[Development and validation of a prediction model for medication adherence in patients receiving allergen sublingual...

Objective: To develop and validate a prediction model for medication adherence among patients receiving allergen sublingual immunotherapy (SLIT). Methods: A prospective cross-sectional study was conducted, and a total of 288 patients who received SLIT treatment at an allergy center in the First Affiliated Hospital with Nanjing Medical University (Jiangsu Province Hospital) from December 2023 to July 2024 were assigned to the modeling group. Additionally, 122 patients from August to October 2024 were assigned to the validation group. Data of patients' general information, medication beliefs, anxiety levels, social support, disease perception, and medication adherence were collected. Single-factor analysis and LASSO regression were utilized to identify potential predictors, and a prediction model for medication adherence was constructed using multifactorial logistic regression. A nomogram was then developed based on the model. The model's discriminatory ability was evaluated using receiver operating characteristic curve (ROC), the area under curve (AUC), sensitivity, and specificity. The model was then validated in the validation cohort. Results: Single-factor analysis and LASSO regression identified a total of nine predictive factors. Logistic regression revealed that medical belief tendency [OR (95%CI)=2.420 (1.116-5.248), P=0.025], the somatic control dimension in self-rating anxiety scales [OR (95%CI)=1.404 (1.241-1.589), P<0.001], the subjective support dimension in social support assessment [OR (95%CI)=0.784 (0.725-0.847), P<0.001], and the cognitive dimension in illness perception [OR (95%CI)=0.725 (0.647-0.813), P<0.001] were independent predictors of medication adherence in patients undergoing SLIT. The AUC value of the model was 0.899 (95%CI=0.863-0.934) in the modeling group and 0.882 (95%CI=0.820-0.944) in the validation group, indicating good discriminatory ability. The optimal cutoff value of the model was 0.493, with a sensitivity of 81.1% and specificity of 85.7% in the modeling group, and a sensitivity of 87.3% and specificity of 82.5% in the validation group. Conclusion: The medication adherence prediction model developed in this study for patients undergoing SLIT exhibits good predictive performance and provides valuable guidance for early intervention by clinical healthcare professionals.