Sat, Dec 21, 2024
[Archive]
Volume 29, Issue 134 (May & June 2021)
J Adv Med Biomed Res 2021, 29(134): 176-182 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Shanbehzadeh M, Nopour R, kazemi-arpanahi H. Determination of the Most Important Diagnostic Criteria for COVID-19: A Step forward to Design an Intelligent Clinical Decision Support System. J Adv Med Biomed Res 2021; 29 (134) :176-182
URL: http://journal.zums.ac.ir/article-1-6160-en.html
URL: http://journal.zums.ac.ir/article-1-6160-en.html
1- Dept. of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran , mostafa.shanbezadeh@gmail.com
2- Dept. of Health Information Technology and Management, School of Paramedical, Tehran University of Medical Sciences, Tehran, Iran
3- Dept. of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran | Dept. of Student Research Committee, Abadan Faculty of Medical Sciences, Abadan, Iran
2- Dept. of Health Information Technology and Management, School of Paramedical, Tehran University of Medical Sciences, Tehran, Iran
3- Dept. of Health Information Technology, Abadan Faculty of Medical Sciences, Abadan, Iran | Dept. of Student Research Committee, Abadan Faculty of Medical Sciences, Abadan, Iran
Abstract: (138114 Views)
Background & Objective: Since the clinical and epidemiologic characteristics of coronavirus disease 2019 (COVID-19) is not well known yet, investigating its origin, etiology, diagnostic criteria, clinical manifestations, risk factors, treatments, and other related aspects is extremely important. In this situation, clinical experts face many uncertainties to make decision about COVID-19 prognosis based on their judgment. Accordingly, this study aimed to determine the diagnostic criteria for COVID-19 as a prerequisite to develop clinical diagnostic models.
Materials & Methods: In this retrospective study, the Enter method of the binary logistic regression (BLR) and the Forward Wald method were used to measure the odds ratio (OR) and the strength of each criterion, respectively. P-value<0.05 was considered as statistically significant for bivariate correlation coefficient.
Results: Phi-Crammer’s examination test showed that 12 diagnostic criteria were statistically important; measuring OR revealed that six criteria had the best diagnostic power. Finally, true classification rate and the area under receiver operative characteristics curve (AUC) were calculated as 90.25% and 0.835, respectively.
Conclusion: Identification of diagnostic criteria has become the standard approach for disease modeling; it helps to design decision support tools. After analyzing and comparing six diagnostic performance measures, we observed that these variables have a high diagnostic power for COVID-19 detection.
Full-Text [PDF 432 kb]
(149995 Downloads)
| | Full-Text (HTML) (3165 Views)
✅ Identification of diagnostic criteria has become the standard approach for disease modeling; it helps to design decision support tools. After analyzing and comparing six diagnostic performance measures, we observed that these variables have a high diagnostic power for COVID-19 detection.
Type of Study: Original Article |
Subject:
Health improvement strategies
Received: 2020/08/9 | Accepted: 2020/09/30 | Published: 2020/12/30
Received: 2020/08/9 | Accepted: 2020/09/30 | Published: 2020/12/30
References
1. Peeri NC, Shrestha N, Rahman MS, et al. The SARS, MERS, and novel coronavirus (COVID-19) epidemics, the newest and biggest global health threats: what lessons have we learned? Int J Epidemiol. 2020; 49(3):717-26 [DOI:10.1093/ije/dyaa033]
2. Qiu H, Wu J, Hong L, Luo Y, Song Q, Chen D. Clinical and epidemiological features of 36 children with coronavirus disease 2019 (COVID-19) in Zhejiang, China: an observational cohort study. The Lancet Infect Dis. 2020. [DOI:10.1016/S1473-3099(20)30198-5]
3. Shi H, Han X, Jiang N, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020; 20(4):425-434 [DOI:10.1016/S1473-3099(20)30086-4]
4. Cascella M, Rajnik M, Cuomo A, Dulebohn SC, Di Napoli R. Features, evaluation and treatment coronavirus (COVID-19). Statpearls [internet]: StatPearls Publishing; 2020.
5. Sohrabi C, Alsafi Z, O'Neill N, et al. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). Int J Surg. 2020;76:71-76 [DOI:10.1016/j.ijsu.2020.02.034]
6. Wang K, Kang S, Tian R, Zhang X, Wang Y. Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area. Clin Radiol. 2020;75(5):341-47 [DOI:10.1016/j.crad.2020.03.004]
7. Liu Y, Wang Z, Ren J, et al. A COVID-19 risk assessment decision support system for general practitioners: design and development study. J Med Internet Res. 2020;22(6):e19786. [DOI:10.2196/19786]
8. Yuan J, Zou R, Zeng L, et al. The correlation between viral clearance and biochemical outcomes of 94 COVID-19 infected discharged patients. Inflamm Res. 2020:1-8. [DOI:10.1007/s00011-020-01342-0]
9. Bai L, Yang D, Wang X, et al. Chinese experts' consensus on the internet of things-aided diagnosis and treatment of coronavirus disease 2019. Clinical eHealth. 2020;3:7-15 [DOI:10.1016/j.ceh.2020.03.001]
10. Bayram M, Springer S, Garvey CK, Özdemir V. COVID-19 digital health innovation policy: A portal to alternative futures in the making. OMICS: J Integ Biol. 2020;24(8):460-69 [DOI:10.1089/omi.2020.0089]
11. Ma LL, Li BH, Jin YH, Deng T, Ren XQ, Zeng XT. Developments, evolution, and implications of national diagnostic criteria for COVID-19 in china. Front Med. 2020;7:242. [DOI:10.3389/fmed.2020.00242]
12. Mahmood A, Gajula C, Gajula P. Clinical and diagnostic criteria of COVID 19; a study of 4659 patients evaluating diagnostic testing and establishing an algorithm. J Med Surg Sci. 2020;2:2.
13. Shipe ME, Deppen SA, Farjah F, Grogan EL. Developing prediction models for clinical use using logistic regression: an overview. J Thoracic Dis. 2019;11(Suppl 4): S574. [DOI:10.21037/jtd.2019.01.25]
14. Agbehadji IE, Awuzie BO, Ngowi AB, Millham RC. Review of big data analytics, artificial intelligence, and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing. Int J Environ Res Public Health. 2020;17(15). [DOI:10.3390/ijerph17155330]
15. Haleem A, Javaid M, Khan IH, Vaishya R. Significant applications of big data in COVID-19 pandemic. Indian J Orthopaedics. 2020;54(4):526-8. [DOI:10.1007/s43465-020-00129-z]
16. Briz-Redon A, Serrano-Aroca A. The effect of climate on the spread of the COVID-19 pandemic: A review of findings, and statistical and modeling techniques. Earth and Environment.2020;44(5):591-604 [DOI:10.1177/0309133320946302]
17. Rehm GB, Woo SH, Chen XL, et al. Leveraging IoTs and machine learning for patient diagnosis and ventilation management in the intensive care unit. IEEE Pervasive Computing. 2020. [DOI:10.1109/MPRV.2020.2986767]
18. Sarkodie SA, Owusu PA. Investigating the cases of novel coronavirus disease (COVID-19) in China using dynamic statistical techniques. Heliyon. 2020;6(4): e03747 [DOI:10.1016/j.heliyon.2020.e03747]
19. Kanagarathinam K, Sekar K. Estimation of the reproduction number and early prediction of the COVID-19 outbreak in India using a statistical computing approach. Epidemiol Health. 2020;42. [DOI:10.4178/epih.e2020028]
20. Mohammed MA, Abdulkareem KH, Al-Waisy AS, et al. Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods. IEEE Access. 2020. [DOI:10.1109/ACCESS.2020.2995597]
21. Alom MZ, Rahman M, Nasrin MS, Taha TM, Asari VK. COVID_MTNet: COVID-19 detection with multi-task deep learning approaches. arXiv preprint arXiv:200403747. 2020.
22. Hussain A, Bhowmik B, do Vale Moreira NC. COVID-19 and diabetes: Knowledge in progress. Diabet Res Clin Pract. 2020;162. [DOI:10.1016/j.diabres.2020.108142]
23. Moujaess E, Kourie HR, Ghosn M. Cancer patients and research during COVID-19 pandemic: A systematic review of current evidence. Criti Rev Oncol Hematol. 2020;150:102972. [DOI:10.1016/j.critrevonc.2020.102972]
24. Deeks JJ, Dinnes J, Takwoingi Y, et al. Diagnosis of SARS-CoV-2 infection and COVID-19: accuracy of signs and symptoms; molecular, antigen, and antibody tests; and routine laboratory markers. Cochrane Database of Systematic Reviews. 2020;2020(4). [DOI:10.1002/14651858.CD013596]
25. Iser BPM, Sliva I, Raymundo VT, Poleto MB, Schulter-Trevisol F, Bobinski F. Suspected COVID-19 case definition: a narrative review of the most frequent signs and symptoms among confirmed cases. Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil. 2020;29(3):e2020233. [DOI:10.5123/S1679-49742020000300018]
26. Jutzeler CR, Bourguignon L, Weis CV, et al. Comorbidities, clinical signs and symptoms, laboratory findings, imaging features, treatment strategies, and outcomes in adult and pediatric patients with COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis. 2020;37: 101825 [DOI:10.1016/j.tmaid.2020.101825]
27. Struyf T, Deeks JJ, Dinnes J, et al. Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease. Cochrane Database Syst Rev. 2020;2020(7): CD013665 [DOI:10.1002/14651858.CD013665]
28. Almeshal AM, Almazrouee AI, Alenizi MR, Alhajeri SN. Forecasting the spread of COVID-19 in Kuwait using compartmental and logistic regression models. Apply Sci. 2020;10(10):3402. [DOI:10.3390/app10103402]
29. Chretien JP, George D, Shaman J, Chitale RA, McKenzie FE. Influenza forecasting in human populations: A scoping review. PLOS ONE. 2014;9(4):e94130. [DOI:10.1371/journal.pone.0094130]
30. Udhaya Kumar S, Thirumal Kumar D, Prabhu Christopher B, George Priya Doss C. The rise and impact of COVID-19 in India. Front Med. 2020;7:250 [DOI:10.3389/fmed.2020.00250]
31. Ng QX, De Deyn MLZQ, Loke W, Chan HW. A framework to deal with uncertainty in the age of COVID-19. Asian J Psychiatr. 2020;54: 102263 [DOI:10.1016/j.ajp.2020.102263]
32. Chater N. Facing up to the uncertainties of COVID-19. Nature Human Behav. 2020;4(5):439. [DOI:10.1038/s41562-020-0865-2]
33. Zhang G, Hu C, Luo L, et al. Clinical features and short-term outcomes of 221 patients with COVID-19 in Wuhan, China. J Clin Virol. 2020:104364. [DOI:10.1016/j.jcv.2020.104364]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Copyright Policy
