Analysis of longitudinal changes and predictive factors of caregiver burden in patients with polytrauma

To investigate the dynamic trajectory of caregiver burden among family caregivers of polytrauma patients at different post-discharge time points, identify key determinants, and explore factors associated with caregiver burden using machine learning methods. This longitudinal study enrolled 275 family caregivers from a trauma center using convenience sampling. Caregiver burden was assessed using the Caregiver Burden Inventory (CBI) at 1 week, 1 month, 3 months, and 6 months post-discharge. Concurrent data collection included patients’ clinical indicators, caregivers’ demographic characteristics, and psychosocial metrics. Candidate variables were initially screened through Pearson correlation analysis (|r| > 0.3), followed by multicollinearity assessment, with subsequent application of eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models for exploratory analysis. Model fit was evaluated using the coefficient of determination (R²). The CBI showed a continuous downward trend: 37.90 ± 15.80 (1 week), 31.60 ± 17.46 (1 month), 23.43 ± 17.78 (3 months), and 16.45 ± 17.88 (6 months), and the differences were statistically significant at different time points ( P < 0.01). The change trend of the scores of each dimension of the CBI was consistent with the total score. In the exploratory machine learning analysis, the XGBoost model showed the highest model fit (R² = 0.99). Features contributing prominently to the model included the degree of left lower limb injury and the scores of each dimension of caregiver burden. Given that the CBI total score is mathematically derived from its subscale scores, the high model fit should be interpreted cautiously and should not be considered evidence of prospective predictive accuracy. Family caregiver burden was gradually relieved with the rehabilitation of patients. Early subjective burden perception and patient injury were associated with caregiver burden. The exploratory machine learning analysis provided additional insight into the relative contributions of factors associated with caregiver burden; however, the observed model performance should not be interpreted as evidence of clinical predictive utility.