{"title":"预测二尖瓣狭窄伴心房扑动的卒中和死亡率:一种机器学习方法","authors":"Amer Rauf MBBS, Asif Ullah MBBS, Usha Rathi MBBS, Zainab Ashfaq MBBS, Hidayat Ullah MBBS, Amna Ashraf MBBS, Jateesh Kumar MBBS, Maria Faraz MS, Waheed Akhtar MBBS, Amin Mehmoodi MD, Jahanzeb Malik MBBS","doi":"10.1111/anec.13078","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Our study hypothesized that an intelligent gradient boosting machine (GBM) model can predict cerebrovascular events and all-cause mortality in mitral stenosis (MS) with atrial flutter (AFL) by recognizing comorbidities, electrocardiographic and echocardiographic parameters.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The machine learning model was used as a statistical analyzer in recognizing the key risk factors and high-risk features with either outcome of cerebrovascular events or mortality.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>A total of 2184 patients with their chart data and imaging studies were included and the GBM analysis demonstrated mitral valve area (MVA), right ventricular systolic pressure, pulmonary artery pressure (PAP), left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and surgery as the most significant predictors of transient ischemic attack (TIA/stroke). MVA, PAP, LVEF, creatinine, hemoglobin, and diastolic blood pressure were predictors for all-cause mortality.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The GBM model assimilates clinical data from all diagnostic modalities and significantly improves risk prediction performance and identification of key variables for the outcome of MS with AFL.</p>\n </section>\n </div>","PeriodicalId":8074,"journal":{"name":"Annals of Noninvasive Electrocardiology","volume":"28 5","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/6d/53/ANEC-28-e13078.PMC10475890.pdf","citationCount":"0","resultStr":"{\"title\":\"Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach\",\"authors\":\"Amer Rauf MBBS, Asif Ullah MBBS, Usha Rathi MBBS, Zainab Ashfaq MBBS, Hidayat Ullah MBBS, Amna Ashraf MBBS, Jateesh Kumar MBBS, Maria Faraz MS, Waheed Akhtar MBBS, Amin Mehmoodi MD, Jahanzeb Malik MBBS\",\"doi\":\"10.1111/anec.13078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>Our study hypothesized that an intelligent gradient boosting machine (GBM) model can predict cerebrovascular events and all-cause mortality in mitral stenosis (MS) with atrial flutter (AFL) by recognizing comorbidities, electrocardiographic and echocardiographic parameters.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>The machine learning model was used as a statistical analyzer in recognizing the key risk factors and high-risk features with either outcome of cerebrovascular events or mortality.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>A total of 2184 patients with their chart data and imaging studies were included and the GBM analysis demonstrated mitral valve area (MVA), right ventricular systolic pressure, pulmonary artery pressure (PAP), left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and surgery as the most significant predictors of transient ischemic attack (TIA/stroke). MVA, PAP, LVEF, creatinine, hemoglobin, and diastolic blood pressure were predictors for all-cause mortality.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>The GBM model assimilates clinical data from all diagnostic modalities and significantly improves risk prediction performance and identification of key variables for the outcome of MS with AFL.</p>\\n </section>\\n </div>\",\"PeriodicalId\":8074,\"journal\":{\"name\":\"Annals of Noninvasive Electrocardiology\",\"volume\":\"28 5\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2023-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/6d/53/ANEC-28-e13078.PMC10475890.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of Noninvasive Electrocardiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/anec.13078\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of Noninvasive Electrocardiology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/anec.13078","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach
Background
Our study hypothesized that an intelligent gradient boosting machine (GBM) model can predict cerebrovascular events and all-cause mortality in mitral stenosis (MS) with atrial flutter (AFL) by recognizing comorbidities, electrocardiographic and echocardiographic parameters.
Methods
The machine learning model was used as a statistical analyzer in recognizing the key risk factors and high-risk features with either outcome of cerebrovascular events or mortality.
Results
A total of 2184 patients with their chart data and imaging studies were included and the GBM analysis demonstrated mitral valve area (MVA), right ventricular systolic pressure, pulmonary artery pressure (PAP), left ventricular ejection fraction (LVEF), New York Heart Association (NYHA) class, and surgery as the most significant predictors of transient ischemic attack (TIA/stroke). MVA, PAP, LVEF, creatinine, hemoglobin, and diastolic blood pressure were predictors for all-cause mortality.
Conclusion
The GBM model assimilates clinical data from all diagnostic modalities and significantly improves risk prediction performance and identification of key variables for the outcome of MS with AFL.
期刊介绍：
The ANNALS OF NONINVASIVE ELECTROCARDIOLOGY (A.N.E) is an online only journal that incorporates ongoing advances in the clinical application and technology of traditional and new ECG-based techniques in the diagnosis and treatment of cardiac patients.
ANE is the first journal in an evolving subspecialty that incorporates ongoing advances in the clinical application and technology of traditional and new ECG-based techniques in the diagnosis and treatment of cardiac patients. The publication includes topics related to 12-lead, exercise and high-resolution electrocardiography, arrhythmias, ischemia, repolarization phenomena, heart rate variability, circadian rhythms, bioengineering technology, signal-averaged ECGs, T-wave alternans and automatic external defibrillation.
ANE publishes peer-reviewed articles of interest to clinicians and researchers in the field of noninvasive electrocardiology. Original research, clinical studies, state-of-the-art reviews, case reports, technical notes, and letters to the editors will be published to meet future demands in this field.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
