{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T20:17:16Z","timestamp":1789503436720,"version":"build-2803163510"},"reference-count":35,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Hypertension is a leading preventable cause of cardiovascular disease, yet a substantial proportion of adults remain undiagnosed, limiting opportunities for early intervention. A predictive model was commissioned by the North West London (NWL) Integrated Care Board to identify undiagnosed hypertension. The model was developed using health records from the Whole Systems Integrated Care (WSIC) database.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n We aimed to independently evaluate the predictive performance of the model as it would be encountered in deployment, how performance varied by demographic characteristics, and practical utility.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n To evaluate the predictive model, we conducted a retrospective cohort study of 1,802,920 individuals aged 16 years or older, registered with a general practice in NWL, and with no prior diagnosis of hypertension from May 2023 to May 2024. We assessed the model\u2019s predictions against recorded hypertension status using medical diagnoses and blood pressure records. Logistic regression models were used to assess the sensitivity and specificity of the model\u2019s predictions by sociodemographic groups. We also compared the model\u2019s performance against a more interpretable regression approach.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The model yielded an overall sensitivity of 62.7% (95% CI 62.5-62.8) and specificity of 60.7% (95% CI 60.5-60.8). Positive predictive value ranged from 31.5% (95% CI 31.2-31.8) to 42.9% (95% CI 42.5-43.2), and negative predictive value ranged from 77.6% (95% CI 77.2-77.9) to 84.9% (95% CI 84.7-85.2). Sensitivity was higher in older adults and Black patients; specificity was higher in younger adults, female patients, and White patients. Overall, sensitivity was higher for those living in areas of higher socioeconomic deprivation, while specificity was lower. These effects plateaued in the 2 least deprived quintiles of deprivation, which were comparable in both sensitivity and specificity. Predictions varied by age, with 96.2% (58,951\/61,281) of those aged 70 to 79 predicted to have hypertension, whereas 0.08% of those aged 20 to 39 were predicted to have the condition. The model\u2019s performance was comparable with a more interpretable logistic regression model.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Despite the model\u2019s relatively good performance for those without hypertension, the positive predictive value was low, and a significant proportion of true cases remained undetected. Furthermore, there was considerable variation in performance associated with demographic characteristics, suggesting tailored approaches to case-finding may be beneficial in ensuring equity across demographic groups. Especially given the importance of understanding possible biases in predictive models, we recommend that, where there is no loss in performance, more parsimonious, transparent models be selected for prediction in health care settings. The findings of this evaluation can guide the practical application of the model, inform enhancements, direct targeted screening initiatives, and support cost-benefit analyses for broader implementation to improve hypertension management.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/87084","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T19:44:13Z","timestamp":1789501453000},"page":"e87084-e87084","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of a National Health Service Machine-Learning Model for Hypertension Case-Finding: Retrospective Cohort Study"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3891-7087","authenticated-orcid":false,"given":"Gloria","family":"Ihenetu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1834-5878","authenticated-orcid":false,"given":"Ahmad","family":"Alkhatib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9700-915X","authenticated-orcid":false,"given":"Vesselin","family":"Novov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9709-7264","authenticated-orcid":false,"given":"Thomas","family":"Beaney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2357-9858","authenticated-orcid":false,"given":"Azeem","family":"Majeed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4589-1743","authenticated-orcid":false,"given":"Paul","family":"Aylin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4735-4856","authenticated-orcid":false,"given":"Thomas","family":"Woodcock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"R1","unstructured":"Risk factors for undiagnosed high blood pressure in England: 2015 to 2019. Office of National Statistics. 2023. URL: https:\/\/www.ons.gov.uk\/peoplepopulationandcommunity\/healthandsocialcare\/healthandwellbeing\/articles\/riskfactorsforundiagnosedhighbloodpressureinengland\/2015to2019#cite-this-article [Accessed 27-08-2026]"},{"issue":"11","key":"R2","doi-asserted-by":"publisher","first-page":"2333","DOI":"10.1007\/s40618-021-01603-7","article-title":"Association of metabolic syndrome with cardiovascular outcomes in hypertensive patients: a systematic review and meta-analysis","volume":"44","author":"Liu","journal-title":"J Endocrinol Invest"},{"key":"R3","unstructured":"Hypertension: how common is it? National Institute for Health and Care Excellence (NICE). URL: https:\/\/cks.nice.org.uk\/topics\/hypertension\/background-information\/prevalence\/ [Accessed 27-08-2026]"},{"issue":"10010","key":"R4","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1016\/S0140-6736(15)00128-2","article-title":"Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013","volume":"386","author":"GBD 2013 Risk Factors Collaborators","journal-title":"Lancet"},{"key":"R5","unstructured":"CVDPREVENT. Data Explorer. 2023. URL: https:\/\/data.cvdprevent.nhs.uk\/data-explorer?period=15&area=1&indicator=11 [Accessed 27-08-2026]"},{"key":"R6","unstructured":"Health matters: combating high blood pressure. GOV.UK. 2017. URL: https:\/\/www.gov.uk\/government\/publications\/health-matters-combating-high-blood-pressure\/health-matters-combating-high-blood-pressure [Accessed 27-08-2026]"},{"key":"R7","unstructured":"Health and care strategy for North West London 2023. North West London\u2019s integrated care partnership; 2023. URL: https:\/\/www.nwlondonicb.nhs.uk\/application\/files\/6016\/9902\/9759\/NW_London_ICS_Health_and_Care_Strategy_2023.pdf [Accessed 27-08-2026]"},{"key":"R8","unstructured":"Heaversedge J . Detect, protect, perfect: why the NWL health system is coming together to find more hypertensive cases this may. Imperial College Health Partners. 2023. URL: https:\/\/imperialcollegehealthpartners.com\/detect-protect-perfect-why-the-nwl-health-system-is-coming-together-to-find-more-hypertensive-cases-this-may\/ [Accessed 27-08-2026]"},{"key":"R9","doi-asserted-by":"crossref","unstructured":"Guirguis-Blake JM Evans CV Webber EM Coppola EL Perdue LA Weyrich MS . Screening for hypertension in adults: an updated systematic evidence review for the US preventive services task force. Agency for Healthcare Research and Quality (US); 2021. URL: https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK570230\/pdf\/Bookshelf_NBK570230.pdf [Accessed 27-08-2026]","DOI":"10.1001\/jama.2020.21669"},{"issue":"10304","key":"R10","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1016\/S0140-6736(21)01330-1","article-title":"Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants","volume":"398","author":"NCD Risk Factor Collaboration (NCD-RisC)","journal-title":"Lancet"},{"key":"R11","doi-asserted-by":"publisher","DOI":"10.1016\/j.lana.2022.100223","article-title":"Drivers and scorecards to improve hypertension control in primary care practice: recommendations from the HEARTS in the Americas Innovation Group","volume":"9","author":"Brettler","journal-title":"Lancet Reg Health Am"},{"issue":"1","key":"R12","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1093\/jamia\/ocw042","article-title":"Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review","volume":"24","author":"Goldstein","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"R13","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.9268","article-title":"Prediction of incident hypertension within the next year: prospective study using statewide electronic health records and machine learning","volume":"20","author":"Ye","journal-title":"J Med Internet Res"},{"key":"R14","doi-asserted-by":"publisher","DOI":"10.2196\/68442","article-title":"Two-year hypertension incidence risk prediction in populations in the desert regions of northwest China: prospective cohort study","volume":"27","author":"Cheng","journal-title":"J Med Internet Res"},{"key":"R15","doi-asserted-by":"publisher","DOI":"10.2196\/52794","article-title":"Machine learning-based prediction for incident hypertension based on regular health checkup data: derivation and validation in 2 independent nationwide cohorts in South Korea and Japan","volume":"26","author":"Hwang","journal-title":"J Med Internet Res"},{"issue":"11","key":"R16","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s11906-022-01212-6","article-title":"Machine learning for hypertension prediction: a systematic review","volume":"24","author":"Silva","journal-title":"Curr Hypertens Rep"},{"key":"R17","unstructured":"The big NWL case find: hypertension. Imperial College Health Partners. 2023. URL: https:\/\/imperialcollegehealthpartners.com\/resource\/the-big-nwl-case-find-hypertension\/ [Accessed 27-08-2026]"},{"issue":"1","key":"R18","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-1082-7","article-title":"How an electronic health record became a real-world research resource: comparison between London\u2019s Whole Systems Integrated Care database and the Clinical Practice Research Datalink","volume":"20","author":"Bottle","journal-title":"BMC Med Inform Decis Mak"},{"issue":"Suppl 1","key":"R19","doi-asserted-by":"publisher","first-page":"e10","DOI":"10.1097\/01.hjh.0001115320.64480.f3","article-title":"May Measurement Month: utilising machine learning to detect hypertension and analyse associated risk factors","volume":"43","author":"Harada","journal-title":"J Hypertens"},{"issue":"23","key":"R20","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.1093\/eurheartj\/ehw302","article-title":"Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges","volume":"38","author":"Goldstein","journal-title":"Eur Heart J"},{"issue":"6464","key":"R21","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","journal-title":"Science"},{"issue":"2","key":"R22","doi-asserted-by":"publisher","first-page":"E167","DOI":"10.1001\/amajethics.2019.167","article-title":"Can AI help reduce disparities in general medical and mental health care?","volume":"21","author":"Chen","journal-title":"AMA J Ethics"},{"key":"R23","unstructured":"Quality and Outcomes Framework (QOF). NHS England. URL: https:\/\/digital.nhs.uk\/data-and-information\/data-tools-and-services\/data-services\/general-practice-data-hub\/quality-outcomes-framework-qof [Accessed 27-08-2026]"},{"key":"R24","unstructured":"Hypertension: what is it? National Institute for Health and Care Excellence (NICE). URL: https:\/\/cks.nice.org.uk\/topics\/hypertension\/background-information\/definition\/ [Accessed 27-08-2026]"},{"issue":"10","key":"R25","doi-asserted-by":"publisher","first-page":"2170","DOI":"10.1002\/sim.2677","article-title":"Confidence intervals for predictive values with an emphasis to case-control studies","volume":"26","author":"Mercaldo","journal-title":"Stat Med"},{"key":"R26","unstructured":"Confusion matrix. ScienceDirect. URL: https:\/\/www.sciencedirect.com\/topics\/computer-science\/confusion-matrix [Accessed 27-08-2026]"},{"key":"R27","unstructured":"Hypertension prevalence estimates for local populations. Public Health England. 2017. URL: https:\/\/www.gov.uk\/government\/publications\/hypertension-prevalence-estimates-for-local-populations [Accessed 27-08-2026]"},{"key":"R28","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.r486","article-title":"Patients face discrimination in primary care, survey finds","volume":"388","author":"O\u2019Dowd","journal-title":"BMJ"},{"key":"R29","unstructured":"Health survey for England 2019 [NS]. NHS England; 2020. URL: https:\/\/digital.nhs.uk\/data-and-information\/publications\/statistical\/health-survey-for-england\/2019? [Accessed 27-08-2026]"},{"issue":"9","key":"R30","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1001\/jama.2013.184182","article-title":"Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries","volume":"310","author":"Chow","journal-title":"JAMA"},{"issue":"8","key":"R31","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2013-003320","article-title":"Do men consult less than women? An analysis of routinely collected UK general practice data","volume":"3","author":"Wang","journal-title":"BMJ Open"},{"issue":"3","key":"R32","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2017-019965","article-title":"Levels of detection of hypertension in primary medical care and interventions to improve detection: a systematic review of the evidence since 2000","volume":"8","author":"Baker","journal-title":"BMJ Open"},{"key":"R33","unstructured":"Core20PLUS5 infographic \u2013 adult. NHS England; 2021. URL: https:\/\/www.england.nhs.uk\/wp-content\/uploads\/2021\/11\/core20plus5-infographic-v3.pdf [Accessed 27-08-2026]"},{"issue":"11","key":"R34","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-042963","article-title":"Evaluation of the uptake and delivery of the NHS Health Check programme in England, using primary care data from 9.5 million people: a cross-sectional study","volume":"10","author":"Patel","journal-title":"BMJ Open"},{"issue":"5","key":"R35","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1093\/jnci\/djad047","article-title":"Ecological and individualistic fallacies in health disparities research","volume":"115","author":"Shih","journal-title":"J Natl Cancer Inst"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T19:44:21Z","timestamp":1789501461000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2026\/1\/e87084"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":35,"URL":"https:\/\/doi.org\/10.2196\/87084","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]},"article-number":"v28i11e87084"}}