In-Hospital Mortality Prediction of Patients Requiring Extracorporeal Membrane Oxygenation Using Composite Lactate Metrics and Weight-of-Evidence Modeling

Clinical Snapshot

Key Findings

• P Value: Composite lactate × bicarbonate metric: FDR-adjusted p = 4.49 × 10⁻⁴

• Effect Size: Machine learning model achieved 85% accuracy (95% CI 80-89%)

• Primary Outcome: In-hospital mortality prediction accuracy

• Nnt Or Sensitivity: 87% precision (95% CI 82-92%) for mortality prediction

• Confidence Interval: AUC 0.879 (95% CI 0.84-0.92) for multilayer perceptron; 0.886 (95% CI 0.853-0.920) for WoE model

Clinical Application

WoE model uses readily available laboratory values, making implementation feasible in most ICUs with ECMO capability Relevant to Australian ECMO centers, though validation needed in local population. Could inform resource allocation and family discussions in Australian ICUs with ECMO programs Adult patients requiring ECMO support, particularly in trauma and critical care settings

Abstract

BACKGROUND: Mortality prognostication in adult patients requiring extracorporeal membrane oxygenation (ECMO) is not accurate or established. We hypothesized that composite lactate-based metrics and machine-learning models would improve in-hospital mortality prognostication compared with lactate alone in this population. STUDY DESIGN: We conducted a retrospective study of adult patients supported with ECMO at a Level I trauma center from 2022 to 2024 (N = 104). Composite metrics were generated by multiplying lactate by acid-base markers and comparing performance to lactate after Benjamini-Hochberg correction to control the false discovery rate. For machine-learning models, 3 multilayer perceptron architectures were developed using laboratory markers from a patient's hospital admission. Feature attribution through local interpretable model-agnostic explanations guided the development of a weight-of-evidence (WoE) model using only initial laboratory values. RESULTS: The composite metric of average arterial lactate × average arterial bicarbonate was the most significantly elevated marker in nonsurvivors and more significant than lactate alone (false discovery rate-adjusted p = 4.49 × 10 -4 vs 2.14 × 10 -3 ). The 100 × 50 multilayer perceptron architecture achieved 85% accuracy (95% CI 80 to 89%), 87% precision (95% CI 82% to 92%), F1 score of 0.81 (95% CI 0.74 to 0.86), and area under the curve of 0.879 (95% CI 0.84 to 0.92). Local interpretable model-agnostic explanations identified kidney replacement therapy, surrogate respiratory quotient, lactate gradient, and arterial bicarbonate as metrics incorporated in the WoE model achieving 80% accuracy (95% CI 76% to 84%), 70% precision (95% CI 63% to 77%), F1 score of 0.72 (95% CI 0.66 to 0.78), and area under the curve of 0.886 (95% CI 0.853 to 0.920). CONCLUSIONS: Composite lactate metrics and the WoE model improved in-hospital mortality prediction in patients requiring ECMO. Prospective studies and external validation are warranted to confirm these findings.

References

1. 1.Singh, P., Rizzo, J. A., Lawson, S., Gutierrez, M., Herico, E., Thomas, J. M., Morris, M. J., Schauer, S. G., April, M. D., & Sobieszczyk, M. (2026). In-Hospital Mortality Prediction of Patients Requiring Extracorporeal Membrane Oxygenation Using Composite Lactate Metrics and Weight-of-Evidence Modeling. Journal of the American College of Surgeons. https://doi.org/10.1097/XCS.0000000000001900