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Allergic Reactions02:06

Allergic Reactions

Overview

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Related Experiment Video

Updated: Jun 11, 2026

Sublingual Immunotherapy as an Alternative to Induce Protection Against Acute Respiratory Infections
16:56

Sublingual Immunotherapy as an Alternative to Induce Protection Against Acute Respiratory Infections

Published on: August 30, 2014

Predicting sublingual immunotherapy efficacy in allergic rhinitis.

Jiayue Wang1, Xiaoning Zhu2, Zan Ding3

  • 1Department of Education, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.

BMC Pulmonary Medicine
|June 10, 2026
PubMed
Summary
This summary is machine-generated.

Predicting sublingual immunotherapy (SLIT) efficacy for allergic rhinitis (AR) is now possible with a new model. This tool helps personalize treatment decisions by analyzing patient data for better outcomes.

Keywords:
Allergic rhinitisEfficacy predictionNomogramPredictive modelRandom forestSublingual immunotherapy

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Area of Science:

  • Immunology
  • Allergology
  • Data Science

Background:

  • Sublingual immunotherapy (SLIT) efficacy for allergic rhinitis (AR) varies, with 30-40% of patients responding poorly.
  • A reliable tool for predicting individualized SLIT efficacy is currently lacking.
  • This study aimed to develop a predictive model for SLIT efficacy in AR patients.

Purpose of the Study:

  • To construct an optimal prediction model for SLIT efficacy in AR patients.
  • To incorporate clinical characteristics, environmental exposures, and immune-inflammatory indicators.
  • To establish a nomogram for intuitive clinical application and personalized treatment decisions.

Main Methods:

  • 346 AR patients receiving SLIT were divided into training (n=242) and validation (n=104) cohorts.
  • Multivariate logistic regression identified independent predictors: disease duration, symptom/medication scores, AC usage, sIgE/tIgE ratio, IL-4, and IL-10.
  • Random forest, SVM, and logistic regression models were compared; a nomogram was constructed and validated.

Main Results:

  • The random forest model showed optimal predictive performance (AUC training: 0.757, validation: 0.729).
  • The nomogram demonstrated good discrimination and calibration, outperforming SVM and logistic regression models.
  • Disease duration, sIgE/tIgE ratio, and baseline medication score were key predictors identified by SHAP analysis.

Conclusions:

  • The developed random forest model and nomogram offer good discrimination, calibration, and clinical utility for predicting SLIT efficacy.
  • This tool enables intuitive, individualized clinical assessment for personalized SLIT decision-making in AR patients.
  • The findings provide a quantitative basis for optimizing AR treatment strategies.