Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining

Jingyi Wu; Guilan Kong; Yu Lin; Hong Chu; Chao Yang; Ying Shi; Haibo Wang; Luxia Zhang

doi:10.21037/atm-20-1006

Development of a scoring tool for predicting prolonged length of hospital stay in peritoneal dialysis patients through data mining

Ann Transl Med. 2020 Nov;8(21):1437. doi: 10.21037/atm-20-1006.

Authors

Jingyi Wu^{1

2}, Guilan Kong^{1

2}, Yu Lin¹, Hong Chu³, Chao Yang³, Ying Shi⁴, Haibo Wang^{1

2

4}, Luxia Zhang^{1

2

3}

Affiliations

¹ National Institute of Health Data Science, Peking University, Beijing, China.
² Advanced Institute of Information Technology, Peking University, Hangzhou, China.
³ Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China.
⁴ China Standard Medical Information Research Center, Shenzhen, China.

Abstract

Background: The hospital admission rate is high in patients treated with peritoneal dialysis (PD), and the length of stay (LOS) in the hospital is a key indicator of medical resource allocation. This study aimed to develop a scoring tool for predicting prolonged LOS (pLOS) in PD patients by combining machine learning and traditional logistic regression (LR).

Methods: This study was based on patient data collected using the Hospital Quality Monitoring System (HQMS) in China. Three machine learning methods, classification and regression tree (CART), random forest (RF), and gradient boosting decision tree (GBDT), were used to develop models to predict pLOS, which is longer than the average LOS, in PD patients. The model with the best prediction performance was used to identify predictive factors contributing to the outcome. A multivariate LR model based on the identified predictors was then built to derive the score assigned to each predictor. Finally, a scoring tool was developed, and it was tested by stratifying PD patients into different pLOS risk groups.

Results: A total of 22,859 PD patients were included in our study, with 25.2% having pLOS. Among the three machine learning models, the RF model achieved the best prediction performance and thus was used to identify the 10 most predictive variables for building the scoring system. The multivariate LR model based on the identified predictors showed good discrimination power with an AUROC of 0.721 in the test dataset, and its coefficients were used as a basis for scoring tool development. On the basis of the developed scoring tool, PD patients were divided into three groups: low risk (≤5), median risk [5-10], and high risk (>10). The observed pLOS proportions in the low-risk, median-risk, and high-risk groups in the test dataset were 11.4%, 29.5%, and 54.7%, respectively.

Conclusions: This study developed a scoring tool to predict pLOS in PD patients. The scoring tool can effectively discriminate patients with different pLOS risks and be easily implemented in clinical practice. The pLOS scoring tool has a great potential to help physicians allocate medical resources optimally and achieve improved clinical outcomes.

Keywords: Length of stay (LOS); logistic regression (LR); machine learning; peritoneal dialysis (PD); scoring methods.