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Background: Fluid balance plays a crucial role in the management of older patients with malnutrition; however, the impact of different fluid balance trajectories on prognosis has not been fully elucidated. This study aimed to evaluate the association between fluid balance trajectories and the risk of 30-day mortality in older patients with malnutrition. Methods: This study was conducted using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patients diagnosed with malnutrition among the older population were included. A group-based trajectory model (GBTM) was applied to identify subgroups of patients with similar trends in fluid balance (FB). Kaplan-Meier survival analysis and Cox proportional hazards regression models were used to assess the associations between different fluid balance trajectories and patient survival. In addition, subgroup analyses and sensitivity analyses were performed to evaluate the robustness of the findings. Results: A total of 1,778 older patients with malnutrition were included. Four distinct fluid balance trajectory patterns were identified: trajectory 1 (T1, persistent positive balance), trajectory 2 (T2, mild negative balance), trajectory 3 (T3, high-level rapid decline), and trajectory 4 (T4, moderate-level rapid decline). Kaplan-Meier survival analysis showed that patients in trajectory 1 (persistent positive balance) had the highest 30-day mortality rate (46.2%), whereas those in trajectory 4 (moderate-level rapid decline) had the lowest mortality risk (26.6%). After adjustment for potential confounders, Cox regression analysis further demonstrated that, compared with trajectory 1 as the reference group, trajectory 4 was significantly associated with a lower risk of mortality hazard ratio (HR) = 0.59, 95% confidence interval (CI): 0.47-0.73. Conclusion: Fluid balance trajectories were significantly associated with prognosis in older patients with malnutrition, and dynamic patterns of fluid balance may aid in stratifying mortality risk. The GBTM approach effectively identified patient subgroups with different risk profiles, providing valuable insights for clinical fluid management.
Zhang et al. (Wed,) studied this question.