Predictive analysis of inflammatory factors in alveolar lavage fluid on the risk of phlegm embolism in patients with severe pneumonia

doi:10.3877/cma.j.issn.1674-6902.2025.06.011

Abstract

Abstract:

Objective

To explore the predictive value of inflammatory factors in bronchoalveolar lavage fluid (BALF) for the risk of sputum plugs in patients with severe pneumonia.

Methods

Seventy-eight patients with severe pneumonia admitted to our hospital from December 2020 to December 2024 were selected as subjects. They were divided into groups based on the presence or absence of sputum plugs found during bronchoscopy: 32 cases with sputum plugs formed the observation group, and 46 cases without sputum plugs formed the control group. Levels of inflammatory factors in BALF were detected. Logistic regression analysis was used to screen characteristic variables associated with the risk of sputum plugs in severe pneumonia. An AI-based model was constructed to predict the risk of sputum plugs in severe pneumonia, and its performance was validated by receiver operating characteristic (ROC) curve analysis.

Results

The levels of interleukin-4 (IL-4) (20.66±4.12) pg/ml, interleukin-6 (IL-6) (116.35±16.75) pg/ml, interleukin-8 (IL-8) (213.68±20.19) pg/ml, interleukin-10 (IL-10) (32.83±4.74) pg/ml, tumor necrosis factor-α (TNF-α) (64.59±8.22) pg/ml and C-reactive protein(CRP)(38.72±5.68)mg/L in the BALF of the observation group were higher than those in the control group IL-4 (18.35±3.27) pg/ml, IL-6 (105.14±15.22) pg/ml, IL-8 (213.68±20.19) pg/ml, IL-10 (29.16±4.09) pg/ml, TNF-α (58.32±7.15) pg/ml and CRP(31.56±4.84)mg/L (t=2.645, 3.017, 4.286, 3.555, 3.493, 5.812; P<0.05). Multivariate logistic regression analysis showed that pleural effusion (OR=1.439), forced expiratory volume in 1 second(FEV₁) (OR=0.844), neutrophils (OR=1.737), CRP (OR=2.015), IL-6 (OR=1.600), IL-8 (OR=1.575), IL-10(OR=1.233), and TNF-α(OR=1.131) were risk factors for sputum plugs in severe pneumonia (P<0.05). ROC curve analysis showed that the area under the curve (AUC) was 0.904, sensitivity was 75.00%, specificity was 93.48%, and the Youden index was 0.685, which was higher than that of any single BALF inflammatory factor.

Conclusion

The AI-based model utilizing BALF inflammatory factors can predict the risk of sputum plugs in patients with severe pneumonia, aiding in the early identification of high-risk patients and the formulation of personalized treatment plans.

Key words: Severe pneumonia, Bronchoalveolar lavage fluid, Inflammatory factors, Risk of phlegm embolism, Artificial intelligence

Baode Liu, Pingping Zheng, Yuxiang Xiao, Ling Shen, Liusheng Wang, Wei Zhang. Predictive analysis of inflammatory factors in alveolar lavage fluid on the risk of phlegm embolism in patients with severe pneumonia[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2025, 18(06): 917-922.

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References 30

1	Liu YN, Zhang YF, Xu Q, et al. Infection and co-infection patterns of community-acquired pneumonia in patients of different ages in China from 2009 to 2020: a national surveillance study[J]. Lancet Microbe, 2023, 4(5): e330-e339.
2	Anderson R, Feldman C. The global burden of community-acquired pneumonia in adults, encompassing invasive pneumococcal disease and the prevalence of its associated cardiovascular events, with a focus on pneumolysin and macrolide antibiotics in pathogenesis and therapy[J]. Int J Mol Sci, 2023, 24(13): 11038.
3	Martin-loeches I, Reyes LF, Rodriguez A. Severe community-acquired pneumonia (sCAP): advances in management and future directions[J]. Thorax, 2025, 80(8): 565-575.
4	何敏，孙一茜，高永霞，等. 经鼻高流量吸氧结合叩背排痰在重症肺炎中的临床应用[J/OL]. 中华肺部疾病杂志(电子版), 2025, 18(1): 168-170.
5	Qu J, Zhang J, Chen Y, et al. Aetiology of severe community acquired pneumonia in adults identified by combined detection methods: a multi-centre prospective study in China[J]. Emerg Microbes Infect, 2022，11(1): 556-566.
6	Rueda ZV, Aguilar Y, Maya MA, et al. Etiology and the challenge of diagnostic testing of community-acquired pneumonia in children and adolescents[J]. BMC Pediatr, 2022, 22(1): 169.
7	Wu X, Sun T, He H, et al. Effect of Metagenomic next-generation sequencing on clinical outcomes of patients with severe community-acquired pneumonia in the ICU: A multicenter, randomized controlled trial[J]. Chest, 2025, 167(2): 362-373.
8	Peng B, Li J, Chen M, et al. Clinical value of glucocorticoids for severe community-acquired pneumonia: A systematic review and meta-analysis based on randomized controlled trials[J]. Medicine (Baltimore), 2023, 102(46): e36047.
9	Zhang P, Liu B, Zhang S, et al. Clinical application of targeted next-generation sequencing in severe pneumonia: a retrospective review[J]. Crit Care, 2024, 28(1): 225.
10	SS L, HB L. Severe mycoplasma pneumoniae pneumonia combined with cold agglutinin disease and pulmonary embolism in childhood: A case report and review of the literature[J]. Afr J Reprod Health, 2024, 28(11): 205-215.
11	Saleem M M, Usman A, Saleem A, et al. Post-traumatic pulmonary embolism in the setting of cough-variant asthma[J]. Radiol Case Rep, 2024, 19(12): 6225-6229.
12	Van Der Velden B, Kuijf H J, Gilhuijs K, et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis[J]. Med Image Anal, 2022, 79: 102470.
13	Dack E, Christe A, Fontanellaz M, et al. Artificial intelligence and interstitial lung disease: diagnosis and prognosis[J]. Invest Radiol, 2023, 58(8): 602-609.
14	Choi Y H, Kim D H, Jeon E T, et al. Cluster analysis of thoracic muscle mass using artificial intelligence in severe pneumonia[J]. Sci Rep, 2024, 14(1): 16912.
15	Zaeri N. Artificial intelligence and machine learning responses to COVID-19 related inquiries[J]. J Med Eng Technol, 2023, 47(6): 301-320.
16	中国医师协会急诊医师分会. 中国急诊重症肺炎临床实践专家共识[J]. 中国急救医学，2016, 36(2): 97-107.
17	Yang J, Li J, Zhang L, et al. Highly diverse sputum microbiota correlates with the disease severity in patients with community-acquired pneumonia: a longitudinal cohort study[J]. Respir Res, 2024, 25(1): 223.
18	韩丽颖，丁思悦，王春玲. 肺康复联合营养干预对矽肺合并慢阻肺患者应用效果[J]. 工业卫生与职业病，2021, 47(1): 78-80.
19	Wang LL, Lu HW, Li LL, et al. Pseudomonas aeruginosa isolation is an important predictor for recurrent hemoptysis after bronchial artery embolization in patients with idiopathic bronchiectasis: a multicenter cohort study[J]. Respir Res, 2023, 24(1): 84.
20	金龙，黄璇，黄超，等. 肺炎支原体感染形成痰栓的危险因素分析[J]. 安徽医学，2024, 45(9): 1146-1150.
21	Green A, Cockroft JL, Kaufman RA, et al. Utility of induced sputum in assessing bacterial etiology for community-acquired pneumonia in hospitalized children[J]. J Pediatric Infect Dis Soc, 2022, 11(6): 274-282.
22	Waagsbø B, Buset EM, Longva JÅ，et al. Diagnostic stewardship aiming at expectorated or induced sputum promotes microbial diagnosis in community-acquired pneumonia[J]. BMC Infect Dis, 2022, 22(1): 203.
23	Paulsson M, Thelaus L, Riesbeck K, et al. Heparin-binding protein in lower airway samples as a biomarker for pneumonia[J]. Respir Res, 2021, 22(1): 174.
24	于燕兴，梅喜庆，刘凤娟，等. 高通量测序重症肺炎肺泡灌洗液病原体的临床应用[J/OL]. 中华肺部疾病杂志(电子版), 2024, 17(5): 785-788.
25	Heitz M, Levrat A, Lazarevic V, et al. Metagenomics for the microbiological diagnosis of hospital-acquired pneumonia and ventilator-associated pneumonia (HAP/VAP) in intensive care unit (ICU): a proof-of-concept study[J]. Respir Res, 2023, 24(1): 285.
26	Pinilla-gonzalez A, Lara-cantón I, Torrejón-rodríguez L, et al. Early molecular markers of ventilator-associated pneumonia in bronchoalveolar lavage in preterm infants[J]. Pediatr Res, 2023, 93(6): 1559-1565.
27	Chrzan R, Wizner B, Sydor W, et al. Artificial intelligence guided HRCT assessment predicts the severity of COVID-19 pneumonia based on clinical parameters[J]. BMC Infect Dis, 2023, 23(1): 314.
28	Mcgonagle D, Sharif K, O′Regan A, et al. The role of cytokines including interleukin-6 in COVID-19 induced pneumonia and macrophage activation syndrome-like disease[J]. Autoimmun Rev, 2020, 19(6): 102537.
29	Qin C, Ma H, Hu M, et al. Performance of artificial intelligence in predicting the prognossis of severe COVID-19: a systematic review and meta-analysis[J]. Front Public Health, 2024, 12: 1371852.
30	Barbieri MA, Battini V, Sessa M. Artificial intelligence for the optimal management of community-acquired pneumonia[J]. Curr Opin Pulm Med, 2024, 30(3): 252-257.

临床资料	观察组(n＝32)	对照组(n＝46)	χ²/t值	P值
胸腔积液[n(%)]	15(46.88)	11(23.91)	4.478	0.034
肺功能(±s)
FEV₁(L)	1.09±0.12	1.27±0.18	5.298	0.000
FVC(L)	1.64±0.17	1.76±0.20	2.850	0.006
PEF(L/s)	1.76±0.24	1.84±0.29	1.328	0.188
实验室指标(±s)
PaO₂(mmHg)	61.62±9.23	62.13±8.52	0.674	0.502
PaO₂/FiO₂	180.74±32.73	182.53±39.26	0.560	0.578
白细胞计数(×10⁹/L)	11.49±1.65	11.63±1.27	0.404	0.688
中性粒细胞(%)	61.45±4.87	56.36±5.12	4.445	0.000
PLT(×10⁹/L)	313.49±30.26	310.01±23.84	0.544	0.589
CRP(mg/L)	38.72±5.68	31.56±4.84	5.812	0.000
PCT(ng/L)	41.24±3.87	40.58±4.21	0.714	0.477
肺实变[n(%)]	18(56.25)	15(32.61)	4.321	0.038
BALF炎症因子(±s)
IL-1(pg/ml)	37.18±6.05	35.26±5.31	1.449	0.153
IL-4(pg/ml)	20.66±4.12	18.35±3.27	2.645	0.011
IL-6(pg/ml)	116.35±16.75	105.14±15.22	3.017	0.004
IL-8(pg/ml)	213.68±20.19	194.34±18.72	4.286	0.000
IL-10(pg/ml)	32.83±4.74	29.16±4.09	3.555	0.001
TNF-α(pg/ml)	64.59±8.22	58.32±7.15	3.493	0.001

临床资料	观察组(n＝32)	对照组(n＝46)	χ²/t值	P值
胸腔积液[n(%)]	15(46.88)	11(23.91)	4.478	0.034
肺功能(±s)
FEV₁(L)	1.09±0.12	1.27±0.18	5.298	0.000
FVC(L)	1.64±0.17	1.76±0.20	2.850	0.006
PEF(L/s)	1.76±0.24	1.84±0.29	1.328	0.188
实验室指标(±s)
PaO₂(mmHg)	61.62±9.23	62.13±8.52	0.674	0.502
PaO₂/FiO₂	180.74±32.73	182.53±39.26	0.560	0.578
白细胞计数(×10⁹/L)	11.49±1.65	11.63±1.27	0.404	0.688
中性粒细胞(%)	61.45±4.87	56.36±5.12	4.445	0.000
PLT(×10⁹/L)	313.49±30.26	310.01±23.84	0.544	0.589
CRP(mg/L)	38.72±5.68	31.56±4.84	5.812	0.000
PCT(ng/L)	41.24±3.87	40.58±4.21	0.714	0.477
肺实变[n(%)]	18(56.25)	15(32.61)	4.321	0.038
BALF炎症因子(±s)
IL-1(pg/ml)	37.18±6.05	35.26±5.31	1.449	0.153
IL-4(pg/ml)	20.66±4.12	18.35±3.27	2.645	0.011
IL-6(pg/ml)	116.35±16.75	105.14±15.22	3.017	0.004
IL-8(pg/ml)	213.68±20.19	194.34±18.72	4.286	0.000
IL-10(pg/ml)	32.83±4.74	29.16±4.09	3.555	0.001
TNF-α(pg/ml)	64.59±8.22	58.32±7.15	3.493	0.001

影响因素	β	标准误	Wald	P值	OR	95%CI
影响因素	β	标准误	Wald	P值	OR	下限	上限
年龄	0.052	0.272	0.037	0.848	1.053	0.618	1.795
胸腔积液	0.364	0.180	4.105	0.043	1.439	1.012	2.045
肺实变	0.299	0.160	3.474	0.062	1.349	0.985	1.847
FEV₁	－0.170	0.071	5.653	0.017	0.844	0.734	0.971
FVC	－0.034	0.045	0.563	0.453	0.967	0.885	1.056
中性粒细胞	0.552	0.274	4.066	0.044	1.737	1.016	2.972
CRP	0.701	0.308	5.180	0.023	2.015	1.102	3.684
IL-4	0.196	0.208	0.887	0.346	1.216	0.809	1.829
IL-6	0.470	0.233	4.085	0.043	1.600	1.014	2.525
IL-8	0.454	0.209	4.726	0.030	1.575	1.046	2.373
IL-10	0.210	0.083	6.403	0.011	1.233	1.048	1.451
TNF-α	0.123	0.049	6.286	0.012	1.131	1.027	1.246

影响因素	β	标准误	Wald	P值	OR	95%CI
影响因素	β	标准误	Wald	P值	OR	下限	上限
年龄	0.052	0.272	0.037	0.848	1.053	0.618	1.795
胸腔积液	0.364	0.180	4.105	0.043	1.439	1.012	2.045
肺实变	0.299	0.160	3.474	0.062	1.349	0.985	1.847
FEV₁	－0.170	0.071	5.653	0.017	0.844	0.734	0.971
FVC	－0.034	0.045	0.563	0.453	0.967	0.885	1.056
中性粒细胞	0.552	0.274	4.066	0.044	1.737	1.016	2.972
CRP	0.701	0.308	5.180	0.023	2.015	1.102	3.684
IL-4	0.196	0.208	0.887	0.346	1.216	0.809	1.829
IL-6	0.470	0.233	4.085	0.043	1.600	1.014	2.525
IL-8	0.454	0.209	4.726	0.030	1.575	1.046	2.373
IL-10	0.210	0.083	6.403	0.011	1.233	1.048	1.451
TNF-α	0.123	0.049	6.286	0.012	1.131	1.027	1.246

相关指标	AUC	敏感性(%)	特异性(%)	95%CI	Youden指数
IL-6	0.738	78.12	69.57	0.626~0.831	0.477
IL-8	0.764	62.50	80.43	0.655~0.853	0.429
IL-10	0.800	75.00	80.43	0.694~0.882	0.554
TNF-α	0.789	59.38	93.48	0.682~0.873	0.529
AI辅助模型	0.904	75.00	93.48	0.815~0.959	0.685

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