支气管哮喘疗效预测模型构建的研究
R256.12; 目的 利用咳喘停穴位贴敷治疗支气管哮喘(简称"哮喘")患者的数据,探讨中医药疗效预测模型构建方法及要点.方法 在哮喘慢病管理科研平台上,选择 2018-2021 年的 6-8 月于江苏省中医院针灸康复科接受咳喘停穴位贴敷治疗 6 周的支气管哮喘患者的数据资料,共 303 例.统计分析使用Python 3.10 软件,对影响因素进行初步筛选,将保留的影响因素采用Logistic回归、支持向量机、K-均值聚类算法、贝叶斯算法、随机森林法和轻量梯度提升机算法(LightGBM)分别构建模型,以哮喘控制测试评分(ACT)、第1 秒用力呼气量(FEV1)及呼出气一氧...
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| Published in | 北京中医药大学学报 Vol. 47; no. 5; pp. 729 - 740 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
南京中医药大学附属医院 江苏省中医院 南京 210029%东南大学%南京中医药大学附属医院 江苏省中医院 南京 210029
01.05.2024
南京中医药大学针药结合教育部重点实验室 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1006-2157 |
| DOI | 10.3969/j.issn.1006-2157.2024.05.018 |
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| Abstract | R256.12; 目的 利用咳喘停穴位贴敷治疗支气管哮喘(简称"哮喘")患者的数据,探讨中医药疗效预测模型构建方法及要点.方法 在哮喘慢病管理科研平台上,选择 2018-2021 年的 6-8 月于江苏省中医院针灸康复科接受咳喘停穴位贴敷治疗 6 周的支气管哮喘患者的数据资料,共 303 例.统计分析使用Python 3.10 软件,对影响因素进行初步筛选,将保留的影响因素采用Logistic回归、支持向量机、K-均值聚类算法、贝叶斯算法、随机森林法和轻量梯度提升机算法(LightGBM)分别构建模型,以哮喘控制测试评分(ACT)、第1 秒用力呼气量(FEV1)及呼出气一氧化氮(FeNO)是否改善为结局指标,对各模型进行比较分析.然后,采用较优的模型,通过在训练集上建模、在验证集上验证,得到准确率,并筛选出重要的影响因素.结果 LightGBM模型被采用.通过LightGBM模型建立的咳喘停穴位贴敷治疗哮喘的疗效预测模型准确率均超过 70%;最终筛选出烟酒嗜好、过敏病史、贴敷时间、治疗前ACT及治疗前FeNO共5 个重要的影响因素.重要影响因素的分级分组与因变量关系的分析结果显示,咳喘停穴位贴敷对无过敏史、无烟酒嗜好和治疗前哮喘控制水平很差(ACT 5～15 分)人群的ACT改善更明显(P<0.05);对于贴敷时间超过3 年的患者FeNO改善较贴敷时间小于等于3 年的更明显(P<0.05).但咳喘停穴位贴敷仅对哮喘控制水平很差(P<0.05)和气道炎症严重(FeNO>50×10-9)的少数患者FeNO(P>0.05)起改善作用.结论 穴位贴敷对哮喘控制水平的改善作用较明显,对气道炎症的改善作用有限;利用哮喘慢病管理科研平台的数据进行预测模型构建具有一定可行性;根据本研究的数据所建立的预测模型经过优化和测试后有可能为临床针对性治疗提供有效的测评工具. |
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| AbstractList | R256.12; 目的 利用咳喘停穴位贴敷治疗支气管哮喘(简称"哮喘")患者的数据,探讨中医药疗效预测模型构建方法及要点.方法 在哮喘慢病管理科研平台上,选择 2018-2021 年的 6-8 月于江苏省中医院针灸康复科接受咳喘停穴位贴敷治疗 6 周的支气管哮喘患者的数据资料,共 303 例.统计分析使用Python 3.10 软件,对影响因素进行初步筛选,将保留的影响因素采用Logistic回归、支持向量机、K-均值聚类算法、贝叶斯算法、随机森林法和轻量梯度提升机算法(LightGBM)分别构建模型,以哮喘控制测试评分(ACT)、第1 秒用力呼气量(FEV1)及呼出气一氧化氮(FeNO)是否改善为结局指标,对各模型进行比较分析.然后,采用较优的模型,通过在训练集上建模、在验证集上验证,得到准确率,并筛选出重要的影响因素.结果 LightGBM模型被采用.通过LightGBM模型建立的咳喘停穴位贴敷治疗哮喘的疗效预测模型准确率均超过 70%;最终筛选出烟酒嗜好、过敏病史、贴敷时间、治疗前ACT及治疗前FeNO共5 个重要的影响因素.重要影响因素的分级分组与因变量关系的分析结果显示,咳喘停穴位贴敷对无过敏史、无烟酒嗜好和治疗前哮喘控制水平很差(ACT 5～15 分)人群的ACT改善更明显(P<0.05);对于贴敷时间超过3 年的患者FeNO改善较贴敷时间小于等于3 年的更明显(P<0.05).但咳喘停穴位贴敷仅对哮喘控制水平很差(P<0.05)和气道炎症严重(FeNO>50×10-9)的少数患者FeNO(P>0.05)起改善作用.结论 穴位贴敷对哮喘控制水平的改善作用较明显,对气道炎症的改善作用有限;利用哮喘慢病管理科研平台的数据进行预测模型构建具有一定可行性;根据本研究的数据所建立的预测模型经过优化和测试后有可能为临床针对性治疗提供有效的测评工具. |
| Abstract_FL | Objective We aimed to investigate the feasibility and method of constructing a traditional Chinese medicine(TCM)curative effect prediction model based on the data of Kechuanting acupoint plastering therapy in the treatment of bronchial asthma(asthma).Methods Data from the Chronic Disease Management Research Platform of 303 patients with asthma who were treated with Kechuanting acupoint plastering therapy for 6 weeks in the Department of Acupuncture and Rehabilitation of Jiangsu Hospital of Traditional Chinese Medicine from June to August 2018 to 2021 were selected.We used Phyton 3.10 for statistical analysis.After data preprocessing,the influencing factors were used to build models by Logistic regression,support vector machine,K-means clustering algorithm,Bayes algorithm,random forest method and Light gradient boosting machine(LightGBM)respectively,with the improvement of asthma control test score(ACT),forced expiratory volume in one second(FEV1)and exhaled nitric oxide(FeNO)as the outcome indicators.Then,the models were compared and analyzed.Subsequently,the superior model was used to establish the efficacy prediction model and verify its stability to obtain the accuracy rate and eliminate the relatively important factors.Results The accuracy rate of the Kechuanting acupoint plastering therapy curative effect prediction model established by the LightGBM model was more than 70%.Five important factors were selected,including allergic history,tabacco and alcohol abuse,plastering duration,ACT before treatment,and FeNO before treatment.According to the classification analysis and the relationship between the important factors and the outcome indicators,Kechuanting acupoint plastering therapy significantly improved the ACT of patients with no history of allergy,no tabacco and alcohol abuse,and poor ACT:5-15 points(P<0.05).Furthermore,Kechuanting acupoint plastering therapy improved FeNO more significantly in patients with more than 3 years of treatment than those with no more than 3 years(P<0.05).However,Kechuanting acupoint plastering therapy only improved FeNO in a few patients with poor asthma control levels(P<0.05)and severe airway inflammation(FeNO>50×10-9)(P>0.05).Conclusion Acupoint plastering application has a significant effect on improving the control level of asthma,but its effect on improving airway inflammation is limited.It is feasible to use data from the chronic disease management research platform to construct the prediction model.After optimization and testing,the predictive model established based on the data of this study may provide an effective evaluation tool for targeted clinical treatment. |
| Author | 李民玺 刘兰英 黄麒东 李逸龙 赵舒梅 赵林度 邵婉琪 龚晓燕 |
| AuthorAffiliation | 南京中医药大学附属医院 江苏省中医院 南京 210029%东南大学%南京中医药大学附属医院 江苏省中医院 南京 210029;南京中医药大学针药结合教育部重点实验室 |
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| Author_FL | HUANG Qidong ZHAO Shumei LIU Lanying SHAO Wanqi LI Yilong LI Minxi ZHAO Lindu GONG Xiaoyan |
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| Author_xml | – sequence: 1 fullname: 黄麒东 – sequence: 2 fullname: 李民玺 – sequence: 3 fullname: 李逸龙 – sequence: 4 fullname: 邵婉琪 – sequence: 5 fullname: 赵舒梅 – sequence: 6 fullname: 龚晓燕 – sequence: 7 fullname: 赵林度 – sequence: 8 fullname: 刘兰英 |
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| DocumentTitle_FL | Construction of a therapeutic effect prediction model for bronchial asthma |
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| Keywords | 轻度梯度提升机算法模型 plaster applied to point LightGBM model 穴位贴敷 bronchial asthma 疗效预测 efficacy predication 支气管哮喘 咳喘停 Kechuanting |
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| PublicationTitle | 北京中医药大学学报 |
| PublicationTitle_FL | Journal of Beijing University of Traditional Chinese Medicine |
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| Publisher | 南京中医药大学附属医院 江苏省中医院 南京 210029%东南大学%南京中医药大学附属医院 江苏省中医院 南京 210029 南京中医药大学针药结合教育部重点实验室 |
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| Snippet | R256.12; 目的 利用咳喘停穴位贴敷治疗支气管哮喘(简称"哮喘")患者的数据,探讨中医药疗效预测模型构建方法及要点.方法 在哮喘慢病管理科研平台上,选择 2018-2021 年的 6-8... |
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| Title | 支气管哮喘疗效预测模型构建的研究 |
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