基于真实世界数据分析中医药非介入治疗肺恶性肿瘤的辨治规律. (Chinese)

Objective: To analyze the discriminative patterns of traditional Chinese medicine (TCM) in the non-interventional treatment of lung malignancies from a real-world perspective using data mining methods based on a clinical medical record information system. Methods: The medical history, symptoms at diagnosis, and medication information of patients with malignant lung cancer diagnosed in Dongzhimen Hospital of Beijing University of Chinese Medicine from January 1, 2015 to December 31, 2021 were collected from the medical record system, and a database was established after excluding the cases using interventional therapy. Microsoft Office Excel 2019 software was used to conduct descriptive statistics on the frequency, four basic properties and five tastes, meridian tropism, and efficacy of frequently used drugs. Microsoft Office Excel 2019 software was also used to data-mine the medical history and symptoms. Apriori algorithm in SPSS Modeler 18.0 software was applied to perform association rule analysis on the core data of high-frequency drugs and symptom information obtained from statistics, and an association network graph was constructed using web nodes. Cluster analysis was conducted using SPSS Statistics 23.0. Results: A total of 119 patients with malignant lung tumors who did not undergo interventional treatment were included. The common TCM symptoms included cough, white phlegm, wheezing, poor appetite, poor sleep, sticky phlegm, constipation, fatigue, chest tightness, and shortness of breath; Warm is the most common type among the four properties of the medicine; sweetness is the most common type in the five flavors of the medicine; and the highest frequency of meridian tropism is lung meridian. The association rule results showed that the combinations with the highest support for binomial association were Pinellia ternata → Chaihu, Pinellia ternata → Gualou, and Almond → Ephedra. The combinations with the highest support for trinomial association were Pinellia ternata → (Poria cocos, Bupleurum chinense), Almonds → (Ephedra, Pinellia ternate), Pinellia ternata → (Almonds, Radix Bupleuri), and Pinellia ternata → (Gualou, Fritillaria). The clustering results revealed three clusters, namely phlegm heat obstructing lung syndrome, lung yin deficiency syndrome, and lung qi deficiency syndrome in succession. Conclusion: The discriminative patterns of TCM in the non-interventional treatment of malignant lung tumors were lung yin deficiency syndrome, phlegm heat obstructing lung syndrome and lung qi deficiency syndrome in the syndrome clusters. The treatment is mainly based on harmonizing Shaoyang, moving qi to relieve cough and asthma, and clearing deficiency heat and resolving phlegm-rheum. The commonly used drugs include Pinellia ternata, Glycyrrhiza, Scutellaria, Poria cocos, and Radix Bupleuri. [ABSTRACT FROM AUTHOR]

目的:基于临床病历信息系统，采用数据挖掘方法从真实世界角度分析中医药非介入治疗肺恶性肿瘤的辨治规律。方 法:收集2015 年1 月1 日至2021 年12 月31 日北京中医药大学东直门医院病历系统中诊断为肺恶性肿瘤的患者病史、刻下症和用 药信息，在剔除使用介入治疗手段的病例后建立数据库。采用Microsoft Office Excel 2019 对高频药物的频数、四气五味、归经及 功效进行描述性统计，并对病史和刻下症反映的症状进行数据化统计；对统计得到的高频药物和症状信息借助SPSS Modeler 18.0 软件中Apriori 算法对核心数据进行关联规则分析，并用Web 节点构建关联网状图；借助SPSS Stastistics 23.0 进行聚类分析。 结果:共纳入119 例未进行介入治疗的肺恶性肿瘤患者，中医常见症状为咳嗽、白痰、喘憋、纳差、眠差、痰黏、便秘、乏力、胸闷、气 短；药物四气以温药最多；五味以甘味最多；归经以肺经频率最高。关联规则结果显示，二项支持度最高的组合为半夏→柴胡、半 夏→瓜蒌、杏仁→麻黄；三项关联支持度最高的组合为半夏→茯苓、柴胡，杏仁→麻黄、半夏，半夏→杏仁、柴胡，半夏→瓜蒌、贝 母。聚类结果聚为3 类，依次为肺阴虚证、痰热壅肺证和肺气虚证。结论:中医药非介入治疗肺恶性肿瘤的中医药辨治规律为: 聚类证型依次为肺阴虚证、痰热壅肺证和肺气虚证。治疗多用和解少阳、行气止咳平喘和清虚热化痰饮为主，药物以半夏、甘草、 黄芩、茯苓、柴胡多见。. [ABSTRACT FROM AUTHOR]

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