| アブストラクト | OBJECTIVE: Atrial fibrillation (AF) screening by age achieves a low yield and misses younger individuals. We aimed to develop an algorithm in nationwide routinely collected primary care data to predict the risk of incident AF within 6 months (Future Innovations in Novel Detection of Atrial Fibrillation (FIND-AF)). METHODS: We used primary care electronic health record data from individuals aged >/=30 years without known AF in the UK Clinical Practice Research Datalink-GOLD dataset between 2 January 1998 and 30 November 2018, randomly divided into training (80%) and testing (20%) datasets. We trained a random forest classifier using age, sex, ethnicity and comorbidities. Prediction performance was evaluated in the testing dataset with internal bootstrap validation with 200 samples, and compared against the CHA(2)DS(2)-VASc (Congestive heart failure, Hypertension, Age >75 (2 points), Stroke/transient ischaemic attack/thromboembolism (2 points), Vascular disease, Age 65-74, Sex category) and C(2)HEST (Coronary artery disease/Chronic obstructive pulmonary disease (1 point each), Hypertension, Elderly (age >/=75, 2 points), Systolic heart failure, Thyroid disease (hyperthyroidism)) scores. Cox proportional hazard models with competing risk of death were fit for incident longer-term AF between higher and lower FIND-AF-predicted risk. RESULTS: Of 2 081 139 individuals in the cohort, 7386 developed AF within 6 months. FIND-AF could be applied to all records. In the testing dataset (n=416 228), discrimination performance was strongest for FIND-AF (area under the receiver operating characteristic curve 0.824, 95% CI 0.814 to 0.834) compared with CHA(2)DS(2)-VASc (0.784, 0.773 to 0.794) and C(2)HEST (0.757, 0.744 to 0.770), and robust by sex and ethnic group. The higher predicted risk cohort, compared with lower predicted risk, had a 20-fold higher 6-month incidence rate for AF and higher long-term hazard for AF (HR 8.75, 95% CI 8.44 to 9.06). CONCLUSIONS: FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF. |
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| ジャーナル名 | Heart (British Cardiac Society) |
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| Pubmed追加日 | 2023/2/10 |
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| 投稿者 | Nadarajah, Ramesh; Wu, Jianhua; Hogg, David; Raveendra, Keerthenan; Nakao, Yoko M; Nakao, Kazuhiro; Arbel, Ronen; Haim, Moti; Zahger, Doron; Parry, John; Bates, Chris; Cowan, Campbel; Gale, Chris P |
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| 組織名 | Leeds Institute for Data Analytics, University of Leeds, Leeds, UK;r.nadarajah@leeds.ac.uk.;Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds,;Leeds, UK.;Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.;Department of Dentistry, University of Leeds, Leeds, UK.;School of Computing, University of Leeds, Leeds, UK.;Faculty of Medicine and Health, University of Leeds, Leeds, UK.;Maximizing Health Outcomes Research Lab, Sapir College, Hof Ashkelon, Israel.;Community Medical Services Division, Clalit Health Services, Tel Aviv, Israel.;Department of Cardiology, Soroka University Medical Center, Beer Sheva, Israel.;Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva,;Israel.;Cardiology, Soroka Medical Center, Beer Sheva, Israel.;The Phoenix Partnership, Leeds, UK.;Cardiology, Leeds General Infirmary, Leeds, UK. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/36759177/ |
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