Development of a Resting Energy Expenditure Estimation in Patients Undergoing Targeted Temperature Management with a...

Targeted temperature management (TTM) directly impacts energy expenditure via temperature modulation and shivering associated with temperature modulating devices. We hypothesized that resting energy expenditure (REE) can be accurately estimated utilizing data obtained from a surface gel pad temperature modulating device (TMD) and demographic factors. Baseline demographic data, along with concurrent temperature, sedation, and shivering data, and indirect calorimetry (IDC) were collected from patients undergoing TTM. The data from the IDC and temperature modulation device (TMD) were synchronized and averaged over 60-second intervals to provide simultaneous comparisons. Heat transfer (calories) was calculated from the TMD by an equation that assessed water temperature from the TMD to the patient, water temperature returning to the TMD, water flow rates, and device mode. A linear regression model was used to determine factors associated with REE as measured by IDC. A difference in the mean between REE and estimated REE was used to assess accuracy. There were 48 assessments conducted in 40 subjects [mean (standard deviation)] age: 58 (14) years, 60% female, body surface area (BSA): 2.0 ± 0.3 who underwent simultaneous assessments. Target temperature was 36-37°C in 75%, with a median Bedside Shivering Assessment Score of 0 (range 0-2). Factors associated with REE on multivariable linear regression included older age (p < 0.001), male sex (p = 0.004), higher BSA (p < 0.001), higher patient temperature (p < 0.001), and lower heat transfer (p = 0.003). Adjusted prediction coefficients from this model were then tested against REE by a Bland-Altman analysis. The difference between difference in resting energy estimation (REEdiff) and measured REE by IDC was 6.2 calories/min (REEdiff: 95% confidence interval: -14.1 calories, 26.5 calories, p = 0.5). We believe that the heat transfer data from the TMD coupled with clinical characteristics of patients can be utilized to calculate the REE for every minute of TTM. These data can be utilized to mitigate the consequences of shivering and malnutrition during TTM.