Application of a prediction model with laboratory indexes in the risk stratification of patients with COVID‑19

Ye,Jiru; Zhang,Xiaoqing; Zhu,Feng; Tang,Yao

doi:10.3892/etm.2021.9613

Application of a prediction model with laboratory indexes in the risk stratification of patients with COVID‑19

Authors:
- Jiru Ye
- Xiaoqing Zhang
- Feng Zhu
- Yao Tang
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Affiliations: Department of Respiratory and Critical Care Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, P.R. China, Department of Respiratory and Critical Care Medicine, Wuxi Fifth People's Hospital, Wuxi, Jiangsu 214000, P.R. China, Department of Tuberculosis, Huaian No. 4 People's Hospital, Huaian, Jiangsu 223000, P.R. China
Published online on: January 5, 2021 https://doi.org/10.3892/etm.2021.9613
Article Number: 182
Copyright: © Ye et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

In the present study, a prediction model with combined laboratory indexes in risk stratification of patients with COVID‑19 was established and tested. The data of 170 patients with COVID‑19 who were divided into an asymptomatic‑moderate group (141 cases) and severe or above group (29 cases) were retrospectively analyzed. The clinical characteristics and laboratory indexes of the two groups were compared. Multivariate logistic regression analysis was performed to construct the prediction model based on laboratory indexes. A receiver operating characteristic (ROC) curve analysis was used to compare the diagnostic efficacy of different indexes. Decision curve analysis (DCA) was performed to quantify and compare the clinical validity of the prediction models. There were significant differences in blood cell count, high‑sensitivity C‑reactive protein (hsCRP) and procalcitonin (PCT) levels between the severe or above group and the asymptomatic‑moderate group (all P<0.05). Among all individual indexes, hsCRP had the highest diagnostic efficacy (area under the curve=0.870), with a sensitivity and specificity of 0.828 and 0.802, respectively. The red blood cell count, hsCRP and PCT were used to construct the prediction model. The AUC of the prediction model was higher than that of hsCRP (0.912 vs. 0.870) but the difference was not significant (P=0.307). DCA suggested that the net benefit of the prediction model was higher than that of hsCRP in most cases and significantly higher than that of PCT, lymphocytes and monocytes. The prediction model with combined laboratory indexes was able to more effectively predict the clinical classification of patients with COVID‑19 and may be used as a tool for risk stratification of patients.

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1	Fan Z, Chen L, Li J, Cheng X, Yang J, Tian C, Zhang Y, Huang S, Liu Z and Cheng J: Clinical features of COVID-19-related liver functional abnormality. Clin Gastroenterol Hepatol. 18:1561–1566. 2020.PubMed/NCBI View Article : Google Scholar
2	Wu J, Song S, Cao HC and Li LJ: Liver diseases in COVID-19: Etiology, treatment and prognosis. World J Gastroenterol. 26:2286–2293. 2020.PubMed/NCBI View Article : Google Scholar
3	Martins-Filho PR, Tavares CSS and Santos VS: Factors associated with mortality in patients with COVID-19. A quantitative evidence synthesis of clinical and laboratory data. Eur J Intern Med. 76:97–99. 2020.PubMed/NCBI View Article : Google Scholar
4	Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ and Prescott HC: Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): A review. JAMA. 324:782–793. 2020.PubMed/NCBI View Article : Google Scholar
5	Liu Q, Song NC, Zheng ZK, Li JS and Li SK: Laboratory findings and a combined multifactorial approach to predict death in critically ill patients with COVID-19: A retrospective study. Epidemiol Infect. 148(e129)2020.PubMed/NCBI View Article : Google Scholar
6	Wang F, Hou H, Luo Y, Tang G, Wu S, Huang M, Liu W, Zhu Y, Lin Q, Mao L, et al: The laboratory tests and host immunity of COVID-19 patients with different severity of illness. JCI Insight. 5(e137799)2020.PubMed/NCBI View Article : Google Scholar
7	Vultaggio A, Vivarelli E, Virgili G, Lucenteforte E, Bartoloni A, Nozzoli C, Morettini A, Berni A, Malandrino D, Rossi O, et al: Prompt predicting of early clinical deterioration of moderate-to-severe COVID-19 patients: Usefulness of a combined score using IL-6 in a preliminary study. J Allergy Clin Immunol Pract. 8:2575–2581.e2. 2020.PubMed/NCBI View Article : Google Scholar
8	Chen G, Wu D, Guo W, Cao Y, Huang D, Wang H, Wang T, Zhang X, Chen H, Yu H, et al: Clinical and immunological features of severe and moderate coronavirus disease 2019. J Clin Invest. 130:2620–2629. 2020.PubMed/NCBI View Article : Google Scholar
9	Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, Villamizar-Peña R, Holguin-Rivera Y, Escalera-Antezana JP, Alvarado-Arnez LE, Bonilla-Aldana DK, Franco-Paredes C, Henao-Martinez AF, et al: Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis. 34(101623)2020.PubMed/NCBI View Article : Google Scholar
10	Zhang B, Zhang J, Chen H, Chen L, Chen Q, Li M, Chen Z, You J, Yang K and Zhang S: Novel coronavirus disease 2019 (COVID-19): Relationship between chest CT scores and laboratory parameters. Eur J Nucl Med Mol Imaging. 47:2083–2089. 2020.PubMed/NCBI View Article : Google Scholar
11	Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Damen JAA, Debray TPA, De Vos M, et al: Prediction models for diagnosis and prognosis of covid-19 infection: Systematic review and critical appraisal. BMJ. 369(m1328)2020.PubMed/NCBI View Article : Google Scholar
12	Lorente-Ros A, Monteagudo Ruiz JM, Rincón LM, Ortega Pérez R, Rivas S, Martínez-Moya R, Sanromán MA, Manzano L, Alonso GL, Ibáñez B and Zamorano JL: Myocardial injury determination improves risk stratification and predicts mortality in COVID-19 patients. Cardiol J. 27:489–496. 2020.PubMed/NCBI View Article : Google Scholar
13	Qin L, Yang Y, Cao Q, Cheng Z, Wang X, Sun Q, Yan F, Qu J and Yang W: A predictive model and scoring system combining clinical and CT characteristics for the diagnosis of COVID-19. Eur Radiol. 30:6797–6807. 2020.PubMed/NCBI View Article : Google Scholar
14	Chen X, Tang Y, Mo Y, Li S, Lin D, Yang Z, Yang Z, Sun H, Qiu J, Liao Y, et al: A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: A multi-center study. Eur Radiol. 30:4893–4902. 2020.PubMed/NCBI View Article : Google Scholar
15	Released by National Health Commission & National Administration of Traditional Chinese Medicine on March 3, 2020. Diagnosis and treatment protocol for novel coronavirus pneumonia (Trial Version 7). Chin Med J (Engl). 133:1087–1095. 2020.PubMed/NCBI View Article : Google Scholar
16	DeLong ER, DeLong DM and Clarke-Pearson DL: Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics. 44:837–845. 1988.PubMed/NCBI
17	Liu YP, Li GM, He J, Liu Y, Li M, Zhang R, Li YL, Wu YZ and Diao B: Combined use of the neutrophil-to-lymphocyte ratio and CRP to predict 7-day disease severity in 84 hospitalized patients with COVID-19 pneumonia: A retrospective cohort study. Ann Transl Med. 8(635)2020.PubMed/NCBI View Article : Google Scholar
18	Su TH and Kao JH: The clinical manifestations and management of COVID-19-related liver injury. J Formos Med Assoc. 119:1016–1018. 2020.PubMed/NCBI View Article : Google Scholar
19	Hu L, Chen S, Fu Y, Gao Z, Long H, Ren HW, Zuo Y, Wang J, Li H, Xu QB, et al: Risk factors associated with clinical outcomes in 323 coronavirus disease 2019 (COVID-19) hospitalized patients in Wuhan, China. Clin Infect Dis. 71:2089–2098. 2020.PubMed/NCBI View Article : Google Scholar
20	Su M, Yuan J, Peng J, Wu M, Yang Y and Peng YG: Clinical prediction model for mortality of adult diabetes inpatients with COVID-19 in Wuhan, China: A retrospective pilot study. J Clin Anesth. 66(109927)2020.PubMed/NCBI View Article : Google Scholar
21	Wang F, Hou H, Wang T, Luo Y, Tang G, Wu S, Zhou H and Sun Z: Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests. Travel Med Infect Dis. 36(101782)2020.PubMed/NCBI View Article : Google Scholar
22	Tian S, Liu H, Liao M, Wu Y, Yang C, Cai Y, Peng Z and Xiao SY: Analysis of mortality in patients with COVID-19: Clinical and laboratory parameters. Open Forum Infect Dis. 7(ofaa152)2020.PubMed/NCBI View Article : Google Scholar
23	Liu J, Li S, Liu J, Liang B, Wang X, Wang H, Li W, Tong Q, Yi J, Zhao L, et al: Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients. EBioMedicine. 55(102763)2020.PubMed/NCBI View Article : Google Scholar
24	Wang L, Liu Y, Zhang T, Jiang Y, Yang S, Xu Y, Song R, Song M, Wang L, Zhang W, et al: Differentiating between 2019 novel coronavirus pneumonia and influenza using a nonspecific laboratory marker-based dynamic nomogram. Open Forum Infect Dis. 7(ofaa169)2020.PubMed/NCBI View Article : Google Scholar
25	Soraya GV and Ulhaq ZS: Crucial laboratory parameters in COVID-19 diagnosis and prognosis: An updated meta-analysis. Med Clin (Barc). 155:143–151. 2020.PubMed/NCBI View Article : Google Scholar
26	Thomas T, Stefanoni D, Dzieciatkowska M, Issaian A, Nemkov T, Hill RC, Francis RO, Hudson KE, Buehler PW, Zimring JC, et al: Evidence of structural protein damage and membrane lipid remodeling in red blood cells from COVID-19 patients. J Proteome Res. 19:4455–4469. 2020.PubMed/NCBI View Article : Google Scholar
27	Wang K, Zuo P, Liu Y, Zhang M, Zhao X, Xie S, Zhang H, Chen X and Liu C: Clinical and laboratory predictors of in-hospital mortality in patients with coronavirus disease-2019: A cohort study in Wuhan, China. Clin Infect Dis. 71:2079–2088. 2020.PubMed/NCBI View Article : Google Scholar
28	Sager R, Kutz A, Mueller B and Schuetz P: Procalcitonin-guided diagnosis and antibiotic stewardship revisited. BMC Med. 15(15)2017.PubMed/NCBI View Article : Google Scholar
29	Hu R, Han C, Pei S, Yin M and Chen X: Procalcitonin levels in COVID-19 patients. Int J Antimicrob Agents. 56(106051)2020.PubMed/NCBI View Article : Google Scholar
30	Lippi G and Plebani M: Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. Clin Chim Acta. 505:190–191. 2020.PubMed/NCBI View Article : Google Scholar
31	Liu F, Li L, Xu M, Wu J, Luo D, Zhu Y, Li B, Song X and Zhou X: Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol. 127(104370)2020.PubMed/NCBI View Article : Google Scholar
32	Fang FC, Naccache SN and Greninger AL: The laboratory diagnosis of COVID-19-frequently-asked questions. Clin Infect Dis, Jun 8, 2020 (Epub ahead of print).
33	Chen R, Sang L, Jiang M, Yang Z, Jia N, Fu W, Xie J, Guan W, Liang W, Ni Z, et al: Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China. J Allergy Clin Immunol. 146:89–100. 2020.PubMed/NCBI View Article : Google Scholar