{
  "abstract": "Introduction Women with gestational diabetes mellitus (GDM) are at seven-fold to ten-fold increased risk of type 2 diabetes mellitus (T2DM) when compared with those who experience a normoglycaemic pregnancy, and the cumulative incidence increases with the time of follow-up post birth. This protocol outlines the development and validation of a risk prediction model assessing the 5-year and 10-year risk of T2DM in women with a prior GDM diagnosis.Methods and analysis Data from all birth mothers and registered births in Victoria and South Australia, retrospectively linked to national diabetes data and pathology laboratory data from 2008 to 2021, will be used for model development and validation of GDM to T2DM conversion. Candidate predictors will be selected considering existing literature, clinical significance and statistical association, including age, body mass index, parity, ethnicity, history of recurrent GDM, family history of T2DM and antenatal and postnatal glucose levels. Traditional statistical methods and machine learning algorithms will explore the best-performing and easily applicable prediction models. We will consider bootstrapping or K-fold cross-validation for internal model validation. If computationally difficult due to the expected large sample size, we will consider developing the model using 80% of available data and evaluating using a 20% random subset. We will consider external or temporal validation of the prediction model based on the availability of data. The prediction model’s performance will be assessed by using discrimination (area under the receiver operating characteristic curve, calibration (calibration slope, calibration intercept, calibration-in-the-large and observed-to-expected ratio), model overall fit (Brier score and Cox-Snell R2) and net benefit (decision curve analysis). To examine algorithm equity, the model’s predictive performance across ethnic groups and parity will be analysed. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis-Artificial Intelligence (TRIPOD+AI) statements will be followed.Ethics and dissemination Ethics approvals have been received from Deakin University Human Research Ethics Committee (2021–179); Monash Health Human Research Ethics Committee (RES-22-0000-048A); the Australian Institute of Health and Welfare (EO2022/5/1369); the Aboriginal Health Research Ethics Committee of South Australia (SA) (04-23-1056); in addition to a Site-Specific Assessment to cover the involvement of the Preventative Health SA (formerly Wellbeing SA) (2023/SSA00065). Project findings will be disseminated in peer-reviewed journals and at scientific conferences and provided to relevant stakeholders to enable the translation of research findings into population health programmes and health policy.",
  "authors": [
    {
      "affiliations": [
        "Deakin Rural Health, School of Medicine, Deakin University, Warrnambool, Victoria, Australia",
        "Geohealth Laboratory, Department of Population Health, Dasman Diabetes Institute, Kuwait City, Al Asimah Governate, Kuwait"
      ],
      "name": "Vincent Versace"
    },
    {
      "affiliations": [
        "Health & Biomedical Research Information Technology Unit (HaBIC R2), Department of General Practice and Primary Care, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Parkville, Victoria, Australia"
      ],
      "name": "Douglas Boyle"
    },
    {
      "affiliations": [
        "Western Health Chronic Disease Alliance and Department of Medicine, Western Health—Melbourne Medical School, University of Melbourne, Parkville, Victoria, Australia",
        "Deakin University, School of Medicine, Warrnambool Campus, Warrnambool, Victoria, Australia"
      ],
      "name": "Edward Janus"
    },
    {
      "affiliations": [
        "Deakin Rural Health, School of Medicine, Deakin University, Warrnambool, Victoria, Australia"
      ],
      "name": "James Dunbar"
    },
    {
      "affiliations": [
        "Deakin Rural Health, School of Medicine, Deakin University, Warrnambool, Victoria, Australia",
        "Geohealth Laboratory, Department of Population Health, Dasman Diabetes Institute, Kuwait City, Al Asimah Governate, Kuwait"
      ],
      "name": "Tesfaye R Feyissa"
    },
    {
      "affiliations": [
        "Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia"
      ],
      "name": "Yitayeh Belsti"
    },
    {
      "affiliations": [
        "Deakin Rural Health, School of Medicine, Deakin University, Warrnambool, Victoria, Australia"
      ],
      "name": "Peta Trinder"
    },
    {
      "affiliations": [
        "Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia"
      ],
      "name": "Joanne Enticott"
    },
    {
      "affiliations": [
        "CSIRO Health and Biosecurity Business Unit, Clayton, Victoria, Australia"
      ],
      "name": "Brett Sutton"
    },
    {
      "affiliations": [
        "The Australian Centre for Behavioural Research in Diabetes, Carlton, Victoria, Australia"
      ],
      "name": "Jane Speight"
    },
    {
      "affiliations": [
        "Eastern Health Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia"
      ],
      "name": "Jacqueline Boyle"
    },
    {
      "affiliations": [
        "Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria, Australia",
        "Diabetes and Endocrinology Units, Monash Health, Clayton, Victoria, Australia"
      ],
      "name": "Shamil Deshan Cooray"
    },
    {
      "affiliations": [
        "Institute for Health Transformation, Faculty of Health, Deakin University, Warrnambool, Victoria, Australia"
      ],
      "name": "Hannah Beks"
    },
    {
      "affiliations": [
        "School of Agriculture and Food Science, University College Dublin, Dublin, Ireland"
      ],
      "name": "Sharleen O’Reilly"
    },
    {
      "affiliations": [
        "Deakin Rural Health, School of Medicine, Deakin University, Warrnambool, Victoria, Australia"
      ],
      "name": "Kevin Mc Namara"
    },
    {
      "affiliations": [
        "SAHMRI Women and Kids, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia"
      ],
      "name": "Alice R Rumbold"
    },
    {
      "affiliations": [
        "Eastern Health Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia"
      ],
      "name": "Siew Lim"
    },
    {
      "affiliations": [
        "Health Economics and Policy Evaluation Research (HEPER) group, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Victoria, Australia"
      ],
      "name": "Zanfina Ademi"
    },
    {
      "affiliations": [
        "Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia"
      ],
      "name": "Helena J Teede"
    }
  ],
  "title": "Developing and validating a risk prediction model for conversion to type 2 diabetes mellitus in women with a history of gestational diabetes mellitus: protocol for a population-based, data-linkage study",
  "uid": "eebb6f5c-66e2-55b3-a849-7b15629ce2cd"
}