title: Diagnostic capability of retinal thickness measures in diabetic peripheral neuropathy.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia. Electronic address: sangeetha.srinivasan@connect.qut.edu.au.
authors.forename: Sangeetha
authors.initials: S
authors.lastname: Srinivasan
authors.affiliation: Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia.
authors.forename: Nicola
authors.initials: N
authors.lastname: Pritchard
authors.affiliation: Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia; School of Medicine, Faculty of Health, Deakin University, Victoria, Australia.
authors.forename: Geoff P
authors.initials: GP
authors.lastname: Sampson
authors.affiliation: Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia.
authors.forename: Katie
authors.initials: K
authors.lastname: Edwards
authors.affiliation: Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia.
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Vagenas
authors.affiliation: Princess Alexandra Hospital, Queensland, Australia; School of Medicine, University of Queensland, Woolloongabba, Queensland, Australia.
authors.forename: Anthony W
authors.initials: AW
authors.lastname: Russell
authors.affiliation: Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Central Manchester University Hospitals Foundation Trust, Manchester, UK.
authors.forename: Rayaz A
authors.initials: RA
authors.lastname: Malik
authors.affiliation: Institute of Health and Biomedical Innovation, Queensland University of Technology, Queensland, Australia.
authors.forename: Nathan
authors.initials: N
authors.lastname: Efron
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D003929:Diabetic Neuropathies / Q000175:diagnosis*; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism; D064368:Healthy Volunteers; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D009412:Nerve Fibers / Q000473:pathology; D012372:ROC Curve; D012160:Retina / Q000000981:diagnostic imaging / Q000473:pathology*; D012165:Retinal Ganglion Cells / Q000473:pathology; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence
chemical_list: D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
keywords: Capa de fibras nerviosas de la retina; Capacidad diagnóstica; Complejo de células ganglionares; Diagnostic capability; Ganglion cell complex; Grosor de la retina; Optical coherence tomography; Retinal nerve fiber layer; Retinal thickness; Tomografía de coherencia óptica; Área bajo la curva