Frontiers in nutrition · 2025
The association between fluid balance trajectories and prognosis in ICU patients with cardiac arrest, a group-based trajectory model analysis.
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Zhang Qitian, Lin Guangyu, Zhang Chunmei
Аннотация
BACKGROUND: The impact of dynamic fluid balance (FB) changes on the prognosis of ICU patients with cardiac arrest (CA) remains unclear. This study aims to explore the association between FB trajectories and the prognosis of such patients.
METHODS: Data were sourced from CA patients in the MIMIC-IV database. A Group-Based Trajectory Model (GBTM) was used to identify patient subgroups with similar FB trajectories. Kaplan-Meier survival curves and Cox regression models were applied to analyze the association between FB trajectories and survival outcomes in CA patients. Subgroup and sensitivity analyses were conducted to further validate the robustness of the results.
RESULTS: A total of 876 CA patients were included. Four distinct FB trajectory patterns were identified, Trajectory 1 (rapid transition to negative balance), Trajectory 2 (stable balance), Trajectory 3 (positive balance gradually decreasing), and Trajectory 4 (decreasing at a high level). Kaplan-Meier survival analysis showed that the survival rate in Trajectory 1 was significantly higher than in the other trajectory groups, with the fluid overload group exhibiting a notably higher mortality risk than the non-overload group. Cox proportional hazards analysis indicated that, after adjusting for various covariates, the survival rate in Trajectory 1 remained significantly higher than in other trajectory groups (Reference, Trajectory 1; Trajectory 2, HR = 1.75 [1.31-2.34], Trajectory 3, HR = 2.02 [1.53, 2.68], Trajectory 4, HR = 1.71 [1.24, 2.37]). Subgroup and sensitivity analyses did not alter these findings.
CONCLUSION: The GBTM method helps to identify subgroups of ICU cardiac arrest patients with distinct risk profiles. Among the dynamic FB types, the group with rapid transition to negative balance at a moderate level (Trajectory 1) showed the best prognosis.
METHODS: Data were sourced from CA patients in the MIMIC-IV database. A Group-Based Trajectory Model (GBTM) was used to identify patient subgroups with similar FB trajectories. Kaplan-Meier survival curves and Cox regression models were applied to analyze the association between FB trajectories and survival outcomes in CA patients. Subgroup and sensitivity analyses were conducted to further validate the robustness of the results.
RESULTS: A total of 876 CA patients were included. Four distinct FB trajectory patterns were identified, Trajectory 1 (rapid transition to negative balance), Trajectory 2 (stable balance), Trajectory 3 (positive balance gradually decreasing), and Trajectory 4 (decreasing at a high level). Kaplan-Meier survival analysis showed that the survival rate in Trajectory 1 was significantly higher than in the other trajectory groups, with the fluid overload group exhibiting a notably higher mortality risk than the non-overload group. Cox proportional hazards analysis indicated that, after adjusting for various covariates, the survival rate in Trajectory 1 remained significantly higher than in other trajectory groups (Reference, Trajectory 1; Trajectory 2, HR = 1.75 [1.31-2.34], Trajectory 3, HR = 2.02 [1.53, 2.68], Trajectory 4, HR = 1.71 [1.24, 2.37]). Subgroup and sensitivity analyses did not alter these findings.
CONCLUSION: The GBTM method helps to identify subgroups of ICU cardiac arrest patients with distinct risk profiles. Among the dynamic FB types, the group with rapid transition to negative balance at a moderate level (Trajectory 1) showed the best prognosis.