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A cerebral arteriovenous malformation (AVM) is a tangle of abnormal blood vessels that irregularly connects arteries and veins. Stereotactic radiosurgery (SRS) has been shown to be an effective treatment for AVM patients, but the factors associated with AVM obliteration remains a matter of debate. In this study, we aimed to develop a model that can predict whether patients with AVM will be cured 36 months after intervention by means of SRS and identify the most important predictors that explain the probability of being cured. A machine learning (ML) approach was applied using decision tree (DT) and logistic regression (LR) techniques on historical data (sociodemographic, clinical, treatment, angioarchitecture, and radiosurgery procedure) of 202 patients with AVM who underwent SRS at the Instituto de Radiocirugía del Perú (IRP) between 2005 and 2018. The LR model obtained the best results for predicting AVM cure with an accuracy of 0.92, sensitivity of 0.93, specificity of 0.89, and an area under the curve (AUC) of 0.98, which shows that ML models are suitable for predicting the prognosis of medical conditions such as AVM and can be a support tool for medical decision-making. In addition, several factors were identified that could explain whether patients with AVM would be cured at 36 months with the highest likelihood: the location of the AVM, the occupation of the patient, and the presence of hemorrhage.

Antibiotics are one of the most frequently prescribed drugs. Unfortunately, they also are the most common cause for self-reported drug allergy, limiting the use of effective therapies. However, evidence shows that more than 90% of patients labeled as allergic to antibiotics are not allergic. Importantly, the label of antibiotic allergy, whether real or not, constitutes a major public health problem as it directly impacts antimicrobial stewardship: it has been associated with broad-spectrum antibiotic use, often resulting in the emergence of bacterial resistance. Therefore, an accurate diagnosis is crucial for de-labeling patients who claim to be allergic but are not really allergic. This review presents allergy methods for achieving successful antibiotic allergy de-labeling. Patient clinical history is often inaccurately reported, thus not being able to de-label most patients. In vitro testing offers a complementary approach but it shows limitations. Immunoassay for quantifying specific IgE is the most used one, although it gives low sensitivity and is limited to few betalactams. Basophil activation test is not validated and not available in all centers. Therefore, true de-labeling still relies on in vivo tests including drug provocation and/or skin tests, which are not risk-exempt and require specialized healthcare professionals for results interpretation and patient management. Moreover, differences on the pattern of antibiotic consumption cause differences in the diagnostic approach among different countries. A multidisciplinary approach is recommended to reduce the risks associated with the reported penicillin allergy label.

Diagnosis of type I hypersensitivity reactions (IgE-mediated reactions) to penicillins is based on clinical history, skin tests (STs), and drug provocation tests (DPTs). Among in vitro complementary tests, the fluoro-enzyme immunoassay (FEIA) ImmunoCAP® (Thermo-Fisher, Waltham, MA, USA) is the most widely used commercial method for detecting drug-specific IgE (sIgE). In this study, we aimed to analyze the utility of ImmunoCAP® for detecting sIgE to penicillin G (PG) and amoxicillin (AX) in patients with confirmed penicillin allergy. The study includes 139 and 250 patients evaluated in Spain and Italy, respectively. All had experienced type I hypersensitivity reactions to penicillins confirmed by positive STs. Additionally, selective or cross-reactive reactions were confirmed by DPTs in a subgroup of patients for further analysis. Positive ImmunoCAP® results were 39.6% for PG and/or AX in Spanish subjects and 52.4% in Italian subjects. When only PG or AX sIgE where analyzed, the percentages were 15.1% and 30.4%, respectively, in Spanish patients; and 38.9% and 46% in Italian ones. The analysis of positive STs showed a statistically significant higher percentage of positive STs to PG determinants in Italian patients. False-positive results to PG (16%) were detected in selective AX patients with confirmed PG tolerance. Low and variable sensitivity values observed in a well-defined population with confirmed allergy diagnosis, as well as false-positive results to PG, suggest that ImmunoCAP® is a diagnostic tool with relevant limitations in the evaluation of subjects with type I hypersensitivity reactions to penicillins.