• H E Hinson, Hannah Radabaugh, Nincheng Li, Toshinori Fukuda, Jeffrey M Pollock, Martin Schreiber, Susan Rowell, and Adam R Ferguson.
• University of California San Francisco, 8785, Neurology, 1001 Potrero Avenue, Building 1, San Francisco, California, United States, 94110; H.E.Hinson@ucsf.edu.
• J. Neurotrauma. 2024 Apr 15.

AbstractProgression of intracranial hemorrhage is a common, potentially devastating complication after moderate/severe traumatic brain injury (TBI). Clinicians have few tools to predict which patients with traumatic intracranial hemorrhage on their initial head computed tomographic scan (hCT) scan will progress. The objective of this investigation was to identify clinical, imaging, and/or protein biomarkers associated with progression of intracranial hemorrhage (PICH) after moderate/severe TBI and to create an accurate predictive model of PICH based on clinical features available at presentation. We analyzed a subset of subjects from the phase II double-blind, multi-center randomized "Prehospital Tranexamic Acid Use for TBI" trial. This subset was limited to the placebo arm of the parent trial with evidence of hemorrhage on the initial head CT and a follow-up CT 6 hours after. PICH was defined as an increase in hemorrhage size by 30% or more, or the development of new hemorrhage in the intra- and extra-axial intracranial vault between the initial and the follow-up head CT. Two independent radiologists evaluated each head CT, and conflicts were adjudicated by a third. Clinical and radiographic characteristics were collected, along with plasma protein biomarkers at admission. Principal component analysis (PCA) was performed, and each principal component (PC) was interrogated for its association with PICH. Finally, expert opinion and recursive feature extraction (RFE) were used to select input features for the construction of several super- vised classification models. Their ability to predict PICH were quantified and compared. In this subset of subjects (n=104), 46% (n=48) demonstrated PICH. Univariate analyses showed no association between PICH and age, sex, admission Glasgow Coma Scale (GCS), GCS motor subscore, presence of midline shift, admission platelet count or admission INR. Radiographic severity scores (Marshall score [P=0.007], Rotterdam score [P=0.004]) and initial hematoma volume [P=0.005] were associated with PICH. Higher levels of admission GFAP (P<0.001) and MAP (P=0.011) were also associated with PICH. Of the PCs, PC1 was significantly associated with PICH (P=0.0125). Using multimodal data input, machine learning classifiers successfully discriminated patients with or without PICH. Models composed of machine selected features performed better than models composed of expert selected variables (reaching an average of 77% accuracy, AUC = 0.78 versus AUC = 0.68 for the expert selected variables). Predictive models utilizing variables measured at admission can accurately predict PICH, confirmed by the 6-hour follow-up head CT. Our best performing models must now be externally validated in a separate cohort of TBI patients with low GCS and initial head CT positive for hemorrhage.

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