Citation Information :
Sabetian G, Azimi A, Kazemi A, Asmarian N, Khaloo V, Zand F, Masjedi M, Shahriarirad R, Shahriarirad S. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022; 26 (6):688-695.
Background: Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19).
Aims and objectives: To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care.
Materials and methods: In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models.
Results: A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O2 saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors.
Conclusion: In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients’ early data, particularly in low- and middle-income countries where their resources are as limited as Iran.
Ghale-Noie ZN, Salmaninejad A, Bergquist R, Mollazadeh S, Hoseini B, Sahebkar A. Genetic aspects and immune responses in Covid-19: Important Organ Involvement. Adv Exp Med Biol 2021;1327:3–22. Epub 2021/07/20. DOI: 10.1007/978-3-030-71697-4_1.
Goshayeshi L, Akbari Rad M, Bergquist R, Allahyari A, Hashemzadeh K, Hoseini B. Demographic and clinical characteristics of severe Covid-19 infections: a cross-sectional study from Mashhad University of Medical Sciences, Iran. BMC Infect Dis 2021;21(1):656. DOI: 10.1186/s12879-021-06363-6.
Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, et al. Detection of SARS-CoV-2 in different types of clinical specimens. JAMA 2020;323(18): 1843–1844. DOI: 10.1001/jama.2020.3786.
Goshayeshi L, Milani N, Bergquist R, Sadrzadeh SM, Rajabzadeh F, Hoseini B. Covid-19 presented only with gastrointestinal symptoms: a case report of a 14-year-old patient. Govaresh 2021;25(4):300–304.
Heo J, Han D, Kim HJ, Kim D, Lee YK, Lim D, et al. Prediction of patients requiring intensive care for COVID-19: development and validation of an integer-based score using data from Centers for Disease Control and Prevention of South Korea. J Intensive Care 2021;9(1):16. DOI: 10.1186/s40560-021-00527-x.
Khoshrounejad F, Hamednia M, Mehrjerd A, Pichaghsaz S, Jamalirad H, Sargolzaei M, et al. Telehealth-based services during the COVID-19 Pandemic: A systematic review of features and challenges. Front Public Health 2021;9:711762. Epub 2021/08/06. DOI: 10.3389/fpubh.2021.711762.
Goyal P, Choi JJ, Pinheiro LC, Schenck EJ, Chen R, Jabri A, et al. Clinical characteristics of Covid-19 in New York City. N Engl J Med 2020;382(24):2372–2374. DOI: 10.1056/NEJMc2010419.
Rahmatinejad Z, Rahmatinejad F, Sezavar M, Tohidinezhad F, Abu-Hanna A, Eslami S. Internal validation and evaluation of the predictive performance of models based on the PRISM-3 (Pediatric Risk of Mortality) and PIM-3 (Pediatric Index of Mortality) scoring systems for predicting mortality in Pediatric Intensive Care Units (PICUs). BMC Pediatr 2022;22(1):199. Epub 2022/04/14. DOI: 10.1186/s12887-022-03228-y.
Rahmatinejad Z, Tohidinezhad F, Rahmatinejad F, Eslami S, Pourmand A, Abu-Hanna A, et al. Internal validation and comparison of the prognostic performance of models based on six emergency scoring systems to predict in-hospital mortality in the emergency department. BMC Emerg Med 2021;21(1):68. Epub 2021/06/12. DOI: 10.1186/s12873-021-00459-7.
Ahouz F, Golabpour A. Predicting the incidence of COVID-19 using data mining. BMC Public Health 2021;21(1):1087. DOI: 10.1186/s12889-021-11058-3.
Cheng FY, Joshi H, Tandon P, Freeman R, Reich DL, Mazumdar M, et al. Using machine learning to predict ICU transfer in hospitalized COVID-19 patients. J Clin Med 2020;9(6):1668. DOI: 10.3390/jcm9061668.
Surme S, Buyukyazgan A, Bayramlar OF, Cinar AK, Copur B, Zerdali E, et al. Predictors of intensive care unit admission or death in patients with coronavirus disease 2019 Pneumonia in Istanbul, Turkey. Jpn J Infect Dis 2021;74(5):458–464. DOI: 10.7883/yoken.JJID.2020.1065.
Cai W, Liu T, Xue X, Luo G, Wang X, Shen Y, et al. CT Quantification and machine-learning models for assessment of disease severity and prognosis of COVID-19 patients. Acad Radiol 2020;27(12):1665–1678. DOI: 10.1016/j.acra.2020.09.004.
Saba T, Abunadi I, Shahzad MN, Khan AR. Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types. Microsc Res Tech 2021;84(7):1462–1474. DOI: 10.1002/jemt.23702.
DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44(3):837–845. PMID: 3203132.
Roumani YF, May JH, Strum DP, Vargas LG. Classifying highly imbalanced ICU data. Health Care Manag Sci 2013;16(2):119–128. DOI: 10.1007/s10729-012-9216-9.
Saniasiaya J, Kulasegarah J. Dizziness and COVID-19. Ear Nose Throat J 2021;100(1):29–30. DOI: 10.1177/0145561320959573.
Peng J, Chen C, Zhou M, Xie X, Zhou Y, Luo CH. A machine-learning approach to forecast aggravation risk in patients with acute exacerbation of chronic obstructive pulmonary disease with clinical indicators. Sci Rep 2020;10(1):3118. DOI: 10.1038/s41598-020-60042-1.
Kazemi A, Kazemi K, Sami A, Sharifian R. Identifying factors that affect patient survival after orthotopic liver transplant using machine-learning techniques. Exp Clin Transplant 2019;17(6):775–783. DOI: 10.6002/ect.2018.0170.
Allenbach Y, Saadoun D, Maalouf G, Vieira M, Hellio A, Boddaert J, et al. Development of a multivariate prediction model of intensive care unit transfer or death: a French prospective cohort study of hospitalized COVID-19 patients. PLoS One 2020;15(10):e0240711. DOI: 10.1371/journal.pone.0240711.
Li J, Chen Z, Nie Y, Ma Y, Guo Q, Dai X. Identification of symptoms prognostic of COVID-19 severity: multivariate data analysis of a case series in Henan province. J Med Internet Res 2020;22(6):e19636. DOI: 10.2196/19636.
Muzzarelli S, Leibundgut G, Maeder MT, Rickli H, Handschin R, Gutmann M, et al. Predictors of early readmission or death in elderly patients with heart failure. Am Heart J 2010;160(2):308–314. DOI: 10.1016/j.ahj.2010.05.007.
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020;395(10229): 1054–1062.
Bauer CMT, Morissette MC, Stampfli MR. The influence of cigarette smoking on viral infections: translating bench science to impact COPD pathogenesis and acute exacerbations of COPD clinically. Chest 2013;143(1):196–206. DOI: 10.1378/chest.12-0930.
McElvaney OJ, McEvoy NL, McElvaney OF, Carroll TP, Murphy MP, Dunlea DM, et al. Characterization of the Inflammatory response to severe COVID-19 illness. Am J Respir Crit Care Med 2020;202(6): 812–821. DOI: 10.1164/rccm.202005-1583OC.
Park JE, Jung S, Kim A, Park JE. MERS transmission and risk factors: a systematic review. BMC Public Health 2018;18(1):574. DOI: 10.1186/s12889-018-5484-8.
Ou M, Zhu J, Ji P, Li H, Zhong Z, Li B, et al. Risk factors of severe cases with COVID-19: a meta-analysis. Epidemiol Infect 2020;148:e175. DOI: 10.1017/S095026882000179X.
Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis 2020;20(6):669–677. DOI: 10.1016/S1473-3099(20)30243-7.
Wu T, Zuo Z, Kang S, Jiang L, Luo X, Xia Z, et al. Multi-organ dysfunction in patients with COVID-19: a systematic review and meta-analysis. Aging Dis 2020;11(4):874–894. DOI: 10.14336/AD.2020.0520.
Chang JC. Acute respiratory distress syndrome as an organ phenotype of vascular microthrombotic disease: based on hemostatic theory and endothelial molecular pathogenesis. Clin Appl Thromb Hemost 2019;25:1–20. DOI: 10.1177/1076029619887437.
Zheng X, Yang H, Li X, Li H, Xu L, Yu Q, et al. Prevalence of kidney injury and associations with critical illness and death in patients with COVID-19. Clin J Am Soc Nephrol 2020;15(11):1549–1556. DOI: 10.2215/CJN.04780420.
Lagunas-Rangel FA. Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe coronavirus disease 2019 (COVID-19): a meta-analysis. J Med Virol 2020;92(10):1733–1734. DOI: 10.1002/jmv.25819.
Zhao Q, Meng M, Kumar R, Wu Y, Huang J, Deng Y, et al. Lymphopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a systemic review and meta-analysis. Int J Infect Dis 2020;96:131–135. DOI: 10.1016/j.ijid.2020.04.086.
Aggarwal S, Garcia-Telles N, Aggarwal G, Lavie C, Lippi G, Henry BM. Clinical features, laboratory characteristics, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19): early report from the United States. Diagnosis (Berl) 2020;7(2):91–96. DOI: 10.1515/dx-2020-0046.
Rahmatinejad Z, Hoseini B, Rahmatinejad F, Abu-Hanna A, Bergquist R, Pourmand A, et al. Internal validation of the predictive performance of models based on three ED and ICU scoring systems to predict inhospital mortality for intensive care patients referred from the Emergency Department. BioMed Research International 2022;2022:3964063. DOI: 10.1155/2022/3964063.