A set of clinical and laboratory markers differentiates hyper-IgE syndrome from severe atopic dermatitis.

Hyper-IgE syndrome (HIES) patients may share many features observed in severe atopic dermatitis (SAD), making a diagnostic dilemma for physicians. Determining clinical and laboratory markers that distinguish both disorders could provide early diagnosis and treatment. We analyzed patients (DOCK8 deficiency:14, STAT3-HIES:10, SAD:10) with early-onset SAD. Recurrent upper respiratory tract infection and pneumonia were significantly frequent in HIES than SAD patients. Characteristic facial appearance, retained primary teeth, skin abscess, newborn rash, and pneumatocele were more predictable for STAT3-HIES, while mucocutaneous candidiasis and Herpes infection were common in DOCK8 deficiency, which were unusual in SAD group. DOCK8-deficient patients had lower CD3+ and CD4+T cells with a senescent phenotype that unique for this form of HIES. Both DOCK8 deficiency and STAT3-HIES patients exhibited reduced switched memory B cells compared to the SAD patients. These clinical and laboratory markers are helpful to differentiate HIES from SAD patients.

Use of Thalamus L-Sign to Differentiate Periventricular Leukomalacia From Neurometabolic Disorders.

PURPOSE: To assess the diagnostic value of the thalamus L-sign on magnetic resonance imaging (MRI) in distinguishing between periventricular leukomalacia and neurometabolic disorders in pediatric patients. METHODS: In this retrospective study, clinical and imaging information was collected from 50 children with periventricular leukomalacia and 52 children with neurometabolic disorders. MRI was used to evaluate the L-sign of the thalamus (ie, injury to the posterolateral thalamus) and the lobar distribution of signal intensity changes. Age, sex, gestational age, and level of Gross Motor Function Classification System (only for periventricular leukomalacia) constituted the clinical parameters. Statistical evaluation of group differences for imaging and clinical variables were conducted using univariable statistical methods. The intra- and inter-observer agreement was evaluated using Cohen's kappa. Univariable or multivariable logistic regression was employed for selection of variables, determining independent predictors, and modeling. RESULTS: The thalamus L-sign was observed in 70% (35/50) of patients in the periventricular leukomalacia group, but in none of the patients with neurometabolic disorder (P < .001). The gestational age between groups varied significantly (P < .001). Involvement of frontal, parietal, and occipital lobes differed significantly between groups (P < .001). In the logistic regression, the best model included negative thalamus L-sign and gestational age, yielding an area under the curve, accuracy, sensitivity, specificity, and precision values of 0.995, 96.1%, 96%, 96.2%, and 96%, respectively. Both the lack of thalamus L-sign and gestational age were independent predictors (P < .001). CONCLUSIONS: The thalamus L-sign and gestational age may be useful in distinguishing between periventricular leukomalacia and neurometabolic disorders.