后疫情阶段重症新型冠状病毒感染患者的临床特征和危险因素分析

张玲玲; 王穆群; 郭锋

doi:10.16766/j.cnki.issn.1674-4152.003870

后疫情阶段重症新型冠状病毒感染患者的临床特征和危险因素分析

doi: 10.16766/j.cnki.issn.1674-4152.003870

1.
安徽医科大学附属宿州医院呼吸与危重症医学科, 安徽宿州 234000
2.
蚌埠医科大学第一附属医院呼吸与危重症医学科, 安徽蚌埠 233004

基金项目:

安徽省科技厅2023年安徽省临床医学研究转化专项项目 202304295107020074

；安徽省宿州市卫生健康科研项目 SZWJ2023a035

详细信息

通讯作者:
王穆群，E-mail：lianyunan1014@126.com

中图分类号: R373.19 R563.14
计量
- 文章访问数: 9
- HTML全文浏览量: 4
- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2024-06-12
- 网络出版日期: 2025-03-27

Analysis of clinical characteristics and risk factors of severe COVID-19 patients in the post-epidemic stage

1.
Department of Respiratory and Critical Care Medicine, Suzhou Hospital of Anhui Medical University (Suzhou Municipal Hospital of Anhui Province), Suzhou, Anhui 234000, China

摘要

摘要: 目的分析新型冠状病毒感染(COVID-19)患者的临床特征, 探索可靠的临床指标来预测易进展为重症的高危人群。方法回顾性分析安徽医科大学附属宿州医院呼吸与危重症医学科2022年12月—2024年2月收治的85例COVID-19患者, 根据临床症状分为非重症组(47例)和重症组(38例), 比较2组患者的临床特征、血常规、C反应蛋白(CRP)、T淋巴细胞亚群之间的差异, 采用logistic回归分析并构建模型, 筛选出重症的危险因素, 并绘制ROC曲线, 比较各危险因素及联合应用对预测重症的价值。结果与非重症组比较, 重症组糖尿病患病率、BMI更高, 嗜酸性粒细胞、CD4⁺T淋巴细胞、CD8⁺T淋巴细胞更低, 差异均有统计学意义(P > 0.05)。逐步logistic回归分析显示CD4⁺T淋巴细胞、糖尿病和BMI是重症的影响因素。CD4⁺T淋巴细胞、BMI和糖尿病预测重症的ROC曲线下面积(AUC)分别为0. 877、0. 748和0. 663;灵敏度分别为81. 6%、60. 5%和36. 8%;特异度分别为85. 1%、93. 6%和95. 7%。而三者联合构建模型的AUC为0. 928, 灵敏度和特异度分别为97. 4%和76. 6%。结论 CD4⁺T淋巴细胞降低单独或联合BMI值高和患糖尿病可以良好地预测COVID-19患者的病情严重性。
- 新型冠状病毒感染 /
- 重症 /
- 危险因素
Abstract: Objective To analyze the clinical features of patients infected with Coronavirus disease 2019 (COVID-19), and to explore reliable clinical indicators for predicting high-risk populations that are likely to progress to severe cases. Methods A retrospective analysis was conducted on 85 patients with confirmed diagnosis of SARS-CoV-2 infection treated at the Department of Respiratory and Critical Care Medicine at the Suzhou Hospital of Anhui Medical University from December 2022 to February 2024. Patients were categorized according to the severity of their clinical symptoms, resulting in the formation of two distinct groups: the non-severe group (n = 47) and the severe group (n = 38). The differences in clinical characteristics, blood routine, C-reactive protein (CRP) and T lymphocyte subsets between the two groups were compared. Logistic regression analysis was applied to identify the risk factors of the severe group and a predictive model was constructed. The receiver operating characteristic (ROC) curve was used to compare the predictive value of each risk factor and their combined application in predicting severity. Results A comparison of the non-severe group with the severe group revealed a higher incidence of comorbidities in the latter, including diabetes. Additionally, the severe group exhibited significantly lower levels of eosinophils, CD4⁺T lymphocytes and CD8⁺T lymphocytes (P > 0.05). Furthermore, a notable difference was observed in the mean BMI between the two groups. The results of the stepwise logistic regression analysis indicated that CD4⁺T lymphocytes, diabetes, and BMI were significant risk factors for the severe group. The area under the receiver operating characteristic curve (AUC) for CD4⁺T lymphocytes, BMI and diabetes were 0. 877, 0. 748 and 0. 663, respectively. The sensitivity of CD4⁺T lymphocytes, BMI and diabetes were 81. 6%, 60. 5% and 36. 8%, respectively; and the specificity were 85. 1%, 93. 6% and 95. 7%, respectively. The AUC for the model constructed by combining the three factors was 0. 928, the sensitivity was 97. 4% and the specificity was 76. 6%. Conclusion A reduced count of CD4⁺T lymphocytes can effectively predict the clinical severity of patients with COVID-19 alone or in conjunction with a high BMI and comorbid diabetes.
- Corona Virus Disease 2019 /
- Severe disease /
- Risk factor

HTML全文

图 1 各项危险因素单独及联合预测COVID-19重症的ROC曲线

Figure 1. ROC curves for individual and combined predictors of severe COVID-19 based on various risk factors

下载: 全尺寸图片幻灯片

表 1 非重症组与重症组COVID-19患者临床特征及实验室指标比较

Table 1. Clinical characteristics and laboratory results compared between non-severe and severe COVID-19 patients

项目	总数(n=85)	重症组(n=38)	非重症组(n=47)	统计量	P值
性别[例(%)]				1.250^a	0.264
男性	48(56.47)	24(63.16)	24(51.06)
女性	37(43.53)	14(36.84)	23(48.94)
年龄(x±s，岁)	68.0±15.4	70.5±15.9	65.9±14.8	1.381^b	0.171
BMI(x±s)	24.1±2.6	25.4±2.9	23.0±1.7	4.837^b	< 0.001
高血压[例(%)]				1.049^a	0.306
无	71(83.53)	30(78.95)	41(87.23)
有	14(16.47)	8(21.05)	6(12.77)
糖尿病[例(%)]				14.602^a	< 0.001
无	69(81.18)	24(63.16)	45(95.74)
有	16(18.82)	14(36.84)	2(4.26)
慢阻肺[例(%)]				< 0.001^a	0.999
无	77(90.59)	34(89.47)	43(91.49)
有	8(9.41)	4(10.53)	4(8.51)
冠心病[例(%)]				0.519^a	0.471
无	72(84.71)	31(81.58)	41(87.23)
有	13(15.29)	7(18.42)	6(12.77)
白细胞计数(x±s，×10⁹/L)	7.0±3.1	6.9±3.6	7.1±2.6	0.409^b	0.684
中性粒细胞计数[M(P₂₅, P₇₅), ×10⁹/L]	4.8(3.4, 6.9)	4.8(2.9, 6.9)	4.8(3.7, 6.6)	-0.248^c	0.804
嗜酸性粒细胞计数[M(P₂₅, P₇₅), ×10⁹/L]	0.01(0.00, 0.09)	0.00(0.00, 0.02)	0.03(0.01, 0.10)	-3.553^c	< 0.001
淋巴细胞计数[M(P₂₅, P₇₅), ×10⁹/L]	1.01(0.75, 1.55)	0.85(0.64, 1.50)	1.07(0.82, 1.63)	-1.485^c	0.137
CRP[M(P₂₅, P₇₅), mg/L]	13.5(4.6, 38.2)	13.6(6.0, 69.1)	13.0(3.3, 28.2)	1.821^c	0.068
CD4⁺T[M(P₂₅, P₇₅)]	335.0(180.0, 525.0)	192.5(97.0, 295.0)	490.0(335.0, 567.0)	-5.941^c	< 0.001
CD8⁺T[M(P₂₅, P₇₅)]	231.0(126.0, 320.0)	117.0(78.0, 169.0)	294.0(231.0, 423.0)	-5.684^c	< 0.001
CD4⁺/CD8⁺[M(P₂₅, P₇₅)]	1.45(0.91, 2.47)	1.47(0.90, 2.60)	1.45(1.09, 2.36)	-0.115^c	0.908
注：^a为χ²值，^b为t值，^c为Z值。

下载: 导出CSV

表 2 COVID-19患者重症影响因素的多因素logistic回归分析

Table 2. Multivariate logistic regression analysis of influencing factors of severe disease in patients with COVID-19

变量	B	SE	Waldχ²	P值	OR(95% CI)
糖尿病	3.225	1.134	8.088	0.004	25.160(2.725~232.210)
BMI	0.272	0.135	4.071	0.044	1.313(1.007~1.710)
CD4⁺T	-0.011	0.003	16.680	< 0.001	0.989(0.983~0.994)

下载: 导出CSV

参考文献(20)

[1]	ANKA A U, TAHIR M I, ABUBAKAR S D, et al. Coronavirus disease 2019 (COVID-19): an overview of the immunopathology, serological diagnosis and management[J]. Scand J Immunol, 2021, 93(4): e12998. DOI: 10.1111/sji.12998.
[2]	CHEN N S, ZHOU M, DONG X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study[J]. Lancet, 2020, 395(10223): 507-513.
[3]	BHAT S A, SINGH G, BHAT W F, et al. Coronavirus disease-2019 and its current scenario: a review[J]. Clinical Ehealth, 2021, 4: 67-73.
[4]	中华人民共和国国家卫生健康委员会. 新型冠状病毒感染诊疗方案(试行第十版)[J]. 中华临床感染病杂志, 2023, 16(1): 1-9. National Health Commission of the People's Republic of China. Diagnosis and treatment plan for COVID-19 (trial version 10)[J]. Chin J Clin Infect Dis, 2023, 16(1): 1-9.
[5]	沈梦媛, 李伟, 颜学兵, 等. 重症新型冠状病毒肺炎的危险因素分析及预测模型构建[J]. 中国感染与化疗杂志, 2022, 22(3): 249-254. SHEN M Y, LI W, YAN X B, et al. Developing a prediction model for severe coronavirus disease 2019 based on the analysis of early predictors[J]. Chin J Infect Chemother, 2022, 22(3): 249-254.
[6]	MIYASHITA N, HIGA F, AOKI Y, et al. Usefulness of the Legionella Score for differentiating from COVID-19 pneumonia to legionella pneumonia[J]. J Infect Chemother, 2022, 28(10): 1353-1357.
[7]	SONI M. Evaluation of eosinopenia as a diagnostic and prognostic indicator in COVID-19 infection[J]. Int J Lab Hematol, 2021, 43(Suppl 1): 137-141.
[8]	MU T, YI Z M, WANG M J, et al. Expression of eosinophil in peripheral blood of patients with COVID-19 and its clinical significance[J]. J Clin Lab Anal, 2021, 35(1): e23620. DOI: 10.1002/jcla.23620.
[9]	LINDSLEY A W, SCHWARTZ J T, ROTHENBERG M E. Eosinophil responses during COVID-19 infections and coronavirus vaccination[J]. J Allergy Clin Immunol, 2020, 146(1): 1-7. doi: 10.1016/j.jaci.2020.04.021
[10]	YUN H, SUN Z R, WU J, et al. Laboratory data analysis of novel coronavirus (COVID-19) screening in 2 510 patients[J]. Clin Chim Acta, 2020, 507: 94-97.
[11]	程玉生, 周云, 朱孟德, 等. 嗜酸性粒细胞减少在新型冠状病毒肺炎患者中的临床意义[J]. 中国呼吸与危重监护杂志, 2021, 20(5): 315-319. CHENG Y S, ZHOU Y, ZHU M D, et al. Clinical significance of eosinopenia in patients with coronavirus disease 2019[J]. Chinese Journal of Respiratory and Critical Care Medicine, 2021, 20(5): 315-319.
[12]	王之旸, 何君, 程杨阳, 等. 2型糖尿病患者感染新型冠状病毒肺炎的临床特征及转归[J]. 中华内分泌代谢杂志, 2020, 36(8): 654-660. WANG Z Y, HE J, CHENG Y Y, et al. Clinical characteristics and outcomes of COVID-19 infected patients with type 2 diabetes[J]. Chin J Endocrinol Metab, 2020, 36(8): 654-660.
[13]	KALLIGEROS M, SHEHADEH F, MYLONA E K, et al. Association of obesity with disease severity among patients with Coronavirus Disease 2019[J]. Obesity (Silver Spring), 2020, 28(7): 1200-1204.
[14]	ANDERSON M R, GELERIS J, ANDERSON D R, et al. Body mass index and risk for intubation or death in SARS-CoV-2 Infection: a retrospective cohort study[J]. Ann Intern Med, 2020, 173(10): 782-790.
[15]	PETTIT N N, MACKENZIE E L, RIGWAY J P, et al. Obesity is associated with increased risk for mortality among hospitalized patients with COVID-19[J]. Obesity (Silver Spring), 2020, 28(10): 1806-1810.
[16]	SAHIN S, SEZER H, CICEK E, et al. The role of obesity in predicting the clinical outcomes of COVID-19[J]. Obes Facts, 2021, 14(5): 481-489.
[17]	祁飞, 夏加伟, 张乐, 等. COVID-19患者急性期T淋巴细胞亚群及血常规的变化[J]. 昆明医科大学学报, 2020, 41(4): 56-59. QI F, XIA J W, ZHANG L, et al. Changes in Acute T lymphocyte Subsets and Blood Routine in Patients with Corona Virus Disease-2019 (COVID-19)[J]. J Kunming Med Univ, 2020, 41(4): 56-59.
[18]	DIAO B, WANG C H, TAN Y J, et al. Reduction and functional exhaustion of T cells in patients with Coronavirus Disease 2019 (COVID-19)[J]. Front Immunol, 2020, 11: 827. DOI: 10.3389/fimmu.2020.00827.
[19]	MAHMOODPOOR A, HOSSEINI M, SOLTANI-ZANGBAR S, et al. Reduction and exhausted features of T lymphocytes under serological changes, and prognostic factors in COVID-19 progression[J]. Mol Immunol, 2021, 138: 121-127.
[20]	洪佳慧, 刘永洋, 房宇坤, 等. CRP/ALB联合肺实变对新型冠状病毒感染严重程度的预测模型[J]. 中华全科医学, 2024, 22(7): 1116-1120. doi: 10.16766/j.cnki.issn.1674-4152.003579 HONG J H, LIU Y Y, FANG Y K, et al. CRP/ALB combined with lung consolidation in predictingthe Severity of COVID-19[J]. Chinese Journal of General Practice, 2024, 22(7): 1116-1120. doi: 10.16766/j.cnki.issn.1674-4152.003579