Tran Quoc Bao Tran MSc , Stefanie Lip MBChB , Clea du Toit MSc , Tejas Kumar Kalaria MRCP , Ravi K. Bhaskar MS , Alison Q. O’Neil EngD , Beata Graff MD, PhD , Michał Hoffmann MD, PhD , Anna Szyndler MD, PhD , Katarzyna Polonis PhD , Jacek Wolf MD, PhD , Sandeep Reddy MBBS, PhD , Krzysztof Narkiewicz MD, PhD , Indranil Dasgupta DM , Anna F. Dominiczak MD, FMedSci , Shyam Visweswaran MD, PhD , Linsay McCallum PhD , Sandosh Padmanabhan MD, PhD
{"title":"评估通过动态血压监测对高血压类型进行诊断分类的机器学习方法","authors":"Tran Quoc Bao Tran MSc , Stefanie Lip MBChB , Clea du Toit MSc , Tejas Kumar Kalaria MRCP , Ravi K. Bhaskar MS , Alison Q. O’Neil EngD , Beata Graff MD, PhD , Michał Hoffmann MD, PhD , Anna Szyndler MD, PhD , Katarzyna Polonis PhD , Jacek Wolf MD, PhD , Sandeep Reddy MBBS, PhD , Krzysztof Narkiewicz MD, PhD , Indranil Dasgupta DM , Anna F. Dominiczak MD, FMedSci , Shyam Visweswaran MD, PhD , Linsay McCallum PhD , Sandosh Padmanabhan MD, PhD","doi":"10.1016/j.cjco.2024.03.005","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>This study employed a multicentre approach involving 3 derivation cohorts from Glasgow, Gdańsk, 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/Target-White-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.</p></div><div><h3>Results</h3><p>Overall, extreme gradient boosting (using XGBoost open source software) showed the highest area under the receiver operating characteristic curve of 0.85-0.88 across derivation cohorts, Glasgow (n = 923; 43% female; age 50.7 ± 16.3 years), Gdańsk (n = 709; 46% female; age 54.4 ± 13 years), and Birmingham (n = 1222; 56% female; age 55.7 ± 14 years). But accuracy (0.57-0.72) and F1 (harmonic mean of precision and recall) scores (0.57-0.69) were low across the 3 patient cohorts. The evaluation cohort (n = 6213; 51% female; age 51.2 ± 10.8 years) indicated elevated 10-year risks of composite cardiovascular events in the Normal/Target-WC and the Hypertension-WC groups, with heightened 27-year all-cause mortality observed in all groups, except the Hypertension-Masked group, compared to the Normal/Target group.</p></div><div><h3>Conclusions</h3><p>ML has limited potential in accurate BP classification when ABPM is unavailable. Larger studies including diverse patient groups and different resource settings are warranted.</p></div>","PeriodicalId":36924,"journal":{"name":"CJC Open","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589790X24001367/pdfft?md5=f906baaf76b4cab310f0b4114739014a&pid=1-s2.0-S2589790X24001367-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring\",\"authors\":\"Tran Quoc Bao Tran MSc , Stefanie Lip MBChB , Clea du Toit MSc , Tejas Kumar Kalaria MRCP , Ravi K. Bhaskar MS , Alison Q. O’Neil EngD , Beata Graff MD, PhD , Michał Hoffmann MD, PhD , Anna Szyndler MD, PhD , Katarzyna Polonis PhD , Jacek Wolf MD, PhD , Sandeep Reddy MBBS, PhD , Krzysztof Narkiewicz MD, PhD , Indranil Dasgupta DM , Anna F. Dominiczak MD, FMedSci , Shyam Visweswaran MD, PhD , Linsay McCallum PhD , Sandosh Padmanabhan MD, PhD\",\"doi\":\"10.1016/j.cjco.2024.03.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>This study employed a multicentre approach involving 3 derivation cohorts from Glasgow, Gdańsk, 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/Target-White-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.</p></div><div><h3>Results</h3><p>Overall, extreme gradient boosting (using XGBoost open source software) showed the highest area under the receiver operating characteristic curve of 0.85-0.88 across derivation cohorts, Glasgow (n = 923; 43% female; age 50.7 ± 16.3 years), Gdańsk (n = 709; 46% female; age 54.4 ± 13 years), and Birmingham (n = 1222; 56% female; age 55.7 ± 14 years). But accuracy (0.57-0.72) and F1 (harmonic mean of precision and recall) scores (0.57-0.69) were low across the 3 patient cohorts. The evaluation cohort (n = 6213; 51% female; age 51.2 ± 10.8 years) indicated elevated 10-year risks of composite cardiovascular events in the Normal/Target-WC and the Hypertension-WC groups, with heightened 27-year all-cause mortality observed in all groups, except the Hypertension-Masked group, compared to the Normal/Target group.</p></div><div><h3>Conclusions</h3><p>ML has limited potential in accurate BP classification when ABPM is unavailable. Larger studies including diverse patient groups and different resource settings are warranted.</p></div>\",\"PeriodicalId\":36924,\"journal\":{\"name\":\"CJC Open\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2589790X24001367/pdfft?md5=f906baaf76b4cab310f0b4114739014a&pid=1-s2.0-S2589790X24001367-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CJC Open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2589790X24001367\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CJC Open","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589790X24001367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Assessing Machine Learning for Diagnostic Classification of Hypertension Types Identified by Ambulatory Blood Pressure Monitoring
Background
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.
Methods
This study employed a multicentre approach involving 3 derivation cohorts from Glasgow, Gdańsk, 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/Target-White-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.
Results
Overall, extreme gradient boosting (using XGBoost open source software) showed the highest area under the receiver operating characteristic curve of 0.85-0.88 across derivation cohorts, Glasgow (n = 923; 43% female; age 50.7 ± 16.3 years), Gdańsk (n = 709; 46% female; age 54.4 ± 13 years), and Birmingham (n = 1222; 56% female; age 55.7 ± 14 years). But accuracy (0.57-0.72) and F1 (harmonic mean of precision and recall) scores (0.57-0.69) were low across the 3 patient cohorts. The evaluation cohort (n = 6213; 51% female; age 51.2 ± 10.8 years) indicated elevated 10-year risks of composite cardiovascular events in the Normal/Target-WC and the Hypertension-WC groups, with heightened 27-year all-cause mortality observed in all groups, except the Hypertension-Masked group, compared to the Normal/Target group.
Conclusions
ML has limited potential in accurate BP classification when ABPM is unavailable. Larger studies including diverse patient groups and different resource settings are warranted.
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