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. 2024 Jan 17:10:1335232.
doi: 10.3389/fmed.2023.1335232. eCollection 2023.

Random forest can accurately predict the technique failure of peritoneal dialysis associated peritonitis patients

Zhiyun Zang #  1 , Qijiang Xu #  1   2 , Xueli Zhou #  1 , Niya Ma  1 , Li Pu  1 , Yi Tang  1 , Zi Li  1
Affiliations

Random forest can accurately predict the technique failure of peritoneal dialysis associated peritonitis patients

Zhiyun Zang et al. Front Med (Lausanne). .

Abstract

Instructions: Peritoneal dialysis associated peritonitis (PDAP) is a major cause of technique failure in peritoneal dialysis (PD) patients. The purpose of this study is to construct risk prediction models by multiple machine learning (ML) algorithms and select the best one to predict technique failure in PDAP patients accurately.

Methods: This retrospective cohort study included maintenance PD patients in our center from January 1, 2010 to December 31, 2021. The risk prediction models for technique failure were constructed based on five ML algorithms: random forest (RF), the least absolute shrinkage and selection operator (LASSO), decision tree, k nearest neighbor (KNN), and logistic regression (LR). The internal validation was conducted in the test cohort.

Results: Five hundred and eight episodes of peritonitis were included in this study. The technique failure accounted for 26.38%, and the mortality rate was 4.53%. There were resignificant statistical differences between technique failure group and technique survival group in multiple baseline characteristics. The RF prediction model is the best able to predict the technique failure in PDAP patients, with the accuracy of 93.70% and area under curve (AUC) of 0.916. The sensitivity and specificity of this model was 96.67 and 86.49%, respectively.

Conclusion: RF prediction model could accurately predict the technique failure of PDAP patients, which demonstrated excellent predictive performance and may assist in clinical decision making.

Keywords: machine learning algorithms; peritoneal dialysis; peritonitis; prediction model; technique failure.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart for the study participants enrollment and outcomes.
Figure 2
Figure 2
Comparison of clinical outcomes between training cohort and test cohort.
Figure 3
Figure 3
ROC curves of prediction models based on machine learning algorithms.
Figure 4
Figure 4
Calibration curves of prediction models. (A) Random forest; (B) LASSO regression; (C) Logistic regression; (D) Decision tree; (E) K-nearest neighbor.
Figure 5
Figure 5
Ranking the importance of variables based on RF model.
See this image and copyright information in PMC

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