A large-scale clinical validation study using nCapp cloud plus terminal by frontline doctors for the rapid diagnosis of COVID-19 and COVID-19 pneumonia in China
Background: The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal: This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods: With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings: We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion: With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:5 |
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| Enthalten in: |
Clinical eHealth - 5(2022), Seite 79-90 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Dawei Yang [VerfasserIn] |
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| Links: |
doi.org [kostenfrei] |
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| Themen: |
COVID-19 |
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| doi: |
10.1016/j.ceh.2022.07.004 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
DOAJ001102184 |
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| 245 | 1 | 2 | |a A large-scale clinical validation study using nCapp cloud plus terminal by frontline doctors for the rapid diagnosis of COVID-19 and COVID-19 pneumonia in China |
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| 520 | |a Background: The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal: This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods: With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings: We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion: With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results. | ||
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| 700 | 0 | |a Chunling Dong |e verfasserin |4 aut | |
| 700 | 0 | |a Maosong Ye |e verfasserin |4 aut | |
| 700 | 0 | |a Yu Xu |e verfasserin |4 aut | |
| 700 | 0 | |a Zhenju Song |e verfasserin |4 aut | |
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| 700 | 0 | |a Hai Yu |e verfasserin |4 aut | |
| 700 | 0 | |a Jian Zhou |e verfasserin |4 aut | |
| 700 | 0 | |a Chunhua Du |e verfasserin |4 aut | |
| 700 | 0 | |a Hongqing Zhao |e verfasserin |4 aut | |
| 700 | 0 | |a Yu Shang |e verfasserin |4 aut | |
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