Infrared thermography-based body-surface thermal inhomogeneity monitoring to assess the severity of hypoperfusion in critically ill patients

Abstract

Background: Uneven body-surface thermal distribution is a manifestation of hypoperfusion and can be quantified by infrared thermography. Our aim was to investigate whether body-surface thermal inhomogeneity could accurately evaluate the severity of patients at risk of hypoperfusion. Methods: This was a prospective cohort study in which infrared thermography images were taken from unilateral legs of critically ill patients at high risk of hypoperfusion in a cardiac surgical intensive care unit. For each patient, five body-surface thermal inhomogeneity parameters, including standard deviation (SD), kurtosis, skewness, entropy, and low-temperature area rate (LTAR), were calculated. Demographic, clinical, and thermal characteristics of deceased and living patients were compared. The risk of mortality and capillary refill time (CRT) were chosen as the primary outcome and benchmarking parameter for hypoperfusion, respectively. The area under the receiver operating characteristic curve (AUROC) was used to evaluate predictive accuracy. Results: Three hundred seventy-three patients were included, and 55 (14.7%) died during hospital stay. Of inhomogeneity parameters, SD (0.738) and LTAR (0.768) had similar AUROC to CRT (0.757) for assessing mortality risk. Besides, there was a tendency for LTAR (1%-3%-7%) and SD (0.81°C-0.88°C-0.94°C) to increase in normotensive, hypotensive, and shock patients. These thermal parameters are associated with CRT, lactate, and blood pressure. The AUROC of a combined prediction incorporating three thermal inhomogeneity parameters (SD, kurtosis, and entropy) was considerably higher at 0.866. Conclusions: Body-surface thermal inhomogeneity provided a noninvasive and accurate assessment of the severity of critically ill patients at high risk of hypoperfusion.

Uses of infrared thermography in acute illness: a systematic review

“…The proportion of studies with a low-risk rating for concerns around the applicability of their findings in the patient selection, index test and reference standard domains were 73, 57 and 77%, respectively. Ten studies scored low risk ratings across all domains ( 11 , 12 , 18 , 20 , 24 , 25 , 27 , 30 , 36 , 39 ). One scored high-risk ratings across all domains ( 16 ).…”

“…The majority of authors identified their studies as exploratory or assessing feasibility ( n = 16) ( 11 , 12 , 14–16 , 19 , 21 , 22 , 26 , 27 , 31 , 32 , 34 , 35 , 37 , 39 ). Few were described as validation or diagnostic accuracy studies ( n = 8) ( 13 , 17 , 18 , 20 , 24 , 33 , 36 , 38 ).…”

“…One study investigated five novel variables to quantify thermal inhomogeneity from IRT images of the anterior thigh in patients following major cardiac surgery with risk factors for hypoperfusion ( 27 ). Two measurements (standard deviation and low-temperature-area-rate) had a similar accuracy to capillary refill time in predicating in-hospital mortality.…”

“…Two measurements (standard deviation and low-temperature-area-rate) had a similar accuracy to capillary refill time in predicating in-hospital mortality. The accuracy of the models improved when multiple variables were combined together or with conventional measures of hypoperfusion ( 27 ). The final adult shock study investigated an association between COVID-19 disease severity and markers of microcirculatory dysfunction, one of which was mean fingertip temperature measured with IRT.…”

Interpreting Infrared Thermography with Deep Learning to Assess the Mortality Risk of Critically Ill Patients at Risk of Hypoperfusion

“…Our group have established a prospective cohort gathering infrared images of the legs of critically ill patients at high risk of hypoperfusion, collecting routine hypoperfusion parameters and following up on their prognosis. Based on these data, we defined parameters reflecting thermal inhomogeneity of body surface (e.g., low temperature area rate [LTAR] and standard deviation [SD]) using traditional mathematical methods and found that these parameters varied among patients with different severity of hypoperfusion and could be used to predict the risk of mortality [ 8 ].…”