The Role of Optical Coherence Tomography Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis.

BACKGROUND AND OBJECTIVES: Recent studies have suggested that intereye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell + inner plexiform (GCIPL) thickness by spectral domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history. METHODS: Participants were from 11 sites within the International Multiple Sclerosis Visual System consortium. pRNFL and GCIPL thicknesses were measured using SD-OCT. A composite score combining OCT and visual measures was compared individual measurements to determine the best model to distinguish PwMS from controls. These methods were also used to distinguish those with a history of ON among PwMS. Receiver operating characteristic (ROC) curve analysis was performed on a training data set (2/3 of cohort) and then applied to a testing data set (1/3 of cohort). Support vector machine (SVM) analysis was used to assess whether machine learning models improved diagnostic capability of OCT. RESULTS: Among 1,568 PwMS and 552 controls, variable selection models identified GCIPL IED, average GCIPL thickness (both eyes), and binocular 2.5% LCLA as most important for classifying PwMS vs controls. This composite score performed best, with area under the curve (AUC) = 0.89 (95% CI 0.85-0.93), sensitivity = 81%, and specificity = 80%. The composite score ROC curve performed better than any of the individual measures from the model (p < 0.0001). GCIPL IED remained the best single discriminator of unilateral ON history among PwMS (AUC = 0.77, 95% CI 0.71-0.83, sensitivity = 68%, specificity = 77%). SVM analysis performed comparably with standard logistic regression models. DISCUSSION: A composite score combining visual structure and function improved the capacity of SD-OCT to distinguish PwMS from controls. GCIPL IED best distinguished those with a history of unilateral ON. SVM performed as well as standard statistical models for these classifications. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that SD-OCT accurately distinguishes multiple sclerosis from normal controls as compared with clinical criteria.

Myasthenia gravis: sleep quality, quality of life, and disease severity.

INTRODUCTION: There is much research on quality of life in myasthenia gravis (MG), and its relationship to disease severity is well-established. However, evidence regarding sleep disturbance in MG is inconclusive. METHODS: To evaluate sleep and quality of life among clinically stable MG patients, 54 subjects were investigated by means of the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale (ESS) and 15-Item-Quality-Of-Life Instrument for MG (MG-QOL15). RESULTS: A pathological PSQI score, which was observed in 59% of patients, was increased in subjects with active disease compared with patients in clinical remission [odds ratio = 4.3; confidence interval 95% (1.0-17.6); P = 0.04]. We found a relationship between PSQI and MG-QOL15 scores in patients with clinically active disease (r = 0.62; P < 0.001). CONCLUSIONS: Our study highlights the high prevalence of sleep disturbance among MG patients. Disease severity may be considered to be a MG-specific risk factor for patient-reported sleep disturbance. The MG-QOL15 and PSQI should be used to estimate the impact of the disease on sleep and quality of life.