Intracranial Densitometry-Augmented Machine Learning Enhances the Prognostic Value of Brain CT in Pediatric Patients With Traumatic Brain Injury: A Retrospective Pilot Study.

Young-Tak Kim,Hakseung Kim,Byung-Mo Oh,Choel-Hui Lee,Young Hun Choi,Won-Sang Cho,Dong-Joo Kim,Byung C Yoon,Jung Bin Kim

doi:10.3389/fped.2021.750272

Abstract

Background: The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models.Objective: This study aimed to employ fully automated quantitative densitometric CT parameters and a cutting-edge machine learning algorithm to construct a robust prognostic model for pediatric TBI.Methods: Fifty-eight pediatric patients with TBI who underwent brain CT were retrospectively analyzed. Intracranial densitometric information was derived from the supratentorial region as a distribution representing the proportion of Hounsfield units. Furthermore, a machine learning-based prognostic model based on gradient boosting (i.e., CatBoost) was constructed with leave-one-out cross-validation. At discharge, the outcome was assessed dichotomously with the Glasgow Outcome Scale (favorability: 1–3 vs. 4–5). In-hospital mortality, length of stay (>1 week), and need for surgery were further evaluated as alternative TBI outcome measures.Results: Densitometric parameters indicating reduced brain density due to subtle global ischemic changes were significantly different among the TBI outcome groups, except for need for surgery. The skewed intracranial densitometry of the unfavorable outcome became more distinguishable in the follow-up CT within 48 h. The prognostic model augmented by intracranial densitometric information achieved adequate AUCs for various outcome measures [favorability = 0.83 (95% CI: 0.72–0.94), in-hospital mortality = 0.91 (95% CI: 0.82–1.00), length of stay = 0.83 (95% CI: 0.72–0.94), and need for surgery = 0.71 (95% CI: 0.56–0.86)], and this model showed enhanced performance compared to the conventional CRASH-CT model.Conclusion: Densitometric parameters indicative of global ischemic changes during the acute phase of TBI are predictive of a worse outcome in pediatric patients. The robustness and predictive capacity of conventional TBI prognostic models might be significantly enhanced by incorporating densitometric parameters and machine learning techniques.

Highlights

Pediatric traumatic brain injury (TBI) accounts for over 500,000 emergency department visits in the United States each year [1]
For initial computed tomography (CT) acquired at admission and follow-up CT acquired within 48 h later, the changes in intracranial densitometry according to the outcome were evaluated
The results indicate that [1] densitometric parameters are independently associated with various TBI outcome measures but not the need for surgery, [2] the prognostic capacity of the conventional corticosteroid randomization after significant head injury (CRASH) model could be enhanced by employing CatBoost rather than traditional logistic regression, and [3] the capacity can be further increased by supplementing densitometric parameters as model inputs

Summary

Introduction

The anatomical and physiological properties (e.g., thinner cranium, less myelinated tissue) of the pediatric brain can result in more rapid and severe development of secondary ischemic-edematous insults after head injury [2,3,4], and worse outcomes, than in adults [5]. Brain computed tomography (CT) remains the gold standard imaging modality during the acute phase of TBI for rapidly evaluating TBI and developing an appropriate intervention strategy [7, 8]. The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models

Methods

Results

Discussion

Conclusion