Intracerebral hemorrhage hospitalization is characterized by two factors: long LOS and uncertainty in LOS. As a measure of resource use, LOS is strongly associated with patient cost, explaining 72–82% of the variation in cost.
The lifetime cost of ICH is more than $123,500, and the mean cost per inpatient day is $1,396. This disease places a heavy burden on the family and society. After the onset of ICH, patients need hospitalization and some of them need surgical treatment. The incidence of ICH was 10–30 cases per 100,000 people/year in 2001 and is expected to double by 2050. According to the statistics, the mortality of ICH is 30–50% every year. Intracerebral hemorrhage (ICH) is one of the most detrimental subtypes of stroke and accounts for 10–15% of all strokes. 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. The Cox-VIMP RSF model can improve prediction performance and is significantly different from the other models. The Cox-VIMP method constructed by us effectively selected significant correlation predictors. 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 demographics, clinical predictors, admission diagnosis, and surgery information from 3,600 ICH patients were used in this study.
The aim of our study was to provide decision support for discharge and admission plans by predicting ICH patients’ LOS probability distribution. The vast majority of patients with intracerebral hemorrhage (ICH) suffer from long and uncertain length of stay (LOS).
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