Prognostic value of lactate clearance, fluid balance, and APACHE II score in patients with cardiogenic shock receiving extracorporeal membrane oxygenation
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective The objective of this study is to examine the prognostic value of lactate clearance, fluid balance, and the Acute Physiology and Chronic Health Evaluation II (APACHE II) score in patients with cardiogenic shock undergoing treatment with extracorporeal membrane oxygenation (ECMO). Methods A retrospective analysis was conducted on 32 patients with cardiogenic shock who underwent ECMO in the Intensive Care Unit of Peking University International Hospital between January 2021 and June 2024. The patients were categorized into a survival group (n = 14) and a non-survival group (n = 18) based on their clinical outcomes. Baseline characteristics, including age, sex, and body weight and so on were collected for both groups. Multivariate logistic regression analysis was used to identify risk factors influencing patient prognosis. The prognostic value of relevant indicators was assessed using receiver operating characteristic curve analysis, while Pearson's correlation analysis was conducted to assess the relationships between specific indicators. Results Lactate clearance was significantly lower in the non-survival group compared to the survival group, while fluid balance and APACHE II scores were notably higher in the non-survival group (p < 0.05). Based on the predictive model, the APACHE II score demonstrated the highest specificity for prognosis at 97.4%, whereas the combined indices exhibited the highest sensitivity at 95.5%. Additionally, lactate clearance revealed a negative correlation with both fluid balance and APACHE II scores (p < 0.05). Conclusion This pilot study demonstrated that lactate clearance, fluid balance, and APACHE II score are valuable prognostic indicators for patients; however, the predictive accuracy of individual indicators is limited. The combined assessment of these indices provides a more robust and reliable predictive performance.
Authors: Qi-Feng Zhang, Shuang-Long Zhang, Gang Li, Miao Guo, Xiao-Xia Qi, Xiao-Hui Xing, Zheng Wang
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4410181964
