Analysis of Coronavirus Disease 2019 Chest High-resolution Computed Tomography Manifestations between Groups with Different Neutrophil- to-Lymphocyte Ratios
-
摘要: 目的：探讨中性粒细胞与淋巴细胞比（NLR）与COVID-19胸部HRCT表现的相关性。材料与方法：回顾性分析132例于2022年12月1日至2023年2月1日就诊于首都医科大学附属北京世纪坛医院感染科确诊COVID-19患者的NLR与胸部HRCT，以NLR截断值3.0把患者分为两组，分析其HRCT影像特征、影像表现模式。对于正态分布的计量资料组间采用连续变量的t检验；对于非正态分布的数据以中位数、四分位数表示，通过Mann-Whitney U检验进行比较；对于计数资料通过频率表示，并采用卡方检验或Fisher精确检验进行组间比较。结果：低NLR组比高NLR组有更多的≤5个病灶及病灶占比≤10％，高NLR组比低NLR组有更多的病灶数目＞10个及病灶占比＞50％；高NLR组比低NLR组更易出现混合密度影、铺路石征、马赛克征、反晕征、胸膜下黑带、拱廊征、牵拉支扩；高NLR组比低NLR组更易表现为非特异性间质性肺炎样、机化性肺炎样、弥漫肺泡损伤样改变。结论：不同NLR其COVID-19胸部HRCT表现不同，高NLR组更易出现混合密度影、铺路石征、马赛克征、反晕征、胸膜下黑带、拱廊征、牵拉支扩；影像表现模式更易表现为非特异性间质型肺炎、机化性肺炎、弥漫性肺泡损伤。
-
关键词:
- 胸部HRCT /
- COVID-19 /
- 中性粒细胞与淋巴细胞比
Abstract: Objective: This study aimed to investigate the correlation between the neutrophil-to-lymphocyte ratio (NLR) and chest high-resolution computed tomography (HRCT) findings of coronavirus disease 2019 (COVID-19). Materials and Methods: NLR and chest HRCT findings of 132 patients diagnosed with COVID-19 in the department of infectious diseases of Beijing Shijitan Hospital Capital Medical University from December 1, 2022 to February 1, 2023 were retrospectively analyzed. The patients were divided into two groups with NLR cut-off value of 3.0, and their HRCT characteristics and imaging manifestation patterns were analyzed. For the measurement data of normal distribution, the t-test of continuous variables was used between the groups. The data of non-normal distribution are expressed as median and quartile and compared using Mann-Whitney U test. The counting data are expressed as frequency, and the chi-squared or Fisher's exact test was used for comparison between the groups. P<0.05 indicates that the difference is statistically significant. Results: The number of lesions ≤5 and the proportion of lesions ≤10% were higher in the low NLR group than that in the high NLR group. The number of lesions >10 and the proportion of lesions >50% were higher in the high NLR group than that in the low NLR group. The high NLR group was prone to mixed density shadow, crazy-paving pattern, mosaic sign, anti-halo sign, subpleural black belt, arcade-like sign than that in the low NLR group. The high NLR group was most likely to have nonspecific interstitial pneumonia-like, organizing pneumonia-like, and diffuse alveolar damage-like patterns than that in the low NLR group. Conclusion: Different NLRs have different manifestations of COVID-19 chest HRCT. The high NLR group is more prone to mixed density shadow, crazy-paving pattern, mosaic sign, anti-halo sign, subpleural black belt, and arcade-like sign, as well as most likely to have radiologic patterns of nonspecific interstitial pneumonia, organizing pneumonia, diffuse alveolar damage.-
Keywords:
- chest HRCT /
- COVID-19 /
- neutrophil-to-lymphocyte ratio
-
-
表 1 低NLR组与高NLR组临床指标统计
Table 1 Statistics of clinical indexes of the low and high NLR groups
临床指标 低NLR组 高NLR组 $Z/{\rm{\chi}}^2$ P 年龄/（岁，$M(Q_1,Q_3)$） 69（59，76） 74（63，82） -1.888 0.059 性别/（男，例（％）） 31（48.4） 40（58.8） 1.431 0.232 临床症状/（例（％）） 发热 64（100.0） 68（100.0） — — 咳嗽 60（93.8） 62（91.2） 0.312 0.577 咳痰 36（56.3） 33（48.5） 0.788 0.375 咽痛 20（31.3） 29（42.6） 1.835 0.176 流涕 17（26.6％） 24（35.3％） 1.174 0.279 实验室指标/（指标值$M(Q_1,Q_3)$） 淋巴细胞百分比/％ 32.0（27.3，35.2） 15.7（10.3，19.1） -9.571 0.000 中性粒细胞百分比/％ 60.3（55.6，64.3） 76.0（73.0，83.3） -9.457 0.000 淋巴细胞/×109/L 1.8（1.5，2.3） 1.01（0.7，1.4） -7.021 0.000 中性粒细胞/×109/L 3.6（2.8，4.4） 5.7（4.0，8.1） -5.780 0.000 注：$M(Q_1,Q_3)$为中位数（第1四分位数，第3四分位数）；NLR为中性粒细胞于淋巴细胞比。 
下载: 导出CSV
表 2 低NLR组与高NLR组HRCT表现统计
Table 2 HRCT statistics of the low and high NLR groups
影像指标 组别 统计检验 低NLR组 高NLR组 $\chi^2 $ P 病灶数目/例（％） ≤5个 13（20.3） 5（7.4） 4.702 0.030 5～10个 13（20.3） 8（11.8） 1.801 0.180 10个 38（59.4） 54（79.4） 6.267 0.012 病灶占比/例（％） ≤10％ 40（62.5） 25（36.8） 8.736 0.003 10％～30％ 13（20.3） 12（17.6） 0.153 0.696 30％～50％ 8（12.5） 14（20.6） 1.553 0.213 ＞50％ 2（3.1） 14（20.6） 9.439 0.002 病变大小/例（％） ≤10 mm 53（82.8） 55（80.9） 0.083 0.774 ≤30 mm 53（82.8） 59（86.8） 0.401 0.527 ＞30 mm 33（51.6） 52（76.5） 8.921 0.003 病变分布/例（％） 中央支气管束周围 51（79.7） 56（82.4） — 0.825 外周胸膜下 63（98.4） 65（95.6） — 0.620 上肺为主 7（10.9） 6（8.8） 0.166 0.684 下肺为主 35（54.7） 29（42.6） 1.914 0.167 病变特征/例（％） GGO 57（89.1） 66（97.1） — 0.089 实变 36（56.3） 27（39.7） 3.617 0.057 混合密度 53（82.8） 64（94.1） 4.183 0.041 CPP 32（50.0） 46（67.6） 4.247 0.039 马赛克征 21（32.8） 41（60.3） 9.997 0.002 晕征 49（76.6） 46（67.6） 1.299 0.254 反晕征 16（25.0） 40（58.8） 15.442 0.000 胸膜下黑带 28（43.8） 47（69.1） 8.647 0.003 拱廊征 16（25.0） 36（52.9） 10.781 0.001 影像表现模式/例（％） NSIP样 18（28.1） 41（60.3） 13.803 0.000 OP样 28（43.8） 47（69.1） 8.647 0.003 DAD样 2（3.1） 14（20.6） 9.439 0.002
下载: 导出CSV
-
[1] LIU Y, DU X, CHEN J, et al. Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19[J]. Journal of Infection, 2020, 81(1): e6−e12. doi: 10.1016/j.jinf.2020.04.002
[2] YANG A P, LIU J P, TAO W Q, et al. The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients[J]. International Immunopharmacology, 2020, 84: 106504. doi: 10.1016/j.intimp.2020.106504
[3] LI X, LIU C, MAO Z, et al. Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: Systematic review and meta-analysis[J]. Critical Care, 2020, 24(1): 647. doi: 10.1186/s13054-020-03374-8
[4] 费明明, 童飞, 陶小根, 等. 中性粒细胞/淋巴细胞比值对新型冠状病毒肺炎患者疾病分型的诊断价值[J]. 中华危重病急救医学, 2020,32(5): 554−558. doi: 10.3760/cma.j.cn121430-20200413-00506 FEI M M, TONG F, TAO X G, et al. Value of neutrophil-to-lymphocyte ratio in the classification diagnosis of coronavirus disease 2019[J]. Chinese Critical Care Medicine, 2020, 32(5): 554−558. (in Chinese). doi: 10.3760/cma.j.cn121430-20200413-00506
[5] 李文平, 张鹏举, 许金环, 等. 免疫检查点抑制剂相关肺炎的临床及CT表现分析[J]. 中华放射学杂志, 2022,56(12): 1352−1358. doi: 10.3760/cma.j.cn112149-20220217-00126 LI W P, ZHANG P J, XU J H, et al. Clinical and CT imaging features of immune checkpoint inhibitor-associated pneumonia[J]. Chinese Journal of Radiology, 2022, 56(12): 1352−1358. (in Chinese). doi: 10.3760/cma.j.cn112149-20220217-00126
[6] BUONACERA A, STANCANERLLI B, COLACI M, et al. Neutrophil to lymphocyte ratio: An emerging marker of the relationships between the immune system and diseases[J]. International Journal of Molecular Sciences, 2022, 23(7): 3636.
[7] FEST J, RUITER T R, GROOT KOERKAMP B, et al. The neutrophil-to-lymphocyte ratio is associated with mortality in the general population: The rotterdam study[J]. European Journal of Epidemiology, 2019, 34(5): 463-470.
[8] SIMADIBRATA D M, CALVIN J, WIJAYA A D, et al. Neutrophil-to-lymphocyte ratio on admission to predict the severity and mortality of COV9 patients: A meta-analysis[J]. American Journal of Emergency Medicine, 2021, 42: 60−69. doi: 10.1016/j.ajem.2021.01.006
[9] CAI J, LI H, ZHANG C, et al. The neutrophil-to-lymphocyte ratio determines clinical efficacy of corticosteroid therapy in patients with COVID-19[J]. Cell Metabolism, 2021, 33(2): 258−269. e3. doi: 10.1016/j.cmet.2021.01.002
[10] BATAH S S, ABRO A T. Pulmonary pathology of ARDS in COVID-19: A pathological review for clinicians[J]. Respiratory Medicine, 2021, 176: 106239. doi: 10.1016/j.rmed.2020.106239
[11] RUBIN G D, YERSON C J, HARAMATI L B, et al. The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the Fleischner Society[J]. Radiology, 2020, 296(1): 172-180.
[12] KWEE T C, KWEE R M. Chest CT in COVID-19: What the radiologist needs to know[J]. Radiographics, 2020, 40(7): 1848−1865. doi: 10.1148/rg.2020200159
[13] SIMPSON S, KAY F U, ABBARA S, et al. Radiological Society of North America expert consensus document on reporting chest CT findings related to COVID-19: Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA[J]. Radiology Cardiothoracic Imaging, 2020, 2(2): e200152. doi: 10.1148/ryct.2020200152
[14] CHO J L, VILLACRESES R, NAGPAL P, et al. Quantitative chest CT assessment of small airways disease in post-acute SARS-CoV-2 infection[J]. Radiology, 2022, 304(1): 185−192. doi: 10.1148/radiol.212170
[15] TOUSSIE D, VOUTSINAS N, FINKELSTEIN M, et al. Clinical and chest radiography features determine patient outcomes in young and middle-aged adults with COVID-19[J]. Radiology, 2020, 297(1): E197−E206. doi: 10.1148/radiol.2020201754
[16] 孙莹, 李玲, 刘晓燕, 等. 早期新型冠状病毒肺炎的胸部薄层平扫CT表现特征[J]. CT理论与应用研究, 2023,32(1): 131−138. DOI: 10.15953/j.ctta.2023.006. SUN Y, LI L, LIU X Y, et al. Characteristics of chest thin-slice non-contrast CT in early novel coronavirus pneumonia[J]. CT Theory and Applications, 2023, 32(1): 131−138. DOI: 10.15953/j.ctta.2023.006. (in Chinese).
[17] LEE J H, KOH J, JEON Y K, et al. An integrated radiologic-pathologic understanding of COVID-19 pneumonia[J]. Radiology, 2023, 306(2): e222600. doi: 10.1148/radiol.222600
[18] KLIGERMAN S J, TNKSJ F R, GALVIN J R. From the radiologic pathology archives: Organization and fibrosis as a response to lung injury in diffuse alveolar damage, organizing pneumonia, and acute fibrinous and organizing pneumonia[J]. Radiographics, 2013, 33(7): 1951−75. doi: 10.1148/rg.337130057
[19] LARICI A R, CICCHETTI G, MARANO R, et al. Multimodality imaging of COVID-19 pneumonia from diagnosis to follow-up: A comprehensive review[J]. European Journal of Radiology, 2020, 131: 109217. doi: 10.1016/j.ejrad.2020.109217
[20] GRASSELLI G, TONETTI T, PROTTI A, et al. Pathophysiology of COVID-19-associated acute respiratory distress syndrome: A multicentre prospective observational study[J]. The Lancet Respiratory Medicine, 2020, 8(12): 1201−1208. doi: 10.1016/S2213-2600(20)30370-2
[21] DURHAN G, DÜZGÜN S A, DEMIRKAZIK F B, et al. Visual and software-based quantitative chest CT assessment of COVID-19: Correlation with clinical findings[J]. Diagnostic Interventional Radiology, 2020, 26(6): 557−564. doi: 10.5152/dir.2020.20407
[22] BERNHEIM A, MEI X, HUANG M, et al. Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection[J]. Radiology, 2020, 295(3): 200463. doi: 10.1148/radiol.2020200463
[23] INOUE A, TAKAHASHI H, IBE T, et al. Comparison of semiquantitative chest CT scoring systems to estimate severity in coronavirus disease 2019 (COVID-19) pneumonia[J]. European Radiology, 2022, 32(5): 3513−3524. doi: 10.1007/s00330-021-08435-2
[24] LI K, FANG Y, LI W, et al. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19)[J]. European Radiology, 2020, 30(8): 4407−4416. doi: 10.1007/s00330-020-06817-6
[25] 杜丹, 谢元亮, 李惠, 等. 人工智能定量测量对新型冠状病毒肺炎患者胸部CT炎性病灶动态变化的评估价值[J]. 中华放射学杂志, 2021,55(3): 250−256. DU D, XIE Y L, LI H, et al. The value of quantitative artificial intelligence measurement in evaluation of CT dynamic changes for COVID-19[J]. Chinese Journal of Radiology, 2021, 55(3): 250−256. (in Chinese).
[26] NAIK B R, SAKALECHA A K, SUNIL B N, et al. Computed tomography severity scoring on high-resolution computed tomography thorax and inflammatory markers with COVID-19 related mortality in a designated COVID hospital[J]. Cureus, 2022, 14(4): e24190.
计量
- 文章访问数: 240
- HTML全文浏览量: 92
- PDF下载量: 294
