定量CT联合中性粒细胞/淋巴细胞比值在慢性阻塞性肺疾病肺功能分级中的判别价值

doi:10.19983/j.issn.2096-8493.20250205

摘要/Abstract

摘要：

目的: 探讨定量CT(quantitative computed tomography,QCT)参数联合外周血中性粒细胞/淋巴细胞比值(neutrophil-to-lymphocyte ratio,NLR)在慢性阻塞性肺疾病(简称“慢阻肺病”)患者肺功能分级中的判别价值。方法: 回顾性纳入2022年1月至2024年4月安徽省亳州市涡阳县人民医院收治的535例慢阻肺病患者。收集患者一般资料、入院24h内实验室检查指标(白细胞计数、中性粒细胞计数、淋巴细胞计数,并计算NLR)以及定量CT参数,包括全肺体积、肺气肿体积及低衰减区域的百分比(low-attenuation area,LAA%)。以肺功能分级(GOLD 1~2级与3~4级)为因变量,进行单因素分析及多因素logistic回归建模,并通过受试者工作特征曲线(receiver operating characteristic curve,ROC)、校准曲线及决策曲线分析(decision curve analysis,DCA)判别模型的区分度、校准度及临床净获益。结果: 单因素ROC分析显示,肺气肿体积对肺功能分级的判别效能最高(AUC=0.740),其次为LAA%(AUC=0.717)、全肺体积(AUC=0.679)、NLR(AUC=0.637)及白蛋白(AUC=0.565)。最终联合模型(全肺体积+LAA%+NLR+白蛋白)的AUC为0.765,高于任一单一指标。多因素logistic回归分析显示,全肺体积、LAA%和NLR与重度分级风险增加相关,白蛋白与重度分级风险降低相关(P值均<0.05),全肺体积(每增加100ml)aOR=1.053(95%CI:1.033~1.072,P<0.001),LAA%(每增加1%)aOR=1.092(95%CI:1.062~1.123,P<0.001),NLR(每增加1单位)aOR=1.060(95%CI:1.014~1.109,P=0.011),白蛋白(每增加1g/L)aOR=0.935(95%CI:0.882~0.990,P=0.022)。模型经bootstrap内部验证显示校准良好(平均绝对误差约0.019),DCA提示在阈值概率0.05~0.30范围内联合模型具有较高的临床净获益。结论: 定量CT参数联合NLR对区分慢阻肺病患者GOLD 3~4级与1~2级具有中等判别能力,且经内部验证显示良好的校准度与潜在临床应用价值,可为慢阻肺病患者的风险分层与临床决策提供参考。

关键词: 肺疾病,慢性阻塞性, 体层摄影术, X线计算机, 中性粒细胞, 淋巴细胞, 肺功能

Abstract:

Objective: To investigate the discriminative value of quantitative computed tomography (QCT) parameters combined with the peripheral blood neutrophil-to-lymphocyte ratio (NLR) for pulmonary function grading in patients with chronic obstructive pulmonary disease (COPD). Methods: A total of 535 patients with COPD admitted to Guoyang County People’s Hospital between January 2022 and April 2024 were retrospectively enrolled. General clinical data, laboratory parameters obtained within 24 hours of admission (including white blood cell count, neutrophil count, lymphocyte count, and the calculated NLR), and QCT parameters were collected, including total lung volume, emphysema volume, and the percentage of low attenuation area (LAA%). Pulmonary function grading (GOLD 1-2 vs. GOLD 3-4) was defined as the dependent variable. Univariable analysis and multivariable logistic regression modeling were performed. Model performance was evaluated using receiver operating characteristic curves (ROC), calibration curves, and decision curve analysis (DCA) in terms of discrimination, calibration, and clinical net benefit. Results: Univariable ROC analysis showed that emphysema volume had the highest discriminative performance for pulmonary function grading (AUC=0.740), followed by LAA% (AUC=0.717), total lung volume (AUC=0.679), NLR (AUC=0.637), and albumin (AUC=0.565). The final combined model (total lung volume+LAA%+NLR+albumin) achieved an AUC of 0.765, which was higher than that of any single indicator. Multivariable logistic regression analysis demonstrated that total lung volume, LAA%, and NLR were independently associated with an increased risk of severe grading, whereas albumin was associated with a decreased risk (all P<0.05). Specifically, the adjusted odds ratios (aORs) were 1.053 per 100 ml increase in total lung volume (95%CI: 1.033-1.072, P<0.001), 1.092 per 1% increase in LAA% (95%CI: 1.062-1.123, P<0.001), 1.060 per 1-unit increase in NLR (95%CI: 1.014-1.109, P=0.011), and 0.935 per 1 g/L increase in albumin (95%CI: 0.882-0.990, P=0.022). Bootstrap internal validation indicated good model calibration (mean absolute error≈0.019), and decision curve analysis showed that the combined model provided higher clinical net benefits within the threshold probability range of 0.05 to 0.30. Conclusion: QCT parameters combined with NLR demonstrate moderate discriminative ability in distinguishing GOLD 3-4 from GOLD 1-2 COPD patients, with good calibration and potential clinical applicability after internal validation, providing a reference for risk stratification and clinical decision-making in COPD patients.

Key words: Pulmonary disease, chronic obstructive, Tomography, X-ray computed, Neutrophil, Lymphocyte, Pulmonary function

中图分类号:

R81
R563

周立游, 姚奇, 杨文想, 张亚康, 赵红. 定量CT联合中性粒细胞/淋巴细胞比值在慢性阻塞性肺疾病肺功能分级中的判别价值[J]. 结核与肺部疾病杂志, 2026, 7(3): 391-397. doi: 10.19983/j.issn.2096-8493.20250205

Zhou Liyou, Yao Qi, Yang Wenxiang, Zhang Yakang, Zhao Hong. Discriminative value of quantitative CT combined with the neutrophil-to-lymphocyte ratio for pulmonary function grading in chronic obstructive pulmonary disease[J]. Journal of Tuberculosis and Lung Disease, 2026, 7(3): 391-397. doi: 10.19983/j.issn.2096-8493.20250205

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组别	患者例数	性别[例,构成比(%)]		年龄 [岁,M(Q₁,Q₃)]	BMI [kg/m²,M(Q₁,Q₃)]
组别	患者例数	男性	女性	年龄 [岁,M(Q₁,Q₃)]	BMI [kg/m²,M(Q₁,Q₃)]
轻-中度组	238	157(66.0)	81(34.0)	72.00(65.25,76.00)	23.30(21.20,25.77)
重-极重度组	297	246(82.8)	51(17.2)	71.00(67.00,76.00)	22.21(19.92,24.60)
统计检验值		χ²=19.315		Z=0.180	Z=3.615
P值		<0.001		0.860	<0.001

组别	患者例数	全肺体积(ml)	肺气肿体积(ml)	LAA%
轻-中度组	238	4164.91(3393.36,4963.12)	225.78(75.13,524.63)	5.44(2.01,11.58)
重-极重度组	297	4956.46(4165.38,5760.58)	545.51(316.09,925.04)	12.05(6.44,18.46)
Z值		-7.114	-9.540	-8.624
P值		<0.001	<0.001	<0.001

变量	轻-中度组(238例)	重-极重度组(297例)	Z值	P值
白细胞计数(×10⁹/L)	6.61(5.11,8.26)	7.06(5.30,9.17)	-2.060	0.039
中性粒细胞计数(×10⁹/L)	4.41(3.24,6.05)	5.08(3.60,7.26)	-3.315	<0.001
中性粒细胞/淋巴细胞	3.13(2.15,4.67)	4.40(2.69,7.41)	-5.457	<0.001
淋巴细胞计数(×10⁹/L)	1.38(0.99,1.79)	1.13(0.78,1.56)	4.451	<0.001
白蛋白(g/L)	39.10(36.80,41.20)	38.10(36.40,40.40)	2.571	0.010
球蛋白(g/L)	27.50(25.20,30.77)	27.10(24.50,31.30)	0.693	0.488

指标	AUC(95%CI)	最佳截断值	敏感度	特异度
肺气肿体积(ml)	0.740(0.698~0.781)	340.90	0.734	0.639
LAA%	0.717(0.674~0.759)	4.57	0.842	0.475
全肺体积(ml)	0.679(0.633~0.724)	4552.30	0.623	0.647
NLR	0.637(0.589~0.684)	5.05	0.455	0.794
白蛋白(g/L)	0.565(0.518~0.611)	39.00	0.603	0.513
联合模型(全肺体积+LAA%+NLR+白蛋白)	0.765(0.724~0.806)	0.55	0.700	0.714

变量	主模型[aOR(95%CI)值]	主模型P值	敏感度分析OR(95%CI)值	敏感度分析P值
全肺体积(每100ml)	1.053(1.033~1.072)	<0.001	1.058(1.035~1.081)	<0.001
LAA%(每1%)	1.092(1.062~1.123)	<0.001	1.098(1.066~1.131)	<0.001
NLR(每增加1)	1.060(1.014~1.109)	0.011	1.064(1.016~1.114)	0.008
白蛋白(g/L,每1单位)	0.935(0.882~0.990)	0.022	0.925(0.870~0.983)	0.012
BMI(kg/m²,每1单位)	-	-	1.018(0.958~1.081)	0.570
年龄(岁,每1单位)	-	-	0.993(0.970~1.016)	0.525
性别(男性和女性)	-	-	0.782(0.463~1.322)	0.359
模型性能(AUC)
主模型AUC	0.765
敏感度分析(BMI+年龄+性别)AUC	0.766(ΔAUC=0.001)