| アブストラクト | OBJECTIVE: To develop prediction models for short-term outcomes following a first acute myocardial infarction (AMI) event (index) or for past AMI events (prevalent) in a national primary care cohort. DESIGN: Retrospective cohort study using logistic regression models to estimate 1-year and 5-year risks of all-cause mortality and composite cardiovascular outcomes. SETTING: Primary care practices in England contributing data to the Clinical Practice Research Datalink (CPRD) Aurum and CPRD GOLD databases between 2006 and 2019. PARTICIPANTS: Patients with an incident (index) or prevalent AMI event. Models were trained on a random 80% sample of CPRD Aurum (n=1018 practices), internally validated on the remaining 20% (n=255) and externally validated using CPRD GOLD (n=248). OUTCOME MEASURES: Discrimination assessed using sensitivity, specificity and area under the receiver operating characteristic curve (AUC). Calibration assessed using calibration plots. RESULTS: In the index (prevalent) cohorts, 94 241 (64 789) patients were included in the training and internal validation sets, and 16 832 (7479) in the external validation set. For the index cohort, AUCs for 1-year [5-year] all-cause mortality were 0.802 (95% CI 0.793 to 0.812) [0.847 (0.841 to 0.853)] internally and 0.800 (0.790 to 0.810) [0.841 (0.835 to 0.847)] externally. For the primary composite outcome (stroke, heart failure and all-cause death), AUCs were 0.763 (0.756 to 0.771) [0.824 (0.818 to 0.830)] internally and 0.748 (0.739 to 0.756) [0.808 (0.801 to 0.815)] externally. Discrimination was higher in the prevalent cohort, particularly for 1-year mortality (AUC: 0.896, 95% CI 0.887 to 0.904). Models excluding treatment variables showed slightly lower but comparable performance. Calibration was acceptable across models. CONCLUSIONS: These models can support clinicians in identifying patients at increased risk of short-term adverse outcomes following AMI, whether newly diagnosed or with a prior history. This can inform monitoring strategies and secondary prevention and guide patient counselling on modifiable risk factors. |
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| 投稿者 | Kontopantelis, Evangelos; Zghebi, Salwa S; Arsene, Corneliu T; Zaman, Azfar G; Chew, Nicholas W S; Wijeysundera, Harindra C; Khunti, Kamlesh; Ashcroft, Darren M; Carr, Matthew; Parisi, Rosa; Mamas, Mamas A |
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| 組織名 | Division of Informatics, Imaging and Data Sciences, School of Health Sciences,;Faculty of Biology, Medicine and Health, The University of Manchester,;Manchester, UK e.kontopantelis@manchester.ac.uk.;Division of Population Health, Health Services Research and Primary Care, School;of Health Sciences, Faculty of Biology, Medicine and Health, The University of;Manchester, Manchester, UK.;Manchester, UK.;Freeman Hospital and Newcastle University, Newcastle upon Tyne, UK.;School of Vascular Biology and Medicine, Newcastle University, Newcastle Upon;Tyne, UK.;Department of Cardiology, National University Heart Centre, National University;Health System, Singapore.;Department of Medicine, Faculty of Medicine, University of Toronto, Toronto,;Ontario, Canada.;Diabetes Research Centre, University of Leicester, Leicester, UK.;Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and;Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health,;The University of Manchester, Manchester, UK.;National Institute for Health and Care Research (NIHR) Greater Manchester Patient;Safety Research Collaboration (PSRC), The University of Manchester, Manchester,;UK.;Keele Cardiovascular Research Group, Centre for Prognosis Research, Institute for;Primary Care and Health Sciences, Keele University, Keele, UK. |
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