A pilot study of the Leicester ED medical infrared imaging protocol in fever and sepsis

Uses of infrared thermography in acute illness: a systematic review

Introduction

Infrared thermography (IRT) is a non-contact, non-ionising imaging modality, providing a visual representation of temperature distribution across a surface.

Methods

We conducted a systematic search of indexed and grey literature for studies investigating IRT applications involving patients in acute care settings. Studies were categorised and described along themes identified iteratively using narrative synthesis. Quality appraisal of included studies was performed using the Quality Assessment tool for Diagnostic Accuracy Studies.

Results

Of 1,060 unique records, 30 studies were included. These were conducted in emergency departments and intensive care units involving adult, paediatric and neonatal patients. IRT was studied for the diagnosis, monitoring or risk stratification of a wide range of individual conditions. IRT was predominantly used to display thermal change associated with localised inflammation or microcirculatory dysfunction. Existing research is largely at an early developmental stage.

Discussion

We recommend that high quality diagnostic validation studies are now required for some clinical applications. IRT has the potential to be a valuable tool in the acute care setting and represents an important area for future research particularly when combined with advances in machine learning technology.

Systematic review registration

CRD 42022327619 (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=327619).

Infrared image method for possible COVID-19 detection through febrile and subfebrile people screening

This study proposed an infrared image-based method for febrile and subfebrile people screening to comply with the society need for alternative, quick response, and effective methods for COVID-19 contagious people screening. The methodology consisted of: (i) Developing a method based on facial infrared imaging for possible COVID-19 early detection in people with and without fever (subfebrile state); (ii) Using 1206 emergency room (ER) patients to develop an algorithm for general application of the method, and (iii) Testing the method and algorithm effectiveness in 2558 cases (RT-qPCR tested for COVID-19) from 227,261 workers evaluations in five different countries. Artificial intelligence was used through a convolutional neural network (CNN) to develop the algorithm that took facial infrared images as input and classified the tested individuals in three groups: fever (high risk), subfebrile (medium risk), and no fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5 °C fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 °C were not enough to detect fever similarly to the proposed CNN algorithm. Most RT-qPCR confirmed COVID-19 (+) cases found in the 2558 cases sample (17 cases/89.5%) belonged to the CNN selected subfebrile group. The COVID-19 (+) main risk factor was to be in the subfebrile group, in comparison to age, diabetes, high blood pressure, smoking and others. In sum, the proposed method was shown to be a potentially important new tool for COVID-19 (+) people screening for air travel and public places in general.

Optical thermography infrastructure to assess thermal distribution in critically ill children

The temperature distribution at the skin surface could be a useful tool to monitor changes in cardiac output. Goal: The aim of this study was to explore infrared thermography as a method to analyze temperature profiles of critically ill children. Methods: Patients admitted to the pediatric intensive care unit (PICU) were included in this study. An infrared sensor was used to take images in clinical conditions. The infrared core and limb temperatures (θ_c & θ_l) were extracted, as well as temperatures along a line drawn between these two regions. Results: The median [interquartile range] θ_c extracted from the images was 33.88°C [32.74-34.19] and the median θ_l was 30.21°C [28.89-33.13]. There was a good correlation between the θ_c and the clinical axillary temperature (rho = 0.39, p-value = 0.016). There was also a good correlation between the θ_c and θ_l (rho = 0.66, p-value = 1.2 e⁻⁰⁵). Conclusion: Thermography was found to be effective to estimate the body temperature. Correlation with specific clinical conditions needs further study.

The effect of constitutive pigmentation on the measured emissivity of human skin

Background

The measurement of body temperature has become commonplace in the current COVID-19 pandemic. Body temperature can be measured using thermal infrared imaging, a safe, non-contact method that relies on the emissivity of the skin being known to provide accurate readings. Skin pigmentation affects the absorption of visible light and enables us to see variations in skin colour. Pigmentation may also affect the absorption of infrared radiation and thus affect thermal imaging. Human skin has an accepted emissivity of 0.98 but the effect of different skin pigmentation on this value is not known. In this study, we investigated the influence of different skin pigmentation on thermal emissivity in 65 adult volunteers.

Methods

A reference object of known emissivity (electrical tape) was applied to participant’s skin on the inner upper arm. Tape and arm were imaged simultaneously using a thermal infrared camera. The emissivity was set on the camera to the known value for electrical tape. The emissivity was altered manually until the skin temperature using thermal imaging software was equal to the initial tape temperature. This provided the calculated emissivity value of the skin. Participants were grouped according to skin pigmentation, quantified using the Fitzpatrick skin phototyping scale and reflectance spectrophotometry. Differences in emissivity values between skin pigmentation groups were assessed by one-way ANOVA.

Results

The mean calculated emissivity for the 65 participants was 0.972 (range 0.96–0.99). No significant differences in emissivity were observed between participants when grouped by skin pigmentation according to the Fitzpatrick scale (p = 0.859) or reflectance spectrophotometry (p = 0.346).

Conclusion

These data suggest that skin pigmentation does not affect thermal emissivity measurement of skin temperature using thermal infrared imaging. This study will aid further research into the application of thermal infrared imaging as a screening or bedside diagnostic tool in clinical practice.

Technological Developments and Strategic Management for Overcoming the COVID-19 Challenge within the Hospital Setting in Israel

The coronavirus disease 2019 (COVID-19) pandemic has remarkably challenged health care organizations and societies. A key strategy for confronting the disease implications on individuals and communities was based on harnessing multidisciplinary efforts to develop technologies for mitigating the disease spread and its deleterious clinical implications. One of the main challenging characteristics of COVID-19 is the provision of medical care to patients with a highly infective disease mandating the use of isolation measures. Such care is complicated by the need for complex critical care, dynamic treatment guidelines, and a vague knowledge regarding the disease's pathophysiology. A second key component of this challenge was the overwhelming surge in patient burden and the relative lack of trained staff and medical equipment which required rapid re-organization of large systems and augmenting health care efficiencies to unprecedented levels. In contrast to the risk management strategies employed to mitigate other serious threats and the billions of dollars that are invested in reducing these risks annually by governments around the world, no such preparation has been shown to be of effect during the current COVID-19 pandemic. Unmet needs were identified within the newly opened COVID-19 departments together with the urgent need for reliable information for effective decision-making at the state level.This review article describes the early research and development response in Israel under the scope of in-hospital patient care, such as non-contact sensing of patients' vital signs, and how it could potentially be weaved into a practical big picture at the hospital or national level using a strategic management system. At this stage, some of the described technologies are still in developmental or clinical evidence generation phases with respect to COVID-19 settings. While waiting for future publications describing the results of the ongoing evidence generation efforts, one should be aware of this trend as these emerging tools have the potential to further benefit patients as well as caregivers and health care systems beyond the scope of the current pandemic as well as confronting future surges in the number of cases.

Mottling score is a strong predictor of 14-day mortality in septic patients whatever vasopressor doses and other tissue perfusion parameters

BACKGROUND

Mottling score, a tissue perfusion parameter, is correlated with outcome in septic shock patients. However, its predictive value on mortality according to prognostic covariates such as vasopressor dose and other tissue perfusion parameters remains unknown.

METHODS

Mottling score and tissue perfusion parameters were recorded at ICU admission (H0), H-6, H 12, and H-24 and used to assess the predictive value of mottling score on 14-day mortality in a development cohort. Results were then validated in an independent cohort of septic shock patients in Brazil.

RESULTS

Overall, 259 patients with sepsis or septic shock were included, 14-day mortality was 37%. Factors associated with death were mottling score (OR 2.26 [95% CI, 1.72-2.97]), arterial lactate level (OR 1.29 [1.11-1.5]), and urine output < 0.5 ml/Kg/h (OR 3.03 [1.37-6.69]). The C statistic for the model was 0.90 in the development cohort and 0.76 in the validation cohort. The predictive value of mottling score was not affected by vasopressor doses (p for interaction = 0.33): OR for mottling score ranged from 2.34 [1.10-3.15] in patients without vasopressor to 3.84 [1.98-7.43] in patients infused with high doses of vasopressor (> 0.8 μg/kg/min). There was no difference in the effect of mottling score on mortality according to mean arterial pressure, heart rate, cardiac index, and urine output, but we found a significant interaction between arterial lactate level and mottling score (p = 0.04). The predictive value of the mottling score remains significant when using the recent SEPSIS-3 definition of septic shock. Finally, a decrease of mottling score during resuscitation was significantly associated with better outcome after adjustment on SOFA score (p = 0.001).

CONCLUSIONS

Our results support the high prognostic value of mottling score for 14-day mortality in septic patients, whatever vasopressor dosage and other perfusion parameters. Mottling score variations during resuscitation are also predictive of mortality.