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

Christopher Melinosky; Shiming Yang; Peter Hu; HsiaoChi Li; Catriona H T Miller; Imad Khan; Colin Mackenzie; Wan-Tsu Chang; Gunjan Parikh; Deborah Stein; Neeraj Badjatia

doi:10.3389/fneur.2018.00761

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

Front Neurol. 2018 Sep 25:9:761. doi: 10.3389/fneur.2018.00761. eCollection 2018.

Authors

Christopher Melinosky^{1

2}, Shiming Yang^{1

3}, Peter Hu^{1

3}, HsiaoChi Li³, Catriona H T Miller⁴, Imad Khan^{1

2}, Colin Mackenzie^{1

3}, Wan-Tsu Chang^{1

5}, Gunjan Parikh^{1

2}, Deborah Stein^{1

6}, Neeraj Badjatia^{1

2}

Affiliations

¹ Program in Trauma, University of Maryland School of Medicine, Baltimore, MD, United States.
² Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, United States.
³ Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, United States.
⁴ Enroute care Division, Department of Aeromedical Research, U.S. Air Force School of Aerospace Medicine, Wright Patterson AFB, Dayton, OH, United States.
⁵ Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, MD, United States.
⁶ Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, United States.

Abstract

Background: 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). Methods: 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. Results: 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 (P < 0.001) and inpatient mortality (P < 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 (P = 0.02), Marshall score (P = 0.001), penetrating injury (P = 0.02), and predictive probability for ND by PPG analysis at 15 min (P = 0.03) were independently associated with inpatient mortality. Conclusions: Analysis of variability and ECG or PPG waveform in the first minutes of resuscitation may represent a non-invasive early marker of future ND.

Keywords: heart rate variability; machine learning; photoplethysmogram; predictive model; traumatic brain injury.