{
  "abstract": "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.",
  "authors": [
    {
      "affiliations": [
        "Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK"
      ],
      "name": "Evangelos Kontopantelis"
    },
    {
      "affiliations": [
        "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"
      ],
      "name": "Salwa S Zghebi"
    },
    {
      "affiliations": [
        "Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK"
      ],
      "name": "Corneliu T Arsene"
    },
    {
      "affiliations": [
        "Freeman Hospital and Newcastle University, Newcastle upon Tyne, UK",
        "School of Vascular Biology and Medicine, Newcastle University, Newcastle Upon Tyne, UK"
      ],
      "name": "Azfar G Zaman"
    },
    {
      "affiliations": [
        "Department of Cardiology, National University Heart Centre, National University Health System, Singapore"
      ],
      "name": "Nicholas W S Chew"
    },
    {
      "affiliations": [
        "Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada"
      ],
      "name": "Harindra C Wijeysundera"
    },
    {
      "affiliations": [
        "Diabetes Research Centre, University of Leicester, Leicester, UK"
      ],
      "name": "Kamlesh Khunti"
    },
    {
      "affiliations": [
        "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"
      ],
      "name": "Darren M Ashcroft"
    },
    {
      "affiliations": [
        "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"
      ],
      "name": "Matthew Carr"
    },
    {
      "affiliations": [
        "Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK"
      ],
      "name": "Rosa Parisi"
    },
    {
      "affiliations": [
        "Keele Cardiovascular Research Group, Centre for Prognosis Research, Institute for Primary Care and Health Sciences, Keele University, Keele, UK"
      ],
      "name": "Mamas A Mamas"
    }
  ],
  "title": "Risk prediction in people with acute myocardial infarction in England: a cohort study using data from 1521 general practices",
  "uid": "e02a0b6d-4cc5-5c34-aa07-9e527ff4814f"
}