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Observational Study
. 2022 Feb:100:13-21.
doi: 10.1016/j.clinbiochem.2021.11.001. Epub 2021 Nov 9.

Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit

Affiliations
Observational Study

Individual outcome prediction models for patients with COVID-19 based on their first day of admission to the intensive care unit

Raúl Rigo-Bonnin et al. Clin Biochem. 2022 Feb.

Abstract

Background: Currently, good prognosis and management of critically ill patients with COVID-19 are crucial for developing disease management guidelines and providing a viable healthcare system. We aimed to propose individual outcome prediction models based on binary logistic regression (BLR) and artificial neural network (ANN) analyses of data collected in the first 24 h of intensive care unit (ICU) admission for patients with COVID-19 infection. We also analysed different variables for ICU patients who survived and those who died.

Methods: Data from 326 critically ill patients with COVID-19 were collected. Data were captured on laboratory variables, demographics, comorbidities, symptoms and hospital stay related information. These data were compared with patient outcomes (survivor and non-survivor patients). BLR was assessed using the Wald Forward Stepwise method, and the ANN model was constructed using multilayer perceptron architecture.

Results: The area under the receiver operating characteristic curve of the ANN model was significantly larger than the BLR model (0.917 vs 0.810; p < 0.001) for predicting individual outcomes. In addition, ANN model presented similar negative predictive value than the BLR model (95.9% vs 94.8%). Variables such as age, pH, potassium ion, partial pressure of oxygen, and chloride were present in both models and they were significant predictors of death in COVID-19 patients.

Conclusions: Our study could provide helpful information for other hospitals to develop their own individual outcome prediction models based, mainly, on laboratory variables. Furthermore, it offers valuable information on which variables could predict a fatal outcome for ICU patients with COVID-19.

Keywords: Artificial neural network; Binary logistic regression; COVID-19; Intensive Care Unit; Laboratory variables; Mortality prediction model.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Comparison of area under the receiver operating characteristic curves among the binary logistic regression and artificial neural network models to separate survival and non-survival COVID-19 patients within their first day in the ICU admission. AUROC = Area under the receiver operating characteristic curve.
Fig. 2
Fig. 2
Variance importance matrix plot of variables collected in the first 24 hours of intensive care unit for artificial neural network in patients with COVID-19. ALB, mass concentration of albumin in plasma; BIL, substance concentration of bilirubin in plasma; CA, substance concentration of calcium(II) in plasma; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration glomerular filtration rate; CL, substance concentration of chloride in plasma; CREA, substance concentration of creatinine; CRP, mass concentration of C-reactive protein in plasma; Days_ adm_hos_ICU, number of days from hospital admission to ICU admission; Days_sym_adm_hos, number of days between appearance of clinical symptoms and admission to the hospital; Days_sym_adm_ICU, number of days between appearance of clinical symptoms and admission to the ICU; DD, mass concentration of D-dimer in plasma; FERRI, mass concentration of ferritin in plasma; GLU, substance concentration of glucose in plasma; LDH, catalytic concentration of lactate dehydrogenase in plasma obtained within the first admission day; K, substance concentration of potassium ion in plasma; PT, relative time of prothrombine in plasma; NA, substance concentration of sodium ion in plasma; TROP-T, mass concentration of troponin T in plasma; UREA, substance concentration of urea in plasma; paCO2, partial pressure of carbon dioxide in arterial blood; paO2, partial pressure of oxygen in arterial blood; apH, pH in arterial blood; aSatO2, substance fraction of oxygen in arterial blood, paO2/FiO2, partial pressure of oxygen in arterial blood/fraction of inspired oxygen quotient value; #BAS, number concentration of basophiles in blood; %BAS, number fraction of basophiles in the leucocytes of the blood; #EOS, number concentration of eosinophils in blood; %EOS, number fraction of eosinophils in the leucocytes of the blood; LEU, number concentration of leucocytes in blood; #LYM, number concentration of lymphocytes in blood; %LYM, number fraction of lymphocytes in the leucocytes of the blood; #MON, number concentration of monocytes in blood obtained; %MON, number fraction of monocytes in the leucocytes of the blood; #NEU, number concentration of neutrophils in blood; %NEU, number fraction of neutrophils in the leucocytes of the blood; PLT, number concentration of platelets in blood; MPV, entitic volum of platelets in blood (mean platelet volume); ERY, number concentration of erythrocytes in blood; HGB, mass concentration of haemoglobin in blood; HCT, volume fraction of erythrocytes in blood (haematocrit); MCV, entitic volum of erythrocytes in blood (mean corpuscular volume); MCH, entitic mass of haemoglobin contained in the erythrocytes of the blood (mean corpuscular haemoglobin); MCHC, mass concentrarion of haemoglobin contained in the erythrocytes of the blood (mean corpuscular haemoglobin concentration); relative distibution width of the erythrocytic volume in the erythrocytes of the blood (red blood cell distribution width).

References

    1. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y.i., Zhang L.i., Fan G., Xu J., Gu X., Cheng Z., Yu T., Xia J., Wei Y., Wu W., Xie X., Yin W., Li H., Liu M., Xiao Y., Gao H., Guo L.i., Xie J., Wang G., Jiang R., Gao Z., Jin Q.i., Wang J., Cao B. novel coronavirus in Wuhan. China, Lancet. 2020;395(10223):497–506. doi: 10.1016/S0140-6736(20)30183-5. - DOI - PMC - PubMed
    1. Chen N., Zhou M., Dong X., Qu J., Gong F., Han Y., Qiu Y., Wang J., Liu Y., Wei Y., Xia J., Yu T., Zhang X., Zhang L.i. novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet. 2020;395(10223):507–513. doi: 10.1016/S0140-6736(20)30211-7. - DOI - PMC - PubMed
    1. Wu Z., McGoogan J.M., Characteristics, of, and, important, lessons, from, the, coronavirus, disease, (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2019;323(2020):1239–1242. doi: 10.1001/jama.2020.2648. - DOI - PubMed
    1. Khan M., Khan H., Khan S., M., Nawaz, M, Epidemiological and clinical characteristics of coronavirus disease (COVID-19) cases at a screening clinic during the early outbreak period: a single-centre study. J. Med. Microbiol. 2020;69:1114–1123. doi: 10.1099/jmm.0.001231. - DOI - PMC - PubMed
    1. Sprung C.L., Joynt G.M., Christian M.D., Truog R.D., Rello J., Nates J.L., Adult I.C.U., triage, during, the, coronavirus, disease, pandemic: who will live and who will die? Recommendations to improve survival. Crit. Care Med. 2019;48(2020):1196–1202. doi: 10.1097/CCM.0000000000004410. - DOI - PMC - PubMed

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