Skin Prick Tests and Allergy Diagnosis

Skin testing remains an essential diagnostic tool in modern allergy practice. A signifi cant variability has been reported regarding technical procedures, interpretation of results and documentation. This review has the aim of consolidating methodological recommendations through a critical analysis on past and recent data. This will allow a better understanding on skin prick test (SPT) history; technique; (contra-) indications; interpretation of results; diagnostic pitfalls; adverse reactions; and variability factors.

Drug Provocation Tests to Betalactam Antibiotics: Experience in a Paediatric Setting

Background: Few studies have been performed in children withs uspected betalactam allergy.We aimed to assess the role of the drug provocation test(DPT)with betalactams in a paediatric setting and to study the association between allergy to betalactam antibiotics and other allergic diseases. Methods:We included all the patients under 15 years old who were consecutively referred to the Immunoallergy Department, Dona Estefânia Hospital,Portugal(January 2002 to April 2008)for a compatible history of allergic reaction to betalactam. All were submitted to a DPT.Children were proposed to performs kintests(ST)to betalactam antibiotics followed by DPT. If they decline ST,a DPT with the culprit drug was performed. Results: We studied 161 children,60%were boys,with a median age of 5years old at the time of the DPT.Thirty-three patients(20.5%)had an immediate reaction and 33(20.5%)a non-immediate reaction. These verity of there porte dreactions was low in most cases. Skin tests to betalactams were performed in 47 children and were positive in 8.DPT was positive inonlyone(3.4%)of the patients skin tested and in 11(13.4%)of those not skin tested. These verity of the DPT reaction was low.Asthma and food allergy were associated with a positive DPT in the later group. Conclusions: DPT seems a safe procedure even in the absence of ST in non-severe cases. This could be a practical optionin infants and pre-school children,where ST are painful and difficult to perform.Additional caution should be taken in children with asthma and food allergy.

Methacholine Dose-Response Slopes from Maximal Bronchial Challenge Tests in Asthmatic Children: Methodological Aspects

To determine whether the slope of a maximal bronchial challenge test (in which FEV1 falls by over 50%) could be extrapolated from a standard bronchial challenge test (in which FEV1 falls up to 20%), 14 asthmatic children performed a single maximal bronchial challenge test with methacholin(dose range: 0.097–30.08 umol) by the dosimeter method. Maximal dose-response curves were included according to the following criteria: (1) at least one more dose beyond a FEV1 ù 20%; and (2) a MFEV1 ù 50%. PD20 FEV1 was calculated, and the slopes of the early part of the dose-response curve (standard dose-response slopes) and of the entire curve (maximal dose-response slopes) were calculated by two methods: the two-point slope (DRR) and the least squares method (LSS) in % FEV1 × umol−1. Maximal dose-response slopes were compared with the corresponding standard dose-response slopes by a paired Student’s t test after logarithmic transformation of the data; the goodness of fit of the LSS was also determined. Maximal dose-response slopes were significantly different (p < 0.0001) from those calculated on the early part of the curve: DRR20% (91.2 ± 2.7 FEV1% z umol−1)was 2.88 times higher than DRR50% (31.6 ± 3.4 DFEV1% z umol−1), and the LSS20% (89.1 ± 2.8% FEV1 z umol−1) was 3.10 times higher than LSS 50% (28.8 ± 1.5%FEV1 z umol−1). The goodness of fit of LSS 50% was significant in all cases, whereas LSS 20% failed to be significant in one. These results suggest that maximal dose-response slopes cannot be predicted from the data of standard bronchial challenge tests.

Optimal Cut-Off Value for Homeostasis Model Assessment (HOMA) Index of Insulin-Resistance in a Population of Patients Admitted Electively in a Portuguese Cardiology Ward

INTRODUCTION: Insulin resistance is the pathophysiological key to explain metabolic syndrome. Although clearly useful, the Homeostasis Model Assessment index (an insulin resistance measurement) hasn't been systematically applied in clinical practice. One of the main reasons is the discrepancy in cut-off values reported in different populations. We sought to evaluate in a Portuguese population the ideal cut-off for Homeostasis Model Assessment index and assess its relationship with metabolic syndrome. MATERIAL AND METHODS: We selected a cohort of individuals admitted electively in a Cardiology ward with a BMI < 25 Kg/m2 and no abnormalities in glucose metabolism (fasting plasma glucose < 100 mg/dL and no diabetes). The 90th percentile of the Homeostasis Model Assessment index distribution was used to obtain the ideal cut-off for insulin resistance. We also selected a validation cohort of 300 individuals (no exclusion criteria applied). RESULTS: From 7 000 individuals, and after the exclusion criteria, there were left 1 784 individuals. The 90th percentile for Homeostasis Model Assessment index was 2.33. In the validation cohort, applying that cut-off, we have 49.3% of individuals with insulin resistance. However, only 69.9% of the metabolic syndrome patients had insulin resistance according to that cut-off. By ROC curve analysis, the ideal cut-off for metabolic syndrome is 2.41. Homeostasis Model Assessment index correlated with BMI (r = 0.371, p < 0.001) and is an independent predictor of the presence of metabolic syndrome (OR 19.4, 95% CI 6.6 - 57.2, p < 0.001). DISCUSSION: Our study showed that in a Portuguese population of patients admitted electively in a Cardiology ward, 2.33 is the Homeostasis Model Assessment index cut-off for insulin resistance and 2.41 for metabolic syndrome. CONCLUSION: Homeostasis Model Assessment index is directly correlated with BMI and is an independent predictor of metabolic syndrome.