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

Dawei Yang, Tao Xu, Xun Wang, Deng Chen, Ziqiang Zhang, Lichuan Zhang, Jie Liu, Kui Xiao, Li Bai, Yong Zhang, Lin Zhao, Lin Tong, Chaomin Wu, Yaoli Wang, Chunling Dong, Maosong Ye, Yu Xu, Zhenju Song, Hong Chen, Jing LiJiwei Wang, Fei Tan, Hai Yu, Jian Zhou, Chunhua Du, Hongqing Zhao, Yu Shang, Linian Huang, Jianping Zhao, Yang Jin, Charles A. Powell, Jinming Yu, Yuanlin Song, Chunxue Bai

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)79-90
Number of pages12
JournalClinical eHealth
StatePublished - Dec 2022


  • COVID-19
  • Infectious disease
  • SARS-CoV-2
  • Smartphone
  • WeChat


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