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  • Title: Antineuronal antibodies and electroneurophysiological studies in pediatric patients with neuropsychiatric systemic lupus erythematosus.
    Author: Mostafa GA, Nazif HK, El-Shahawi HH, Abd El-Aziz MM, Hassan MA.
    Journal: Pediatr Allergy Immunol; 2009 Mar; 20(2):192-9. PubMed ID: 18422890.
    Abstract:
    Neuropsychiatric systemic lupus erythematosus (NPSLE) is one of the most difficult manifestations of lupus to diagnose. Measurement of serum brain autoantibodies and assessment of cognitive function by electroneurophysiological studies (electroencephalogram (EEG) and P300) have contributed to an earlier and a more specific diagnosis of NPSLE. Thus, we were stimulated to assess the value of serum antineuronal antibodies and electroneurophysiological studies in diagnosis and early prediction of NPSLE. To investigate this, assessment of serum antineuronal antibodies and cognitive function (clinically and by electroneurophysiological studies) was done in 30 lupus patients [14 (46.7%) with and 16 (53.3%) without clinical evidence of NPSLE] in comparison with 30 healthy matched subjects. Patients without clinical evidence of NPSLE were followed-up clinically by monthly neuropsychiatric evaluation for 18 months. Seropositivity for antineuronal antibodies and abnormalities of EEG and P300 (prolonged latency and/or low amplitude) were found in 60%, 50% and 70%, respectively of lupus patients. During follow-up, 8 out of the 16 patients without clinical evidence of NPSLE developed such evidence [six (75%) had antineuronal seropositivity, five (62.5%) had abnormal EEG, six (75%) had P300 abnormalities and all had at least one abnormal result of these parameters at the time of initial evaluation before clinical presentation of NPSLE]. In conclusion, serum antineuronal antibodies and electroneurophysiological studies may be reliable parameters for diagnosis and early prediction of NPSLE, especially when combined together, before clinical manifestations ensue. Further studies on a large scale are warranted to evaluate the predictive value of these parameters in NPSLE.
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