Cargando…
Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis
BACKGROUND: Computed tomography (CT) scans are increasingly available in clinical care globally. They enable a rapid and detailed assessment of tissue and organ involvement in disease processes that are relevant to diagnosis and management, particularly in the context of the COVID-19 pandemic. OBJEC...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609195/ https://www.ncbi.nlm.nih.gov/pubmed/33001830 http://dx.doi.org/10.2196/19424 |
_version_ | 1783604977514577920 |
---|---|
author | Wu, Lianpin Jin, Qike Chen, Jie He, Jiawei Brett-Major, David M Dong, Jianghu James |
author_facet | Wu, Lianpin Jin, Qike Chen, Jie He, Jiawei Brett-Major, David M Dong, Jianghu James |
author_sort | Wu, Lianpin |
collection | PubMed |
description | BACKGROUND: Computed tomography (CT) scans are increasingly available in clinical care globally. They enable a rapid and detailed assessment of tissue and organ involvement in disease processes that are relevant to diagnosis and management, particularly in the context of the COVID-19 pandemic. OBJECTIVE: The aim of this paper is to identify differences in the CT scan findings of patients who were COVID-19 positive (confirmed via nucleic acid testing) to patients who were confirmed COVID-19 negative. METHODS: A retrospective cohort study was proposed to compare patient clinical characteristics and CT scan findings in suspected COVID-19 cases. A multivariable logistic model with LASSO (least absolute shrinkage and selection operator) selection for variables was used to identify the good predictors from all available predictors. The area under the curve (AUC) with 95% CI was calculated for each of the selected predictors and the combined selected key predictors based on receiver operating characteristic curve analysis. RESULTS: A total of 94 (56%) patients were confirmed positive for COVID-19 from the suspected 167 patients. We found that elderly people were more likely to be infected with COVID-19. Among the 94 confirmed positive patients, 2 (2%) patients were admitted to an intensive care unit. No patients died during the study period. We found that the presence, distribution, and location of CT lesions were associated with the presence of COVID-19. White blood cell count, cough, and a travel history to Wuhan were also the top predictors for COVID-19. The overall AUC of these selected predictors is 0.97 (95% CI 0.93-1.00). CONCLUSIONS: Taken together with nucleic acid testing, we found that CT scans can allow for the rapid diagnosis of COVID-19. This study suggests that chest CT scans should be more broadly adopted along with nucleic acid testing in the initial assessment of suspected COVID-19 cases, especially for patients with nonspecific symptoms. |
format | Online Article Text |
id | pubmed-7609195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-76091952020-11-16 Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis Wu, Lianpin Jin, Qike Chen, Jie He, Jiawei Brett-Major, David M Dong, Jianghu James JMIR Public Health Surveill Original Paper BACKGROUND: Computed tomography (CT) scans are increasingly available in clinical care globally. They enable a rapid and detailed assessment of tissue and organ involvement in disease processes that are relevant to diagnosis and management, particularly in the context of the COVID-19 pandemic. OBJECTIVE: The aim of this paper is to identify differences in the CT scan findings of patients who were COVID-19 positive (confirmed via nucleic acid testing) to patients who were confirmed COVID-19 negative. METHODS: A retrospective cohort study was proposed to compare patient clinical characteristics and CT scan findings in suspected COVID-19 cases. A multivariable logistic model with LASSO (least absolute shrinkage and selection operator) selection for variables was used to identify the good predictors from all available predictors. The area under the curve (AUC) with 95% CI was calculated for each of the selected predictors and the combined selected key predictors based on receiver operating characteristic curve analysis. RESULTS: A total of 94 (56%) patients were confirmed positive for COVID-19 from the suspected 167 patients. We found that elderly people were more likely to be infected with COVID-19. Among the 94 confirmed positive patients, 2 (2%) patients were admitted to an intensive care unit. No patients died during the study period. We found that the presence, distribution, and location of CT lesions were associated with the presence of COVID-19. White blood cell count, cough, and a travel history to Wuhan were also the top predictors for COVID-19. The overall AUC of these selected predictors is 0.97 (95% CI 0.93-1.00). CONCLUSIONS: Taken together with nucleic acid testing, we found that CT scans can allow for the rapid diagnosis of COVID-19. This study suggests that chest CT scans should be more broadly adopted along with nucleic acid testing in the initial assessment of suspected COVID-19 cases, especially for patients with nonspecific symptoms. JMIR Publications 2020-10-20 /pmc/articles/PMC7609195/ /pubmed/33001830 http://dx.doi.org/10.2196/19424 Text en ©Lianpin Wu, Qike Jin, Jie Chen, Jiawei He, David M Brett-Major, Jianghu James Dong. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 20.10.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Wu, Lianpin Jin, Qike Chen, Jie He, Jiawei Brett-Major, David M Dong, Jianghu James Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis |
title | Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis |
title_full | Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis |
title_fullStr | Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis |
title_full_unstemmed | Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis |
title_short | Diagnostic Accuracy of Chest Computed Tomography Scans for Suspected Patients With COVID-19: Receiver Operating Characteristic Curve Analysis |
title_sort | diagnostic accuracy of chest computed tomography scans for suspected patients with covid-19: receiver operating characteristic curve analysis |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609195/ https://www.ncbi.nlm.nih.gov/pubmed/33001830 http://dx.doi.org/10.2196/19424 |
work_keys_str_mv | AT wulianpin diagnosticaccuracyofchestcomputedtomographyscansforsuspectedpatientswithcovid19receiveroperatingcharacteristiccurveanalysis AT jinqike diagnosticaccuracyofchestcomputedtomographyscansforsuspectedpatientswithcovid19receiveroperatingcharacteristiccurveanalysis AT chenjie diagnosticaccuracyofchestcomputedtomographyscansforsuspectedpatientswithcovid19receiveroperatingcharacteristiccurveanalysis AT hejiawei diagnosticaccuracyofchestcomputedtomographyscansforsuspectedpatientswithcovid19receiveroperatingcharacteristiccurveanalysis AT brettmajordavidm diagnosticaccuracyofchestcomputedtomographyscansforsuspectedpatientswithcovid19receiveroperatingcharacteristiccurveanalysis AT dongjianghujames diagnosticaccuracyofchestcomputedtomographyscansforsuspectedpatientswithcovid19receiveroperatingcharacteristiccurveanalysis |