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Helmink MAG, Hageman SHJ, Eliasson B, Sattar N, Visseren FLJ, Dorresteijn JAN, Harris K, Peters SAE, Woodward M, Szentkúti P, Højlund K, Henriksen JE, Sørensen HT, Serné EH, van Sloten TT, Thomsen RW, Westerink J. Lifetime and 10-year cardiovascular risk prediction in individuals with type 1 diabetes: The LIFE-T1D model. Diabetes Obes Metab 2024; 26:2229-2238. [PMID: 38456579 DOI: 10.1111/dom.15531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/14/2024] [Accepted: 02/16/2024] [Indexed: 03/09/2024]
Abstract
AIMS To develop and externally validate the LIFE-T1D model for the estimation of lifetime and 10-year risk of cardiovascular disease (CVD) in individuals with type 1 diabetes. MATERIALS AND METHODS A sex-specific competing risk-adjusted Cox proportional hazards model was derived in individuals with type 1 diabetes without prior CVD from the Swedish National Diabetes Register (NDR), using age as the time axis. Predictors included age at diabetes onset, smoking status, body mass index, systolic blood pressure, glycated haemoglobin level, estimated glomerular filtration rate, non-high-density lipoprotein cholesterol, albuminuria and retinopathy. The model was externally validated in the Danish Funen Diabetes Database (FDDB) and the UK Biobank. RESULTS During a median follow-up of 11.8 years (interquartile interval 6.1-17.1 years), 4608 CVD events and 1316 non-CVD deaths were observed in the NDR (n = 39 756). The internal validation c-statistic was 0.85 (95% confidence interval [CI] 0.84-0.85) and the external validation c-statistics were 0.77 (95% CI 0.74-0.81) for the FDDB (n = 2709) and 0.73 (95% CI 0.70-0.77) for the UK Biobank (n = 1022). Predicted risks were consistent with the observed incidence in the derivation and both validation cohorts. CONCLUSIONS The LIFE-T1D model can estimate lifetime risk of CVD and CVD-free life expectancy in individuals with type 1 diabetes without previous CVD. This model can facilitate individualized CVD prevention among individuals with type 1 diabetes. Validation in additional cohorts will improve future clinical implementation.
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Affiliation(s)
- Marga A G Helmink
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Steven H J Hageman
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Björn Eliasson
- Department of Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Sciences, University of Glasgow, Glasgow, UK
| | - Frank L J Visseren
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jannick A N Dorresteijn
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Katie Harris
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Sanne A E Peters
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
- The George Institute for Global Health, Imperial College London, London, UK
| | - Mark Woodward
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- The George Institute for Global Health, Imperial College London, London, UK
| | - Péter Szentkúti
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Kurt Højlund
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Jan Erik Henriksen
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Henrik Toft Sørensen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Erik H Serné
- Department of Vascular Medicine, Amsterdam University Medical Center, Location AMC, Amsterdam, The Netherlands
| | - Thomas T van Sloten
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Reimar W Thomsen
- Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
| | - Jan Westerink
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Internal Medicine, Isala, Zwolle, The Netherlands
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Kanbour S, Harris C, Lalani B, Wolf RM, Fitipaldi H, Gomez MF, Mathioudakis N. Machine Learning Models for Prediction of Diabetic Microvascular Complications. J Diabetes Sci Technol 2024; 18:273-286. [PMID: 38189280 PMCID: PMC10973856 DOI: 10.1177/19322968231223726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
IMPORTANCE AND AIMS Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN). METHODS A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics. RESULTS Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance. CONCLUSIONS AND RELEVANCE There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.
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Affiliation(s)
| | - Catharine Harris
- Division of Endocrinology, Diabetes,
& Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Benjamin Lalani
- Division of Endocrinology, Diabetes,
& Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
| | - Risa M. Wolf
- Division of Pediatric Endocrinology,
Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund
University Diabetes Centre, Lund University, Malmö, Sweden
| | - Maria F. Gomez
- Department of Clinical Sciences, Lund
University Diabetes Centre, Lund University, Malmö, Sweden
| | - Nestoras Mathioudakis
- Division of Endocrinology, Diabetes,
& Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD,
USA
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Khan SS, Coresh J, Pencina MJ, Ndumele CE, Rangaswami J, Chow SL, Palaniappan LP, Sperling LS, Virani SS, Ho JE, Neeland IJ, Tuttle KR, Rajgopal Singh R, Elkind MSV, Lloyd-Jones DM. Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association. Circulation 2023; 148:1982-2004. [PMID: 37947094 DOI: 10.1161/cir.0000000000001191] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.
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