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中华实验和临床感染病杂志(电子版) ›› 2026, Vol. 20 ›› Issue (01) : 46 -53. doi: 10.3877/cma.j.issn.1674-1358.2026.01.008

论著

重症肺炎患者肺泡灌洗液中核因子κB表达与计算机断层扫描影像特征的相关性及其对转归的预测价值
庞秋菊, 陈灿, 任宝恒†(), 韩菊玲, 王婧   
  1. 723000　汉中市，三二〇一医院呼吸与危重症医学科
  • 收稿日期:2025-05-27 出版日期:2026-02-15
  • 通信作者: 任宝恒

Correlation between nuclear factor-κB expression in bronchoalveolar lavage fluid and computed tomography imaging features in patients with severe pneumonia and its predictive value for clinical outcomes

Qiuju Pang, Can Chen, Baoheng Ren†(), Juling Han, Jing Wang   

  1. Department of Respiratory and Critical Care Medicine, 3201 Hospital, Hanzhong 723000, China
  • Received:2025-05-27 Published:2026-02-15
  • Corresponding author: Baoheng Ren
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引用本文：

庞秋菊, 陈灿, 任宝恒, 韩菊玲, 王婧. 重症肺炎患者肺泡灌洗液中核因子κB表达与计算机断层扫描影像特征的相关性及其对转归的预测价值[J/OL]. 中华实验和临床感染病杂志(电子版), 2026, 20(01): 46-53.

Qiuju Pang, Can Chen, Baoheng Ren, Juling Han, Jing Wang. Correlation between nuclear factor-κB expression in bronchoalveolar lavage fluid and computed tomography imaging features in patients with severe pneumonia and its predictive value for clinical outcomes[J/OL]. Chinese Journal of Experimental and Clinical Infectious Diseases(Electronic Edition), 2026, 20(01): 46-53.

目的

探究重症肺炎患者肺泡灌洗液（BALF）中核因子-κB（NF-κB）表达与计算机断层扫描（CT）影像特征的相关性及其对转归的预测价值。

方法

选取2022年2月至2024年1月三二〇一医院急诊及急诊重症监护室（EICU）收治的128例重症肺炎患者作为研究对象，根据治疗后临床转归分为好转组（86例）和恶化组（42例）。比较两组患者的年龄、最高体温、高血压、肿瘤坏死因子-α（TNF-α）、C-反应蛋白（CRP）、白细胞介素-6（IL-6）及降钙素原（PCT）等临床资料及CT影像特征。采用重复测量方差分析比较两组患者治疗过程中NF-κB水平的动态变化。采用多元线性回归分析CT影像特征与NF-κB水平的关系。通过Logistic回归模型绘制NF-κB水平与患者病情恶化发生的概率曲线，再采用广义混合效应模型分析NF-κB水平与病情恶化的相关性。采用受试者工作特征曲线（ROC）分析NF-κB水平对病情恶化的预测效能。采用Kaplan-Meier分析NF-κB表达水平与预后的关系。

结果

恶化组患者年龄[（63.58±8.07）岁 vs.（59.26±7.73）岁：t=2.926、P=0.004]、最高体温[（38.64±2.58） ℃ vs.（37.26±2.14） ℃：t=3.198、P=0.002]、高血压占比（30.95% vs. 13.95%：χ2=5.188、P=0.023）、肿瘤坏死因子-α（TNF-α）[（2.15±0.46）pg/ml vs.（1.68±0.32）pg/ml：t=6.723、P＜0.001]、C-反应蛋白（CRP）[（35.67±6.85） mg/L vs.（25.48±5.19） mg/L：t=9.361、P＜0.001]、白细胞介素-6（IL-6）[（15.64±2.09）pg/ml vs.（11.75±1.36）pg/ml：t=12.648、P＜0.001]及降钙素原（PCT）[（7.89±1.43）µg/L vs.（6.84±1.26）µg/L：t=4.233、P＜0.001]水平显著高于好转组患者，差异均有统计学意义。恶化组患者中斑片CT影像改变（78.57% vs. 60.47%：χ2=4.147、P=0.042）、肺实变（61.90% vs. 41.86%：χ2=4.539、P=0.033）、磨玻璃样改变（64.29% vs. 39.53%：χ2=6.930、P=0.008）、累及肺叶≥2个（69.05% vs. 40.70%：χ2=9.072、P=0.003）、支气管壁增厚（66.67% vs. 36.05%：χ2=10.648、P=0.001）、片状实变影（42.86% vs. 24.42%：χ2=4.529、P=0.033）、肺门淋巴结肿大（33.33% vs. 12.79%：χ2=7.577、P=0.006）、胸腔积液（59.52% vs. 31.40%：χ2=9.256、P=0.002）和肺不张（45.24% vs. 18.60%：χ2=10.075、P=0.002）患者占比均显著高于好转组，差异均有统计学意义。重复测量方差分析结果显示，两组患者NF-κB水平的时间效应、组间效应和交互效应均具有统计学意义（P均＜0.001）。多元线性回归分析结果显示，NF-κB水平与斑片影像改变（OR=1.614、P=0.033）、肺实变（OR=1.846、P=0.009）、磨玻璃样改变（OR=1.889、P=0.012）、累及肺叶≥2个（OR=1.436、P=0.007）、支气管壁增厚（OR=1.428、P=0.008）、片状实变影（OR=2.106、P=0.020）、肺门淋巴结肿大（OR=1.862、P=0.001）、胸腔积液（OR=1.731、P=0.005）以及肺不张（OR=1.895、P=0.014）等CT影像特征均存在独立相关性，差异均有统计学意义。Logistic回归模型分析显示，不同NF-κB水平值均有相对应的重症肺炎患者病情恶化发生概率，不同恶化发生概率也有相对应的NF-κB水平值。NF-κB水平升高（NF-κB：14.57～17.14 ng/L：OR=1.171、95%CI：1.024～1.579、P=0.014，NF-κB＞17.14 ng/L：OR=1.162、95%CI：1.059～2.857、P=0.005）为重症肺炎患者病情恶化风险升高的影响因素。ROC曲线分析显示，NF-κB水平对重症肺炎患者病情恶化具有一定预测价值（AUC=0.896，灵敏度和特异度分别为84.25%和80.79%）。NF-κB低表达患者1年总生存率高于高表达患者（89.06% vs. 48.44%，Log-rank χ2=24.582、P＜0.001）。

结论

BALF中NF-κB高表达的重症肺炎患者病情恶化风险更高，且NF-κB表达水平与CT影像特征密切相关。

关键词: 重症肺炎, 肺泡灌洗液, 核因子-κB, 计算机断层扫描, 影像特征
Objective

To investigate the correlation between nuclear factor-κB (NF-κB) expression in bronchoalveolar lavage fluid (BALF) and computed tomography (CT) imaging features, and its predictive value for prognosis of patients with severe pneumonia.

Methods

Total of 128 patients with severe pneumonia admitted to the Emergency Department and Emergency Intensive Care Unit (EICU) of 3201 Hospital from February 2022 to January 2024 were enrolled, according to the clinical outcomes after treatment, they were divided into improved group (86 cases) and deterioration group (42 cases). Clinical data including age, maximum body temperature, hypertension, tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), interleukin-6 (IL-6), procalcitonin (PCT) and CT imaging features were compared between the two groups, respectively. The dynamic changes of NF-κB levels during treatment were analyzed by repeated-measuressis of variance. The relationship between CT imaging features and NF-κB level were analyzed by multiple linear regression. The probability curve of NF-κB level associated with clinical deterioration was ploted by the Logistic regression model, and the correlation between NF-κB level and disease deterioration were analyzed by generalized mixed-effects model. The predictive efficacy of NF-κB level for disease deterioration was evaluated by receiver operating characteristic (ROC) curve analysis. The association between NF-κB expression and survival prognosis was assessed by Kaplan-Meier analysis.

Results

Age [(63.58±8.07) years old vs. (59.26±7.73) years old: t=2.926, P=0.004], maximum body temperature [(38.64±2.58) ℃ vs. (37.26±2.14) ℃: t=3.198, P=0.002], proportion of hypertension (30.95% vs. 13.95% : χ2=5.188, P=0.023), TNF-α [(2.15±0.46) pg/ml vs. (1.68±0.32) pg/ml: t=6.723, P<0.001], CRP [(35.67±6.85) mg/L vs. (25.48±5.19) mg/L: t=9.361, P<0.001], IL-6 [(15.64±2.09) pg/ml vs. (11.75±1.36) pg/ml: t=12.648, P<0.001] and PCT [(7.89±1.43) µg/L vs. (6.84±1.26) µg/L: t=4.233, P<0.001] of patients in deterioration group were significantly higher than those of improved group, all with significant differences. The deterioration group also showed significantly higher frequencies of CT imaging features: patchy opacities (78.57% vs. 60.47%: χ2=4.147, P=0.042), lung consolidation (61.90% vs. 41.86%: χ2=4.539, P=0.033), ground-glass like changes (64.29% vs. 39.53%: χ2=6.930, P=0.008), involvement of≥2 lung lobes (69.05% vs. 40.70%: χ2=9.072, P=0.003), bronchial wall thickening (66.67% vs. 36.05%: χ2=10.648, P=0.001), patellar consolidation shadow (42.86% vs. 24.42%: χ2=4.529, P=0.033), hilar lymph node enlargement (33.33% vs. 12.79%: χ2=7.577, P=0.006), pleural effusion (59.52% vs. 31.40%: χ2=9.256, P=0.002) and atelectasis (45.24% vs. 18.60%: χ2=10.075, P=0.002) than those of improved group, all with significant differences. The results of repeated measurement ANOVA showed that the time effect, intergroup effect and interaction effect of NF-κB level in both groups were statistically significant (all P<0.001). The results of multiple linear regression analysis showed that NF-κB level was independently correlated with patchy opacities (OR=1.614, P=0.033), lung consolidation (OR=1.846, P=0.009), ground glass change (OR=1.889, P=0.012), involvement of≥2 lung lobes (OR=1.436, P=0.007), bronchial wall thickening (OR=1.428, P=0.008), lamella consolidation shadow (OR=2.106, P=0.020), hilar lymph node enlargement (OR=1.862, P=0.001), pleural effusion (OR=1.731, P=0.005) and atelectasis (OR=1.895、P=0.014), all with significant differences. Logistic regression analysis indicated that distinct NF-κB values corresponded to specific probabilities of disease deterioration, and vice versa. Elevated NF-κB level (NF-κB: 14.57-17.14 ng/L: OR=1.171, 95%CI: 1.024-1.579, P=0.014; NF-κB>17.14 ng/L: OR=1.162, 95%CI: 1.059-2.857, P=0.005) was risk factor for increased deterioration in patients with severe pneumonia (P=0.014, 0.005). ROC analysis demonstrated that NF-κB expression had a certain predictive value for the deterioration of patients with severe pneumonia (AUC=0.896, sensitivity and specificity were 84.25% and 80.79%, respectively). The one-year overall survival of patients with NF-κB low expression was higher than that of patients with NF-κB high expression (89.06% vs. 48.44%: Log-rank χ2=24.582, P<0.001).

Conclusions

NF-κB high expression in BALF is associated with an increased risk of clinical deterioration of patients with severe pneumonia, and NF-κB expression is closely correlated with CT imaging features.

Key words: Severe pneumonia, Broncho alveolar lavage fluid, Nuclear factor κB, Computed tomography, Imaging feature
表1 恶化组和好转组重症肺炎患者一般资料
指标 恶化组（42例） 好转组（86例） 统计量 P值
性别 χ2=0.429 0.512a
男 26（61.90） 48（55.81）
女 16（38.10） 38（44.19）
年龄（
±s，岁）		 63.58±8.07 59.26±7.73 t=2.926 0.004
BMI（
±s，kg/m2）		 23.56±1.82 23.38±1.76 t=0.537 0.592
入住ICU时间（
±s，d）		 15.31±2.74 15.08±2.36 t=0.491 0.625
机械通气时间（
±s，d）		 13.86±2.33 13.45±1.68 t=1.137 0.258
收缩压（
±s，mmHg）		 111.39±9.55 112.21±9.74 t=0.450 0.653
舒张压（
±s，mmHg）		 81.53±6.09 81.29±5.78 t=0.217 0.829
最高体温（
± s，℃）		 38.64±2.58 37.26±2.14 t=3.198 0.002
吸烟史 [例（%）] 18（42.86） 34（39.53） χ2=0.129 0.719a
饮酒史 [例（%）] 29（69.05） 52（60.47） χ2=0.895 0.344a
糖尿病史 [例（%）] 3（7.14） 8（9.30） χ2=0.168 0.682b
高血压 [例（%）] 13（30.95） 12（13.95） χ2=5.188 0.023a
高脂血症 [例（%）] 9（21.43） 14（16.28） χ2=0.508 0.476a
病原体种数 [例（%）] χ2=2.255 0.133a
＜2种 18（42.86） 49（56.98）
≥2种 24（57.14） 37（43.02）
肺部病变 [例（%）] χ2=0.445 0.505a
单侧 15（35.71） 36（41.86）
双侧 27（64.29） 50（58.14）
RBC（
±s，×1012/L）		 5.24±0.42 5.36±0.58 t=1.195 0.234
WBC（
±s，×1012/L）		 12.87±2.45 12.24±2.18 t=1.473 0.143
TNF-α（
± s，pg/ml）		 2.15±0.46 1.68±0.32 t=6.723 ＜0.001
CRP（
±s，mg/L）		 35.67±6.85 25.48±5.19 t=9.361 ＜0.001
IL-6（
±s，pg/ml）		 15.64±2.09 11.75±1.36 t=12.648 ＜0.001
Hb（
±s，×1012/L）		 146.93±18.96 146.27±18.75 t=0.186 0.853
PCT（
±s，µg/L）		 7.89±1.43 6.84±1.26 t=4.233 ＜0.001
PLT（
±s，×109/L）		 314.06±25.17 305.48±22.76 t=1.934 0.055
BUN（
±s，mmol/L）		 4.39±0.46 4.26±0.42 t=1.593 0.114
表2 恶化组和好转组重症肺炎患者CT影像特征 [例（%）]
CT影像特征 恶化组（42例） 好转组（86例） χ2值 P值
斑片影像改变 33（78.57） 52（60.47） 4.147 0.042
肺实变 26（61.90） 36（41.86） 4.539 0.033
磨玻璃样改变 27（64.29） 34（39.53） 6.930 0.008
累及肺叶数 9.072 0.003
≥2个 29（69.05） 35（40.70）
＜2个 13（30.95） 51（59.30）
支气管壁增厚 28（66.67） 31（36.05） 10.648 0.001
片状实变影 18（42.86） 21（24.42） 4.529 0.033
肺门淋巴结肿大 14（33.33） 11（12.79） 7.577 0.006
胸腔积液 25（59.52） 27（31.40） 9.256 0.002
肺不张 19（45.24） 16（18.60） 10.075 0.002
表3 恶化组和好转组重症肺炎患者BALF中NF-κB水平（
±s，ng/L）
组别 例数 插管后2天 插管后6天 撤机后2天 F值 P值
好转组 86 15.12±3.05 10.36±1.47 6.25±0.48 52.974 ＜0.001
恶化组 42 16.38±3.34 18.14±4.28 24.76±4.95 44.492 0.022
t值 2.127 15.174 34.488
P值 0.035 ＜0.001 ＜0.001
表4 重症肺炎患者CT影像特征与NF-κB水平的关系
CT影像特征 调整前 调整后
OR值 95%CI P值 OR值 95%CI P值
斑片影像改变 1.426 1.115～1.764 0.031 1.614 1.362～2.747 0.033
肺实变 1.459 1.133～1.827 0.006 1.846 1.487～2.334 0.009
磨玻璃样改变 1.639 1.216～2.323 0.014 1.889 1.401～2.989 0.012
累及肺叶数≥2个 1.391 1.163～1.985 0.009 1.436 1.221～1.847 0.007
支气管壁增厚 1.428 1.014～2.693 0.005 1.428 1.149～1.634 0.008
片状实变影 1.022 1.003～1.756 0.017 2.106 1.952～3.019 0.020
肺门淋巴结肿大 1.679 1.328～2.966 0.002 1.862 1.329～2.056 0.001
胸腔积液 1.452 1.283～1.894 0.004 1.731 1.208～2.379 0.005
肺不张 1.621 1.178～2.253 0.011 1.895 1.247～2.804 0.014
图1 重症肺炎患者NF-κB值与病情恶化概率的单因素Logistic曲线
表5 重症肺炎患者不同NF-κB表达水平与病情恶化的广义混合效应模型
NF-κB（ng/L） β值 S.E.值 Wald χ2值 OR值 95%CI P值
≤14.57 — — — Ref（1.00） Ref（1.00） —
14.57～17.14 0.158 0.121 1.702 1.171 1.024～1.579 0.014
≥17.14 0.150 0.112 1.797 1.162 1.059～2.857 0.005
图2 NF-κB水平预测重症肺炎患者病情恶化的ROC曲线
图3 NF-κB水平与重症肺炎患者生存率的关系
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