BACKGROUND: There is a scarcity of prospective studies investigating the relative roles of skin prick and intradermal testing, serum-specific Immunoglobulin E, and extended oral challenges in diagnosing children with reported beta-lactam allergies.
OBJECTIVE: To determine the sensitivity and specificity of skin testing and serum-specific Immunoglobulin E in children with beta-lactam allergies, with immediate and non-immediate historic reactions.
METHODS: Four hundred children with parent-reported beta-lactam allergies were recruited into an open-label prospective study. Detailed allergy histories were collected. Those with medically observed and documented histories of anaphylaxis, requiring epinephrine, or SCARs were excluded. In total, 380 children underwent all testing modalities and a direct provocation test. Each child was followed up for a minimum of three years.
RESULTS: True allergy in children was uncommon, 8·3% reacted to the direct provocation challenge or the 5-day extended oral provocation challenge. Children reporting cephalosporin allergy or a reaction within one year were more likely to react to direct provocation testing. The sensitivity, specificity, and positive predictive value of skin testing was 12·5%, 98·8% and 20·0% for direct challenge outcomes, 4·76%, 99·0% and 25·0% for extended challenge outcomes, and 6·9%, 99·0% and 40·0% for both challenges combined. Follow-up investigations revealed that 5·7% of children had a mild repeat reaction and 2·7% continued to avoid the culprit despite successful delabeling. The relabeling rate for children readmitted to hospital was 15% with the relabeing being unfounded.
CONCLUSIONS: Genuine beta-lactam allergies were rare, with over 90% of children effectively delabeled. Skin and serum-specific Immunoglobulin E testing did not aid the diagnosis of beta-lactam antibiotic allergy in children, regardless of medical history. Extended oral challenges proved valuable in confirming allergies and boosted parental confidence.

In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff-to-patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different beacons by relabeling to the locations with less samples, resolving data imbalance. Standard deviation and Kullback-Leibler divergence between minority and majority classes are used to measure signal pattern to find matching beacons to relabel. By matching beacons between classes, two variations of relabeling are implemented, specifically full and partial matching. The performance is evaluated using the real-world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. A Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the beacon data with our proposed relabeling method for data augmentation, we achieve a higher minority class F1-score compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected beacon data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

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OBJECTIVE: Prudent handling of reported antibiotic allergies is an important aspect of antibiotic stewardship. The Dutch Working Party on Antibiotic Policy (SWAB) constituted a multidisciplinary expert committee to provide evidence-based recommendations for bedside decision-making in antibiotic therapy in patients that report an antibiotic allergy.
METHODS: The guideline committee generated 12 key questions, most of which were population, intervention, comparison, and outcome questions relevant to both children and adults with suspected antibiotic allergies. For each question, a systematic literature search was performed and reviewed for the best available evidence according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. The quality of evidence was graded from very low to high, and recommendations were formulated in structured discussions as strong or weak.
RESULTS: Sixty recommendations were provided for suspected allergy to β-lactam antibiotics (BLAs) and non-β-lactam antibiotics. Owing to the absence of randomized controlled trials in this field, the underlying evidence was predominantly graded as low or very low. Available data support that a detailed allergy history should always be performed and critically appraised. When cross-allergy between BLA groups is not to be expected due to the absence of molecular similarity of the side chains, the patient can be safely exposed to the alternative BLA. An exception to this rule is severe delayed-type reactions in which re-exposure to a BLA should only be considered after consultation with a multidisciplinary team.
CONCLUSIONS: Accumulated scientific data now support a more liberal approach that better balances the benefits of treatment with first choice and usually smaller spectrum antibiotics with appropriate avoidance of antibiotics in case of a truly high risk of a (severe) allergic reaction. In The Netherlands, a formal guideline was developed that provides recommendations for the approach toward suspected allergy to BLA and frequently used non-β-lactam antibiotics, thereby strongly supporting antimicrobial stewardship.

BACKGROUND: Beta-lactam antibiotic allergy labels are highly prevalent, though, rarely indicate an allergic intolerance. These patient-reported allergies lead to broad-spectrum antibiotic use, conferred resistance, increased expense and side effects.
OBJECTIVE: To implement and assess the impact of a history-based clinical guideline that directs antibiotic management and beta-lactam allergy relabeling for patients reporting beta-lactam allergies.
METHODS: Patients with beta-lactam allergy labels were identified by our trained multi-disciplinary team in diverse clinical settings. This quality improvement project was conducted to evaluate the safety and impact of the guideline on antibiotic use by comparing prescribing practices before and after guideline implementation.
RESULTS: 79 patients with beta-lactam allergies were identified (penicillins-90%, cephalosporins-10%). After guideline implementation, outcomes of allergy relabeling included: complete removal indicating an unlikely true allergy (27%), updated to detail successfully tolerated beta-lactam courses (48%), or no change was made to current label (25%). Beta-lactam antibiotic courses before and after guideline implementation compared to total antibiotic courses: allergy removed (44% versus 70%; p<0.0001), allergy updated (32% versus 68%; p<0.0001), and no change (27% versus 41%; p=0.08). Compared to before guideline implementation, beta-lactam antibiotics were three times more likely to be prescribed after allergy assessment (odds ratio, 3.22; 95% confidence interval, 2.4-4.3; p<0.05).
CONCLUSIONS: The implementation of the beta-lactam allergy clinical guideline resulted in the majority of patients\' allergy labels being removed or advantageously updated. These allergy label changes correlated with a significant increase in the percent of beta-lactam antibiotics prescribed. After guideline implementation, beta-lactam antibiotics had a three-fold increased odds of being prescribed independent of allergy label outcome.

This paper considers the reflection unidentifiability problem in confirmatory factor analysis (CFA) and the associated implications for Bayesian estimation. We note a direct analogy between the multimodality in CFA models that is due to all possible column sign changes in the matrix of loadings and the multimodality in finite mixture models that is due to all possible relabelings of the mixture components. Drawing on this analogy, we derive and present a simple approach for dealing with reflection in variance in Bayesian factor analysis. We recommend fitting Bayesian factor analysis models without rotational constraints on the loadings-allowing Markov chain Monte Carlo algorithms to explore the full posterior distribution-and then using a relabeling algorithm to pick a factor solution that corresponds to one mode. We demonstrate our approach on the case of a bifactor model; however, the relabeling algorithm is straightforward to generalize for handling multimodalities due to sign invariance in the likelihood in other factor analysis models.