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Secondary neurologic decline (ND) after traumatic brain injury (TBI) is independently associated with outcome, but robust predictors of ND are lacking. In this retrospective analysis of consecutive isolated TBI admissions to the R. Adams Cowley Shock Trauma Center between November 2015 and June 2018, we aimed to develop a triage decision support tool to quantify risk for early ND. Three machine learning models based on clinical, physiologic, or combined characteristics from the first hour of hospital resuscitation were created. Among 905 TBI cases, 165 (18%) experienced one or more ND events (130 clinical, 51 neurosurgical, and 54 radiographic) within 48 h of presentation. In the prediction of ND, the clinical plus physiologic data model performed similarly to the physiologic only model, with concordance indices of 0.85 (0.824–0.877) and 0.84 (0.812–0.868), respectively. Both outperformed the clinical only model, which had a concordance index of 0.72 (0.688–0.759). This preliminary work suggests that a data-driven approach utilizing physiologic and basic clinical data from the first hour of resuscitation after TBI has the potential to serve as a decision support tool for clinicians seeking to identify patients at high or low risk for ND.

In this study, we hypothesized that immersive virtual reality (VR) environments may reduce pain in patients with acute traumatic injuries, including traumatic brain injuries. We performed a randomized within-subject study in patients hospitalized with acute traumatic injuries, including traumatic brain injury with moderate pain (numeric pain score ≥3 of 10). We compared 3 conditions: (1) an immersive VR environment (VR Blu), (2) a content control with the identical environment delivered through nonimmersive tablet computer (Tablet Blu), and (3) a second control composed of donning VR headgear without content to control for placebo effects and sensory deprivation (VR Blank). We enrolled 60 patients, and 48 patients completed all 3 conditions. Objective and subjective data were analyzed using linear mixed-effects models. Controlling for demographics, baseline pain, and injury severity, we found differences by conditions in relieving pain (F2,75.43 = 3.32, P = 0.042). VR Blu pain reduction was greater than Tablet Blu (−0.92 vs −0.16, P = 0.043), but VR Blu pain reduction was similar to VR Blank (−0.92 vs −1.24, P = 0.241). VR Blu was perceived as most effective by patients for pain reduction (F2,66.84 = 16.28, P < 0.001), and changes in measures of parasympathetic activity including heart rate variability (F2,55.511 = 7.87, P < 0.001) and pupillary maximum constriction velocity (F2,61.41 = 3.50, 1-tailed P = 0.038) echoed these effects. There were no effects on opioid usage. These findings outlined a potential clinical benefit for mollifying pain related to traumatic injuries.

Introduction The annual mortality and national expense of the opioid crisis continue to rise in the USA (130 deaths/day, $50 billion/year). Opioid use disorder usually starts with the prescription of opioids for a medical condition. Its risk is associated with greater pain intensity and coping strategies characterised by pain catastrophising. Non-pharmacological analgesics in the hospital setting are critical to abate the opioid epidemic. One promising intervention is virtual reality (VR) therapy. It has performed well as a distraction tool and pain modifier during medical procedures; however, little is known about VR in the acute pain setting following traumatic injury. Furthermore, no studies have investigated VR in the setting of traumatic brain injury (TBI). This study aims to establish the safety and effect of VR therapy in the inpatient setting for acute traumatic injuries, including TBI. Methods and analysis In this randomised within-subjects clinical study, immersive VR therapy will be compared with two controls in patients with traumatic injury, including TBI. Affective measures including pain catastrophising, trait anxiety and depression will be captured prior to beginning sessions. Before and after each session, we will capture pain intensity and unpleasantness, additional affective measures and physiological measures associated with pain response, such as heart rate and variability, pupillometry and respiratory rate. The primary outcome is the change in pain intensity of the VR session compared with controls. Ethics and dissemination Dissemination of this protocol will allow researchers and funding bodies to stay abreast in their fields through exposure to research not otherwise widely publicised. Study protocols are compliant with federal regulation and University of Maryland Baltimore’s Human Research Protections and Institutional Review Board (protocol number HP-00090603). Study results will be published on completion of enrolment and analysis, and deidentified data can be shared by request to the corresponding author. Trial registration number NCT04356963; Pre-results.

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