[Predictive model of curative effect of mite subcutaneous immunotherapy in 5-18 years of age patients with allergic asthma]

Abstract

Objective: To analyze the factors affecting the efficacy of mite subcutaneous immunotherapy (SCIT) in allergic asthma patients aged 5-18 years, and to find the best predictive model for the curative effect. Methods: The data of 688 patients aged 5-18 years with allergic asthma who completed more than 3 years of mite SCIT from December 2006 to November 2021 in the Department of Respiratory Medicine, Children's Hospital Affiliated to Nanjing Medical University were retrospectively analyzed. Male, results of skin prick test (SPT), age, daily medication score (DMS), visual analogue scale (VAS) score, and enrollment season were defined as independent variables. R language models, including Logistic regression model, random forest model and extreme gradient boosting (XGboost) model, were used to analyze the impact of these independent variables on the outcomes. The receiver operating characteristic curve was applied to compare the predictive ability of the models. Hypothesis testing of the area under curve (AUC) of the 3 models was performed using DeLong test. Results: There were 435 males and 253 females in the 688 patients. There were 349 patients aged 5-<8 years, 240 patients aged 8-<11 years, and 99 patients aged 11-18 years. SPT showed that 429 cases (62.4%) were only allergic to mite, and 259 cases (37.7%) were also allergic to other allergens. According to the efficacy after 3 years of SCIT, 351 cases (51.0%) discontinued the treatment and 337 cases (49.0%) required continued treatment. The DMS was 4 (3, 6) at initiation, 3 (2, 5) at 3 months, 3 (2, 5) at 4 months, 2 (1, 3) at 12 months, and 0 (0, 1) at 3 years of SCIT treatment. The VAS was 3.5 (2.5, 5.2) at initiation, 3.2 (2.2, 4.8) at 3 months, 2.6 (1.4, 4.1) at 4 months, 1.0 (0.6, 1.8) at 12 months, and 0.5 (0, 1.2) at 3 years of treatment. At 3, 4, and 12 months, the rate of decline in DMS was 0 (0, 20%), 16.7% (0, 33.3%), and 50.0% (31.0%, 75.0%), respectively; and the VAS decreased by 7.1% (3.2%,13.8%), 27.6% (16.7%,44.4%), and 70.2% (56.1%, 82.3%), respectively. Regarding the enrollment season, 99 cases were in spring, 230 cases in summer, 171 cases in autumn, and 188 cases in winter. The R language Logistic regression model found that DMS>3 points at 3 months (OR=-3.5, 95%CI:-4.3--2.7, P<0.01), male (OR=-1.7, 95%CI:-2.3--1.0), P<0.01), DMS decline rate>16.7% at 4 months (OR=-1.6, 95%CI:-2.3--0.8, P<0.01) and DMS decline rate>0 at 3 months (OR=-0.7, 95%CI:-1.3--0.2, P<0.05) had higher possibility of drug discontinuation; whereas, the decline rate of DMS at 12 months>50.0% (OR=0.7, 95%CI: 0.1-1.3, P<0.05), VAS at 12 months>1.0 points (OR=0.9, 95%CI: 0.3-1.6, P<0.05), and initial VAS<4.0 points (OR=1.0, 95%CI: 0.4-1.6, P<0.01) had lower possibility of drug discontinuation. Both the random forest model and the XGboost model showed that DMS>3 points at 3 months (mean decrease accuracy=30.9, importance=0.45) had the greatest impact on drug discontinuation. The AUC of the random forest model was the largest at 0.900, with an accuracy of 78.2% and a sensitivity of 84.5%. Logistic regression model had AUC of 0.891, accuracy of 80.0%, and sensitivity of 80.0%; XGboost model had AUC of 0.886, accuracy of 76.9%, and sensitivity of 84.5%. The AUC of the pairwise comparison model by DeLong test found that all three models could be used for the prediction of this data set (all P>0.05). Conclusions: The more drugs used to control the primary disease, and the more careful reduction of the control medicine after starting SCIT treatment, the more favorable it is to stop all drugs after 3 years. The random forest model is the best predictive model for the efficacy of mite SCIT in asthmatic children.