Srinivasan Sangeetha, Pritchard Nicola, Sampson Geoff P, Edwards Katie, Vagenas Dimitrios, Russell Anthony W, Malik Rayaz A, Efron Nathan
Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia.
Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia.
J Optom. 2017 Oct-Dec;10(4):215-225. doi: 10.1016/j.optom.2016.05.003. Epub 2016 Jul 14.
To examine the diagnostic capability of the full retinal and inner retinal thickness measures in differentiating individuals with diabetic peripheral neuropathy (DPN) from those without neuropathy and non-diabetic controls.
Individuals with (n=44) and without (n=107) diabetic neuropathy and non-diabetic control (n=42) participants underwent spectral domain optical coherence tomography (SDOCT). Retinal thickness in the central 1mm zone (including the fovea), parafovea and perifovea was assessed in addition to ganglion cell complex (GCC) global loss volume (GCC GLV) and focal loss volume (GCC FLV), and retinal nerve fiber layer (RNFL) thickness. Diabetic neuropathy was defined using a modified neuropathy disability score (NDS) recorded on a 0-10 scale, wherein, NDS ≥3 indicated neuropathy and NDS indicated <3 no neuropathy. Diagnostic performance was assessed by areas under the receiver operating characteristic curves (AUCs), 95 per cent confidence intervals (CI), sensitivities at fixed specificities, positive likelihood ratio (+LR), negative likelihood ratio (-LR) and the cut-off points for the best AUCs obtained.
The AUC for GCC FLV was 0.732 (95% CI: 0.624-0.840, p<0.001) with a sensitivity of 53% and specificity of 80% for differentiating DPN from controls. Evaluation of the LRs showed that GCC FLV was associated with only small effects on the post-test probability of the disease. The cut-off point calculated using the Youden index was 0.48% (67% sensitivity and 73% specificity) for GCC FLV. For distinguishing those with neuropathy from those without neuropathy, the AUCs of retinal parameters ranged from 0.508 for the central zone to 0.690 for the inferior RNFL thickness. For distinguishing those with moderate or advanced neuropathy from those with mild or no neuropathy, the inferior RNFL thickness demonstrated the highest AUC of 0.820, (95% CI: 0.731-0.909, p<0.001) with a sensitivity of 69% and 80% specificity. The cut-off-point for the inferior RNFL thickness was 97μm, with 81% sensitivity and 72% specificity.
The GCC FLV can differentiate individuals with diabetic neuropathy from healthy controls, while the inferior RNFL thickness is able to differentiate those with greater degrees of neuropathy from those with mild or no neuropathy, both with an acceptable level of accuracy. Optical coherence tomography represents a non-invasive technology that aids in detection of retinal structural changes in patients with established diabetic neuropathy. Further refinement of the technique and the analytical approaches may be required to identify patients with minimal neuropathy.
研究全视网膜厚度和视网膜内层厚度测量值在鉴别糖尿病周围神经病变(DPN)患者与无神经病变者及非糖尿病对照者方面的诊断能力。
对患有(n = 44)和未患有(n = 107)糖尿病神经病变的个体以及非糖尿病对照者(n = 42)进行了光谱域光学相干断层扫描(SDOCT)。除了评估神经节细胞复合体(GCC)整体损失体积(GCC GLV)、局灶性损失体积(GCC FLV)和视网膜神经纤维层(RNFL)厚度外,还评估了中心1mm区域(包括黄斑中心凹)、黄斑旁和黄斑周围的视网膜厚度。使用改良的神经病变残疾评分(NDS)(范围为0 - 10分)来定义糖尿病神经病变,其中,NDS≥3表示存在神经病变,NDS < 3表示无神经病变。通过受试者操作特征曲线(AUC)下的面积、95%置信区间(CI)、固定特异性下的敏感性、阳性似然比(+LR)、阴性似然比(-LR)以及获得的最佳AUC的截断点来评估诊断性能。
GCC FLV的AUC为0.732(95% CI:0.624 - 0.840,p < 0.001),在区分DPN与对照者时,敏感性为53%,特异性为80%。似然比评估显示,GCC FLV对疾病的检验后概率影响较小。使用约登指数计算的GCC FLV截断点为0.48%(敏感性为67%,特异性为73%)。对于区分有神经病变者与无神经病变者,视网膜参数的AUC范围从中心区域的0.508到下方RNFL厚度的0.690。对于区分中度或重度神经病变者与轻度或无神经病变者,下方RNFL厚度的AUC最高,为0.820(95% CI:0.731 - 0.909,p < 0.001),敏感性为69%,特异性为80%。下方RNFL厚度的截断点为97μm,敏感性为81%,特异性为72%。
GCC FLV能够区分糖尿病神经病变患者与健康对照者,而下方RNFL厚度能够区分神经病变程度较重者与轻度或无神经病变者,二者均具有可接受的准确性。光学相干断层扫描是一种无创技术,有助于检测已确诊糖尿病神经病变患者的视网膜结构变化。可能需要进一步完善该技术和分析方法,以识别轻度神经病变患者。