[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125786-en":3,"doc-seo-125786-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125786,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring","Blood pressure misclassification leads to inappropriate treatment, motivating evaluation of machine learning as a dependable alternative to ambulatory BP monitoring for defining BP status. A multicentre study used three derivation cohorts from Glasgow, Gdansk, and Birmingham plus an independent evaluation cohort. Seven ML algorithms classified patients into five groups based on office BP, ABPM, and clinical, laboratory, and demographic data. Cox models estimated 10-year cardiovascular outcomes and 27-year all-cause mortality risks, showing limited classification accuracy when ABPM is unavailable.","CJC Open 6 (2024) 798e804  \nOriginal Article  \nAssessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure  \nMonitoring  \nTran Quoc Bao Tran, MSc,a, z Stefanie Lip, MBChB,a, z Clea du Toit, MSc,a,z  \nTejas Kumar Kalaria, MRCP,b Ravi K. Bhaskar, MS,c Alison Q. O’Neil, EngD,d Beata Graff, MD, PhD,e Michał Hoffmann, MD, PhD,e Anna Szyndler, MD, PhD,e  \nKatarzyna Polonis, PhD,e Jacek Wolf, MD, PhD,e Sandeep Reddy, MBBS, PhD,f Krzysztof Narkiewicz, MD, PhD,e Indranil Dasgupta, DM,b Anna F. Dominiczak, MD, FMedSci,a Shyam Visweswaran, MD, PhD,g Linsay McCallum, PhD,a and Sandosh Padmanabhan, MD, PhDa  \na School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, United Kingdom  \nb University Hospitals Birmingham NHS Foundation Trust, Heartlands Hospital, Bordesley Green East, Birmingham, United Kingdom  \ncMabgenex, Hyderabad, Telangana, India  \nd Canon Medical Research Europe, Bonnington Bond, Edinburgh, United Kingdom  \ne Department of Hypertension and Diabetology, Medical University of Gdask, Gdask, Poland  \nf School of Medicine, Deakin University, Geelong, Victoria, Australia  \ngDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA  \nABSTRACT  \nBackground: Inaccurate blood pressure (BP) classification results in inappropriate treatment. We tested whether machine learning (ML), using routine clinical data, can serve as a reliable alternative to ambulatory BP monitoring (ABPM) in classifying BP status.  \nMethods: This study employed a multicentre approach involving 3 derivation cohorts from Glasgow, Gdask, and Birmingham, and a fourth independent evaluation cohort. ML models were trained using office BP, ABPM, and clinical, laboratory, and demographic data, collected from patients referred for hypertension assessment. Seven ML algorithms were trained to classify patients into 5 groups, named as follows: Normal/Target; Hypertension-Masked; Normal/TargetWhite-Coat (WC); Hypertension-WC; and Hypertension. The 10-year cardiovascular outcomes and 27-year all-cause mortality risks were calculated for the ML-derived groups using the Cox proportional hazards model.  \nReceived for publication December 14, 2023 . Accepted March 11, 2024 . zEqual contribution.  \nCorresponding author: Dr Sandosh Padmanabhan, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8TA, United Kingdom. Tel.: þ44 141 330 2228.  \nE-mail: [sandosh.padmanabhan@glasgow.ac.uk](sandosh.padmanabhan@glasgow.ac.uk)  \n[See page 804 for disclosure information.](See page 804 for disclosure information.)  \n􀀁 􀀁  \nRESUME  \nContexte : Les erreurs dans la classification des valeurs de la pression artrielle (PA) entraînent une inadquation du traitement. Nous avonstâch de dterminer si l’apprentissage machine, à l’aide de donnescliniques routinières, constituait une solution de rechange fiable à la surveillance ambulatoire de la PA pour dfinir le statut de la PA. Mthodologie : Cette tude a utilis une approche multicentrique incluant trois cohortes de drivation de Glasgow, Gdask et Birmingham, et une quatrième cohorte d’valuation indpendante. Les modèles d’apprentissage machine ont t dvelopps en analysant les donnes dmographiques, les valeurs de la PA mesure au cabinet, les donn preuves de laboratoire recueillies auprès de patients adresss pour une valuation de l’hypertension. Sept algorithmes d’apprentissage machine ont t appliqus pour classer les patients en cinq groupes : Normale/Cible; Hypertension-Masque; Normal/Cible-Blouse blanche;  \nClinical guidelines now recommend out-of-office blood pressure (BP) measurements using ambulatory BP monitoring (ABPM) to screen and diagnose hypertension and monitor on-treatment BP control, as clinic or office BP (oBP) measurement is prone to error.1-3 Compared to oBP, ABPM is a superior predictor of hypertension-mediated organ damage, and cardiovascular disease (CVD) morbidity and mortality,3  \n[https://doi.org","cbCaitdbM629ZXOK","https://ap.wps.com/l/cbCaitdbM629ZXOK","pdf",357403,1,7,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To test whether machine learning using routine clinical data can reliably classify hypertension status as an alternative when ambulatory BP monitoring is unavailable.\"},{\"question\":\"How were the machine learning models trained and evaluated?\",\"answer\":\"Seven ML algorithms were trained on office BP, ABPM, and clinical, laboratory, and demographic data from three derivation cohorts, then evaluated in an independent cohort.\"},{\"question\":\"What did the study find about classification performance?\",\"answer\":\"Extreme gradient boosting achieved the highest ROC area, but overall accuracy and F1 scores remained low across the derivation cohorts, indicating limited potential for accurate classification without ABPM.\"}]","Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring | 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is the main goal of this study?","Question",{"text":75,"@type":76},"To test whether machine learning using routine clinical data can reliably classify hypertension status as an alternative when ambulatory BP monitoring is unavailable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models trained and evaluated?",{"text":80,"@type":76},"Seven ML algorithms were trained on office BP, ABPM, and clinical, laboratory, and demographic data from three derivation cohorts, then evaluated in an independent cohort.",{"name":82,"@type":73,"acceptedAnswer":83},"What did the study find about classification performance?",{"text":84,"@type":76},"Extreme gradient boosting achieved the highest ROC area, but overall accuracy and F1 scores remained low across the derivation cohorts, indicating limited potential for accurate classification without 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