Capacity Evaluation of Diagnostic Tests For COVID-19 Using Multicriteria Decision-Making Techniques

In December 2019, cases of pneumonia were detected in Wuhan, China, which were caused by the highly contagious coronavirus. This study is aimed at comparing the confusion regarding the selection of effective diagnostic methods to make a mutual comparison among existing SARS-CoV-2 diagnostic tests and at determining the most effective one. Based on available published evidence and clinical practice, diagnostic tests of coronavirus disease (COVID-19) were evaluated by multi-criteria decision-making (MCDM) methods, namely, fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS). Computerized tomography of chest (chest CT), the detection of viral nucleic acid by polymerase chain reaction, cell culture, CoV-19 antigen detection, CoV-19 antibody IgM, CoV-19 antibody IgG, and chest X-ray were evaluated by linguistic fuzzy scale to compare among the diagnostic tests. This scale consists of selected parameters that possessed different weights which were determined by the experts' opinions of the field. The results of our study with both proposed MCDM methods indicated that the most effective diagnosis method of COVID-19 was chest CT. It is interesting to note that the methods that are consistently used in the diagnosis of viral diseases were ranked in second place for the diagnosis of COVID-19. However, each country should use appropriate diagnostic solutions according to its own resources. Our findings also show which diagnostic systems can be used in combination.

[1]  G. Gao,et al.  A Novel Coronavirus from Patients with Pneumonia in China, 2019 , 2020, The New England journal of medicine.

[2]  Q. Tao,et al.  Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases , 2020, Radiology.

[3]  Ting Yu,et al.  Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study , 2020, The Lancet.

[4]  Jingwen Huang,et al.  Combining entropy weight and TOPSIS method for information system selection , 2008, 2008 IEEE Conference on Cybernetics and Intelligent Systems.

[5]  Jun Liu,et al.  Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing , 2020, Radiology.

[6]  Fei Ye,et al.  An extended TOPSIS model based on the Possibility theory under fuzzy environment , 2014, Knowl. Based Syst..

[7]  Y. Hu,et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China , 2020, The Lancet.

[8]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[9]  A. M. Leontovich,et al.  The species Severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2 , 2020, Nature Microbiology.

[10]  Bingliang Zeng,et al.  Diagnosis of the Coronavirus disease (COVID-19): rRT-PCR or CT? , 2020, European Journal of Radiology.

[11]  Junzhang Tian,et al.  CT screening for early diagnosis of SARS-CoV-2 infection , 2020, The Lancet Infectious Diseases.

[12]  Heshui Shi,et al.  Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study , 2020, The Lancet Infectious Diseases.

[13]  Jean Pierre Brans,et al.  HOW TO SELECT AND HOW TO RANK PROJECTS: THE PROMETHEE METHOD , 1986 .

[14]  T. Egglin,et al.  Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT , 2020, Radiology.

[15]  Michael Bell,et al.  Initial Public Health Response and Interim Clinical Guidance for the 2019 Novel Coronavirus Outbreak — United States, December 31, 2019–February 4, 2020 , 2020, MMWR. Morbidity and mortality weekly report.

[16]  Jie Dong,et al.  Identification of a novel coronavirus causing severe pneumonia in human: a descriptive study , 2020, Chinese medical journal.

[17]  T. Franquet Imaging of pulmonary viral pneumonia. , 2011, Radiology.

[18]  C. D. Dela Cruz,et al.  Epidemiologic and Clinical Characteristics of Novel Coronavirus Infections Involving 13 Patients Outside Wuhan, China. , 2020, JAMA.

[19]  Francesco Mojoli,et al.  Performance of VivaDiag COVID‐19 IgM/IgG Rapid Test is inadequate for diagnosis of COVID‐19 in acute patients referring to emergency room department , 2020, Journal of medical virology.

[20]  Thomas Spengler,et al.  Fuzzy outranking for environmental assessment. Case study: iron and steel making industry , 2000, Fuzzy Sets Syst..

[21]  I. Ozsahin,et al.  A Fuzzy PROMETHEE Approach for Breast Cancer Treatment Techniques , 2018 .

[22]  Amjad D. Al-Nasser,et al.  SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far , 2020, Pathogens.

[23]  Victor M Corman,et al.  Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[24]  Ren-He Xu,et al.  COVID-19: what has been learned and to be learned about the novel coronavirus disease , 2020, International journal of biological sciences.

[25]  K. Yuen,et al.  Clinical Characteristics of Coronavirus Disease 2019 in China , 2020, The New England journal of medicine.

[26]  Yicheng Fang,et al.  Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR , 2020, Radiology.

[27]  Qi Jin,et al.  Profiling Early Humoral Response to Diagnose Novel Coronavirus Disease (COVID-19) , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[28]  Pei-Hsuan Tsai,et al.  Financial Service of Wealth Management Banking: Balanced Scorecard Approach , 2008 .

[29]  M. Seong,et al.  Virus Isolation from the First Patient with SARS-CoV-2 in Korea , 2020, Journal of Korean medical science.

[30]  P. Vincke,et al.  Note-A Preference Ranking Organisation Method: The PROMETHEE Method for Multiple Criteria Decision-Making , 1985 .