Epidemiology and Infectious DiseasesEpidemiology and Infectious Diseases1560-95292411-3026Eco-Vector8762110.17816/EID87621Research ArticleClinical and laboratory predictors of poor outcome in COVID-19 patientsLizinfeldIrina A.<p>MD</p>irinalizinfeld@gmail.comhttps://orcid.org/0000-0002-8114-1002PshenichnayaNatalia Yu.<p>MD, Dr. Sci. (Med.), Professor</p>natalia-pshenichnaya@yandex.ruhttps://orcid.org/0000-0003-2570-711XBunyaevaOlga V.<p>MD</p>olya-bunyaeva@mail.ruhttps://orcid.org/0000-0002-4889-5566ShilkinaIrina M.<p>MD</p>shim-48@mail.ruhttps://orcid.org/0000-0002-9900-038XShmailenkoOlga A.<p>MD</p>Shmailenko@mail.ruhttps://orcid.org/0000-0002-4680-590XGopatsaGalina V.<p>MD, Cand. Sci. (Med.)</p>GopatsaG@mail.ruhttps://orcid.org/0000-0001-8703-7671SiziakinDmitrii V.<p>MD, Dr. Sci. (Med.), Professor</p>Siziakin@gmail.comhttps://orcid.org/0000-0001-7125-1374ChigaevaEvgeniia V.<p>MD, Cand. Sci. (Med.)</p>ChigaevaEV@gmail.comCentral Research Institute of EpidemiologyDomodedovo Central City HospitalCity Hospital № 1 N.A. Semashko, City Hospital No. 1 named after N.A. Semashko of Rostov-on-DonRostov State Medical University071020222715141611202126012022Copyright © 2022, Lizinfeld I.A., Pshenichnaya N.Y., Bunyaeva O.V., Shilkina I.M., Shmailenko O.A., Gopatsa G.V., Siziakin D.V., Chigaeva E.V.2022<p><em><strong>BACKGROUND:</strong> </em>Many researchers have reported numerous predictors of severe COVID-19 and poor prognosis. However, to make a quick decision, the doctor needs to have a certain set of data that he can use in routine practice to predict the outcome in patients with this disease.</p>
<p><em><strong>AIMS:</strong> </em>This study aimed to develop and describe a predictive model for determining an unfavorable outcome in COVID-19 patients based on age, objective, laboratory and instrumental data, and comorbid pathology.</p>
<p><em><strong>MATERIALS AND METHODS:</strong></em> The study included 447 patients with a laboratory-confirmed diagnosis of COVID-19 who underwent inpatient treatment in the period from March 2020 to January 2021. Discriminant analysis was used with cross-validation to build a predictive model.</p>
<p><em><strong>RESULTS:</strong></em> Based on discriminant analysis, a predictive model was developed to predict the outcome in patients with COVID-19. Evaluation of clinical findings, such as respiratory rate, heart rate, SpO2, laboratory data, and computed tomography results on admission to the hospital, showed their significance as predictors of poor outcome. The discrimination constant was 0.4435. The sensitivity of the model is 96.4%, and the specificity is 90.4%.</p>
<p><em><strong>CONCLUSION:</strong></em> The developed model will help medical institutions predict the outcome of the disease when a patient is admitted to the hospital and, on this basis, optimize and prioritize the provision of necessary medical care.</p>COVID-19predictive modelpredictors of poor outcomelaboratory dataCOVID-19прогностическая модельпредикторы неблагоприятного исходалабораторные данные[Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet. 2020;395(10223):497–506. doi: 10.1016/S0140-6736(20)30183-5][Wu Z, McGoogan JM. Characteristics of and important lessons from the Coronavirus Disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239–1242. doi: 10.1001/jama.2020.2648][Shi C, Wang L, Ye J, et al. Predictors of mortality in patients with coronavirus disease 2019: a systematic review and meta-analysis. BMC Infect Dis. 2021;21(1):663. doi: 10.1186/s12879-021-06369-0][Grasselli G, Zangrillo A, Zanella A, et al. Baseline characteristics and outcos of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy. JAMA. 2020;323(16):1574–1581. doi: 10.1001/jama.2020.5394][Sisó-Almirall A, Kostov B, Mas-Heredia M, et al. Prognostic factors in Spanish COVID-19 patients: a case series from Barcelona. PLoS One. 2020;15(8):e0237960. doi: 10.1371/journal.pone.0237960][De Souza FS, Hojo-Souza NS, Batista BD, et al. On the analysis of mortality risk factors for hospitalized COVID-19 patients: a data-driven study using the major Brazilian database. PLoS One. 2021;16(3):e0248580. doi: 10.1371/journal.pone.0248580][Lai CC, Wang CY, Wang YH, et al. Global epidemiology of coronavirus disease 2019 (COVID-19): disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status. Int J Antimicrob Agents. 2020;55(4):105946. doi: 10.1016/j.ijantimicag.2020.105946][Glybochko PV, Fomin VV, Moiseev SV, et al. Risk factors for the early development of septic shock in patients with severe COVID-19. Ther Arch. 2020;92(11):17–23. (In Russ). doi: 10.26442/00403660.2020.11.000780][Klypa TV, Bychinin MV, Mandel IA, et al. Clinical characteristics of patients admitted to an ICU with COVID-19. Predictors of the severe disease. Clin Pract. 2020;11(2):6–20. (In Russ). doi: 10.17816/clinpract34182][Kiss S, Gede N, Hegyi P, et al. Early changes in laboratory parameters are predictors of mortality and ICU admission in patients with COVID-19: a systematic review and meta-analysis. Med Microbiol Immunol. 2021;210(1)33–47. doi: 10.1007/s00430-020-00696-w][Chung M, Bernheim A, Mei X, et al. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology. 2020;295(1):202–207. doi: 10.1148/radiol.2020200230][Durhan G, Düzgün AS, Demirkazık BF, et al. Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings. Diagn Interv Radiol. 2020;26(6):557–564. doi: 10.5152/dir.2020.20407][Burian E, Jungmann F, Kaissis GA, et al. Intensive care risk estimation in COVID-19 pneumonia based on clinical and imaging parameters: experiences from the munich cohort. J Clin Med. 2020; 9(5):1514. doi: 10.3390/jcm9051514][Avdeev SN, Adamyan LV, Alekseeva EI, et al. Prevention, diagnosis and treatment of new coronavirus infection (COVID-19). Temporary methodological recommendations. Version 9 from 26.10.2020. Moscow; 2020. 236 p. (In Russ).][Liu J, Liu Y, Xiang P, et al. Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J Transl Med. 2020;18(1):206. doi: 10.1186/s12967-020-02374-0][Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a meta-analysis. Clin Chim Acta. 2020;506:145–148. doi: 10.1016/j.cca.2020.03.022][Yang M, Ng MH, Li CK. Thrombocytopenia in patients with severe acute respiratory syndrome (review). Hematology. 2005;10(2): 101–105. doi: 10.1080/10245330400026170][Assaf D, Gutman Y, Neuman Y, et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med. 2020;15(8):1435–1443. doi: 10.1007/s11739-020-02475-0]