Presentation (Project) Title

Revisiting the Predictive Value of Heart Rate Relative to PTSD and Depression in Trauma Center Patients: Does it Matter When it’s Measured?

Mentor Information

Brian Bunnell (College of Medicine Psychiatry and Behavioral Neurosciences)

Presentation Format

Event

Abstract

Background: Between 20 and 40% of traumatically injured patients develop posttraumatic stress disorder (PTSD) and/ or depression within a year post-injury. The ACS Committee on Trauma (COT) recommends assessment and monitoring of PTSD and depression symptoms by trauma centers. Resources that efficiently enhance capacity to identify good candidates for mental health follow-up are therefore a priority. Data on patient vitals such as heart rate (HR) are routinely collected and stored in electronic medical records (EMRs), and their utility for identifying patients at-risk for PTSD has been explored previously but with varying results. Further, few studies assessed HR at more than one time-point during hospital admission and even fewer examined the relation between HR and depression at follow-up. Methods: We examined data from a prospective clinical sample of 389 patients admitted to a Level I trauma center to examine the predictive value of HR relative to elevated PTSD and depression 30-days post-injury using ROC curve analyses. Patient admission, discharge, and mean HR data were extracted from the EMR. A phone-based depression screening was conducted ~30-days post-injury (median days = 39) using the PTSD Checklist for DSM-5 (PCL-5) and the Patient Health Questionnaire-9 (PHQ-9). Results: Results of the ROC curve analyses predicting elevated PTSD at 30-days were significant for admission (AUC = 0.62 p < 0.001) and mean (AUC = 0.59, p = 0.01) HR. Optimal cutoffs for predicting elevated PTSD at 30-days were 91.5 and 87.5 BPM for admission and mean HR, respectively. Results of the ROC curve analyses predicting elevated depression at 30-days were significant for admission (AUC = 0.60, p = 0.002) and mean (AUC = 0.57, p = 0.04) HR. Optimal cutoffs for predicting elevated depression at 30-days were 90.5 and 87.5 BPM for admission and mean HR, respectively. Discharge HR did not significantly predict PTSD or depression at 30-days. Conclusions: The results of this study support the prior literature demonstrating the predictive value of HR relative to elevated PTSD and depression. However, the results suggested an optimal HR ~5 BPM lower than these studies. Continued assessment of the predictive value of physiological variables such as HR that are automatically captured and continuously monitored in EMRs is important given the increasing need for efficient and cost-effective risk assessment in trauma centers, as recommended by the ACS COT.

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Revisiting the Predictive Value of Heart Rate Relative to PTSD and Depression in Trauma Center Patients: Does it Matter When it’s Measured?

Background: Between 20 and 40% of traumatically injured patients develop posttraumatic stress disorder (PTSD) and/ or depression within a year post-injury. The ACS Committee on Trauma (COT) recommends assessment and monitoring of PTSD and depression symptoms by trauma centers. Resources that efficiently enhance capacity to identify good candidates for mental health follow-up are therefore a priority. Data on patient vitals such as heart rate (HR) are routinely collected and stored in electronic medical records (EMRs), and their utility for identifying patients at-risk for PTSD has been explored previously but with varying results. Further, few studies assessed HR at more than one time-point during hospital admission and even fewer examined the relation between HR and depression at follow-up. Methods: We examined data from a prospective clinical sample of 389 patients admitted to a Level I trauma center to examine the predictive value of HR relative to elevated PTSD and depression 30-days post-injury using ROC curve analyses. Patient admission, discharge, and mean HR data were extracted from the EMR. A phone-based depression screening was conducted ~30-days post-injury (median days = 39) using the PTSD Checklist for DSM-5 (PCL-5) and the Patient Health Questionnaire-9 (PHQ-9). Results: Results of the ROC curve analyses predicting elevated PTSD at 30-days were significant for admission (AUC = 0.62 p < 0.001) and mean (AUC = 0.59, p = 0.01) HR. Optimal cutoffs for predicting elevated PTSD at 30-days were 91.5 and 87.5 BPM for admission and mean HR, respectively. Results of the ROC curve analyses predicting elevated depression at 30-days were significant for admission (AUC = 0.60, p = 0.002) and mean (AUC = 0.57, p = 0.04) HR. Optimal cutoffs for predicting elevated depression at 30-days were 90.5 and 87.5 BPM for admission and mean HR, respectively. Discharge HR did not significantly predict PTSD or depression at 30-days. Conclusions: The results of this study support the prior literature demonstrating the predictive value of HR relative to elevated PTSD and depression. However, the results suggested an optimal HR ~5 BPM lower than these studies. Continued assessment of the predictive value of physiological variables such as HR that are automatically captured and continuously monitored in EMRs is important given the increasing need for efficient and cost-effective risk assessment in trauma centers, as recommended by the ACS COT.