Early Prognostication of Critical Patients With Spinal Cord Injury

Abstract

Study Design: A retrospective case-series. Objective: The study aims to use machine-learning (ML) to predict the discharge destination of spinal cord injury (SCI) patients in the intensive care unit (ICU). Summary of Background Data: Prognostication following SCI is vital, especially for critical patients who need intensive care. Methods: Clinical data of patients diagnosed with SCI were extracted from a publicly available ICU database. The firstly recorded data of the included patients were used to develop a total of 98 ML classifiers, seeking to predict discharge destination (e.g. death, further medical care, home). The micro-average area under the curve (AUC) was the main indicator to assess discrimination. The best average-AUC classifier and the best death-sensitivity classifier were integrated into an ensemble classifier. The discrimination of the ensemble classifier was compared with top death-sensitivity classifiers and top average-AUC classifiers. Additionally, prediction consistency and clinical utility were also assessed. Results: A total of 1485 SCI patients were included. The ensemble classifier had a micro-average AUC of 0.851, which was only slightly inferior to the best average-AUC classifier (P=0.10) The best average-AUC classifier death sensitivity was much lower than that of the ensemble classifier. The ensemble classifier had a death sensitivity of 0.452, which was inferior to top 8 death-sensitivity classifiers, whose micro-average AUC were inferior to the ensemble classifier (P<0.05). Additionally, the ensemble classifier demonstrated a comparable Brier score and superior Net benefit in the decision curve analysis, when compared to the performance of the origin classifiers. Conclusions: The ensemble classifier shows an overall superior performance in predicting discharge destination considering discrimination ability, prediction consistency and clinical utility. This classifier system may aid in the clinical management of critical SCI patients in the early phase following injury. Level of Evidence: 3