Continuous Vital Sign Analysis to Predict Secondary Neurological Decline After Traumatic Brain Injury

In the acute resuscitation period after traumatic brain injury (TBI), one of the goals is to identify those at risk for secondary neurological decline (ND), represented by a constellation of clinical signs that can be identified as objective events related to secondary brain injury and independently...

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Bibliographic Details
Published in:	Frontiers in neurology Vol. 9; p. 761
Main Authors:	Melinosky, Christopher, Yang, Shiming, Hu, Peter, Li, HsiaoChi, Miller, Catriona H T, Khan, Imad, Mackenzie, Colin, Chang, Wan-Tsu, Parikh, Gunjan, Stein, Deborah, Badjatia, Neeraj
Format:	Journal Article
Language:	English
Published:	Switzerland Frontiers Media S.A 25-09-2018
Subjects:	heart rate variability machine learning Neurology photoplethysmogram predictive model traumatic brain injury photoplethysmogram traumatic brain injury predictive model machine learning heart rate variability
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Summary:	In the acute resuscitation period after traumatic brain injury (TBI), one of the goals is to identify those at risk for secondary neurological decline (ND), represented by a constellation of clinical signs that can be identified as objective events related to secondary brain injury and independently impact outcome. We investigated whether continuous vital sign variability and waveform analysis of the electrocardiogram (ECG) or photoplethysmogram (PPG) within the first hour of resuscitation may enhance the ability to predict ND in the initial 48 hours after traumatic brain injury (TBI). Retrospective analysis of ND in TBI patients enrolled in the prospective Oximetry and Noninvasive Predictors Of Intervention Need after Trauma (ONPOINT) study. ND was defined as any of the following occurring in the first 48 h: new asymmetric pupillary dilatation (>2 mm), 2 point GCS decline, interval worsening of CT scan as assessed by the Marshall score, or intervention for cerebral edema. Beat-to-beat variation of ECG or PPG, as well as waveform features during the first 15 and 60 min after arrival in the TRU were analyzed to determine physiologic parameters associated with future ND. Physiologic and admission clinical variables were combined in multivariable logistic regression models predicting ND and inpatient mortality. There were 33 (17%) patients with ND among 191 patients (mean age 43 years old, GCS 13, ISS 12, 69% men) who met study criteria. ND was associated with ICU admission ( < 0.001) and inpatient mortality ( < 0.001). Both ECG (AUROC: 0.84, 95% CI: 0.76,0.93) and PPG (AUROC: 0.87, 95% CI: 0.80, 0.93) analyses during the first 15 min of resuscitation demonstrated a greater ability to predict ND then clinical characteristics alone (AUROC: 0.69, 95% CI: 0.59, 0.8). Age ( = 0.02), Marshall score ( = 0.001), penetrating injury ( = 0.02), and predictive probability for ND by PPG analysis at 15 min ( = 0.03) were independently associated with inpatient mortality. Analysis of variability and ECG or PPG waveform in the first minutes of resuscitation may represent a non-invasive early marker of future ND.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Sara Stern-Nezer, University of California, Irvine, United States; Syed Omar Shah, Thomas Jefferson University, United States This article was submitted to Neurocritical and Neurohospitalist Care, a section of the journal Frontiers in Neurology Edited by: Wengui Yu, University of California, Irvine, United States
ISSN:	1664-2295 1664-2295
DOI:	10.3389/fneur.2018.00761