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Cited by 206Diabetes, Cardiovascular Risks, and LipoproteinsDiabetes Treatment and ManagementDiabetes Management and Research

Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials

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Basu, Sanjay, Sussman, Jeremy B, Berkowitz, Seth A, Hayward, Rodney A, Yudkin, John S·2017·The Lancet Diabetes & Endocrinology
DOI 10.1016/s2213-8587(17)30221-8PMC PMC5769867
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Abstract

Background In view of substantial mis-estimation of risks of diabetes complications using existing equations, we sought to develop updated Risk Equations for Complications Of type 2 Diabetes (RECODe). Methods To develop and validate these risk equations, we used data from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD, n=9635; 2001-09) and validated the equations for microvascular events using data from the Diabetes Prevention Program Outcomes Study (DPPOS, n=1018; 1996-2001), and for cardiovascular events using data from the Action for Health in Diabetes (Look AHEAD, n=4760; 2001-12). Microvascular outcomes were nephropathy, retinopathy, and neuropathy. Cardiovascular outcomes were myocardial infarction, stroke, congestive heart failure, and cardiovascular mortality. We also included all-cause mortality as an outcome. We used a cross-validating machine learning method to select predictor variables from demographic characteristics, clinical variables, comorbidities, medications, and biomarkers into Cox proportional hazards models for each outcome. The new equations were compared to older risk equations by assessing model discrimination, calibration, and the net reclassification index. Findings All equations had moderate internal and external discrimination (C-statistics 0·55-0·84 internally, 0·57-0·79 externally) and high internal and external calibration (slopes 0·71-1·31 between observed and estimated risk). Our equations had better discrimination and calibration than the UK Prospective Diabetes Study Outcomes Model 2 (for microvascular and cardiovascular outcomes, C-statistics 0·54-0·62, slopes 0·06-1·12) and the American College of Cardiology/American Heart Association Pooled Cohort Equations (for fatal or non-fatal myocardial infarction or stroke, C-statistics 0·61-0·66, slopes 0·30-0·39). Interpretation RECODe might improve estimation of risk of complications for patients with type 2 diabetes. Funding National Institute for Diabetes and Digestive and Kidney Disease, National Heart, Lung and Blood Institute, and National Institute on Minority Health and Health Disparities, National Institutes of Health, and US Department of Veterans Affairs.

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  • Researchers and clinicians are using artificial intelligence to integrate electronic health records and social risk factors, such as neighborhood characteristics, to predict outcomes important to individuals with diabetes, including poor control.

    “researchers and clinicians are using artificial intelligence (AI) to integrate electronic health records (EHRs) and social risk factors, such as neighborhood characteristics, to predict outcomes important to individuals with diabetes, including poor control [17-22].”

    From An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study (2023) · cites this paper as [18]

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Authors
Basu, S., Sussman, J. B., Berkowitz, S. A., Hayward, R. A., & Yudkin, J. S.
Affiliations
  • Massachusetts General Hospital (US)
  • University of Michigan (US)
  • Harvard University (US)
  • University College London (GB)
Corresponding author
Basu, Sanjay
ORCID
Venue
The Lancet Diabetes & Endocrinology
Publisher
Elsevier BV
Published
2017-10
Volume
5
Issue
10
Pages
788-798
ISSN
2213-8587 (ISSN-L)
DOI
10.1016/s2213-8587(17)30221-8
PMC
PMC5769867
OpenAlex
W2743505738
Cited by
206
References
11
Citations by year
2026: 21 · 2025: 23 · 2024: 31 · 2023: 33 · 2022: 30 · 2021: 29 · 2020: 16 · 2019: 13 · 2018: 8 · 2017: 2
FWCI
8.44
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • The Lancet Diabetes & Endocrinology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Heart, Lung, and Blood Institute (R01HL132814, K08HL121056)
  • National Institute of Diabetes and Digestive and Kidney Diseases (K23DK109200, L30 DK103291, P60DK020572, P30DK092926)
  • National Institute on Minority Health and Health Disparities (DP2MD010478, U54MD010724)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
Keywords
diabetes complications, risk prediction equations, RECODE, microvascular complications, kidney disease, retinopathy, diabetic neuropathy, cardiovascular events, myocardial infarction, stroke, congestive heart failure, cardiovascular mortality, all-cause mortality, risk prediction model validation, Pooled Cohort Equations, individual patient data
MeSH terms
Aged, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Diabetic Angiopathies, Female, Humans, Male, Middle Aged, Risk Factors, Randomized Controlled Trials as Topic, Risk Assessment

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