天津医药 ›› 2026, Vol. 54 ›› Issue (2): 189-195.doi: 10.11958/20252555

• 临床研究 • 上一篇    下一篇

COPD进展为Ⅱ型呼吸衰竭预警模型的建立与验证

陈丽1(), 陈楠2   

  1. 1 唐山中心医院全科医疗科(邮编063000)
    2 唐山市工人医院内分泌科
  • 收稿日期:2025-07-21 修回日期:2025-10-13 出版日期:2026-02-15 发布日期:2026-02-12
  • 作者简介:陈丽(1983),女,主治医师,主要从事普通内科方面研究。E-mail:cucumber99923@163.com

Establishment and verification of the early warning model for COPD progressing to type II respiratory failure

CHEN Li1(), CHEN Nan2   

  1. 1 Department of General Medicine, Tangshan Central Hospital, Tangshan 063000, China
    2 Department of Endocrinology, Tangshan Workers' Hospital
  • Received:2025-07-21 Revised:2025-10-13 Published:2026-02-15 Online:2026-02-12

摘要:

目的 建立并验证慢性阻塞性肺疾病(COPD)进展为Ⅱ型呼吸衰竭潜在高危人群的早期风险预警模型。方法 采用分层随机抽样法将符合纳入标准的297例急性期住院治疗后转为稳定期的COPD患者按照7∶3分为建模集(n=208)与验证集(n=89)。将建模集患者按照再次急性加重是否进展为Ⅱ型呼吸衰竭分为进展组(n=81)和非进展组(n=127)。比较进展组与非进展组临床资料,采用随机森林初步筛选预测特征变量,经最小绝对收缩和选择算子(LASSO)回归进一步压缩筛选出重要预测特征变量,构建并验证列线图预警识别模型。结果 建模集与验证集患者年龄、性别、体质量指数、既往史、稳定期治疗方案、病程、COPD防治全球倡仪(GOLD)肺功能分级、过去1年急性加重次数、肌少症及实验室检查指标[白细胞、血红蛋白、血小板、中性粒细胞、嗜酸性粒细胞(EOS)、白蛋白、肺泡表面活性蛋白-D(SP-D)]比较差异无统计学意义;建模集中进展组年龄、GOLD肺功能分级、过去1年急性加重次数≥2次患者占比、肌少症患者占比、EOS、SP-D高于非进展组,病程长于非进展组,白蛋白低于非进展组(P<0.05),基于此随机森林筛选出前6位重要特征变量依次为病程、GOLD肺功能分级、过去1年急性加重次数、肌少症、EOS、SP-D,经LASSO进一步压缩后最终确定GOLD肺功能分级、过去1年急性加重次数、肌少症、EOS、SP-D是COPD进展为Ⅱ型呼吸衰竭的重要预测因子(P<0.05);基于此构建的预警识别模型一致性指数(C-index)为0.904;受试者工作特征曲线显示,在建模集与验证集中,该模型的曲线下面积分别为0.904(95%CI:0.860~0.948)、0.924(95%CI:0.861~0.986);校准曲线、决策曲线结果显示,该模型具有良好的校准性、临床适用性。结论 基于预测因子构建的列线图预警识别模型具有良好的预测性能及临床适用性。

关键词: 肺疾病,慢性阻塞性, 呼吸功能不全, 列线图, 影响因素, 风险预测模型

Abstract:

Objective To establish and validate an early risk warning model for the potential high-risk population of chronic obstructive pulmonary disease (COPD) progressing to type II respiratory failure. Methods Stratified random sampling method was used to divide 297 COPD patients who met the inclusion criteria after acute hospitalization into stable stage into the modeling set (n=208) and the validation set (n=89) according to the ratio of 7∶3. The patients in the modeling set were divided into the progressive group (n=81) and the non-progressive group (n=127) according to whether the re-acute exacerbation progressed to type II respiratory failure. The clinical data of the progressive group and the non-progressive group were compared. The random forest was used to preliminarily screen the predictive feature variables. Least absolute shrinkage and selection operator(LASSO) regression was used to further compress and screen the important predictive feature variables, and the nomogram early warning recognition model was constructed and verified. Results There were no significant differences in age, gender, body mass index (BMI), past history, stable treatment plan, course of disease, Global Initiative for Chronic Obstructive Lung Disease (GOLD) lung function classification, number of acute exacerbations in the past year, sarcopenia and laboratory examination indexes [white blood cells, hemoglobin, platelets, neutrophils, eosinophils(EOS), albumin and pulmonary surfactant protein-D(SP-D)] between the modeling set and the validation set. The age, GOLD lung function classification, proportion of patients with acute exacerbation ≥ 2 times in the past year, proportion of patients with sarcopenia, EOS and SP-D were higher in the progressive group than those in the non-progressive group, the course of disease was longer than that of the non-progressive group, and the albumin level was lower than that in the non-progressive group (P<0.05). Based on this random forest, the top six important characteristic variables were course of disease, GOLD lung function classification, the number of acute exacerbations in the past year, sarcopenia, EOS and SP-D. After further compression by LASSO, GOLD lung function classification, the number of acute exacerbations in the past year, sarcopenia, EOS and SP-D were finally determined to be important predictors of COPD progression to type Ⅱ respiratory failure (P<0.05). Based on this, the consistency index(C-index)of the early warning identification model was 0.904. Receiver operating characteristic (ROC) curve showed that the area under the curve of the model was 0.904(95%CI: 0.860-0.948) and 0.924(95%CI: 0.861-0.986) in the modeling set and the validation set, respectively. The results of calibration curve and decision curve showed that the model had good calibration and clinical applicability. Conclusion The nomogram early warning recognition model based on predictors has good predictive performance and clinical applicability.

Key words: pulmonary disease, chronic obstructive, respiratory insufficiency, nomograms, influencing factors, risk prediction model

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