However, there is limited research exploring this issue in the setting of Brugada syndrome (BrS).This was a retrospective territory-wide study of BrS patients from Hong Kong. Healthcare resource utilisation for accident and emergency (A&E), inpatient and specialist outpatient attendances were analyzed over a 19-year period, with their associated costs presented in US dollars. A total of 507 BrS patients with a mean presentation age of 49.9 ± 16.3 years old were included. Of these, 384 patients displayed spontaneous type 1 electrocardiographic (ECG) Brugada pattern and 77 patients had presented with ventricular tachycardia/ventricular fibrillation (VT/VF). At the individual patient level, the median annualized costs were $110 (52-224) at the (A&E) setting, $6812 (1982-32414) at the inpatient setting and $557 (326-1001) for specialist outpatient attendances. Patients with initial VT/VF presentation had overall greater costs in inpatient ($20161 [9147-189215] vs $5290 [1613-24937],P < 0.0001) and specialist outpatient setting ($776 [438-1076] vs $542 [293-972],P = 0.015) compared to those who did not present VT. In addition, patients without Type 1 ECG pattern had greater median costs in the specialist outpatient setting ($7036 [3136-14378] vs $4895 [2409-10554],p=0.019). There is a greater health care demand in the inpatient and specialist outpatient settings for BrS patients. The most expensive attendance type was inpatient setting stay at $6812 per year. The total median annualized cost of BrS patients without VT/VF presentation was 78% lower compared to patients with VT/VF presentation.
Collapse
Affiliation(s)
- Sharen Lee
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, Hong Kong, P. R. China-UK
| | - Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, Hong Kong, P. R. China-UK
| | - Oscar Hou In Chou
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, Hong Kong, P. R. China-UK
| | - Teddy Tai Loy Lee
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, Hong Kong, P. R. China-UK
| | - Danny Radford
- Kent and Medway Medical School, Canterbury, Kent, UK
| | | | - Wing Tak Wong
- School of Life Sciences, Chinese University of Hong Kong, Hong Kong, Hong Kong, P. R. China
| | - Shuk Han Cheng
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, Hong Kong, P.R.China
| | - Ngai Shing Mok
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong Hospital Authority, Hong Kong, Hong Kong, P.R.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, Tianjin, P.R.China.
| | - Gary Tse
- Kent and Medway Medical School, Canterbury, Kent, UK; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, Tianjin, P.R.China; School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China.
| |
Collapse
|
13
|
Barker J, Li X, Khavandi S, Koeckerling D, Mavilakandy A, Pepper C, Bountziouka V, Chen L, Kotb A, Antoun I, Mansir J, Smith-Byrne K, Schlindwein FS, Dhutia H, Tyukin I, Nicolson WB, Ng GA. Machine learning in sudden cardiac death risk prediction: a systematic review. Europace 2022; 24:1777-1787. [PMID: 36201237 DOI: 10.1093/europace/euac135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 07/07/2022] [Indexed: 11/23/2022] Open
Abstract
AIMS Most patients who receive implantable cardioverter defibrillators (ICDs) for primary prevention do not receive therapy during the lifespan of the ICD, whilst up to 50% of sudden cardiac death (SCD) occur in individuals who are considered low risk by conventional criteria. Machine learning offers a novel approach to risk stratification for ICD assignment. METHODS AND RESULTS Systematic search was performed in MEDLINE, Embase, Emcare, CINAHL, Cochrane Library, OpenGrey, MedrXiv, arXiv, Scopus, and Web of Science. Studies modelling SCD risk prediction within days to years using machine learning were eligible for inclusion. Transparency and quality of reporting (TRIPOD) and risk of bias (PROBAST) were assessed. A total of 4356 studies were screened with 11 meeting the inclusion criteria with heterogeneous populations, methods, and outcome measures preventing meta-analysis. The study size ranged from 122 to 124 097 participants. Input data sources included demographic, clinical, electrocardiogram, electrophysiological, imaging, and genetic data ranging from 4 to 72 variables per model. The most common outcome metric reported was the area under the receiver operator characteristic (n = 7) ranging between 0.71 and 0.96. In six studies comparing machine learning models and regression, machine learning improved performance in five. No studies adhered to a reporting standard. Five of the papers were at high risk of bias. CONCLUSION Machine learning for SCD prediction has been under-applied and incorrectly implemented but is ripe for future investigation. It may have some incremental utility in predicting SCD over traditional models. The development of reporting standards for machine learning is required to improve the quality of evidence reporting in the field.
Collapse
Affiliation(s)
- Joseph Barker
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Cardiology Department, Glenfield Hospital, University Hospitals Leicester, Leicester, UK
| | - Xin Li
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- School of Engineering, University of Leicester, Leicester, UK
| | - Sarah Khavandi
- Faculty of Medicine, Imperial College School of Medicine, Imperial College London, London, UK
| | - David Koeckerling
- Division of Angiology, Swiss Cardiovascular Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Akash Mavilakandy
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Coral Pepper
- Library and Information Service, University Hospitals of Leicester NHS Trust, Leicester, UK
| | | | - Long Chen
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
| | - Ahmed Kotb
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Cardiology Department, Glenfield Hospital, University Hospitals Leicester, Leicester, UK
| | - Ibrahim Antoun
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | | | - Karl Smith-Byrne
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Fernando S Schlindwein
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- School of Engineering, University of Leicester, Leicester, UK
| | - Harshil Dhutia
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Cardiology Department, Glenfield Hospital, University Hospitals Leicester, Leicester, UK
| | - Ivan Tyukin
- Department of Mathematics, University of Leicester, Leicester, UK
| | - William B Nicolson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Cardiology Department, Glenfield Hospital, University Hospitals Leicester, Leicester, UK
| | - G Andre Ng
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- Cardiology Department, Glenfield Hospital, University Hospitals Leicester, Leicester, UK
- Cardiovascular Theme, National Institute for Health Research, Leicester Biomedical Research Centre, Leicester, UK
| |
Collapse
|
14
|
Comparing the performance of published risk scores in Brugada syndrome: a multi-center cohort study. Curr Probl Cardiol 2022; 47:101381. [PMID: 36058344 DOI: 10.1016/j.cpcardiol.2022.101381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 08/26/2022] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. The present study evaluated the predictive performance of different risk scores in an Asian BrS population and its intermediate risk subgroup. METHODS This retrospective cohort study included consecutive patients diagnosed with BrS from January 1st, 1997 to June 20th, 2020 from Hong Kong. The primary outcome is sustained ventricular tachyarrhythmias. Two novel risk risk scores and seven machine learning-based models (random survival forest, Ada boost classifier, Gaussian naïve Bayes, light gradient boosting machine, random forest classifier, gradient boosting classifier and decision tree classifier) were developed. The area under the receiver operator characteristic (ROC) curve (AUC) [95% confidence intervals] was compared between the different models. RESULTS This study included 548 consecutive BrS patients (7% female, age at diagnosis: 50±16 years, follow-up: 84±55 months). For the whole cohort, the score developed by Sieira et al. showed the best performance (AUC: 0.806 [0.747-0.865]). A novel risk score was developed using the Sieira score and additional variables significant on univariable Cox regression (AUC: 0.855 [0.808-0.901]). A simpler score based on non-invasive results only showed a statistically comparable AUC (0.784 [0.724-0.845]), improved using random survival forests (AUC: 0.942 [0.913-0.964]). For the intermediate risk subgroup (N=274), a gradient boosting classifier model showed the best performance (AUC: 0.814 [0.791-0.832]). CONCLUSION A simple risk score based on clinical and electrocardiographic variables showed a good performance for predicting VT/VF, improved using machine learning. Abstract: The management of Brugada Syndrome (BrS) patients at intermediate risk of arrhythmic events remains controversial. This study evaluated the predictive performance of published risk scores in a cohort of BrS patients from Hong Kong (N=548) and its intermediate risk subgroup (N=274). A novel risk score developed by modifying the best performing existing score (by. Sieira et al.) showed an area under the curve of 0.855 and 0.760 for the whole BrS cohort and the intermediate risk subgroup, respectively. The performance of the different scores was significantly improved machine learning-based methods, such as random survival forests and gradient boosting classifier.
Collapse
|
15
|
Chung CT, Lee S, Zhou J, Chou OHI, Lee TTL, Leung KSK, Jeevaratnam K, Wong WT, Liu T, Tse G. Clinical Characteristics, Genetic Basis and Healthcare Resource Utilisation and Costs in Patients with Catecholaminergic Polymorphic Ventricular Tachycardia: A Retrospective Cohort Study. Rev Cardiovasc Med 2022; 23:276. [PMID: 39076628 PMCID: PMC11266943 DOI: 10.31083/j.rcm2308276] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/31/2022] [Accepted: 06/20/2022] [Indexed: 10/11/2023] Open
Abstract
Background This study examined the clinical characteristics, genetic basis, healthcare utilisation and costs of catecholaminergic ventricular tachycardia (CPVT) patients from a Chinese city. Methods This was a territory-wide retrospective cohort study of consecutive CPVT patients at public hospitals or clinics in Hong Kong. Healthcare resource utilisation for accident and emergency (A&E), inpatient and outpatient attendances were analysed over 19 years (2001-2019) followed by calculations of annualised costs (in USD). Results Sixteen patients with a median presentation age (interquartile range (IQR) of 11 (9-14) years old) were included. Fifteen patients (93.8%) were initially symptomatic. Ten patients had both premature ventricular complexes (PVCs) and ventricular tachycardia/fibrillation (VT/VF). One patient had PVCs without VT/VF. Genetic tests were performed on 14 patients (87.5%). Eight (57.1%) tested positive for the ryanodine receptor 2 (RyR2) gene. Seven variants have been described elsewhere (c.14848G > A, c.12475C > A, c.7420A > G, c.11836G > A, c.14159T > C, c.10046C > T and c.7202G > A). c.14861C > G is a novel RyR2 variant not been reported outside this cohort. Patients were treated with beta-blockers (n = 16), amiodarone (n = 3) and verapamil (n = 2). Sympathectomy (n = 8) and implantable-cardioverter defibrillator implantation (n = 3) were performed. Over a median follow-up of 13.3 years (IQR: 8.4-18.1) years, six patients exhibited incident VT/VF. At the patient level, the median (IQR) annualised costs for A&E, inpatient and outpatient attendances were $ 66 (40-95), $ 10521 (5240-66887) and $ 791 (546-1105), respectively. Conclusions All patients presented before the age of 19. The yield of genetic testing was 57%. The most expensive attendance type was inpatient stays, followed by outpatients and A&E attendances.
Collapse
Affiliation(s)
- Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, 999077 Hong Kong, China
| | - Sharen Lee
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, 999077 Hong Kong, China
| | - Jiandong Zhou
- School of Data Science, City University of Hong Kong, 999077 Hong Kong, China
| | - Oscar Hou In Chou
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, 999077 Hong Kong, China
| | - Teddy Tai Loy Lee
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, 999077 Hong Kong, China
| | - Keith Sai Kit Leung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, 999077 Hong Kong, China
| | - Kamalan Jeevaratnam
- Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH Guildford, UK
| | - Wing Tak Wong
- State Key Laboratory of Agrobiotechnology (CUHK), School of Life Sciences, Chinese University of Hong Kong, 999077 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, 300211 Tianjin, China
| | - Gary Tse
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, 999077 Hong Kong, China
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, China
| |
Collapse
|
16
|
Zhou J, Chou OHI, Wong KHG, Lee S, Leung KSK, Liu T, Cheung BMY, Wong ICK, Tse G, Zhang Q. Development of an Electronic Frailty Index for Predicting Mortality and Complications Analysis in Pulmonary Hypertension Using Random Survival Forest Model. Front Cardiovasc Med 2022; 9:735906. [PMID: 35872897 PMCID: PMC9304657 DOI: 10.3389/fcvm.2022.735906] [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] [Received: 07/03/2021] [Accepted: 04/20/2022] [Indexed: 12/14/2022] Open
Abstract
Background The long-term prognosis of the cardio-metabolic and renal complications, in addition to mortality in patients with newly diagnosed pulmonary hypertension, are unclear. This study aims to develop a scalable predictive model in the form of an electronic frailty index (eFI) to predict different adverse outcomes. Methods This was a population-based cohort study of patients diagnosed with pulmonary hypertension between January 1st, 2000 and December 31st, 2017, in Hong Kong public hospitals. The primary outcomes were mortality, cardiovascular complications, renal diseases, and diabetes mellitus. The univariable and multivariable Cox regression analyses were applied to identify the significant risk factors, which were fed into the non-parametric random survival forest (RSF) model to develop an eFI. Results A total of 2,560 patients with a mean age of 63.4 years old (interquartile range: 38.0–79.0) were included. Over a follow-up, 1,347 died and 1,878, 437, and 684 patients developed cardiovascular complications, diabetes mellitus, and renal disease, respectively. The RSF-model-identified age, average readmission, anti-hypertensive drugs, cumulative length of stay, and total bilirubin were among the most important risk factors for predicting mortality. Pair-wise interactions of factors including diagnosis age, average readmission interval, and cumulative hospital stay were also crucial for the mortality prediction. Patients who developed all-cause mortality had higher values of the eFI compared to those who survived (P < 0.0001). An eFI ≥ 9.5 was associated with increased risks of mortality [hazard ratio (HR): 1.90; 95% confidence interval [CI]: 1.70–2.12; P < 0.0001]. The cumulative hazards were higher among patients who were 65 years old or above with eFI ≥ 9.5. Using the same cut-off point, the eFI predicted a long-term mortality over 10 years (HR: 1.71; 95% CI: 1.53–1.90; P < 0.0001). Compared to the multivariable Cox regression, the precision, recall, area under the curve (AUC), and C-index were significantly higher for RSF in the prediction of outcomes. Conclusion The RSF models identified the novel risk factors and interactions for the development of complications and mortality. The eFI constructed by RSF accurately predicts the complications and mortality of patients with pulmonary hypertension, especially among the elderly.
Collapse
Affiliation(s)
- Jiandong Zhou
- Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Oscar Hou In Chou
- Frailty Assessment Unit, Cardiovascular Analytics Group, Hong Kong, Hong Kong SAR, China
- Division of Clincal Pharmacology, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Ka Hei Gabriel Wong
- Frailty Assessment Unit, Cardiovascular Analytics Group, Hong Kong, Hong Kong SAR, China
| | - Sharen Lee
- Frailty Assessment Unit, Cardiovascular Analytics Group, 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
| | - Bernard Man Yung Cheung
- Division of Clincal Pharmacology, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Ian Chi Kei Wong
- Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Medicines Optimisation Research and Education, UCL School of Pharmacy, London, United Kingdom
| | - Gary Tse
- Frailty Assessment Unit, Cardiovascular Analytics Group, Hong Kong, Hong Kong SAR, China
- Kent and Medway Medical School, Canterbury, United Kingdom
- *Correspondence: Qingpeng Zhang
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Gary Tse ;
| |
Collapse
|
17
|
Lakhani I, Zhou J, Lee S, Li KHC, Leung KSK, Hui JMH, Lee YHA, Li G, Liu T, Wong WT, Wong ICK, Mok NS, Mak CM, Zhang Q, Tse G. A Territory-Wide Study of Arrhythmogenic Right Ventricular Cardiomyopathy Patients from Hong Kong. Rev Cardiovasc Med 2022; 23:231. [PMID: 39076921 PMCID: PMC11266799 DOI: 10.31083/j.rcm2307231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 05/12/2022] [Accepted: 05/16/2022] [Indexed: 10/11/2023] Open
Abstract
Background Arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) is a hereditary disease characterized by fibrofatty infiltration of the right ventricular myocardium that predisposes affected patients to malignant ventricular arrhythmias, dual-chamber cardiac failure and sudden cardiac death (SCD). The present study aims to investigate the risk of detrimental cardiovascular events in an Asian population of ARVC/D patients, including the incidence of malignant ventricular arrhythmias, new-onset heart failure with reduced ejection fraction (HFrEF), as well as long-term mortality. Methods and Results This was a territory-wide retrospective cohort study of patients diagnosed with ARVC/D between 1997 and 2019 in Hong Kong. This study consisted of 109 ARVC/D patients (median age: 61 [46-71] years; 58% male). Of these, 51 and 24 patients developed incident VT/VF and new-onset HFrEF, respectively. Five patients underwent cardiac transplantation, and 14 died during follow-up. Multivariate Cox regression identified prolonged QRS duration as a predictor of VT/VF (p < 0.05). Female gender, prolonged QTc duration, the presence of epsilon waves and T-wave inversion (TWI) in any lead except aVR/V1 predicted new-onset HFrEF (p < 0.05). The presence of epsilon waves, in addition to the parameters of prolonged QRS duration and worsening ejection fraction predicted all-cause mortality (p < 0.05). Clinical scores were developed to predict incident VT/VF, new-onset HFrEF and all-cause mortality, and all were significantly improved by machine learning techniques. Conclusions Clinical and electrocardiographic parameters are important for assessing prognosis in ARVC/D patients and should in turn be used in tandem to aid risk stratification in the hospital setting.
Collapse
Affiliation(s)
- Ishan Lakhani
- Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China
| | - Jiandong Zhou
- School of Data Science, City University of Hong Kong, Hong Kong, China
| | - Sharen Lee
- Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China
| | | | | | - Jeremy Man Ho Hui
- Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China
| | - Yan Hiu Athena Lee
- Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China
| | - Guoliang Li
- Arrhythmia Unit, Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, 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, 300211 Tianjin, China
| | - Wing Tak Wong
- State Key Laboratory of Agrobiotechnology (CUHK), School of Life Sciences, Chinese University of Hong Kong, Hong Kong, China
| | - Ian Chi Kei Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- School of Pharmacy, University College London, WC1E 6BT London, UK
| | - Ngai Shing Mok
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong Hospital Authority, Hong Kong, China
| | - Chloe Miu Mak
- Department of Pathology, Hong Kong Children’s Hospital, Hospital Authority, Hong Kong, China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong, 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, 300211 Tianjin, China
| |
Collapse
|
18
|
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.
Collapse
|
19
|
Aziz HM, Zarzecki MP, Garcia-Zamora S, Kim MS, Bijak P, Tse G, Won HH, Matusik PT. Pathogenesis and Management of Brugada Syndrome: Recent Advances and Protocol for Umbrella Reviews of Meta-Analyses in Major Arrhythmic Events Risk Stratification. J Clin Med 2022; 11:jcm11071912. [PMID: 35407520 PMCID: PMC8999897 DOI: 10.3390/jcm11071912] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/14/2022] [Accepted: 03/25/2022] [Indexed: 12/13/2022] Open
Abstract
Brugada syndrome (BrS) is a primary electrical disease associated with life-threatening arrhythmias. It is estimated to cause at least 20% of sudden cardiac deaths (SCDs) in patients with normal cardiac anatomy. In this review paper, we discuss recent advances in complex BrS pathogenesis, diagnostics, and current standard approaches to major arrhythmic events (MAEs) risk stratification. Additionally, we describe a protocol for umbrella reviews to systematically investigate clinical, electrocardiographic, electrophysiological study, programmed ventricular stimulation, and genetic factors associated with BrS, and the risk of MAEs. Our evaluation will include MAEs such as sustained ventricular tachycardia, ventricular fibrillation, appropriate implantable cardioverter–defibrillator therapy, sudden cardiac arrest, and SCDs from previous meta-analytical studies. The protocol was written following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) guidelines. We plan to extensively search PubMed, Embase, and Scopus databases for meta-analyses concerning risk-stratification in BrS. Data will be synthesized integratively with transparency and accuracy. Heterogeneity patterns across studies will be reported. The Joanna Briggs Institute (JBI) methodology, A MeaSurement Tool to Assess systematic Reviews 2 (AMSTAR 2), and the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) are planned to be applied for design and execution of our evidence-based research. To the best of our knowledge, these will be the first umbrella reviews to critically evaluate the current state of knowledge in BrS risk stratification for life-threatening ventricular arrhythmias, and will potentially contribute towards evidence-based guidance to enhance clinical decisions.
Collapse
Affiliation(s)
- Hasina Masha Aziz
- Faculty of Medicine, Jagiellonian University Medical College, 31-530 Kraków, Poland;
| | - Michał P. Zarzecki
- Department of Anatomy, Jagiellonian University Medical College, 31-034 Kraków, Poland;
| | | | - Min Seo Kim
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul 06351, Korea;
| | - Piotr Bijak
- John Paul II Hospital, 31-202 Kraków, Poland;
| | - Gary Tse
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, China;
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300070, China
- Kent and Medway Medical School, University of Kent and Canterbury Christ Church University, Canterbury CT2 7FS, UK
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea;
| | - Paweł T. Matusik
- Department of Electrocardiology, Institute of Cardiology, Faculty of Medicine, Jagiellonian University Medical College, 31-202 Kraków, Poland
- Department of Electrocardiology, The John Paul II Hospital, 31-202 Kraków, Poland
- Correspondence:
| |
Collapse
|
20
|
Chung CT, Bazoukis G, Radford D, Coakley-Youngs E, Rajan R, Matusik PT, Liu T, Letsas K, Lee S, Tse G. Predictive risk models for forecasting arrhythmic outcomes in Brugada syndrome: A focused review. J Electrocardiol 2022; 72:28-34. [PMID: 35287003 DOI: 10.1016/j.jelectrocard.2022.02.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 02/19/2022] [Accepted: 02/20/2022] [Indexed: 12/20/2022]
|
21
|
Liang Y, Li X, Tse G, King E, Roever L, Li G, Liu T. Syncope Prediction Scores in the Emergency Department. Curr Cardiol Rev 2022; 18:1-7. [PMID: 35319380 PMCID: PMC9896417 DOI: 10.2174/1573403x18666220321104129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 12/03/2021] [Accepted: 01/10/2022] [Indexed: 11/22/2022] Open
Abstract
Syncope is a common clinical presentation defined as a transient loss of consciousness (TLOC) due to cerebral hypoperfusion, characterized by a rapid onset, short duration, and spontaneous complete recovery. Different clinical decision rules (CDRs) and risk stratification scores have been developed to predict short- and long-term risks for adverse outcomes after syncope. The central theme of these prediction systems is consistent with the ESC syncope guidelines. Initial assessment according to the ESC guideline is essential until an optimal and well-validated risk score is available. The focus should be accurate risk stratification to allow prevention of adverse outcomes and optimize the use of limited healthcare resources. In this review article, we summarize and critically appraise the evidence regarding the CDRs for patients presenting with syncope.
Collapse
Affiliation(s)
- Yan Liang
- Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Pingjiang Road, Hexi District, Tianjin 300211, People’s Republic of China
| | - Xiulian Li
- Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Pingjiang Road, Hexi District, Tianjin 300211, People’s Republic of China
| | - Gary Tse
- Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Pingjiang Road, Hexi District, Tianjin 300211, People’s Republic of China
- Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China
| | - Emma King
- Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Pingjiang Road, Hexi District, Tianjin 300211, People’s Republic of China
- Cardiovascular Analytics Group, Laboratory of Cardiovascular Physiology, Hong Kong, China
| | | | - Guangping Li
- Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Pingjiang Road, Hexi District, Tianjin 300211, People’s Republic of China
| | - Tong Liu
- Department of Cardiology, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Pingjiang Road, Hexi District, Tianjin 300211, People’s Republic of China
| |
Collapse
|
22
|
Lee S, Zhou J, Jeevaratnam K, Wong WT, Wong ICK, Mak C, Mok NS, Liu T, Zhang Q, Tse G. Paediatric/young versus adult patients with long QT syndrome. Open Heart 2021; 8:openhrt-2021-001671. [PMID: 34518285 PMCID: PMC8438947 DOI: 10.1136/openhrt-2021-001671] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 08/02/2021] [Indexed: 12/20/2022] Open
Abstract
Introduction Long QT syndrome (LQTS) is a less prevalent cardiac ion channelopathy than Brugada syndrome in Asia. The present study compared the outcomes between paediatric/young and adult LQTS patients. Methods This was a population-based retrospective cohort study of consecutive patients diagnosed with LQTS attending public hospitals in Hong Kong. The primary outcome was spontaneous ventricular tachycardia/ventricular fibrillation (VT/VF). Results A total of 142 LQTS (mean onset age=27±23 years old) were included. Arrhythmias other than VT/VF (HR 4.67, 95% CI (1.53 to 14.3), p=0.007), initial VT/VF (HR=3.25 (95% CI 1.29 to 8.16), p=0.012) and Schwartz score (HR=1.90 (95% CI 1.11 to 3.26), p=0.020) were predictive of the primary outcome for the overall cohort, while arrhythmias other than VT/VF (HR=5.41 (95% CI 1.36 to 21.4), p=0.016) and Schwartz score (HR=4.67 (95% CI 1.48 to 14.7), p=0.009) were predictive for the adult subgroup (>25 years old; n=58). A random survival forest model identified initial VT/VF, Schwartz score, initial QTc interval, family history of LQTS, initially asymptomatic and arrhythmias other than VT/VF as the most important variables for risk prediction. Conclusion Clinical and ECG presentation varies between the paediatric/young and adult LQTS population. Machine learning models achieved more accurate VT/VF prediction.
Collapse
Affiliation(s)
- Sharen Lee
- Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration
| | - Jiandong Zhou
- School of Data Science, City University of Hong Kong, Hong Kong, People's Republic of China
| | - Kamalan Jeevaratnam
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK
| | - Wing Tak Wong
- School of Life Sciences, Chinese University of Hong Kong, Hong Kong, People's Republic of China
| | - Ian Chi Kei Wong
- Research Department of Practice and Policy, University College London School of Pharmacy, London, UK
| | - Chloe Mak
- Department of Pathology, Hong Kong Children's Hospital, Hong Kong, People's Republic of China
| | - Ngai Shing Mok
- Department of Medicine and Geriatrics, Princess Margaret Hospital, Hong Kong, People's Republic of 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, People's Republic of China
| | - Qingpeng Zhang
- School of Data Science, City University of Hong Kong, Hong Kong, People's Republic of China
| | - Gary Tse
- Cardiovascular Analytics Group, Hong Kong, China-UK Collaboration .,Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK.,Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin, People's Republic of China
| |
Collapse
|
23
|
Ventrella P, Delgrossi G, Ferrario G, Righetti M, Masseroli M. Supervised machine learning for the assessment of Chronic Kidney Disease advancement. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 209:106329. [PMID: 34418814 DOI: 10.1016/j.cmpb.2021.106329] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Chronic Kidney Disease (CKD) is a condition characterized by a progressive loss of kidney function over time caused by many diseases. The most effective weapons against CKD are early diagnosis and treatment, which in most of the cases can only postpone the onset of complete kidney failure. The CKD grading system is classified based on the estimated Glomerular Filtration Rate (eGFR), and it helps to stratify patients for risk, follow up and management planning. This study aims to effectively predict how soon a CKD patient will need to be dialyzed, thus allowing personalized care and strategic planning of treatment. METHODS To accurately predict the time frame within which a CKD patient will necessarily have to be dialyzed, a computational model based on a supervised machine learning approach is developed. Many techniques, regarding both information extraction and model training phases, are compared in order to understand which approaches are most effective. The different models compared are trained on the data extracted from the Electronic Medical Records of the Vimercate Hospital. RESULTS As final model, we propose a set of Extremely Randomized Trees classifiers considering 27 features, including creatinine level, urea, red blood cells count, eGFR trend (which is not even the most important), age and associated comorbidities. In predicting the occurrence of complete renal failure within the next year rather than later, it obtains a test accuracy of 94%, specificity of 91% and sensitivity of 96%. More and shorter time-frame intervals, up to 6 months of granularity, can be specified without relevantly worsening the model performance. CONCLUSIONS The developed computational model provides nephrologists with a great support in predicting the patient's clinical pathway. The model promising results, coupled with the knowledge and experience of the clinicians, can effectively lead to better personalized care and strategic planning of both patient's needs and hospital resources.
Collapse
Affiliation(s)
| | - Giovanni Delgrossi
- ASST Vimercate, Via Santi Cosma e Damiano, 10, 20871 Vimercate (MB), Italy
| | | | - Marco Righetti
- ASST Vimercate, Via Santi Cosma e Damiano, 10, 20871 Vimercate (MB), Italy
| | - Marco Masseroli
- DEIB, Politecnico di Milano, Piazza L. Da Vinci 32, 20133 Milan (MI), Italy
| |
Collapse
|
24
|
Lee S, Zhou J, Guo CL, Wong WT, Liu T, Wong ICK, Jeevaratnam K, Zhang Q, Tse G. Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death. ENDOCRINOLOGY DIABETES & METABOLISM 2021; 4:e00240. [PMID: 34277965 PMCID: PMC8279628 DOI: 10.1002/edm2.240] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/08/2021] [Accepted: 02/09/2021] [Indexed: 12/11/2022]
Abstract
Introduction The present study evaluated the application of incorporating non‐linear J/U‐shaped relationships between mean HbA1c and cholesterol levels into risk scores for predicting acute myocardial infarction (AMI) and non‐AMI‐related sudden cardiac death (SCD) respectively, amongst patients with type 2 diabetes mellitus. Methods This was a territory‐wide cohort study of patients with type 2 diabetes mellitus above the age 40 and free from prior AMI and SCD, with or without prescriptions of anti‐diabetic agents between January 1st, 2009 to December 31st, 2009 at government‐funded hospitals and clinics in Hong Kong. Patients recruited were followed up until 31 December 2019 or their date of death. Risk scores were developed for predicting incident AMI and non‐AMI‐related SCD. The performance of conditional inference survival forest (CISF) model compared to that of random survival forests (RSF) model and multivariate Cox model. Results This study included 261 308 patients (age = 66.0 ± 11.8 years old, male = 47.6%, follow‐up duration = 3552 ± 1201 days, diabetes duration = 4.77 ± 2.29 years). Mean HbA1c and low high‐density lipoprotein‐cholesterol (HDL‐C) were significant predictors of AMI on multivariate Cox regression. Mean HbA1c was linearly associated with AMI, whilst HDL‐C was inversely associated with AMI. Mean HbA1c and total cholesterol were significant multivariate predictors with a J‐shaped relationship with non‐AMI‐related SCD. The AMI and SCD risk scores had an area under the curve (AUC) of 0.666 (95% confidence interval (CI) = [0.662, 0.669]) and 0.677 (95% CI = [0.673, 0.682]), respectively. CISF significantly improves prediction performance of both outcomes compared to RSF and multivariate Cox models. Conclusion A holistic combination of demographic, clinical and laboratory indices can be used for the risk stratification of patients with type 2 diabetes mellitus for AMI and SCD.
Collapse
Affiliation(s)
- Sharen Lee
- Cardiovascular Analytics Group Laboratory of Cardiovascular Physiology Hong Kong China
| | - Jiandong Zhou
- School of Data Science City University of Hong Kong Hong Kong Hong Kong China
| | - Cosmos Liutao Guo
- Li Ka Shing Institute of Health Sciences Chinese University of Hong Kong Hong Kong China
| | - Wing Tak Wong
- School of Life Sciences Chinese University of Hong Kong 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 China
| | - Ian Chi Kei Wong
- Department of Pharmacology and Pharmacy University of Hong Kong Pokfulam Hong Kong China.,Medicines Optimisation Research and Education (CMORE UCL School of Pharmacy London UK
| | | | - Qingpeng Zhang
- School of Data Science City University of Hong Kong Hong Kong Hong Kong 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
| |
Collapse
|