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

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][6][7][8][9][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.

Length of hospital stay and associated factors among adult surgical patients admitted to a surgical ward in Amhara Regional State Comprehensive Specialized Hospitals, Ethiopia

“…As a result, these factors increased their treatment durations and investigations, which in turn increased their length of hospital stay. Additionally, the length of hospital stays is increased by inadequate systems for exchanging patient information among hospitals when referring [55]. A key area emerging from the current results is that the source of referral is one of the factors that leads to prolonged hospital stays.…”

Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury

“…Most available studies [12][13][14]44 included demographic data, vital signs, clinical examinations and laboratory values as potential predictors to develop ML classifiers for predicting LoS-ICU or LoS-hospital. Except for the data mentioned above, only a few studies included treatment information, 43 source information 45 to develop ML classifiers. The present study also included treatment information and extra information like the sum of diagnosis, source of admission, LoS before ICU admission, care unit, among others.…”

Artificial intelligence in peritoneal dialysis: general overview

“…The scoring system took advantage of the superior prediction performance of the machine learning model and the interpretability of the traditional LR model. The RF model had the best prediction performance among the three machine learning models in terms of overall prediction performance, discrimination, and calibration and thus was used to identify the 10 most predictive variables for building the scoring system [ 25 ]. In 2021, they developed the pLOS prediction model using a stacking model constructed with SVM, RF and k-NN algorithms and conducted validation.…”