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

洪佳慧; 刘永洋; 房宇坤; 仇奕尹; 方凯; 陈清勇

doi:10.16766/j.cnki.issn.1674-4152.003579

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

doi: 10.16766/j.cnki.issn.1674-4152.003579

1.
浙江中医药大学第二临床学院，浙江杭州 310053
2.
中国人民解放军联勤保障部队第九〇三医院呼吸与危重症医学科，浙江杭州 310000
3.
温州医科大学附属第一医院呼吸内科，浙江温州 325015
4.
浙江衢化医院呼吸内科，浙江衢州 324000

基金项目:

浙江省医药卫生科技计划项目 2020KY1025

详细信息

通讯作者:
陈清勇，E-mail: cqyong117@163.com

中图分类号: R563.1 R445
计量
- 文章访问数: 183
- HTML全文浏览量: 102
- PDF下载量: 7
- 被引次数: 0
出版历程
- 收稿日期: 2023-08-30
- 网络出版日期: 2024-09-05

CRP/ALB combined with lung consolidation in predicting the severity of COVID-19

1.
The Second Clinical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310053, China

摘要

摘要: 目的新型冠状病毒感染(COVID-19，简称新冠感染)已成为全球大流行疾病，严重者可导致多器官损伤综合征，死亡率高，因此迫切需要可靠的生物标志物，用来早期筛选出可能进展为重症感染者，这对于改善临床预后具有重要意义。方法回顾性分析2022年12月8日—2023年1月31日因首次感染新冠病毒就诊于中国人民解放军联勤保障部队第九〇三医院呼吸与危重症医学科的94例患者的临床资料，依据新型冠状病毒感染诊疗方案(试行第十版)的分型，将其分为2组，其中轻型和中型为非重症组(57例)，重型和危重型为重症组(37例)。通过单因素分析筛选出2组差异有统计学意义的指标，再通过多因素logistic回归分析筛选出影响早期新冠感染严重程度的独立危险因素。采用ROC曲线分析对应指标对早期新冠感染严重程度分层的预测价值，最后用列线图评估预测模型的检验效能。结果 C反应蛋白(CRP)/白蛋白(ALB)、肺实变、冠心病可作为新冠感染严重程度的预测因素，其中CRP/ALB的ROC曲线下面积为0.762(95% CI：0.663~0.862，P < 0.001)，肺实变曲线下面积为0.682(95% CI：0.573~0.791，P=0.003)，冠心病曲线下面积为0.638(95% CI：0.520~0.756，P=0.024)。CRP/ALB联合肺实变预测指标AUC为0.801(95% CI：0.708~0.894)。绘制列线图预测模型及其校正曲线，结果表明该预测模型具有较高的准确性。结论 CRP/ALB联合肺实变能较好地预测新型冠状病毒感染严重程度。
- 新型冠状病毒感染 /
- C反应蛋白与白蛋白的比值 /
- 肺实变 /
- 严重程度 /
- 预测模型
Abstract: Objective Corona Virus Disease 2019 (COVID-19) has become a global pandemic disease, which can lead to multiple organ damage syndrome and high mortality. Therefore, there is an urgent need for reliable biomarkers that can be used for early screening for possible progression to severe infection, which is important for improving clinical prognosis. Methods For this retrospective study, basic information and clinical data from December 8, 2022 to January 31, 2023 were extracted from the medical records of patients with COVID-19 admitted to the 903rd Hospital of the People's Liberation Army. On the basis of a new type of coronavirus infection diagnosis and treatment plan 10 (trial version) of parting, after into and exclusion criteria, the patients were divided into two groups, non-severe group (n=57) and severe group (n=37). Univariate analysis was used to screen out statistically significant indicators between the two groups, then multivariate analysis logistic regression was used to screen out independent risk factors affecting the severity of early COVID-19. The receiver operating characteristic curve was used to evaluate and analyze the predictive value of the corresponding indicators for the severity stratification of COVID-19 in the early stage. Finally, a nomogram was used to evaluate the test efficacy of the prediction model. Results The C-reactive protein/albumin ratio (CRP/ALB), lung consolidation and coronary heart disease were predictors of severity prediction, the area under the ROC of CRP/ALB was 0.762 (95% CI: 0.663-0.862, P < 0.001), the area under the ROC of lung consolidation was 0.682 (95% CI: 0.573-0.791, P=0.003), and the area under the ROC of coronary heart disease was 0.638(95% CI: 0.520-0.756, P=0.024). The AUC of CRP/ALB and lung consolidation was 0.801(95% CI: 0.708-0.894). The nomogram prediction model and its calibration curve showed that the prediction model had high accuracy with the actual situation. Conclusion CRP/ALB combined with lung consolidation can predict the severity of COVID-19.
- Corona Virus Disease 2019 /
- The C-reactive protein/albumin ratio /
- Lung consolidation /
- Severity /
- Prediction model

HTML全文

图 1 CRP/ALB、肺实变、冠心病三者单独预测新冠感染严重程度的ROC曲线

Figure 1. The ROC curve for CRP/ALB, pulmonary consolidation, and CDH as independent predictors of COVID-19 infection severity

下载: 全尺寸图片幻灯片

图 2 联合指标预测新冠感染严重程度的ROC曲线

Figure 2. ROC curve for combined indicators predicting the severity of COVID-19

下载: 全尺寸图片幻灯片

图 3 基于肺实变、CRP/ALB构建的预测新冠感染严重程度模型列线图

Figure 3. Nomogram for predicting COVID-19 severity based on lung consolidation and CRP/ALB

下载: 全尺寸图片幻灯片

图 4 预测新冠感染严重程度模型的校正曲线

Figure 4. Calibration curves for the model predicting COVID-19 severity

下载: 全尺寸图片幻灯片

表 1 2组新冠感染患者基本资料比较

Table 1. Comparison of basic characteristics between two groups of COVID-19 patients

项目	非重症组 (n=57)	重症组 (n=37)	统计量	P值
年龄(x±s，岁)	57.51±17.67	78.24±11.61	3.264^a	0.001
性别[例(%)]			8.636^b	0.003
男性	31(54.4)	31(83.8)
女性	26(45.6)	6(16.2)
吸烟史[例(%)]			1.920^b	0.166
有	10(17.5)	11(29.7)
无	47(82.5)	26(70.3)
饮酒史[例(%)]			3.293^b	0.070
有	7(12.3)	10(27.0)
无	50(87.7)	27(73.0)
基础合并症[例(%)]
高血压	30(52.6)	21(56.8)	0.154^b	0.695
糖尿病	14(24.6)	8(21.6)	0.108^b	0.742
脑卒中	5(8.8)	8(21.6)	3.109^b	0.078
冠心病	12(21.1)	18(48.6)	7.863^b	0.005
慢性肾脏病	3(5.3)	5(13.5)	1.045^b	0.307
肿瘤	4(7.0)	4(10.8)	0.071^b	0.791
支气管扩张	1(1.8)	4(10.8)	2.077^b	0.150
器官移植	2(3.5)	1(2.7)	0.147^b	0.702
慢性肝病	8(14.0)	8(21.6)	0.914^b	0.339
注：^a为t值，^b为χ²值。

下载: 导出CSV

表 2 2组新冠感染患者生命体征比较

Table 2. Comparison of vital signs between two groups of COVID-19 patients

组别	例数	体温 [M(P₂₅, P₇₅), ℃]	呼吸≥30次/min [例(%)]	心率 (x±s, 次/min)
非重症组	57	36.50(36.30，36.80)	0	88.44±12.61
重症组	37	36.60(36.25，37.20)	1(2.7)	90.46±16.04
统计量		-1.363^a		0.681^c
P值		0.173	0.394^b	0.497
注：^a为Z值，^b为采用Fisher精确检验，^c为t值。

下载: 导出CSV

表 3 2组新冠感染患者血常规比较

Table 3. Comparison of blood routine tests between two groups of COVID-19 patients

组别	例数	白细胞 [M(P₂₅, P₇₅)，×10⁹/L]	中性粒细胞 [M(P₂₅, P₇₅)，×10⁹/L]	淋巴细胞 [M(P₂₅, P₇₅)，×10⁹/L]	NLR [M(P₂₅, P₇₅)]	红细胞总数 (x±s，×10¹²/L)	血红蛋白 (x±s，g/L)
非重症组	57	5.28(3.50，6.61)	3.47(2.48，5.36)	0.89(0.61，1.49)	3.67(2.52，5.50)	4.15±0.62	127.35±18.41
重症组	37	6.12(4.30，7.86)	4.79(2.96，6.37)	0.66(0.51，1.61)	7.22(3.30，10.86)	3.92±0.70	123.46±20.13
统计量		-1.656^a	-2.067^a	-2.183^a	-3.022^a	1.672^b	0.965^b
P值		0.098	0.039	0.029	0.003	0.098	0.337

组别	例数	Hb/Hb-SD (x±s)	血小板数目 [M(P₂₅, P₇₅)，×10⁹/L]	血小板数目/淋巴细胞[M(P₂₅, P₇₅)]	CRP [M(P₂₅, P₇₅)，mg/L]	CRP/ALB [M(P₂₅, P₇₅)]
非重症组	57	2.91±0.48	191.00(152.00，236.00)	185.41(133.29, 316.33)	12.67(3.11，32.20)	0.35(0.08，0.90)
重症组	37	2.67±0.58	160.00(117.50，209.00)	231.82(142.83, 422.83)	58.67(19.88，75.59)	1.77(0.54，2.49)
统计量		2.168^b	-1.916^a	-0.948^a	-4.299^a	-4.276^a
P值		0.033	0.055	0.343	<0.001	<0.001
注：^a为Z值，^b为t值。

下载: 导出CSV

表 4 2组新冠感染患者生化、凝血功能指标比较

Table 4. Comparison of biochemical and coagulation functions between two groups of COVID-19 patients

项目	类别	非重症组(n=57)	重症组(n=37)	统计量	P值
生化指标	总蛋白[M(P₂₅, P₇₅)，g/L]	66.7(63.7，71.0)	63.6(60.2，67.4)	-3.019^a	0.003
	白蛋白(x±s，g/L)	36.27±4.67	32.84±6.26	3.038^b	0.003
	球蛋白[M(P₂₅, P₇₅)，g/L]	30.00(28.45，32.05)	29.80(27.65，32.65)	-0.550^a	0.583
肝功能指标	总胆红素[M(P₂₅, P₇₅)，μmol/L]	10.3(7.7，15.3)	12.3(9.1，16.4)	-1.869^a	0.062
	直接胆红素[M(P₂₅, P₇₅)，μmol/L]	2.5(1.9，3.7)	4.0(3.0，4.8)	-4.134^a	<0.001
	间接胆红素[M(P₂₅, P₇₅)，μmol/L]	7.60(5.65，11.15)	8.30(6.55，12.25)	-0.778^a	0.437
凝血功能指标	D-二聚体[M(P₂₅, P₇₅)，μg/L]	550(295，995)	1 210(655，2 025)	-3.410^a	0.001
肾功能指标	尿素氮[M(P₂₅, P₇₅)，mmol/L]	5.24(4.39，6.66)	6.04(4.02，8.60)	-1.029^a	0.303
	肌酐[M(P₂₅, P₇₅)，μmol/L]	66.0(52.0，80.0)	73.0(56.5，93.0)	-1.494^a	0.135
注：^a为Z值，^b为t值。

下载: 导出CSV

表 5 2组新冠感染患者胸部CT情况比较[例(%)]

Table 5. Comparison of chest CT findings between two groups of COVID-19 patients[cases(%)]

项目	非重症组(n=57)	重症组(n=37)	χ²值	P值
肺部结节			1.065	0.302
有	13(22.8)	12(32.4)
无	44(77.2)	25(67.6)
胸腔积液			5.528	0.019
有	13(22.8)	17(45.9)
无	44(77.2)	20(54.1)
肺实变			12.591	<0.001
有	27(47.4)	31(83.8)
无	30(52.6)	6(16.2)
毛玻璃影			1.713	0.191
有	37(64.9)	19(51.4)
无	20(35.1)	18(48.6)
铺路石症			2.510	0.113
有	0	3(8.1)
无	57(100.0)	34(91.9)

下载: 导出CSV

表 6 新冠感染严重程度影响因素的多因素logistic回归分析

Table 6. Multivariate logistic regression analysis of factors influencing COVID-19 severity

变量	B	SE	Waldχ²	P值	OR值	95% CI
年龄	-0.001	0.024	0.003	0.959	0.999	0.952~1.048
性别	1.185	0.724	2.680	0.102	3.271	0.792~13.517
冠心病	-1.611	0.760	4.492	0.034	0.200	0.045~0.886
中性粒细胞	-0.014	0.220	0.004	0.947	0.986	0.641~1.516
淋巴细胞	-0.549	0.839	0.427	0.513	0.578	0.111~2.994
NLR	0.018	0.090	0.041	0.839	1.018	0.853~1.215
Hb/Hb-SD	-0.813	0.729	1.244	0.265	0.443	0.106~1.851
CRP/ALB	0.741	0.361	4.215	0.040	2.099	1.034~4.258
总蛋白	-0.133	0.115	1.338	0.247	0.875	0.698~1.097
白蛋白	0.169	0.140	1.460	0.227	1.185	0.900~1.559
直接胆红素	0.140	0.193	0.529	0.467	1.150	0.789~1.679
D-二聚体	0.000	0.000	0.054	0.816	1.000	1.000~1.000
胸腔积液	0.107	0.880	0.019	0.890	1.113	0.246~5.037
肺实变	-1.833	0.691	7.429	0.006	0.152	0.039~0.589

下载: 导出CSV

参考文献(17)

[1]	QIN P, DU E Z, LUO W T, et al. Characteristics of the life cycle of Porcine Deltacoronavirus (PDCoV) in vitro: replication kinetics, cellular ultrastructure and virion morphology, and evidence of inducing autophagy[J]. Viruses, 2019, 11(5): 455. DOI: 10.3390/v11050455.
[2]	WANG J, ZHU K, XUE Y, et al. Research progress in the treatment of complications and sequelae of COVID-19[J]. Front Med, 2021, 8: 757605. DOI: 10.3389/fmed.2021.757605.
[3]	MACHHI J, HERSKOVITZ J, SENAN A M, et al. The natural history, pathobiology, and clinical manifestations of SARS-CoV-2 infections[J]. J Neuroimmune Pharmacol, 2020, 15(3): 359-386. doi: 10.1007/s11481-020-09944-5
[4]	ALPDAGTAS S, ILHAN E, UYSAL E, et al. Evaluation of current diagnostic methods for COVID-19[J]. APL Bioeng, 2020, 4(4): 041506. DOI: 10.1063/5.0021554.
[5]	KERMALI M, KHALSA R K, PILLAI K, et al. The role of biomarkers in diagnosis of COVID-19: a systematic review[J]. Life Sci, 2020, 254: 117788. DOI: 10.1016/j.lfs.2020.117788.
[6]	SCHNEIDER M. The role of biomarkers in hospitalized COVID-19 patients with systemic manifestations[J]. Biomark Insights, 2022, 17: 11772719221108909. DOI: 10.1177/11772719221108909.
[7]	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. https://www.cnki.com.cn/Article/CJFDTOTAL-JZYL202304001.htm NATIONAL Health Commission of the PRC. Diagnosis and treatment plan for COVID-19 (trial version 10)[J]. Chinese Journal of Clinical Infectious Diseases, 2023, 16(1): 1-9. https://www.cnki.com.cn/Article/CJFDTOTAL-JZYL202304001.htm
[8]	SILVA D L, LIMA C M, MAGALHÀES V C R, et al. Fungal and bacterial coinfections increase mortality of severely ill COVID-19 patients[J]. J Hosp Infect, 2021, 113: 145-154. doi: 10.1016/j.jhin.2021.04.001
[9]	BATTAGLINI D, LOPES-PACHECO M, CASTRO-FARIA-NETO H C, et al. Laboratory biomarkers for diagnosis and prognosis in COVID-19[J]. Front Immunol, 2022, 13: 857573. DOI: 10.3389/fimmu.2022.857573.
[10]	MOLINS B, FIGUERAS-ROCA M, VALERO O, et al. C-reactive protein isoforms as prognostic markers of COVID-19 severity[J]. Front Immunol, 2023, 13: 1105343. DOI: 10.3389/fimmu.2022.1105343.
[11]	LI J Y, LI M, ZHENG S S, et al. Plasma albumin levels predict risk for nonsurvivors in critically ill patients with COVID-19[J]. Biomark Med, 2020, 14(10): 827-837. doi: 10.2217/bmm-2020-0254
[12]	师明明, 王雅杰. 血清PCT、ALB及D-D水平与新型冠状病毒肺炎患者病情严重程度间的相关性分析[J]. 标记免疫分析与临床, 2023, 30(6): 918-922. https://www.cnki.com.cn/Article/CJFDTOTAL-BJMY202306004.htm SHI M M, WANG Y J. A correlation analysis between serum PCT, ALB and D-D levels and disease severity in patients with novel coronavirus pneumonia[J]. Labeled Immunoassays and Clinical Medicine, 2023, 30(6): 918-922. https://www.cnki.com.cn/Article/CJFDTOTAL-BJMY202306004.htm
[13]	马旭灿, 丁颖威, 章炳文. 丙种球蛋白冲击治疗重症社区获得性肺炎的疗效及对患者血清免疫蛋白和淋巴细胞亚群的影响[J]. 中华全科医学, 2021, 19(9): 1497-1499, 1583. doi: 10.16766/j.cnki.issn.1674-4152.002095 MA X C, DING Y W, ZHANG B W. Effect of gamma globulin pulse therapy on patients with severe community-acquired pneumonia and on serum immune proteins and lymphocyte subsets[J]. Chinese Journal of General Practice, 2021, 19(9): 1497-1499, 1583. doi: 10.16766/j.cnki.issn.1674-4152.002095
[14]	KARAKOYUN I, COLAK A, TURKEN M, et al. Diagnostic utility of C-reactive protein to albumin ratio as an early warning sign in hospitalized severe COVID-19 patients[J]. Int Immunopharmacol, 2021, 91: 107285. DOI: 10.1016/j.intimp.2020.107285.
[15]	JOVIČIĆ B P, RAKOVIĆ I, GAVRILOVIĆ J, et al. Vitamin D, albumin, and D-dimer as significant prognostic markers in early hospitalization in patients with COVID-19[J]. J Clin Med, 2023, 12(8): 2825. DOI: 10.3390/jcm12082825.
[16]	ZHANG C, ZHENG F X, WU X Y. Predictive value of C-reactive protein-to-albumin ratio for risk of 28-day mortality in patients with severe pneumonia[J]. J Lab Med, 2023, 47(3): 115-120. doi: 10.1515/labmed-2022-0114
[17]	MALÉCOT N, CHRUSCIEL J, SANCHEZ S, et al. Chest CT characteristics are strongly predictive of mortality in patients with COVID-19 pneumonia: a multicentric cohort study[J]. Acad Radiol, 2022, 29(6): 851-860. doi: 10.1016/j.acra.2022.01.010