Application of an infrared thermography‐based model to detect pressure injuries: a prospective cohort study. (13th June 2022)

• Record Type: Journal Article
• Authors: Jiang, Xiaoqiong; Wang, Yu; Wang, Yuxin; Zhou, Min; Huang, Pan; Yang, Yufan; Peng, Fang; Wang, Haishuang; Li, Xiaomei; Zhang, Liping; Cai, Fuman
• Is Part Of: British journal of dermatology. Volume 187:Number 4(2022)
• Page Start: 571
• Page End: 579
• Publication Date: 2022-06-13
• Subjects: Dermatology -- Periodicals; Skin -- Diseases -- Periodicals; 616.5
• DOI: 10.1111/bjd.21665
• Languages: English
• ISSNs: 0007-0963

Abstract

Abstract: Background: It is challenging to detect pressure injuries at an early stage of their development. Objectives: To assess the ability of an infrared thermography (IRT)‐based model, constructed using a convolution neural network, to reliably detect pressure injuries. Methods: A prospective cohort study compared validity in patients with pressure injury ( n = 58) and without pressure injury ( n = 205) using different methods. Each patient was followed up for 10 days. Results: The optimal cut‐off values of the IRT‐based model were 0·53 for identifying tissue damage 1 day before visual detection of pressure injury and 0·88 for pressure injury detection on the day visual detection is possible. Kaplan–Meier curves and Cox proportional hazard regression model analysis showed that the risk of pressure injury increased 13‐fold 1 day before visual detection with a cut‐off value higher than 0·53 [hazard ratio (HR) 13·04, 95% confidence interval (CI) 6·32–26·91; P < 0·001]. The ability of the IRT‐based model to detect pressure injuries [area under the receiver operating characteristic curve (AUC)lag 0 days, 0·98, 95% CI 0·95–1·00] was better than that of other methods. Conclusions: The IRT‐based model is a useful and reliable method for clinical dermatologists and nurses to detect pressure injuries. It can objectively and accurately detect pressure injuries 1 day before visual detection and is therefore able to guide prevention earlier than would otherwise be possible. What is already known about this topic? Detection of pressure injuries at an early stage is challenging. Infrared thermography can be used for the physiological and anatomical evaluation of subcutaneous tissue abnormalities. A convolutional neural network is increasingly used in medical imaging analysis. What does this study add? The optimal cut‐off values of the IRT‐based model were 0·53 for identifying tissue damage 1 day before visual detection of pressure injury and 0·88 for pressure injury detection on the day visual detection is possible. Infrared thermography‐based models can be used by clinical dermatologists and nurses to detect pressure injuries at an early stage objectively and accurately. Abstract : IRT‐based model is a useful and reliable method for early identification of pressure induced tissue damage by clinical nurses. This model can objectively and accurately detect PIs one day before visual cues and help guide prevention. Linked Comment: L.J. Gould and E. White-Chu. Br J Dermatol 2022; 187:456 . Plain language summary available online