{
  "abstract": "Introduction Preventable morbidity and mortality from chronic obstructive pulmonary disease (COPD) accrue from major adverse cardiovascular events (MACEs) and acute exacerbations of COPD (AECOPD). The study aims to summarise models for the prediction of these cardiopulmonary events in community-based settings.Methods We searched for studies of multivariable models derived, validated or augmented for the prediction of cardiopulmonary events in COPD and used community-based data sources using MEDLINE and Embase from inception through 10 April 2025. Discrimination measures for the model with C-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, and heterogeneity and risk of bias assessments were undertaken.Results No models were identified that predicted cardiopulmonary events in COPD using community-based data. Of the 71 models included, 5 predicted cardiovascular events, 32 predicted AECOPD and 30 predicted all-cause mortality. None were eligible for meta-analysis for the prediction of cardiovascular events or AECOPD. For all-cause mortality, age, dyspnoea and airflow obstruction—surprise question (ADO-SQ) (0.763, 95% CI 0.533 to 0.942) and body mass index, airflow obstruction, dyspnoea score and exercise capacity (BODE) (0.753, 95% CI 0.583 to 0.907) demonstrated good prediction performance, while ADO (0.638, 95% CI 0.443 to 0.827) demonstrated adequate prediction performance. The risk of bias was high for 57.9% of studies, and none had clinical utility evaluated.Conclusions Despite the high burden of MACE and AECOPD, there is an absence of community-based models that predict this composite outcome. Models to identify individuals with COPD at high risk of cardiopulmonary events could enable targeted clinical intervention.PROSPERO registration number CRD420251026275.",
  "authors": [
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK",
        "Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK"
      ],
      "name": "Tobin Joseph"
    },
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK"
      ],
      "name": "Keerthenan Raveendra"
    },
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK",
        "Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK"
      ],
      "name": "Mohammad Haris"
    },
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK"
      ],
      "name": "Jasmin Kirupananthan"
    },
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK"
      ],
      "name": "Amaan Aslam"
    },
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK"
      ],
      "name": "Alexandra Mircescu"
    },
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK"
      ],
      "name": "Ashmit Bhardwaj"
    },
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK"
      ],
      "name": "Aidan Wong"
    },
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK",
        "Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK"
      ],
      "name": "Ramesh Nadarajah"
    },
    {
      "affiliations": [
        "Observational and Pragmatic Research Institute, Singapore",
        "University of Aberdeen Academic Primary Care Research Group, Aberdeen, UK"
      ],
      "name": "David B Price"
    },
    {
      "affiliations": [
        "Medicine, University of Alberta, Edmonton, Alberta, Canada"
      ],
      "name": "Mohit Bhutani"
    },
    {
      "affiliations": [
        "Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK",
        "Leeds Institute for Data Analytics, University of Leeds, Leeds, UK",
        "Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK"
      ],
      "name": "Chris Gale"
    }
  ],
  "title": "Community-based prediction models of cardiovascular events, acute exacerbations and all-cause mortality in individuals with chronic obstructive pulmonary disease: a systematic review and meta-analysis on behalf of the International Cardiovascular and Respiratory Alliance",
  "uid": "35dbfc04-0828-50a8-a399-a3c6a051555b"
}