Key result
A machine learning prognostic model using conditional inference trees with readily available pre-ECMO variables predicted in-hospital mortality with an area under the curve of 0.70.
Why the study?
VA-ECMO therapy is associated with considerable mortality, and personalized survival predictions using machine learning may assist clinical decision-making prior to ECMO insertion.
Can a prognostic model using conditional inference trees accurately predict in-hospital mortality in patients undergoing VA-ECMO therapy?
Cohort (n=837)
Yes
Can a prognostic model using conditional inference trees accurately predict in-hospital mortality in patients undergoing VA-ECMO therapy?
Effect estimate: AUC 0.70 (95% CI 0.66-0.74)
Conditional inference trees can provide moderate accuracy in predicting in-hospital mortality for patients undergoing VA-ECMO using readily available pre-initiation variables.
BACKGROUND: Despite increasing use and understanding of the process, veno-arterial extracorporeal membrane oxygenation (VA-ECMO) therapy is still associated with considerable mortality. Personalized and quick survival predictions using machine learning methods can assist in clinical decision making before ECMO insertion. METHODS: This is a multicenter study to develop and validate an easy-to-use prognostic model to predict in-hospital mortality of VA-ECMO therapy, using unbiased recursive partitioning with conditional inference trees. We compared two sets with different numbers of variables (small and comprehensive), all of which were available just before ECMO initiation. The area under the curve (AUC), the cross-validated Brier score, and the error rate were applied to assess model performance. Data were collected retrospectively between 2007 and 2019. RESULTS: 837 patients were eligible for this study; 679 patients in the derivation cohort (median (IQR) age 60 (49 to 69) years; 187 (28%) female patients) and a total of 158 patients in two external validation cohorts (median (IQR) age 57 (49 to 65) and 70 (63 to 76) years). For the small data set, the model showed a cross-validated error rate of 35.79% and an AUC of 0.70 (95% confidence interval from 0.66 to 0.74). In the comprehensive data set, the error rate was the same with a value of 35.35%, with an AUC of 0.71 (95% confidence interval from 0.67 to 0.75). The mean Brier scores of the two models were 0.210 (small data set) and 0.211 (comprehensive data set). External validation showed an error rate of 43% and AUC of 0.60 (95% confidence interval from 0.52 to 0.69) using the small tree and an error rate of 35% with an AUC of 0.63 (95% confidence interval from 0.54 to 0.72) using the comprehensive tree. There were large differences between the two validation sets. CONCLUSIONS: Conditional inference trees are able to augment prognostic clinical decision making for patients undergoing ECMO treatment. They may provide a degree of accuracy in mortality prediction and prognostic stratification using readily available variables.
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Braun et al. (2023) conducted a cohort in Cardiopulmonary failure requiring VA-ECMO (n=837). Pre-ECMO clinical variables (prognostic model) was evaluated on In-hospital mortality prediction (AUC of small data set model) (AUC 0.70, 95% CI 0.66-0.74). A machine learning prognostic model using conditional inference trees with readily available pre-ECMO variables predicted in-hospital mortality with an area under the curve of 0.70.
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