Development and Validation of a Lifetime Risk Model for Kidney Failure and Treatment Benefit in Type 2 Diabetes

Østergaard, Helena Bleken; Read, Stephanie H.; Sattar, Naveed; Franzén, Stefan; Halbesma, Nynke; Dorresteijn, Jannick A.N.; Westerink, Jan; Visseren, Frank L J; Eliasson, Björn; Leeuw, Joep van der

doi:10.2215/cjn.05020422

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…We identified 74 studies that met our eligibility criteria. Of these, 66 studies presented the original version of the ML model, 10-75 and an additional eight studies externally validated these models. 76-…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Models for Prediction of Diabetic Microvascular Complications

Kanbour,

Harris,

Lalani

et al. 2024

J Diabetes Sci Technol

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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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Section: Resultsmentioning

confidence: 99%

Machine Learning Models for Prediction of Diabetic Microvascular Complications

Kanbour,

Harris,

Lalani

et al. 2024

J Diabetes Sci Technol

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“…Microvascular complications not only significantly reduce the quality of life, but also lead to a higher risk of macrovascular complications and mortality 45 . For individuals with type 2 diabetes, a recently developed model can be used to estimate the lifetime risk of end‐stage kidney disease, 46 but this model has not been validated in individuals with type 1 diabetes. For individuals with type 1 diabetes, existing models can only predict shorter‐term risk of microvascular outcomes 47,48 .…”

Section: Discussionmentioning

confidence: 99%

Lifetime and 10‐year cardiovascular risk prediction in individuals with type 1 diabetes: The LIFE‐T1D model

Helmink,

Hageman,

Eliasson

et al. 2024

Diabetes Obesity Metabolism

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AimsTo 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 MethodsA 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.ResultsDuring 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.ConclusionsThe 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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“…68–72 Longer-term studies are emerging to provide direct evidence for links between these factors and lifetime risk of total CVD, ASCVD, and HF. 73–77 In addition to greater burden of CVD, data demonstrate earlier onset of CVD among those with poor CKM health. 78–80 Herein, we review the evidence for risk markers of CKM health and their utility to improve precision and accuracy of risk assessment.…”

Section: Rationale For the Development Of Novel Risk Prediction Equat...mentioning

confidence: 99%

Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association

Khan,

Coresh,

Pencina

et al. 2023

Circulation

103

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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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Development and Validation of a Lifetime Risk Model for Kidney Failure and Treatment Benefit in Type 2 Diabetes

Cited by 5 publications

References 45 publications

Machine Learning Models for Prediction of Diabetic Microvascular Complications

Machine Learning Models for Prediction of Diabetic Microvascular Complications

Lifetime and 10‐year cardiovascular risk prediction in individuals with type 1 diabetes: The LIFE‐T1D model

Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association

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