einstein (São Paulo). 28/ago/2023;21:eAO0071.
Outcome prediction for critical care patients with respiratory neoplasms using a multilayer perceptron neural network
DOI: 10.31744/einstein_journal/2023AO0071
Highlights
Two artificial neural networks, type of multilayer perceptron, and a random forest model were developed.
Multilayer perceptron and random forest were compared to the OASIS, SAPS, SAPS II, SAPS III, LODS, SOFA, and EVCI Scores.
Discrimination was assessed with the receiver operator characteristic and precision-recall curves.
Calibration of the predictive models was evaluated with the Brier Score.
ABSTRACT
Objective
The variation in mortality rates of intensive care unit oncological patients may imply that clinical characteristics and prognoses are very different between specific subsets of patients with cancer. The specific characteristics of patients with cancer have not been included as risk factors in the established severity-of-illness scoring systems and comorbidity scores, showing limitations in predicting mortality risk. This study aimed to devise a predictive tool for in-hospital mortality for adult patients with a respiratory neoplasm admitted to the intensive care unit, using
an artificial neural network
Methods
A total of 1,221 stays in the intensive care unit from the Beth Israel Deaconess Medical Center were studied. The primary endpoint was the all-cause inhospital mortality prediction. An artificial neural network was developed and compared with six severity-of-illness scores and one comorbidity score. Model building was based on important predictors of lung cancer mortality, such as several laboratory parameters, demographic parameters, organ-supporting treatments, and other clinical information. Discrimination and calibration were assessed.
Results
The AUROC for the multilayer perceptron was 0.885, while it was <0.74 for the conventional systems. The AUPRC for the multilayer perceptron was 0.731, whereas it was ≤0.482 for the conventional systems. The superiority of multilayer perceptron was statistically significant for all pairwise AUROC and AUPRC comparisons. The Brier Score was better for the multilayer perceptron (0.109) than for OASIS (0.148), SAPS III (0.163), and SAPS
II (0.154).
Conclusion
Discrimination was excellent for multilayer perceptron, which may be a valuable tool for assessing critically ill patients with lung cancer.
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