Rozłucka Lesia, Rymarczyk Barbara, Gawlik Radosław, Glück Joanna
Department of Internal Medicine, Allergology and Clinical Immunology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-752 Katowice, Poland.
J Clin Med. 2024 Nov 29;13(23):7267. doi: 10.3390/jcm13237267.
The decision whether to de-label patient with suspected BL hypersensitivity is based on risk stratification. The aim of this study was to prepare a characteristic of diagnostic risk groups and to create a model enabling the identification of the low-risk diagnostic group. We analyzed the medical records of patients hospitalized due to suspected hypersensitivity to BL antibiotics. Based on their medical-history data, patients were divided into three diagnostic risk groups, using the criteria proposed by Shenoy et al. Univariate and multivariate analysis models were used to create a diagnostic tool. Among 263 patients referred for BL hypersensitivity diagnosis, 88 (33.5%) were allocated to group I, 129 (49%) to group II, and 46 (17.5%) to group III. There were significant differences between diagnostic risk groups regarding history of hypersensitivity to penicillins ( < 0.001), cephalosporins ( < 0.001), >1 BL ( < 0.05), several episodes of BL hypersensitivity ( < 0.001), medical intervention ( < 0.001), documented hypersensitivity ( < 0.001), time from drug intake to symptoms ( < 0.001), and time from hypersensitivity to diagnosis ( < 0.001). In total, 81 patients (30.8%) were de-labeled: 52 (59.8%) in group I, 27 (20.9%) in group II, and 2 (4.3%) in group III. The univariate analysis model of the low-diagnostic-risk group applied to the de-labeled part showed 90% specificity and 21.93% sensitivity. NPV and PPV were estimated at 72.04% and 49.53%, respectively. The multivariate model had high specificity but low sensitivity; its NPV was 76%, with 68% PPV. The tool enabling the identification of low-diagnostic-risk patients based on anamnesis is not sensitive enough to de-label patients on its basis.
对于疑似对BL抗生素过敏的患者是否取消过敏标签的决定基于风险分层。本研究的目的是制定诊断风险组的特征,并创建一个能够识别低风险诊断组的模型。我们分析了因疑似对BL抗生素过敏而住院的患者的病历。根据他们的病史数据,采用Shenoy等人提出的标准将患者分为三个诊断风险组。使用单变量和多变量分析模型创建诊断工具。在263例被转诊进行BL过敏诊断的患者中,88例(33.5%)被分配到I组,129例(49%)被分配到II组,46例(17.5%)被分配到III组。诊断风险组在青霉素过敏史(<0.001)、头孢菌素过敏史(<0.001)、>1种BL抗生素过敏(<0.05)、多次BL过敏发作(<0.001)、医疗干预(<0.001)、有记录的过敏(<0.001)、从服药到出现症状的时间(<0.001)以及从过敏到诊断的时间(<0.001)方面存在显著差异。共有81例患者(30.8%)被取消过敏标签:I组52例(59.8%),II组27例(20.9%),III组2例(4.3%)。应用于被取消过敏标签部分的低诊断风险组单变量分析模型显示特异性为90%,敏感性为21.93%。阴性预测值和阳性预测值分别估计为72.04%和49.53%。多变量模型具有高特异性但低敏感性;其阴性预测值为76%,阳性预测值为68%。基于问诊来识别低诊断风险患者的工具不够敏感,并不能据此为患者取消过敏标签。