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

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Abstract

Background

Machine learning is increasingly recognized as a viable approach for identifying risk factors associated with diabetic kidney disease (DKD). However, the current state of real-world research lacks a comprehensive systematic analysis of the predictive performance of machine learning (ML) models for DKD.

Objectives

The objectives of this study were to systematically summarize the predictive capabilities of various ML methods in forecasting the onset and the advancement of DKD, and to provide a basic outline for ML methods in DKD.

Methods

We have searched mainstream databases, including PubMed, Web of Science, Embase, and MEDLINE databases to obtain the eligible studies. Subsequently, we categorized various ML techniques and analyzed the differences in their performance in predicting DKD.

Results

Logistic regression (LR) was the prevailing ML method, yielding an overall pooled area under the receiver operating characteristic curve (AUROC) of 0.83. On the other hand, the non-LR models also performed well with an overall pooled AUROC of 0.80. Our t-tests showed no statistically significant difference in predicting ability between LR and non-LR models (t = 1.6767, p > 0.05).

Conclusion

All ML predicting models yielded relatively satisfied DKD predicting ability with their AUROCs greater than 0.7. However, we found no evidence that non-LR models outperformed the LR model. LR exhibits high performance or accuracy in practice, while it is known for algorithmic simplicity and computational efficiency compared to others. Thus, LR may be considered a cost-effective ML model in practice.

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Abbreviations

Area under the receiver operating characteristic curve:

AUROC

Biological age:

BA

Body mass index:

BMI

Chronological age:

CA

Concordance index:

C-index

Creatinine:

Cr

Diabetic kidney disease:

DKD

Estimated Glomerular Filtration Rate:

eGFR

False negatives:

FN

False positives:

FP

Kidney Age Index:

KAI

Logistic regression:

LR

Low-Density Lipoprotein Cholesterol:

LDL-C

Machine learning:

ML

Prediction Model Risk of Bias Assessment Tool:

PROBAST

Synthetic Minority Over-sampling Technique:

SMOTE

Summary ROC:

SROC

Systolic blood pressure:

SBP

Transparent Reporting of a Multivariate Predictive Models for Individual Prognosis or Diagnosis:

TRIPOD

True negatives:

TN

True positives:

TP

True negative rate:

TNR

True positive rate:

TPR

Urine Albumin Creatinine Ratio:

UACR

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Acknowledgements

The authors thank the staff and participants of the study for their indispensable contributions.

Funding

This study was funded by Tianjin Science and Technology Major Special Project and Engineering Public Health Science and Technology Major Special Project (No.21ZXGWSY00100), Tianjin Natural Science Foundation Key Projects (No.22JCZDJC00590), Tianjin Key Medical Discipline (Specialty) Construct Project (No.TJYXZDXK-032A), Scientific Research Funding of Tianjin Medical University Chu Hsien-I Memorial Hospital (No.ZXY-ZDSYSZD-1), China Endocrine Metabolism Talent Research Fund (No. 2022-N-02-07). The funder was not for profit.

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Author notes

  1. These authors contributed equally: Lianqin Chen, Xian Shao.

Authors and Affiliations

  1. NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134, China

    Lianqin Chen, Xian Shao & Pei Yu

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Contributions

L.C. and X.S. contributed equally to this study. L.C. and X.S. acquired the data, prepared the Figs, and wrote the manuscript; P.Y. designed this study and revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Pei Yu.

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The authors declare no competing interests.

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Written informed consent for publication was obtained from all participants. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The meta-analysis was registered on PROSPERO (reference number CRD 42022357770).

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Chen, L., Shao, X. & Yu, P. Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis. Endocrine (2023). https://doi.org/10.1007/s12020-023-03637-8

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