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. 2019 Jan 27:2019:4571636.
doi: 10.1155/2019/4571636. eCollection 2019.

Predicting Intracerebral Hemorrhage Patients' Length-of-Stay Probability Distribution Based on Demographic, Clinical, Admission Diagnosis, and Surgery Information

Li Luo  1 , Xueru Xu  1 , Yan Jiang  2 , Wei Zhu  2
Affiliations

Predicting Intracerebral Hemorrhage Patients' Length-of-Stay Probability Distribution Based on Demographic, Clinical, Admission Diagnosis, and Surgery Information

Li Luo et al. J Healthc Eng. .

Abstract

The vast majority of patients with intracerebral hemorrhage (ICH) suffer from long and uncertain length of stay (LOS). The aim of our study was to provide decision support for discharge and admission plans by predicting ICH patients' LOS probability distribution. The demographics, clinical predictors, admission diagnosis, and surgery information from 3,600 ICH patients were used in this study. We used univariable Cox analysis, multivariable Cox analysis, Cox-variable of importance (Cox-VIMP) analysis, and an intersection analysis to select predictors and used random survival forests (RSF)-a method in survival analysis-to predict LOS probability distribution. The Cox-VIMP method constructed by us effectively selected significant correlation predictors. The Cox-VIMP RSF model can improve prediction performance and is significantly different from the other models. The Cox-VIMP can contribute to the screening of predictors, and the RSF model can be established through those predictors to predict the probability distribution of LOS in each patient.

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Figures

Figure 1
Figure 1
VIMP for predictors with VIMP > 0.
Figure 2
Figure 2
Average C-indexes of nested analysis after 5-fold cross validation.
Figure 3
Figure 3
Non-disease predictors in the four schemes.
Figure 4
Figure 4
Main diagnosis predictors in each scheme.
Figure 5
Figure 5
Preexisting diseases diagnosis predictors for each scheme.
Figure 6
Figure 6
Surgery predictors of each scheme.
Figure 7
Figure 7
C-index box plot of each model.
See this image and copyright information in PMC

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