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Predictors of mortality in VA-ECMO patients: A retrospective cohort analysis using LASSO Regression
Session:
SESSÃO DE POSTERS 35 - DOENÇAS CARDIOVASCULARES - CHOQUE CARDIOGÉNICO 1
Speaker:
Marta Leite
Congress:
CPC 2025
Topic:
E. Coronary Artery Disease, Acute Coronary Syndromes, Acute Cardiac Care
Theme:
14. Acute Cardiac Care
Subtheme:
14.4 Acute Cardiac Care – Cardiogenic Shock
Session Type:
Cartazes
FP Number:
---
Authors:
Marta Leite; Inês Neves; Fábio Nunes; Mariana Brandão; Pedro Teixeira; Marisa Silva; Gustavo Pires-Morais; Marta Ponte; Adelaide Dias; Pedro Braga; Daniel Caeiro; Ricardo Fontes-Carvalho
Abstract
<p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><span style="font-size:12.0pt"><strong>Background:</strong> Venoarterial extracorporeal membrane oxygenation (VA-ECMO) serves as a critical rescue support in patients with refractory cardiogenic shock (CS), yet mortality rates remain high. Identifying clinical predictors of mortality in this population could aid in optimizing patient selection and timing of intervention.</span></span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><span style="font-size:12.0pt"><strong>Methods: </strong>We conducted a retrospective observational study, encompassing patients admitted with cardiogenic shock and treated with VA-ECMO from 2011 to 2023 in our center. Key patient data, including demographics, comorbidities, clinical presentation, ECMO-related complications, and outcomes, were extracted from medical records. This single-center study analyzed clinical predictors of mortality in a cohort of VA-ECMO patients, utilizing a LASSO logistic regression model for feature selection and risk estimation. LASSO regularization was used to enhance the model’s predictive accuracy, with hyperparameters optimized via cross-validation. Model performance was evaluated by metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (ROC AUC).</span></span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><span style="font-size:12.0pt"><strong>Results:</strong> From January 2011 to October 2023 our center treated a total of 85 patients in VA-ECMO (mean age 54.5 ± 11.9 years-old; 61.2% male). The final model is resumed in Figure 1 and identified several significant predictors of mortality, including gender, use of an unloading device, invasive mechanical ventilation, and a higher SAVE score. Notably, the SAVE score exhibited the largest association with mortality, with an odds ratio of 1.46 (46% increase in odds), followed by male gender (odds ratio: 1.26, 26% increase). Model performance showed moderate discriminative ability, with an ROC AUC of 0.638, accuracy of 44.4%, and a Brier score of 0.243. Sensitivity analysis indicated a slight improvement in mortality prediction when stratifying patients by SAVE score and use of mechanical ventilation.</span></span></span></p> <p style="text-align:justify"><span style="font-size:11pt"><span style="font-family:Calibri,sans-serif"><span style="font-size:12.0pt"><strong>Conclusion: </strong>This study highlights specific clinical features, notably the SAVE score and the presence of invasive ventilation, as significant clinical predictors of mortality in VA-ECMO patients with cardiogenic shock. Although model accuracy was moderate, these findings underscore the importance of early risk stratification and may guide candidate selection. </span></span></span></p>
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