SFDI biomarkers provide a quantitative ulcer risk metric and can be used to predict diabetic foot ulcer onset
SFDI biomarkers provide a quantitative ulcer risk metric and can be used to predict diabetic foot ulcer onset
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DOI：
10.1016/j.jdiacomp.2020.107624
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发表时间：
2020-09-01
影响因子：
3
通讯作者：
Yu, Kalvin
中科院分区：
文献类型：
--
作者：
Lee, Suzette;Mey, Leann;Yu, Kalvin
目标：每年，高达4%的糖尿病患者患有慢性足部溃疡。定量实时检测以确定有溃疡风险的患者可以指导预防性护理。在这里，我们评估了一种非侵入性的光学成像技术，空间频域成像(SFDI)是否可以识别出溃疡风险最高的患者，并预测溃疡发作。方法：我们在南加州凯撒永久医院对252名糖尿病患者进行了成像。SIDI衍生的微循环生物标志物在有和没有溃疡病史的受试者之间以及1年后有或没有发生溃疡的受试者之间进行比较。结果：高危受试者的足部乳头状真皮中的血红蛋白(Hbt(1))显著降低，同时由于提取不良而导致更高的氧合(sto(2))。这些受试者在网状真皮(HBT(2))和组织散射(与皮肤结构有关)中也有更均匀的血红蛋白扩散。基于HBT(1)和组织散射的预测发现了新的溃疡，其敏感性/特异性分别为68.8%/64.8%和75.0%/69.1%。结论：这些结果表明，SFDI血红蛋白分布和氧合生物标志物为溃疡危险分层和溃疡发病预测提供了定量依据。(C)2020作者。
Aims: Annually, up to 4% of people with diabetes present with a chronic foot ulcer. Quantitative real-time testing to identify patients at risk for ulceration can guide preventative care. Here, we assess whether a non-invasive optical imaging technique, Spatial Frequency Domain Imaging (SFDI), can identify patients at the highest risk for ulceration and predict ulcer onset.Methods: We imaged 252 subjects with diabetes at Kaiser Permanente, Southern California. SIDI derived tissue biomarkers of microcirculation were compared between subjects with and without a history of ulceration, and subjects who did or did not develop ulcers after 1 year.Results: Feet of subjects at the highest risk (i.e. history of ulceration) had significantly lower hemoglobin in the papillary dermis (HbT(1)), along with higher oxygenation (StO(2)) due to poor extraction. These subjects also had more homogeneous hemoglobin spread in the reticular dermis (HbT(2)) and tissue scattering (related to skin structure). Prediction based on HbT(1) and tissue scattering identified new ulcerations and performed with sensitivity/specificity of 68.8%/64.8% and 75.0%/69.1%, respectively.Conclusion: These results show that SFDI hemoglobin distribution and oxygenation biomarkers provide a quantitative basis for ulcer risk stratification and ulcer onset prediction. (C) 2020 The Authors.