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A machine learning-based assistant tool for early frailty screening of patients receiving maintenance hemodialysis

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Abstract

Purpose

To develop an assistant tool based on machine learning for early frailty screening in patients receiving maintenance hemodialysis.

Methods

This is a single-center retrospective study. 141 participants’ basic information, scale results and laboratory findings were collected and the FRAIL scale was used to assess frailty. Then participants were divided into the frailty group (n = 84) and control group (n = 57). After feature selection, data split and oversampling, ten commonly used binary machine learning methods were performed and a voting classifier was developed.

Results

The grade results of Clinical Frailty Scale, age, serum magnesium, lactate dehydrogenase, comorbidity and fast blood glucose were considered to be the best feature set for early frailty screening. After abandoning models with overfitting or poor performance, the voting classifier based on Support Vector Machine, Adaptive Boosting and Naive Bayes achieved a good screening performance (sensitivity: 68.24% ± 8.40%, specificity:72.50% ± 11.81%, F1 score: 72.55% ± 4.65%, AUC:78.38% ± 6.94%).

Conclusion

A simple and efficient early frailty screening assistant tool for patients receiving maintenance hemodialysis based on machine learning was developed. It can provide assistance on frailty, especially pre-frailty screening and decision-making tasks.

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Data availability

The data sets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Key R&D Program of China (2020YFC2005600), West China Nursing Discipline Development Special Fund Project, Sichuan University (HXHL21015), Major Research Programs of Science & Technology Department of Sichuan Province (2022ZDZX0032) and Project of Sichuan Luzhou Science and Technology Bureau (2022CDLZ-25).

Author information

Author notes

  1. Wenmei Lv and Hualong Liao have contributed equally to this work as first authors.

  2. Huaihong Yuan and Yu Chen have contributed equally to this work as corresponding authors.

Authors and Affiliations

  1. Department of Nephrology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, 610041, Sichuan, China

    Wenmei Lv, Xue Wang, Yuan Peng, Xianghong Li & Huaihong Yuan

  2. Department of Applied Mechanics, College of Architecture and Environment, Sichuan University, Chengdu, 610065, Sichuan, China

    Hualong Liao & Yu Chen

  3. National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China

    Shaobin Yu

  4. Department of Nephrology, Kidney Research Institute, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China

    Ping Fu

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  1. Wenmei Lv
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Contributions

HY and YC were the principal leaders. They conceived this study, participated in design and coordination. WL and HL participated in design and coordination, helped to draft manuscript. Data were analysed by HL and XW. SY, YP, and XL searched literatures, collected the data of patients receiving maintenance hemodialysis. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Huaihong Yuan or Yu Chen.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval and consent to participate

This study was performed in line with the principles of the Declaration of Helsinki. This study received approval from the Ethics Committee of Sichuan University (ethical approval number: 2020[1002]) and was performed in accordance with the principles of the Declaration of Helsinki. The requirement for informed consent was waived by the Ethics Committee of Sichuan University because this was an observational, cross-sectional study and patients’ identifying information had been removed.

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Lv, W., Liao, H., Wang, X. et al. A machine learning-based assistant tool for early frailty screening of patients receiving maintenance hemodialysis. Int Urol Nephrol 56, 223–235 (2024). https://doi.org/10.1007/s11255-023-03640-y

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