A multi-regression approach to improve optical coherence tomography diagnostic accuracy in multiple sclerosis patients without previous optic neuritis

Background: Optical coherence tomography (OCT) is a retinal imaging system that may improve the diagnosis of multiple sclerosis (MS) persons, but the evidence is currently equivocal. To assess whether compensating the peripapillary retinal nerve fiber layer (pRNFL) thickness for ocular anatomical features as well as the combination with macular layers can improve the capability of OCT in differentiating non-optic neuritis eyes of relapsing-remitting MS patients from healthy controls. Methods: 74 MS participants (n = 129 eyes) and 84 age- and sex-matched healthy controls (n = 149 eyes) were enrolled. Macular ganglion cell complex (mGCC) thickness was extracted and pRNFL measurement was compensated for ocular anatomical factors. Thickness measurements and their corresponding areas under the receiver operating characteristic curves (AUCs) were compared between groups. Results: Participants with MS showed significantly thinner mGCC, measured and compensated pRNFL (p ≤ 0.026). Compensated pRNFL achieved better performance than measured pRNFL for MS differentiation (AUC, 0.75 vs 0.80; p = 0.020). Combining macular and compensated pRNFL parameters provided the best discrimination of MS (AUC = 0.85 vs 0.75; p < 0.001), translating to an average improvement in sensitivity of 24 percent for differentiation of MS individuals. Conclusion: The capability of OCT in MS differentiation is made more robust by accounting OCT scans for individual anatomical differences and incorporating information from both optic disc and macular regions, representing markers of axonal damage and neuronal injury, respectively.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversityNational Medical Research Council (NMRC)National Research Foundation (NRF)Published versionThis work was funded by grants from the National Medical Research Council (CG/C010A/2017_SERI; OFIRG/0048/2017; OFLCG/004c/2018; TA/MOH-000249-00/2018 MOH-OFIRG20nov-0014 and NMRC/CG2/004b/2022-SERI), National Research Foundation Singapore (NRF2019-THE002-0006 and NRF-CRP24-2020-0001), A*STAR (A20H4b0141), the Singapore Eye Research Institute & Nanyang Technological University (SERI-NTU Advanced Ocular Engineering (STANCE) Program), the Duke-NUS Medical School (Duke-NUS-KP(Coll)/2018/0009A), and the SERI-Lee Foundation (LF1019-1) Singapore