肺纤维化患者血瘀证的诊断预测模型*
作者：孙 放1，蔡 松2，罗 雪3，张立山4
单位：1.上海中医药大学附属曙光医院，上海 201203； 2.广州医科大学附属中医医院，广东 广州 510130； 3.中直机关医院，北京 100039； 4.北京中医药大学东直门医院，北京 100700
引用：引用：孙放，蔡松，罗雪，张立山.肺纤维化患者血瘀证的诊断预测模型[J].中医药导报,2025,31(7):68-72.
DOI：10.13862/j.cn43-1446/r.2025.07.011
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摘要：目的：利用Logistic回归分析建立肺纤维化血瘀证诊断预测模型。方法：纳入符合诊断标准的肺纤维化患者285例，收集患者的一般资料，由2名副主任医师及以上职称的专家采集患者的中医四诊资料并判定中医证型，运用SPSS 26.0软件对数据进行统计分析，通过Logistic回归分析建立肺纤维化血瘀证的诊断模型。结果：肺纤维化患者血瘀证占71.2%，基于Logistic回归分析建立了肺纤维化及不同诊断分类肺纤维化的血瘀证诊断模型，经拟合优度检验和ROC检验，模型具有较好的校准能力，有较高的预测价值。结论：血瘀证在肺纤维化人群中具有普遍性，研究构建的诊断模型可协助诊断肺纤维化血瘀证，但研究结果的准确性尚待大样本临床研究进一步验证。
关键词：肺纤维化；血瘀证；诊断模型；Logistic回归
Abstract：
Objective:
To establish a diagnostic model for pulmonary fibrosis of blood stasis syndrome
using Logistic regression analysis. Methods: A total of 285 patients with
pulmonary fibrosis who met the diagnostic criteria were included, and their general
medical history data were collected. A total of two experts, each with a rank
of deputy chief physician or higher, gathered the patients' four-diagnostic
data of traditional Chinese medicine to determine the syndrome types. The data
were analyzed using SPSS 26.0 software. Through scientific analysis, a
diagnostic model for pulmonary fibrosis of blood stasis syndrome was developed
via Logistic regression analysis. Results: The findings indicate that blood
stasis syndrome was present in 71.2% of the patients with pulmonary fibrosis.
Based on Logistic regression analysis, a diagnostic model for pulmonary
fibrosis with varying diagnostic categories, as well as blood stasis syndrome
in pulmonary fibrosis, was established. Following goodness-of-fit and ROC tests,
the model demonstrated better calibration ability, with higher predictive
value. Conclusion: Blood stasis syndrome is prevalent among individuals with
pulmonary fibrosis. The diagnostic model constructed in the study can assist in
the diagnosis of pulmonary fibrosis of blood stasis syndrome, but the accuracy
of these findings requires further validation through large-scale clinical
studies.
Key words：pulmonary fibrosis; blood stasis syndrome; diagnostic model; Logistic regression
发布时间：2026-01-06
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