埃利斯波特
医学
药物过敏
激发试验
药品
逻辑回归
过敏
内科学
接收机工作特性
免疫学
胃肠病学
药理学
病理
T细胞
免疫系统
替代医学
作者
Yuda Chongpison,Sira Sriswasdi,Supranee Buranapraditkun,Pattarawat Thantiworasit,Pawinee Rerknimitr,Pungjai Mongkolpathumrat,Leena Chularojanamontri,Yuttana Srinoulprasert,Ticha Rerkpattanapipat,Kumutnart Chanprapaph,Wareeporn Disphanurat,Panlop Chakkavittumrong,Napatra Tovanabutra,Chutika Srisuttiyakorn,Chonlaphat Sukasem,Papapit Tuchinda,Padcha Pongcharoen,Jettanong Klaewsongkram
标识
DOI:10.1016/j.jaci.2023.08.026
摘要
Background
Diagnosing drug-induced allergy, especially nonimmediate phenotypes, is challenging. Incorrect classifications have unwanted consequences. Objective
We sought to evaluate the diagnostic utility of IFN-γ ELISpot and clinical parameters in predicting drug-induced nonimmediate hypersensitivity using machine learning. Methods
The study recruited 393 patients. A positive patch test or drug provocation test (DPT) was used to define positive drug hypersensitivity. Various clinical factors were considered in developing random forest (RF) and logistic regression (LR) models. Performances were compared against the IFN-γ ELISpot-only model. Results
Among the 102 patients who had 164 DPTs, most patients had severe cutaneous adverse reactions (35/102, 34.3%) and maculopapular exanthems (33/102, 32.4%). Common suspected drugs were antituberculosis drugs (46/164, 28.1%) and β-lactams (42/164, 25.6%). Mean (SD) age of patients with DPT was 52.7 (20.8) years. IFN-γ ELISpot, fixed drug eruption, Naranjo categories, and nonsteroidal anti-inflammatory drugs were the most important features in all developed models. The RF and LR models had higher discriminating abilities. An IFN-γ ELISpot cutoff value of 16.0 spot-forming cells/106 PBMCs achieved 94.8% specificity and 57.1% sensitivity. Depending on clinical needs, optimal cutoff values for RF and LR models can be chosen to achieve either high specificity (0.41 for 96.1% specificity and 0.52 for 97.4% specificity, respectively) or high sensitivity (0.26 for 78.6% sensitivity and 0.37 for 71.4% sensitivity, respectively). Conclusions
IFN-γ ELISpot assay was valuable in identifying culprit drugs, whether used individually or incorporated in a prediction model. Performances of RF and LR models were comparable. Additional test datasets with DPT would be helpful to validate the model further.
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