Development and validation of an algorithm to predict the treatment modality of burn wounds using thermographic scans: Prospective cohort study

BackgroundThe clinical evaluation of a burn wound alone may not be adequate to predict the severity of the injury nor to guide clinical decision making. Infrared thermography provides information about soft tissue viability and has previously been used to assess burn depth. The objective of this study was to determine if temperature differences in burns assessed by infrared thermography could be used predict the treatment modality of either healing by re-epithelization, requiring skin grafts, or requiring amputations, and to validate the clinical predication algorithm in an independent cohort.Methods and findingsTemperature difference (ΔT) between injured and healthy skin were recorded within the first three days after injury in previously healthy burn patients. After discharge, the treatment modality was categorized as re-epithelization, skin graft or amputation. Potential confounding factors were assessed through multiple linear regression models, and a prediction algorithm based on the ΔT was developed using a predictive model using a recursive partitioning Random Forest machine learning algorithm. Finally, the prediction accuracy of the algorithm was compared in the development cohort and an independent validation cohort. Significant differences were found in the ΔT between treatment modality groups. The developed algorithm correctly predicts into which treatment category the patient will fall with 85.35% accuracy. Agreement between predicted and actual treatment for both cohorts was weighted kappa 90%.ConclusionInfrared thermograms obtained at first contact with a wounded patient can be used to accurately predict the definitive treatment modality for burn patients. This method can be used to rationalize treatment and streamline early wound closure.背景 仅通过临床评估烧伤创面，可能不足以预测损伤严重程度，亦无法指导临床决策。红外热成像（infrared thermography）可提供软组织活性相关信息，此前已被应用于烧伤深度评估。本研究旨在明确：通过红外热成像评估的烧伤部位温差，是否可用于预测患者的治疗方案——即通过再上皮化愈合、需皮肤移植，抑或需截肢，并在独立队列中验证该临床预测算法。 方法与结果 本研究纳入既往健康的烧伤患者，于伤后3日内记录损伤皮肤与健康皮肤间的温差（ΔT）。患者出院后，将其治疗方案归类为再上皮化愈合、皮肤移植或截肢。通过多元线性回归模型评估潜在混杂因素，并采用递归分割随机森林（Random Forest）机器学习算法构建基于ΔT的预测模型。最终，在开发队列与独立验证队列中对比该算法的预测精度。结果显示，不同治疗方案组间的ΔT存在显著差异。所构建的算法对患者治疗类别的预测准确率达85.35%。两个队列中预测结果与实际治疗方案的加权Kappa值为90%，一致性良好。 结论 接诊烧伤患者时首次获取的红外热成像图，可用于精准预测烧伤患者的最终治疗方案。该方法可用于优化治疗决策、简化早期创面闭合流程。