基于实验室指标的新型冠状病毒肺炎和甲型流感鉴别诊断模型的建立及其临床意义

doi:10.13481/j.1671-587X.20220232

Abstract

Abstract: Objective

To explore the differences in laboratory indicators test results of coronavirus disease 2019 （COVID-19） and influenza A and to establish a differential diagnosis model for the two diseases， and to clarify the clinical significance of the model for distinguishing the two diseases.

Methods

A total of 56 common COVID-19 patients and 54 influenza A patients were enrolled， and 24 common COVID-19 patients and 30 influenza A patients were used for model validation. The average values of the laboratory indicators of the patients 5 d after admission were calculated， and the elastic network model and the stepwise Logistic regression model were used to screen the indicators for identifying COVID-19 and influenza A. Elastic network models were used for the first round of selection， in which the optimal cutoff of lambda was chosen by performing 10-fold cross validations. With different random seeds， the elastic net models were fit for 200 times to select the high-frequency indexes （frequency>90%）.A Logistic regression model with AIC as the selection criterions was used in the second round of screening uses； a nomogram was used to represent the final model； an independent data were used as an external validation set， and the area under the curve （AUC） of the validation set were calculate to evaluate the predictive the performance of the model.

Results

After the first round of screening， 16 laboratory indicators were selected as the high-frequency indicators. After the second round of screening， albumin/globulin （A/G），total bilirubin （TBIL） and erythrocyte volume （HCT） were identified as the final indicators. The model had good predictive performance， and the AUC of the verification set was 0.844（95% CI：0.747－0.941）.

Conclusion

A differential diagnosis model for COVID-19 and influenza A based on laboratory indicators is successfully established， and it will help clinical and timely diagnosis of both diseases.

Key words: Coronavirus disease 2019, Influenza A, Diagnostic model, Albumin, Globulin

CLC Number:

R446.6

Dongyang XING,Suyan TIAN,Yukun CHEN,Jinmei WANG,Xuejuan SUN,Shanji LI,Jiancheng XU. Establishment of differential diagnostic model for COVID-19 and influenza A based on laboratory indicators and its clinical significance[J].Journal of Jilin University(Medicine Edition), 2022, 48(2): 518-526.

Figures/Tables 8

Tab.1

Tab. 2

Tab. 3

Tab. 4

Laboratory findings at onset to hospital admission"

Parameter	Reference interval	COVID-19 [M (P₂₅－P₇₅)]	Influenza A [M (P₂₅－P₇₅)]	P
Hematological parameter
WBC(×10⁹ L^－1)	3.50－9.50	5.22 (3.90－6.30)	7.33(5.44－9.40)	<0.001
NE (×10⁹ L^－1)	1.80－6.30	3.45 (2.48－4.38)	5.43 (3.73－6.59)	<0.001
LY (×10⁹ L^－1)	1.10－3.20	1.35 (0.90－1.66)	1.20 (0.70－1.49)	0.049
EO (×10⁹ L^－1)	0.02－0.52	0.03 (0.00－0.04)	0.08 (0.00－0.12)	0.094
BA(×10⁹ L^－1)	0.00－0.06	0.02 (0.00－0.03)	0.03 (0.01－0.04)	0.199
MO (×10⁹ L^－1)	0.10－0.60	0.36 (0.24－0.50)	0.59 (0.40－0.76)	<0.001
RBC (×10¹² L^－1)	4.30－5.80	4.69 (4.29－5.09)	4.08 (3.40－4.66)	<0.001
HGB(g·L^－1)	130.00－175.00	145.00 (133.00－158.00)	125.98 (104.00－142.50)	<0.001
HCT (L·L^－1)	0.40－0.50	0.42 (0.39－0.45)	0.37 (0.31－0.42)	<0.001
MCV (fL)	82.00－100.00	89.68 (86.58－92.60)	91.34 (87.15－93.55)	0.201
MCH (pg)	27.00－34.00	30.82 (30.00－32.03)	30.90 (29.60－32.45)	0.810
MCHC (g·L^－1)	316.00－354.00	344.69 (340.75－349.00)	337.79 (327.50－347.50)	<0.001
RDW (%)	11.00－16.00	11.65 (11.20－11.90)	13.76 (12.55－14.65)	<0.001
Platelets (×10⁹ L^－1)	125.00－350.00	201.83 (159.25－230.50)	193.08 (125.00－281.00)	0.342
MPV (fL)	6.50－12.00	9.40 (8.58－10.10)	10.79 (9.70－11.55)	<0.001
PDW (%)	9.00－17.00	12.81 (10.65－14.35)	12.32 (10.50－12.90)	0.216
PCT (%)	0.11－0.29	0.19 (0.14－0.20)	0.20 (0.14－0.29)	0.485
Biochemical parameter
AST (U·L^－1)	13.00－40.00	29.30 (20.00－30.00)	52.50 (18.20－78.35)	0.041
ALT (U·L^－1)	7.00－50.00	34.23 (19.00－45.00)	43.25 (12.80－63.95)	0.617
GGT (U·L^－1)	7.00－60.00	34.30 (15.00－40.00)	72.33 (18.50－113.70)	<0.001
CHE (U·L^－1)	5 000－12 000	8 032 (6 885－9 045)	4 865 (3 811－5 828)	<0.001
TP (g·L^－1)	65.0－85.0	68.43 (65.00－72.00)	60.60 (56.30－64.60)	<0.001
ALB (g·L^－1)	40.0－55.0	44.30 (42.20－45.90)	30.95 (26.55－35.70)	<0.001
GLB (g·L^－1)	20.0－40.0	24.08 (21.60－26.70)	29.65 (26.35－33.30)	<0.001
A/G	1.20－2.40	1.89 (1.66－2.06)	1.08 (0.86－1.18)	<0.001
TBIL (μmol·L^－1)	≤23.00	10.00 (6.70－13.20)	19.61 (9.80－25.50)	<0.001
DBIL(μmol·L^－1)	≤8.00	4.10 (3.10－5.10)	8.78 (3.00－9.55)	<0.001
IBIL (μmol·L^－1)	5.10－21.40	5.87 (3.40－7.50)	10.83 (6.45－13.15)	<0.001
Urea (mmol·L^－1)	3.10－8.00	3.96 (3.07－4.75)	6.12 (3.88－8.24)	<0.001
Cr (μmol·L^－1)	57.00－97.00	67.90 (57.50－77.00)	70.08 (47.25－84.38)	0.207
CO₂-CP (mmol·L^－1)	22.00－29.00	20.74 (18.9－22.4)	25.78 (24.23－27.80)	<0.001
Glu (mol·L^－1)	3.89－6.11	6.62 (5.53－6.79)	6.38 (5.01－7.26)	0.392
K⁺ (mmol·L^－1)	3.50－5.30	4.14 (3.90－4.40)	3.95 (3.60－4.30)	0.036
Na⁺ (mmol·L^－1)	137.00－147.00	138.50 (137.00－140.00)	136.74 (134.00－140.00)	0.021
Cl^－ (mmol·L^－1)	99.00－110.00	99.73 (97.25－102.00)	101.54 (98.00－104.00)	0.030

Tab. 4

Fig. 1

Fig. 2

Fig. 3

Fig. 4

References 30

1	ZHANG Z， NAVARESE E P， ZHENG B， et al.Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome［J］. J Evid Based Med，2020，13（4）： 301-312.
2	LEI H， LI Y， XIAO S， et al.Routes of transmission of influenza A H1N1， SARS CoV， and norovirus in air cabin： Comparative analyses［J］.Indoor Air，2018，28（3）： 394-403.
3	SULLIVAN S J， JACOBSON R M， DOWDLE W R， et al.2009 H1N1 influenza［J］. Mayo Clin Proc，2010， 85（1）： 64-76.
4	SCHOEN K， HORVAT N， GUERREIRO N F C， et al.Spectrum of clinical and radiographic findings in patients with diagnosis of H1N1 and correlation with clinical severity［J］. BMC Infect Dis，2019，19（1）： 964.
5	GAO H N， LU H Z， CAO B， et al.Clinical findings in 111 cases of influenza A （H7N9） virus infection［J］. N Engl J Med，2013，368（24）： 2277-2285.
6	COBB N L， SATHE N A， DUAN K I， et al.Comparison of clinical features and outcomes in critically Ill patients hospitalized with COVID-19 versus influenza［J］.Ann Am Thorac Soc，2021，18（4）：632-640.
7	SUN Y， KOH V， MARIMUTHU K， et al.Epidemiological and clinical predictors of COVID-19［J］. Clin Infect Dis，2020，71（15）： 786-792.
8	TIAN S， ZHU X， SUN X， et al.A Prognostic model to predict recovery of COVID-19 patients based on longitudinal laboratory findings［J］.Virol Sin，2020，35（6）： 811-819.
9	WANG L， GAO P， ZHANG M， et al.Prevalence and ethnic pattern of diabetes and prediabetes in China in 2013［J］. JAMA，2017，317（24）： 2515-2523.
10	WANG L， CHANG L S， LEE I K， et al.Clinical diagnosis of pandemic A（H1N1） 2009 influenza in children with negative rapid influenza diagnostic test by lymphopenia and lower C-reactive protein levels［J］. Influenza Other Respir Viruses，2014， 8（1）： 91-98.
11	LI Y， WANG H， WANG F， et al.Comparison of hospitalized patients with pneumonia caused by COVID-19 and influenza A in children under 5 years［J］. Int J Infect Dis，2020，98： 80-83.
12	CHEN Y， LIANG W， YANG S， et al.Human infections with the emerging avian influenza A H7N9 virus from wet market poultry： clinical analysis and characterisation of viral genome［J］.Lancet，2013，381（9881）： 1916-1925.
13	ONG A K， CHEN M I， LIN L， et al.Improving the clinical diagnosis of influenza--a comparative analysis of new influenza A （H1N1） cases［J］. PLoS One，2009，4（12）： e8453.
14	FLICK H， DRESCHER M， PRATTES J， et al.Predictors of H1N1 influenza in the emergency department： proposition for a modified H1N1 case definition［J］. Clin Microbiol Infect，2014，20（2）： O105-108.
15	KUMAR A， ZARYCHANSKI R， PINTO R， et al.Critically ill patients with 2009 influenza A（H1N1） infection in Canada［J］.JAMA，2009，302（17）：1872-1879.
16	RODRIGUEZ-NORIEGA E， GONZALEZ-DIAZ E， MORFIN-OTERO R， et al.Hospital triage system for adult patients using an influenza-like illness scoring system during the 2009 pandemic--Mexico［J］.PLoS One，2010，5（5）： e10658.
17	PEREZ-PADILLA R， DE LA ROSA-ZAMBONI D， PONCE DE LEON S， et al.Pneumonia and respiratory failure from swine-origin influenza A （H1N1） in Mexico［J］. N Engl J Med，2009，361（7）： 680-689.
18	NSEIR S， CAVESTRI B， DI POMPEO C， et al.Factors predicting bacterial involvement in severe acute exacerbations of chronic obstructive pulmonary disease［J］. Respiration，2008，76（3）： 253-260.
19	SALVAGNO G L， SANCHIS-GOMAR F， PICANZA A， et al.Red blood cell distribution width： A simple parameter with multiple clinical applications［J］. Crit Rev Clin Lab Sci，2015，52（2）： 86-105.
20	WEISS G， GOODNOUGH L T.Anemia of chronic disease［J］. N Engl J Med，2005， 352（10）： 1011-1023.
21	CHEN N， 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.
22	SKEVAKI C， FRAGKOU P C， CHENG C， et al.Laboratory characteristics of patients infected with the novel SARS-CoV-2 virus［J］. J Infect，2020，81（2）： 205-212.
23	ZHANG J， ZHAO Y， CHEN Y.Laboratory findings in patients with avian-origin influenza A （H7N9） virus infections［J］. J Med Virol，2014，86（5）： 895-898.
24	TO K K， SONG W， LAU S Y， et al.Unique reassortant of influenza A（H7N9） virus associated with severe disease emerging in Hong Kong［J］. J Infect， 2014，69（1）： 60-68.
25	PAN F， XIAO X， GUO J， et al.No evidence of severe acute respiratory syndrome-coronavirus 2 in semen of males recovering from coronavirus disease 2019［J］. Fertil Steril，2020，113（6）： 1135-1139.
26	ABELLEIRA R， RUANO-RAVINA A， LAMA A， et al.Influenza A H1N1 community-acquired pneumonia： characteristics and risk factors-A case-control study［J］. Can Respir J，2019，2019： 4301039.
27	CHENG X， LIU YM， LI H， et al.Metformin is associated with higher incidence of acidosis， but not mortality， in individuals with COVID-19 and pre-existing type 2 diabetes［J］.Cell Metablism，2020，32（4）： 537-547.e533.
28	TANG X， DU R H， WANG R， et al.Comparison of hospitalized patients with ARDS caused by COVID-19 and H1N1［J］. Chest，2020，158（1）： 195-205.
29	ZHANG Y， LIU J， YU L， et al.Prevalence and characteristics of hypoxic hepatitis in the largest single-centre cohort of avian influenza A（H7N9） virus-infected patients with severe liver impairment in the intensive care unit［J］. Emerg Microbes Infect，2016，5： e1.
30	MAKKOCH J， PRACHAYANGPRECHA S， PAYUNGPORN S， et al.Erythrocyte binding preference of human pandemic influenza virus a and its effect on antibody response detection［J］. Ann Lab Med，2012，32（4）： 276-282.

Hospital	Biochemical equipment	Hematological equipment
First Hospital of Jilin University
Instrument	7600-210	XN-9000	CS-5100
Manufacturer	Hitachi High-Technologies	Sysmex Corp.	Sysmex Corp.
Origin	Tokyo, Japan	Hyogo, Japan	Hyogo, Japan
Changchun Infectious Disease Hospital
Instrument	CS-T300	DF53	OCG-102
Manufacturer	Dirui Industrial Co.	Dymind Biotechnology Co	Wondfo Biotech Co.
Origin	Changchun, China	Shenzhen, China	Guangzhou, China
Siping Infectious Disease Hospital
Instrument	Pointcare M3i	ABX Pentra XL 80	CS-2500
Manufacturer	Mnchip Technology Co.	Horiba Medical	Sysmex Corp.
Origin	Tianjin, China	Montpellier, France	Hyogo, Japan
Jilin Infectious Disease Hospital
Instrument	AU480	XN-1000	SF-8100
Manufacturer	Beckman Coulter Inc.	Sysmex Corp.	Beijing Succeeder Technology Inc.
Origin	Brea, the United States	Hyogo, Japan	Beijing, China

Item	COVID-19 (n=56)	Influenza A (n=54)	P
Hospitalized time（t/d)	18 (15－20) 18 (11－20)	7 (5－10) 6 (4－9)	<0.001
Male	31 (55.4)	36 (66.7)	0.246
Age(year)	40 (28－51)	63 (48－71)	<0.001
Clinical characteristics
Fever	46 (82.1)	29 (53.7)	0.002
Cough	44 (78.6)	19 (35.2)	<0.001
Hemoptysis	1 (1.8)	1 (1.9)	0.743
Headache	6 (10.7)	2 (3.7)	0.271
Chest pain	2 (3.6)	2 (3.7)	0.677
Fatigue	20 (35.7)	9 (16.7)	0.030
Muscle pain	9 (16.1)	4 (7.4)	0.238
Dyspnea	6 (10.7)	11 (20.4)	0.193
Abdominal pain or diarrhea	6 (10.7)	6 (11.1)	0.593
Data were presented as median (P₂₅－P₇₅) or No.(%).

Item	COVID-19 (n=24)	Influenza A (n=30)	P
Hospitalized time(t/d)	21 (19－24) 18 (11－20)	3 (2－4) 6 (4－9)	<0.001
Male	11 (46)	17 (57)	0.429
Age(year)	40 (31－49)	54 (40－72)	0.011
Clinical characteristics
Fever	20 (83)	16 (53)	0.020
Cough	15 (63)	8 (27)	0.008
Hemoptysis	1 (4)	0 (0)	0.690
Headache	4 (17)	2 (7)	0.245
Chest pain	1 (4)	0 (0)	0.259
Fatigue	9 (38)	4 (13)	0.039
Muscle pain	3 (13)	2 (7)	0.462
Dyspnea	2 (8)	1 (3)	0.425
Abdominal pain or diarrhea	1 (4)	1 (3)	0.872
Data were presented as median (P₂₅－P₇₅) or No. (%).

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Establishment of differential diagnostic model for COVID-19 and influenza A based on laboratory indicators and its clinical significance

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