Journal of Clinical Immunology and Allergy

Description

In computed tomography and fluoroscopic procedures, iodinated radiocontrast media is used to make structures more visible. In addition to pharmacological toxicity, iRCM can cause immediate or delayed Drug Hypersensitivity Reactions (DHR). Depending on the results of the skin test, either allergic or non-allergic DHR can be classified. Albeit these differentiation specialists are for the most part viewed as protected, particularly the nonionic low or iso-osmolar items at present utilized, IHRs and NIHRs are accounted for in around 1%-3% of iRCM infusions. At our center, the primary strategy for reducing iRCM allergic DHR has been to avoid the iRCM that is causing the reaction, prohibit cross-reactive iRCMs, and use drug allergy testing to find non-cross-reactive agents. This strategy is in line with the European recommendations on allergy work-up for iRCM, which were recently published and now include algorithms for emergency situations in which a drug allergy work-up is not possible. Alternately, in certain circumstances, premedication or avoidance of the iRCM that is causing the problem can be used; nonetheless, there is restricted proof that this procedure can forestall intermittent responses. Skin testing is a useful tool for diagnosing iRCM allergic DHRs, and ST may play a significant role in selecting a safe alternative to iRCM for allergic patients with a specificity of 96% to 100%. Between two and six months after the reaction, up to 50% of patients with IHRs and 47% of patients with NIHRs have positive ST.

Cluster Analysis of Data from a Large Drug Allergy and Hypersensitivity Database

The Negative Predictive Value (NPV) of iRCM ST is greater than 90% in actual clinical settings. Accuracy medication is an original way to deal with patient administration that depends on various endotypes. Clinical phenotypes that are linked to underlying endotypes have been defined significantly in asthma. Except for tryptase and a few serum-specific IgEs, there are no biological markers that can be used to identify DHR-specific phenotypes and endotypes. Clinical phenotyping of DHRs to iRCM currently follows a classification that has been established a priori based on patient characteristics, clinical history, and drug allergy work-up results. We hypothesized in this study that clinically relevant groupings that will replace the existing a priori classifications could be created and described among a diverse group of patients who have a suspicion of iRCM DHRs. Unsupervised machine learning techniques that can be used to identify distinct subgroups or clusters within a set of data are referred to as cluster analysis. Using cluster analysis of data from a large drug allergy and hypersensitivity database, the purpose of this study was to identify distinct characteristics among patients with a suspicion of DHR to iRCM. The Drug Allergy and Hypersensitivity Database (DAHD) of the Allergy Unit at the University Hospital of Montpellier in Montpellier, France, was used in a retrospective study. The analysis included all referred patients with either confirmed positive ST or confirmed negative ST between February 2001 and December 2019.597 (2001–2014) of this population have previously been analyzed. The University Hospital of Montpellier's Institutional Review Board approved this study's protocol. At the time of the allergy workup, written informed consent was obtained from the study participants. Recovered information included segment information, side effects, and sequence of the DHR, guilty party iRCM utilized during the methodology, postpone between the response, and the date of tests, order, and recognized iRCMs from positive ST results. Except for a select few NIHR cases, our study protocol did not include drug provocation tests for iRCM. The most severe reaction was included in the analysis if more than one DHR occurred. The quality of the models was rated as fair, poor, or good. A group proportion sub-par compared to 3 was thought of as satisfactory. The importance chart of the variables was examined for each cluster. In order to construct the cluster, all patients who were referred to iRCM on the basis of suspicion of DHR were included.

Determination of the Number of Clusters Based on a Statistical Measure

Based on the patient's medical history, index reactions were categorized as either IHR or NIHR.As previously mentioned, a set of ten iRCMs that are available in France were typically used for ST. Skin prick tests were performed briefly, and if they came back negative after 15 minutes, intradermal tests were performed. IHRs had their IDT read at 20 minutes, while NIHRs had their IDT read delayed for up to 7 days. An immediate reading was always carried out for patients whose chronology was unknown. The allergist made the decision to show a delayed reading based on the reaction's semiology. The allergy team's contact information was provided to all patients upon discharge for future requests. The chronology of the positive ST was assigned independently of that of the index reaction, but it was compared to that of the index reaction using the same classification. SPSS Statistics version 18.0 was used to carry out each and every analysis. Numbers and percentages (percent) are used to display categorical data. Non-normally distributed continuous data are expressed as the median and interquartile range or range. The Mann-Whitney U test was used to compare continuous non-normally distributed data, whereas the Chi-square test was used to compare categorical data between groups. Risk factors for having a positive ST to iRCM were identified through the use of binary logistic regression. Multivariate analysis included parameters with a p-value less than 0.25 in univariate logistic regression. It is considered statistically significant when the p-value is less than 0.05. The cluster numbers were determined using the Two-Step cluster method. Two-step cluster analysis is a hybrid method that first divides groups using a distance measure and then uses probabilistic methods to find the best subgroup model. When compared to more conventional methods, this one has a number of advantages, such as the ability to handle large datasets, the simultaneous use of categorical and continuous variables, and the determination of the number of clusters based on a statistical measure of fit rather than an arbitrary choice. Because it prevented any intervention in the analysis, automatic selection was chosen. Better models are indicated by smaller BIC values. For cluster building, the following variables were used: (i) clinical manifestations such as anaphylactic shock, anaphylaxis, urticaria/angioedema, maculopapular exanthema, isolated malaise, isolated bronchospasm, MPE with signs of severity, fixed drug eruption, and other manifestations such as intense isolated signs requiring medical intervention such as cardiac signs, digestive signs, arthralgias, and unknown; (ii) clinical manifestation ii) The index reaction's chronology; iii) the time between the index reaction and the ST; iv) the RCMs at fault;and (v) the ratio of DHR episodes to iRCMs. Evaluation fields included the test result, the number of confirmed positive STs to iRCMs, and the chronology of reactivity.