Predictive value of the random forest model based on bioelectrical impedance analysis parameter trajectories for short-term prognosis in stroke patients

The short-term prognosis of stroke patients is mainly influenced by the severity of the primary disease at admission and the trend of disease development during the acute phase (1-7 days after admission). The aim of this study is to explore the relationship between the bioelectrical impedance analysis (BIA) parameter trajectories during the acute phase of stroke patients and their short-term prognosis, and to investigate the predictive value of the prediction model constructed using BIA parameter trajectories and clinical indicators at admission for short-term prognosis in stroke patients. A total of 162 stroke patients were prospectively enrolled, and their clinical indicators at admission and BIA parameters during the first 1-7 days of admission were collected. A Group-Based Trajectory Model (GBTM) was employed to identify different subgroups of longitudinal trajectories of BIA parameters during the first 1-7 days of admission in stroke patients. The random forest algorithm was applied to screen BIA parameter trajectories and clinical indicators with predictive value, construct prediction models, and perform model comparisons. The outcome measure was the Modified Rankin Scale (mRS) score at discharge. PA in BIA parameters can be divided into four separate trajectory groups. The incidence of poor prognosis (mRS: 4-6) at discharge was significantly higher in the "Low PA Rapid Decline Group" (85.0%) than in the "High PA Stable Group " (33.3%) and in the "Medium PA Slow Decline Group "(29.5%) (all P