The factor analysis approach to mortality prediction in COVID-19 severe disease using laboratory values: a retrospective study

Year 2022, , 528 - 533, 15.03.2022

Umut Kara , Fatih Şimşek Mehmet Özgür Özhan Mehmet Emin Ince , Gökhan Özkan Serkan Şenkal , Ahmet Coşar

https://doi.org/10.32322/jhsm.1052191

Cited By: 4

Abstract

Aim: Factor analysis is a statistical approach used mainly in social science scale development systems. The aim of this study was to evaluate the performance of factorial structures formed by laboratory values in predicting mortality in severe COVID-19 patients.
Material and Method: The study included 281 patients diagnosed with ‘‘severe coronavirus infection’’ according to the WHO COVID-19 clinical management guideline who were treated in a 13-bed adult tertiary-level critical care unit of a tertiary level hospital. For a total of 23 variables (ALT, AST, BUN, creatinine, Na, K, LDH, CRP, CK, ferritin, D-dimer, INR, TB, Glu, NLR, WBC, fibrinogen, % NEU, PLT, HTC, % LYM, TLC, Alb), laboratory values were collected. A two-step method was used to determine if exploratory factors might be used in place of laboratory variables. First, the ability of individual laboratory variables to predict mortality was obtained by analysis of the receiver operating characteristic (ROC) analysis. Then, the ability of factors created from these variables to predict mortality was measured using ROC analysis. The area under curve (AUC) values were compared between the two conditions.
Results: The Kaiser-Meyer-Olkin (KMO) value calculated using factor analysis on the variables was found to be 0.661. The significance level of the Bartlett’s Test was <0.001. The correlation matrix determinant was found to be 0.001. CRP, ferritin, LDH, D-dimer, PLT, and TLC all had AUC values >0.6. A five-factor structure was created based on the Scree Plot. The fifth factor, which included CRP, fibrinogen, and ferritin, was the highest for predicting mortality (AUC: 0.677). According to the individual laboratory variables, the first factor comprising TLC, CK, and NLR, had the most remarkable success (AUC: 0,642).
Conclusions: The factor analysis approach can be used to present an alternative perspective for predicting mortality in COVID-19 critical disease. The factor including CRP, fibrinogen, and ferritin predicted mortality at the highest rate in this study.

Keywords

Covid-19, factor analysis, critically ill, mortality, Coronavirus disease 2019

References

• World Health Organization (WHO). Coronavirus Disease (COVID-19) Dashboard. WHO. Accessed date: September 26 2020; Available from: https://covid19.who.int/, (n.d.).
• Wynants L, Van Calster B, Collins GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369: m1328
• Doganci S, Ince ME, Ors N, et al. A new COVID-19 prediction scoring model for in-hospital mortality: experiences from Turkey, single center retrospective cohort analysis. Eur Rev Med Pharmacol Sci 2020; 24: 10247-57.
• Sperrin M, Grant SW, Peek N. Prediction models for diagnosis and prognosis in Covid-19. BMJ 2020; 369: m1464.
• Wu C, Chen X, Cai Y, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med 2020; 180: 934-43.
• Güngörer B. Baseline demographic, clinical and laboratory risk factors for predicting admission to intensive care unit in patients diagnosed with COVID-19 in the emergency department. Anatolian Curr Med J 2021; 3: 279-83.
• Yıldırım Ö, Bayram M, Özmen RS, et al. Evaluation of hematological indices in terms of COVID-19 related mortality and ICU admission. J Health Sci Med 2021; 4: 666-9.
• Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. 2020; 323: 1061-9.
• Tavşançıl E. Tutumların ölçülmesi ve SPSS ile veri analizi. Ankara, Nobel Yayın 2002
• World Health Organization. Clinical management of COVID-19: living guidance. 25.01. 2021. WHO/2019-nCoV/clinical/2021.1
• IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.
• Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: A meta-analysis. Clinica Chimica Acta 2020; 506: 145-8.
• Kırıcı Berber N, Ulutaş Ö, Sarıcı A, et al. Clinical features and laboratory values associated with disease severity in Covid 19 patients: a single center experience. J Health Sci Med 2021; 4: 18-22.
• Antunez Muiños PJ, López Otero D, Amat-Santos IJ, et al. The COVID-19 lab score: an accurate dynamic tool to predict in-hospital outcomes in COVID-19 patients. Scientifics Reports 2021; 11: 9361.
• Arslan K, Baş S. Frequency of troponin elevations in patients with COVID-19 and clinical course in these patients. Anatolian Curr Med J 2022; 4: 95-102.
• Huang I, Pranata R. Lymphopenia in severe coronavirus disease-2019 (COVID-19): systematic review and meta-analysis. J Intensive Care 2020; 8: 36.
• 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. 206.
• García LF. Immune response, inflammation, and the clinical spectrum of COVID-19. Frontiers in immunology 2020; 11: 1441.
• Büyüköztürk Ş. Faktör analizi: temel kavramlar ve ölçek geliştirmede kullanımı. kuram ve uygulamada eğitim yönetimi. 2001; 32: 470-83.
• Leoni MLG, Lombardelli L, Colombi D, et al. Prediction of 28-day mortality in critically ill patients with COVID-19: development and internal validation of a clinical prediction model. PLoS One 2021; 16: e0254550.
• Altschul DJ, Unda SR, Benton J, et al. A novel severity score to predict inpatient mortality in COVID-19 patients. Scientific Reports 2020; 10: 16726.
• Luo Y, Wu J, Lu J, et al. Investigation of COVID-19-related symptoms based on factor analysis. Ann Palliat Med 2020; 9: 1851-8.