生物标志物联合肺功能预测COPD急性加重风险的模型研究

郭立娟; 马方旭; 鲍洁; 王爽; 李毅

doi:10.16766/j.cnki.issn.1674-4152.004446

生物标志物联合肺功能预测COPD急性加重风险的模型研究

doi: 10.16766/j.cnki.issn.1674-4152.004446

郭立娟¹,
马方旭¹,
鲍洁¹,
王爽²,
李毅^3, ,

1.
河北省胸科医院(河北省肺癌防治研究中心)呼吸与危重症医学科，河北石家庄 050000
2.
河北省胸科医院(河北省肺癌防治研究中心)肺功能室，河北石家庄 050000
3.
河北省胸科医院(河北省肺癌防治研究中心)全科医学科，河北石家庄 050000

基金项目:

河北省医学科学研究项目 20242171

详细信息

通讯作者:
李毅，E-mail：15633035308@163.com

中图分类号: R563
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出版历程
- 收稿日期: 2025-12-11

A model study of biomarkers combined with lung function to predict the risk of acute exacerbations of COPD

GUO Lijuan¹,
MA Fangxu¹,
BAO Jie¹,
WANG Shuang²,
LI Yi^{3
, ,}

1.
Department of Respiratory and Critical Care Medicine, Hebei Provincial Chest Hospital(Hebei Lung Cancer Prevention and Treatment Research Center), Shijiazhuang, Hebei 050000, China

摘要

摘要: 目的探讨可溶性致癌抑制因子2(sST2)、甲壳质酶蛋白40(YKL-40)、白细胞介素-10(IL-10)和肺表面活性蛋白D(SP-D)联合肺功能指标构建的预测模型对慢性阻塞性肺疾病(COPD)急性加重风险的早期评估价值。方法选取2023年1月—2024年12月河北省胸科医院收治的120例COPD患者作为研究对象，根据是否发生急性加重分为稳定期组(60例)和急性加重期组(60例)。检测所有患者血清sST2、YKL-40、IL-10和SP-D水平，并进行肺功能检查。采用多因素logistic回归分析确定COPD急性加重的独立危险因素，并构建联合预测模型。采用ROC曲线评估各指标及联合模型的预测效能。结果急性加重期组患者血清sST2、YKL-40和SP-D水平显著高于稳定期组(P < 0.05)，IL-10水平和FEV₁%pred显著低于稳定期组(P < 0.05)。多因素logistic回归分析显示，sST2、YKL-40、IL-10、SP-D和FEV₁%pred均是COPD急性加重的独立预测因素(P < 0.05)。联合预测模型的曲线下面积(AUC)为0.923(95% CI：0.881~0.965)，灵敏度为86.7%，特异度为88.3%，预测性能显著优于单一指标。结论 sST2、YKL-40、IL-10、SP-D联合肺功能指标构建的预测模型对COPD急性加重风险具有良好的预测价值，可用于早期风险评估和临床干预指导。
- 慢性阻塞性肺疾病 /
- 生物标志物 /
- 肺功能 /
- 预测模型 /
- 早期风险评估
Abstract: Objective To investigate the value of a combined lung function index model constructed from soluble cancer suppressor factor 2 (sST2), chitinase protein 40 (YKL-40), interleukin-10 (IL-10), and pulmonary surfactant D (SP-D) in early risk assessment of chronic obstructive pulmonary disease (COPD) acute exacerbations. Methods A total of 120 COPD patients admitted at Hebei Provincial Chest Hospital between January 2023 and December 2024 were selected as subjects, and divided into stable phase group (60 cases) and acute exacerbation phase group (60 cases) based on whether they experienced acute exacerbations. All patients ' serum levels of sST2, YKL-40, IL-10, and SP-D were measured, along with pulmonary function tests. Multivariate logistic regression was used to identify independent risk factors for COPD acute exacerbations, and a combined prediction model was established. The predictive efficacy of each indicator and the combined model was evaluated using receiver-operating characteristic (ROC) curves. Results Patients in the acute exacerbation group showed significantly higher serum levels of sST2, YKL-40, and SP-D compared to the stable phase group (P < 0.05), while IL-10 and FEV₁%pred were markedly lower (P < 0.05). Multivariate logistic regression analysis demonstrated that all four biomarkers-sST2, YKL-40, IL-10, SP-D, and FEV₁% pred-were independent predictors of COPD acute exacerbations (P < 0.05). The combined predictive model achieved an area under the curve (AUC) of 0.923 (95% CI: 0.881-0.965), with a sensitivity of 86.7% and specificity of 88.3%, demonstrating superior predictive performance compared to single biomarkers. Conclusion The integrated pulmonary function biomarker model combining sST2, YKL-40, IL-10, SP-D provides reliable prediction for COPD acute exacerbations, offering valuable insights for early risk assessment and clinical intervention guidance.
- Chronic obstructive pulmonary disease /
- Biomarkers /
- Pulmonary function /
- Predictive models /
- Early risk assessment
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图 1 生物标志物联合肺功能预测COPD急性加重风险的预测价值

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表 1 2组COPD患者基线资料比较

Table 1. Comparison of baseline characteristics between two groups of COPD patients

项目	稳定期组(n=60)	急性加重期组(n=60)	统计量	P值
年龄(x±s，岁)	65.3±8.7	67.2±9.3	1.186^a	0.238
性别(男/女，例)	38/22	42/18	0.625^b	0.429
BMI(x±s)	22.6±3.2	21.8±3.5	1.318^a	0.190
吸烟史[例(%)]	35(58.3)	38(63.3)	0.312^b	0.576
合并糖尿病[例(%)]	12(20.0)	15(25.0)	0.434^b	0.510
合并冠状动脉疾病[例(%)]	10(16.7)	14(23.3)	0.877^b	0.349
COPD病程[M(P₂₅, P₇₅)，年]	7.5(4.0, 11.0)	10.0(6.0, 14.0)	-2.614^c	0.009
急性加重病史[M(P₂₅, P₇₅)，次/年]	1.0(0.5, 2.0)	2.0(1.0, 3.0)	-3.875^c	< 0.001
注：^a为t值，^b为χ²值，^c为Z值。

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表 2 2组COPD患者生物标志物水平和肺功能指标比较(x±s)

Table 2. Comparison of biomarker levels and pulmonary function indicators between two groups of COPD patients (x±s)

项目	稳定期组(n=60)	急性加重期组(n=60)	t值	P值
sST2(ng/mL)	25.16±6.83	38.92±9.74	8.926	< 0.001
YKL-40(ng/mL)	58.77±15.66	69.58±9.08	4.512	< 0.001
IL-10(pg/mL)	15.23±4.68	10.45±3.92	6.075	< 0.001
SP-D(ng/mL)	82.34±18.65	108.92±22.73	7.099	< 0.001
FEV₁%pred(%)	47.32±10.85	39.18±9.26	4.378	< 0.001
FEV₁/FVC(%)	56.78±9.63	54.92±8.74	1.136	0.258

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表 3 COPD急性加重危险因素的多因素logistic回归分析

Table 3. Multivariate logistic regression analysis of risk factors for acute exacerbation of COPD

变量	B	SE	Wald χ²	P值	OR值	95% CI
sST2	0.118	0.039	9.121	0.003	1.125	1.042~1.215
YKL-40	0.052	0.022	5.432	0.020	1.053	1.008~1.101
IL-10	-0.083	0.039	4.524	0.034	0.920	0.852~0.994
SP-D	0.021	0.007	8.763	0.003	1.021	1.007~1.036
FEV₁%pred	-0.102	0.025	16.318	< 0.001	0.903	0.860~0.949
常量	-2.503	0.745	11.293	0.001	0.082

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表 4 各指标及联合模型预测COPD急性加重风险的效能比较

Table 4. Comparison of predictive performance of individual indicators and the combined model for acute exacerbation of COPD

项目	AUC	95% CI	灵敏度(%)	特异度(%)	最佳截断值
sST2	0.834	0.762~0.907	71.7	70.0	32.18 ng/mL
YKL-40	0.745	0.655~0.835	64.8	71.2	63.50 ng/mL
IL-10	0.787	0.705~0.870	68.3	66.7	12.35 pg/mL
SP-D	0.809	0.730~0.888	70.0	68.3	96.45 ng/mL
FEV₁%pred	0.692	0.594~0.790	65.0	63.3	42.85%
联合模型	0.923	0.881~0.965	86.7	88.3

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