变应性鼻炎患者舌下免疫治疗发生脱落风险的列线图预测模型  

The nomogram prediction model for the risk of dropout in sublingual immunotherapy of patients with allergic rhinitis

作　　者：彭聪 易烛光[1] 叶惠平 刘丹[2] 吴敏 Peng Cong;Yi Zhuguang;Ye Huiping;Liu Dan;Wu Min(Department of Otorhinolaryngology,Guizhou Provincial People′s Hospital,Guiyang 550002,China;Department of Otorhinolaryngology,Huangshi Central Hospital,Affiliated Hospital of Hubei Polytechnic University,Huangshi 435000,China)

机构地区：[1]贵州省人民医院耳鼻咽喉科,贵阳550002 [2]黄石市中心医院(湖北理工学院附属医院)耳鼻咽喉科,黄石435000

出　　处：《中华耳鼻咽喉头颈外科杂志》2025年第3期330-337,共8页Chinese Journal of Otorhinolaryngology Head and Neck Surgery

基　　金：贵州省卫健委课题(gzwkj2021-324)。

摘　　要：目的建立并外部验证一个针对变应性鼻炎(AR)患者在接受舌下免疫治疗(sublingual immunotherapy,SLIT)时发生脱落风险的列线图预测模型。方法2016年2月至2019年12月期间,分别在贵州省人民医院和黄石市中心医院收集358例和259例符合入排标准的接受SLIT的AR患者资料,包括患者的一般信息、尘螨特异性免疫球蛋白E(sIgE)分级、过敏原种类等22项临床数据。将贵州省人民医院的数据作为建模组,黄石市中心医院的数据作为外部验证组,应用多因素Cox回归模型筛选SLIT脱落的独立影响因素,并建立列线图预测模型。结果多因素Cox回归分析结果表明,尘螨sIgE分级(Ⅱ~Ⅳ级)(HR=1.48,95%CI:1.16~1.88)、合并其他变应性疾病(HR=0.47,95%CI:0.37~0.61)、鼻结膜炎生活质量量表(Rhinoconjunctivitis Quality of Life Questionnaire,RQLQ)评分(HR=0.98,95%CI:0.97~1.00)、微信管理(HR=0.77,95%CI:0.60~0.98)、治疗有效(HR=0.72,95%CI:0.56~0.92)、年龄(5~17岁,HR=0.50,95%CI:0.36~0.71;≥60岁,HR=1.42,95%CI:1.08~1.87)、家庭月收入(<5000元,HR=1.44,95%CI:1.09~1.90;>20000元,HR=0.66,95%CI:0.44~0.99)、过敏原种类(单一螨虫,HR=0.70,95%CI:0.49~0.93;合并花粉或真菌,HR=1.45,95%CI:1.02~2.04)以及起效时间<3个月(HR=0.73,95%CI:0.56~0.94)均是SLIT治疗发生脱落的显著因素(P<0.05)。随访第3年时,建模组列线图模型曲线下面积(AUC)值为0.913(95%CI:0.881~0.943);验证组为0.886(95%CI:0.838~0.933)。校准曲线和决策曲线分析可显示该模型在建模组和验证组中与实际脱落的一致性及临床获益。此外,Brier评分为0.29,进一步证实了模型的预测准确性。结论本研究成功开发了一种基于列线图的AR患者SLIT治疗脱落预测模型,可协助临床医护人员有效筛选高风险患者,制定更个性化的治疗方案,提高患者依从性。ObjectiveTo develop and externally validate a nomogram prediction model for assessing the risk of treatment dropout in allergic rhinitis(AR)patients undergoing sublingual immunotherapy(SLIT).MethodsBetween February 2016 and December 2019,data from 358 and 259 AR patients undergoing SLIT were collected from Guizhou Provincial People′s Hospital and Huangshi Central Hospital,respectively.The data included general patient information,dust mite sIgE levels,allergen types,and 22 other clinical variables.Data from Guizhou Provincial People′s Hospital were used as the training set,while data from Huangshi Central Hospital were served as the external validation set.A multivariable Cox regression model was used to identify independent factors associated with SLIT dropout and to develop a nomogram prediction model.ResultsMultivariate Cox regression analysis identified several significant factors influencing SLIT dropout,including dust mite sIgE levels(GradeⅡ-Ⅳ;HR=1.48,95%CI:1.16-1.88),presence of other allergic diseases(HR=0.47,95%CI:0.37-0.61),Rhinoconjunctivitis Quality of Life Questionnaire(RQLQ)score(HR=0.98,95%CI:0.97-1.00),WeChat management(HR=0.77,95%CI:0.60-0.98),treatment efficacy(HR=0.72,95%CI:0.56-0.92),age(5-17 years,HR=0.50,95%CI:0.36-0.71;≥60 years,HR=1.42,95%CI:1.08-1.87),household income(<5000 CNY,HR=1.44,95%CI:1.09-1.90;>20000 CNY,HR=0.66,95%CI:0.44-0.99),allergen types(single dust mite,HR=0.70,95%CI:0.49-0.93;and combined pollen or mold,HR=1.45,95%CI:1.02-2.04),and time to efficacy<3 months(HR=0.73,95%CI:0.56-0.94),all P<0.05.At the third-year follow-up,the area under curve(AUC)for the nomogram model was 0.913(95%CI:0.881-0.943)in the training group and 0.886(95%CI:0.838-0.933)in the validation group.Calibration and decision curve analyses demonstrated the model′s consistency with actual dropout rates and clinical benefit in both groups.Additionally,a Brier score of 0.29 further confirmed the model′s predictive accuracy.ConclusionWe successfully develop a nomogram-based prediction model for S

关 键 词：鼻炎 变应性 舌下免疫治疗 列线图 治疗依从性 预测模型 

分 类 号：R765.21[医药卫生—耳鼻咽喉科]