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高分辨率光谱分析准确识别糖尿病患者感染性慢性足部溃疡中的细菌特征。

High-Resolution Spectral Analysis Accurately Identifies the Bacterial Signature in Infected Chronic Foot Ulcers in People With Diabetes.

作者信息

Poosapadi Arjunan Sridhar, Tint Aye Nyein, Aliahmad Behzad, Kumar Dinesh Kant, Shukla Ravi, Miller Julie, Zajac Jeffrey D, Wang Gayathiri, Viswanathan Rekha, Ekinci Elif Ilhan

机构信息

1 Biosignals Lab, RMIT University, Melbourne, Victoria, Australia.

2 Department of Medicine, Austin Health, The University of Melbourne, Melbourne, Victoria, Australia.

出版信息

Int J Low Extrem Wounds. 2018 Jun;17(2):78-86. doi: 10.1177/1534734618785844.

Abstract

Diabetic foot infections are a major cause of hospitalization, and delayed treatment can lead to numerous complications. The aim of this research was to investigate high-resolution spectroscopy of the wound center and periwound area for real-time estimation of multispectral signature of bacteria at the base of diabetic foot ulcers. We investigated the spectrum of the reflected visual light from diabetic foot ulcers and developed a method that identifies the presence of bacteria in the wound infections. We undertook a prospective pilot study on 18 patients with type 1 and type 2 diabetes and chronic diabetic foot ulcers. The spectral coefficients were directly compared with the results from the wound swab. The results of the multispectral analysis demonstrated 100% sensitivity, with 100% negative predictive values of identifying the presence of the bacteria, which was the cause of the infection in the wound. The results of our study suggest that the changes in the multispectral properties of the wound can be used to identify the presence of bacteria in the infected area using a noninvasive device without any contact with the wound. This technique holds great promise for real-time objective evaluation of the wound infection status beyond the standard visual assessment of diabetic foot ulcers.

摘要

糖尿病足感染是住院的主要原因,延迟治疗会导致多种并发症。本研究的目的是研究伤口中心和伤口周围区域的高分辨率光谱,以实时估计糖尿病足溃疡底部细菌的多光谱特征。我们研究了糖尿病足溃疡反射可见光的光谱,并开发了一种识别伤口感染中细菌存在的方法。我们对18例1型和2型糖尿病合并慢性糖尿病足溃疡患者进行了一项前瞻性试点研究。将光谱系数与伤口拭子的结果直接进行比较。多光谱分析结果显示,在识别导致伤口感染的细菌存在方面,灵敏度为100%,阴性预测值为100%。我们的研究结果表明,伤口多光谱特性的变化可用于通过非侵入性设备在不接触伤口的情况下识别感染区域中细菌的存在。这项技术对于超越糖尿病足溃疡标准视觉评估的伤口感染状况的实时客观评估具有巨大潜力。

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