Thermography may aid in selecting most effective burn wound Tx


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Validation of an algorithm for using digital thermography to predict the best treatment modality for burn wounds has found the algorithm was 90% accurate both in the development cohort of patients, as well as in an independent validation cohort.

The algorithm was developed in an attempt to address some of the limitations of predicting the course of a burn wound based on clinical examination alone. In a press release on Jan. 31, 2019 from McGill University, which conducted the research in collaboration with in collaboration with Universidad Autonoma de San Luis Potosi in Mexico, researchers note that burns can change clinical characteristics in the first few days after the injury. This variability has been shown to result in attempts to determine the best course of treatment from clinical examination alone to be inaccurate in 30 to 50% of cases.

As well, guidelines that suggest patients be followed for between 10 to 15 days before starting surgical interventions to wounds add uncertainty as to which treatment a patient will receive when they first present to the hospital, according to the release.

The experimental approach is based in part on the idea that the difference in temperature between wounded and healthy skin can suggest how effectively the wound is receiving the blood necessary for healing.

“Digital infrared thermography allows us to visualize the heat emitted by objects,” explains Dr. José Luis Ramírez García Luna, a PhD candidate in the department of experimental surgery at McGill and the corresponding author on the research, in the release. “In the human body, heat emission depends on blood flow to the tissues, thus it is an indirect measurement of blood perfusion to the skin or a wound bed. Previous research has shown that the temperature of the skin correlates well with blood flow to it and that there are differences in the temperature pattern of healing vs. non-healing wounds.”

The researchers devised a clinical prediction rule for the type of treatment a patient with a large burn in an extremity would receive based on the thermal characteristics of the initial lesion. In a prospective cohort study, digital thermograms were taken of patients’ burn lesions, and the difference in average temperature between the burn and healthy adjacent skin was recorded. The surgical team then treated the patient as they normally would.

Once the patient was discharged, the treatment given to them was categorized as: re-epithelization if the wound healed on its own; skin graft if the patient received at least one such graft; or as amputation if any extremity had to be removed due to extensive damage.

A machine learning method was then used to create a prediction model to categorize treatment based on the clinical data and the thermograms. With this method investigators discovered that the temperature difference between the healthy skin and the wound alone was enough to make the prediction.

“We believe our method could become a useful tool for the early assessment of patients in the emergency department, as well as in the later stages of patient care to identify dead tissue, distinguish between partial and full-thickness burns and detect complications such as the presence of infection or lack of blood flow,” said Dr. Ramírez García Luna.

“In our opinion, the most relevant contribution of our method to wound care is in allowing the clinical team to rapidly and objectively determine the treatment modality that is most likely needed, thereby preventing unnecessary procedures or delays in surgery,” he said.

“Because this has implications in selecting which patients may benefit more from certain interventions, and which will not, it is very likely that it can be translated to lowering costs related to treatment and hospital stay, as well as in reducing the length of hospital stay for the patients.”

The research was published online in PLOS One (Nov. 14, 2018).

#McGill #UniversidadAutonomadeSanLuisPotosi #DrJoseLuisRamirezGarciaLuna #burnwounds #Thermography #Treatmentselection

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