Risk Factors and Nomogram for Predicting Relapse Risk in Pediatric Neuromyelitis Optica Spectrum Disorders

Abstract

BackgroundNeuromyelitis optica spectrum disorders (NMOSDs) are attack-relapsing autoimmune inflammatory diseases of the central nervous system, which are characterized by the presence of serological aquaporin-4 (AQP4) antibody. However, this disorder is uncommon in children, and AQP4 antibody was often found to be seronegative. However, some pediatric patients diagnosed with NMOSDs were tested to be positive for myelin oligodendrocyte glycoprotein (MOG) antibody. The previous investigations of pediatric NMOSDs were usually focused on the clinical presentation, treatment responses, and long-term prognoses, but little is known about the risk factors predicting NMOSD relapse attacks in a shorter time, especially, for Chinese children.MethodsWe retrospectively identified 64 Chinese pediatric patients, including 39 positive for AQP4 antibody, 12 positive for MOG antibody, and the rest negative for AQP4 and MOG antibodies. Independent risk factors predicting relapse in 1-year follow-up were extracted by multivariate regression analysis to establish a risk score model, its performance evaluation was analyzed using receiver operating characteristic (ROC) curve, and the independent risk factors related to relapse manifestation were also explored through multivariate logistic analysis. A nomogram was generated to assess relapse attacks in 1-year follow-up. Thirty-five patients from 3 other centers formed an external cohort to validate this nomogram.ResultsFour independent relapsed factors included discharge Expanded Disability Status Scale (EDSS) (p = 0.017), mixed-lesion onset (p = 0.010), counts (≧1) of concomitant autoantibodies (p = 0.015), and maintenance therapy (tapering steroid with mycophenolate mofetil (MMF), p = 0.009; tapering steroid with acetazolamide (AZA), p = 0.045; and tapering steroid only, p = 0.025). The risk score modeled with these four factors was correlated with the likelihood of relapse in the primary cohort (AUC of 0.912) and the validation cohort (AUC of 0.846). Also, our nomogram exhibited accurate relapse estimate in the primary cohort, the validation cohort, and the whole cohort, but also in the cohorts with positive/negative AQP4 antibody, and noticeably, it performed predictive risk improvement better than other factors in the concordance index (C-index), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ConclusionsThe risk score and nomogram could facilitate accurate prognosis of relapse risk in 1-year follow-up for pediatric NMOSDs and help clinicians provide personalized treatment to decrease the chance of relapse.

Construction and analysis of a nomogram prediction model for post-infectious bronchiolitis obliterans in children with adenovirus pneumonia after invasive mechanical ventilation

“…Wang et al constructed a nomogram to predict the probability of psychosocial and behavioral problems in children and adolescents from an online questionnaire involving 12,186 children (6–11 years) and adolescents (12–16 years) [ 16 ]. Zhang et al retrospectively analyzed the clinical baseline characteristics of 64 children in China and further created a recurrence risk model of children Neurophyllitis optica spectrum disorders using a nomogram [ 17 ]. Cheng et al analyzed the clinical characteristics and risk factors for the development of PIBO in children (0–14 years old) with refractory Mycoplasma pneumoniae pneumonia (MPP) and constructed a nomogram to predict the risk ratio for the development of PIBO [ 18 ].…”

The risk assessment of relapse among newly enrolled participants in methadone maintenance treatment: A group-LASSO based Bayesian network study

“…On the other hand, the developed tools were usually constructed using the logistic regression model ( 23 ) or the Cox proportion hazard model ( 24 ). These methods did not perform well in dealing with the dependence on variables, which is the character of relapse.…”