Nonpharmacologic Pain Management Among Hospitalized Inpatients: A Randomized Waitlist-Controlled Trial of Standard Virtual Reality (CGI VR) Versus Video Capture VR (360 degrees 3D/Stereoscopic Video Capture VR).

OBJECTIVES: Nonpharmacologic pain management strategies are needed because of the growing opioid epidemic. While studies have examined the efficacy of virtual reality (VR) for pain reduction, there is little research in adult inpatient settings, and no studies comparing the relative efficacy of standard animated computer-generated imagery (CGI) VR to Video Capture VR (360 degrees 3D/stereoscopic Video Capture VR). Here, we report on a randomized controlled trial of the relative efficacy of standard CGI VR versus Video Capture VR (matched for content) and also compared the overall efficacy of VR to a waitlist control group. MATERIALS AND METHODS: Participants (N=103 hospitalized inpatients reporting pain) were randomized to 1 of 3 conditions: (1) waitlist control, (2) CGI VR, or (3) Video Capture VR. The VR and waitlist conditions were 10 minutes in length. Outcomes were assessed pretreatment, post-treatment, and after a brief follow-up. RESULTS: Consistent with hypotheses, both VR conditions reduced pain significantly more relative to the waitlist control condition (d=1.60, P<0.001) and pain reductions were largely maintained at the brief follow-up assessment. Both VR conditions reduced pain by ∼50% and led to improvements in mood, anxiety, and relaxation. Contrary to prediction, the Video Capture VR condition was not significantly more effective at reducing pain relative to the CGI VR condition (d=0.25, P=0.216). However, as expected, patients randomized to the Video Capture VR rated their experience as more positive and realistic (d=0.78, P=0.002). DISCUSSION: Video Capture VR was as effective as CGI VR for pain reduction and was rated as more realistic.

Reviews of Modeling Techniques for Neuromonitoring Depth of Anesthesia.

This article reviews the various modeling techniques for neuromonitoring depth of anesthesia (DOA). Traditional techniques such as parametric, predictive, optimal, and adaptive modeling; proportional, integral, derivative (PID) modeling; together with modern techniques such as bispectral-based, artificial neural-network-based, fuzzy logic, and neuro-fuzzy modeling, bring us to the current state of the art in DOA neuromonitoring. This article reviews historical information about each of the modern techniques and provides an example demonstrating its implementation; reviews drug pharmacokinetic/pharmacodynamic (PK/PD) and drug interaction PK/PD modeling techniques for a balanced total intravenous anesthesia (TIVA) administration; and discusses the existing technical problems and clinical challenges, suggesting new techniques necessary for the future development of a DOA monitoring and control system.

Modeling for neuromonitoring depth of anesthesia.

This article reviews the various modeling techniques for neuromonitoring depth of anesthesia (DOA). Traditional techniques such as parametric, predictive, optimal, and adaptive modeling, proportional, integral, derivative (PID) modeling, together with modern techniques such as bispectral-based, artificial neural-network-based, fuzzy logic, and neuro-fuzzy modeling, bring us to the current state of the art in DOA neuromonitoring. This article reviews historical information about each of the modern techniques and provides an example demonstrating its implementation; reviews drug pharmacokinetic/pharmacodynamic (PK/PD) and drug interaction PK/PD modeling techniques for a balanced total intravenous anesthesia (TIVA) administration; and discusses the existing technical problems and clinical challenges, suggesting new techniques necessary for the future development of a DOA monitoring and control system.