Development and validation of a risk score for diabetic kidney disease prediction in type 2 diabetes patients: a machine learning approach

Sarkhosh, Seyyed Mahdi Hosseini; Hemmatabadi, Mahboobeh; Esteghamati, Alireza

doi:10.1007/s40618-022-01919-y

Cited by 9 publications

(13 citation statements)

References 29 publications

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“…(ACR, HbA1c, hypertension, diabetic duration, eGFR and cardiovascular disease) using recursive feature elimination with cross-validation and develop a risk model with a AUC of 0.755 for predicting renal dysfunction using multivariate LR algorithm. By comparison, the AUC in the present study was a little higher than that in the study of Hosseini Sarkhosh et al (23), although both of the two studies had six predictive features. Perhaps, the most likely reasons for the different performance of the prediction models in the two studies were the study population and the predictive variables.…”

Section: Discussioncontrasting

confidence: 80%

“…In the present study, six predictive features were determined by LASSO regression, and LR algorithms was performed to establish the prediction model for the occurrence of renal dysfunction. The study by Hosseini Sarkhosh et al (23) determined six predictors The correlations between the potential continuous variables for the univariate analysis (p ≤ 0.1). HbA1c, glycated hemoglobin; UA, uric acid; ACR, albumin to creatinine ratio; URBP/Cr, retinol-binding protein to creatinine ratio; UTRF/Cr, transferrin to creatinine ratio; UORM/Cr, alpha-1-acidglycoprotein to creatinine ratio; A1MCR, alpha-1-microglobulin to creatinine ratio.…”

Section: Discussionmentioning

confidence: 99%

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Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Sun

et al. 2023

Front. Endocrinol.

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BackgroundOur previous cross-sectional study has demonstrated the independently non-linear relationship between fasting C-peptide with renal dysfunction odds in patients with type 2 diabetes (T2D) in China. This longitudinal observational study aims to explore the role of serum C-peptide in risk prediction of new-onset renal dysfunction, then construct a predictive model based on serum C-peptide and other clinical parameters.MethodsThe patients with T2D and normal renal function at baseline were recruited in this study. The LASSO algorithm was performed to filter potential predictors from the baseline variables. Logistic regression (LR) was performed to construct the predictive model for new-onset renal dysfunction risk. Power analysis was performed to assess the statistical power of the model.ResultsDuring a 2-year follow-up period, 21.08% (35/166) of subjects with T2D and normal renal function at baseline progressed to renal dysfunction. Six predictors were determined using LASSO regression, including baseline albumin-to-creatinine ratio, glycated hemoglobin, hypertension, retinol-binding protein-to-creatinine ratio, quartiles of fasting C-peptide, and quartiles of fasting C-peptide to 2h postprandial C-peptide ratio. These 6 predictors were incorporated to develop model for renal dysfunction risk prediction using LR. Finally, the LR model achieved a high efficiency, with an AUC of 0.83 (0.76 - 0.91), an accuracy of 75.80%, a sensitivity of 88.60%, and a specificity of 70.80%. According to the power analysis, the statistical power of the LR model was found to be 0.81, which was at a relatively high level. Finally, a nomogram was developed to make the model more available for individualized prediction in clinical practice.ConclusionOur results indicated that the baseline level of serum C-peptide had the potential role in the risk prediction of new-onset renal dysfunction. The LR model demonstrated high efficiency and had the potential to guide individualized risk assessments for renal dysfunction in clinical practice.

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Section: Discussioncontrasting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Sun

et al. 2023

Front. Endocrinol.

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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%

“…Similar observations were made in studies that kept other model characteristics constant while only varying their machine learning technique. 32,44,60,62,67,69,71,73 For example, in the case of DR, the study by Zhao et al 62 compared five ML techniques: XGBoost (area under the curve [AUC]: 0.91 [0.9–0.93]), RF (AUC: 0.87 [0.86–0.89]), Logistic Regression (AUC: 0.81 [0.79–0.83]), SVM (AUC: 0.80 [0.78–0.82]), and K-NN (AUC: 0.63 [0.6–0.66]). In the case of DKD, the study by Dong et al 60 compared seven ML techniques: LightGBM (AUC: 0.82 [0.75–0.88]), AdaBoost (AUC: 0.81 [0.74–0.87]), Neural Network (AUC: 0.80 [0.73–0.87]), Logistic Regression (AUC: 0.80 [0.73–0.87]), XGBoost (AUC: 0.78 [0.71–0.85]), Support Vector Machine (AUC: 0.79 [0.72–0.86]), and DT (AUC: 0.58 [0.5–0.67]).…”

Section: Resultsmentioning

confidence: 99%

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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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Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis

Chen,

Shao,

2023

Endocrine

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Development and validation of a risk score for diabetic kidney disease prediction in type 2 diabetes patients: a machine learning approach

Cited by 9 publications

References 29 publications

Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Machine Learning Models for Prediction of Diabetic Microvascular Complications

Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis

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