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Neuromyelitis optica (NMO) and neuromyelitis optica spectrum disorders (NMOSD), previously known as Devic's syndrome, are a group of inflammatory disorders of the central nervous system (CNS) characterized by severe, immune-mediated demyelination and axonal damage, predominantly targeting optic nerves and the spinal cord typically associated with a disease-specific serum NMO-IgG antibody that selectively binds aquaporin-4 (AQP4). The classic and best-defined features of NMOSD include acute attacks of bilateral or rapidly sequential optic neuritis (leading to visual loss) or transverse myelitis (often causing limb weakness and bladder dysfunction) or both with a typically relapsing course. The diagnosis of NMO/NMOSD requires a consistent history and examination with typical clinical presentations, findings on spinal cord neuroimaging with MRI, cerebrospinal fluid analysis along with determination of AQP4-IgG serum autoantibody status, and exclusion of other disorders. Two major advances in this field has been the development of diagnostic criteria and treatment recommendations. Consensus diagnostic criteria have been established and were recently revised and published in 2015, enhancing the ability to make a diagnosis and appropriately evaluate these disorders. Expert recommendations and uncontrolled trials form the basis of treatment guidelines. All patients with suspected NMOSD should be treated for acute attacks as soon as possible with high-dose intravenous methylprednisolone -1 gram daily for three to five consecutive days and in some cases, plasma exchange should be used. It is recommended that every patient with NMOSD be started on an immunosuppressive agent, such as, azathioprine, methotrexate, or mycophenolate and in some cases, rituximab, soon after the acute attack and usually be treated for about 5 years after the attack. These advances have helped improve the prognosis and outcome in these disorders.

ObjectiveLupus nephritis (LN) is an immune complex-mediated glomerular and tubulointerstitial disease in patients with SLE. Prediction of outcomes at the onset of LN diagnosis can guide decisions regarding intensity of monitoring and therapy for treatment success. Currently, no machine learning model of outcomes exists. Several outcomes modelling works have used univariate or linear modelling but were limited by the disease heterogeneity. We hypothesised that a combination of renal pathology results and routine clinical laboratory data could be used to develop and to cross-validate a clinically meaningful machine learning early decision support tool that predicts LN outcomes at approximately 1 year.MethodsTo address this hypothesis, patients with LN from a prospective longitudinal registry at the Medical University of South Carolina enrolled between 2003 and 2017 were identified if they had renal biopsies with International Society of Nephrology/Renal Pathology Society pathological classification. Clinical laboratory values at the time of diagnosis and outcome variables at approximately 1 year were recorded. Machine learning models were developed and cross-validated to predict suboptimal response.ResultsFive machine learning models predicted suboptimal response status in 10 times cross-validation with receiver operating characteristics area under the curve values >0.78. The most predictive variables were interstitial inflammation, interstitial fibrosis, activity score and chronicity score from renal pathology and urine protein-to-creatinine ratio, white blood cell count and haemoglobin from the clinical laboratories. A web-based tool was created for clinicians to enter these baseline clinical laboratory and histopathology variables to produce a probability score of suboptimal response.ConclusionGiven the heterogeneity of disease presentation in LN, it is important that risk prediction models incorporate several data elements. This report provides for the first time a clinical proof-of-concept tool that uses the five most predictive models and simplifies understanding of them through a web-based application.