Frontiers in cardiovascular medicine · 2026
Fluid balance trajectories and prognosis in patients with acute myocardial infarction complicated by cardiogenic shock: a group-based trajectory model approach.
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Zhang Chunmei, Xie Zhiyi, Lin Guangyu, Zhang Qitian
Аннотация
BACKGROUND: Fluid management is crucial in the treatment of patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS), yet the optimal strategy remains unclear. This study aims to evaluate the association between fluid balance (FB) trajectories and prognosis in AMI-CS patients.
METHODS: This study utilized data from the MIMIC-IV database, including patients diagnosed with AMI-CS. A Group-Based Trajectory Model (GBTM) was applied to identify patient groups with similar FB trends. The association between different FB trajectories and patient survival was assessed using Kaplan-Meier survival analysis and Cox regression models. Additionally, subgroup and sensitivity analyses were conducted to validate the robustness of the results.
RESULTS: A total of 533 AMI-CS patients were included. The 4-group trajectory model showed good fit (AIC = 19,937.75; minimum AvePP = 0.81). Four FB trajectory patterns were identified: trajectory 1 (stable negative balance), trajectory 2 (rapid decline to negative balance), trajectory 3 (persistent positive balance), and trajectory 4 (high-level decreasing). Kaplan-Meier survival analysis revealed that patients in trajectories 1 and 2 had higher survival rates, while those in the fluid overload group had a significantly higher risk of death compared to the non-overload group. Cox regression analysis further demonstrated that, compared to trajectory 2, trajectory 3 was associated with a significantly increased mortality risk, while trajectory 1 showed no statistically significant difference. Subgroup and sensitivity analyses were consistent, confirming the robustness of the study findings.
CONCLUSION: Among the dynamic FB patterns in AMI-CS patients, stable negative balance or rapid transition to negative balance is associated with the best prognosis. The GBTM approach helps identify different risk strata within the AMI-CS patient population.
METHODS: This study utilized data from the MIMIC-IV database, including patients diagnosed with AMI-CS. A Group-Based Trajectory Model (GBTM) was applied to identify patient groups with similar FB trends. The association between different FB trajectories and patient survival was assessed using Kaplan-Meier survival analysis and Cox regression models. Additionally, subgroup and sensitivity analyses were conducted to validate the robustness of the results.
RESULTS: A total of 533 AMI-CS patients were included. The 4-group trajectory model showed good fit (AIC = 19,937.75; minimum AvePP = 0.81). Four FB trajectory patterns were identified: trajectory 1 (stable negative balance), trajectory 2 (rapid decline to negative balance), trajectory 3 (persistent positive balance), and trajectory 4 (high-level decreasing). Kaplan-Meier survival analysis revealed that patients in trajectories 1 and 2 had higher survival rates, while those in the fluid overload group had a significantly higher risk of death compared to the non-overload group. Cox regression analysis further demonstrated that, compared to trajectory 2, trajectory 3 was associated with a significantly increased mortality risk, while trajectory 1 showed no statistically significant difference. Subgroup and sensitivity analyses were consistent, confirming the robustness of the study findings.
CONCLUSION: Among the dynamic FB patterns in AMI-CS patients, stable negative balance or rapid transition to negative balance is associated with the best prognosis. The GBTM approach helps identify different risk strata within the AMI-CS patient population.