ABSTRACT
Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) is a life-threatening hypersensitivity reaction to medications characterized by keratinocyte apoptosis and skin detachment. IL-15 serum levels have been associated with severity and prognosis of SJS/TEN. We have measured IL-15 concentrations in serum and blister fluid (BF) from patients with SJS/TEN by ELISA and used quantitative RT-PCR to analyze the expression of IL15 and IL15RA (encoding for IL-15 Receptor-α chain) genes in peripheral blood and BF cells, including isolated monocytes, and in affected skin. A positive correlation was found between IL-15 serum levels and a percent of detached skin. BF concentrations were higher, but no correlation was found. Higher IL15 and IL15RA gene expression levels were found in skin-infiltrating blister fluid cells compared to peripheral mononuclear cells. Moreover, IL15RA transcripts were barely detected in healthy skin, being the highest expression levels found in samples from two SJS/TEN patients who did not survive. The cutaneous expression of IL-15Rα in SJS/TEN may provide an explanation to the tissue-specific immune cytotoxic response in this clinical entity, and the results suggest that the effects of IL-15 in SJS/TEN patients may be dependent on the expression of its private receptor IL-15Rα in affected skin.
ABSTRACT
BACKGROUND: An Ommaya reservoir can be used to treat posthemorrhagic hydrocephalus secondary to intraventricular hemorrhage of prematurity until an acceptable weight can be obtained to place a permanent shunt. Identifying newborns at higher risk of developing shunt conversion may improve the management of these patients. This study aimed to develop a predictive algorithm for conversion of an Ommaya reservoir to a permanent shunt using artificial intelligence techniques and classical statistics. METHODS: A database of 43 preterm patients weighing ≤1500 g with posthemorrhagic hydrocephalus (Papile grades III and IV with Levene ventricular index >4 mm above the 97th percentile) managed with an Ommaya reservoir at our institution between 2002 and 2017 was used to train a k-nearest neighbor algorithm. Validation of results was done with cross-validation technique. Three scenarios were calculated: 1) considering all features regardless whether or not they are correlated with the output variable; 2) considering the features as predictors if they have a correlation >30% with the output variable; 3) considering the output of the previous analysis. RESULTS: When considering the outputs of a previous multivariate analysis, the algorithm reached 86% of cross-validation accuracy. CONCLUSIONS: The use of machine learning-based algorithms can help in early identification of patients with permanent need of a shunt. We present a predictive algorithm for a permanent shunt with an accuracy of 86%; accuracy of the algorithm can be improved with larger volume of data and previous analysis.
