Objective Aimed to summarize the characteristics of chest CT imaging in Chinese hospitalized patients with Coronavirus Disease 2019 (COVID-19) to provide reliable evidence for further guiding clinical routine. Methods PubMed, Embase and Web of Science databases were searched to identify relevant articles involving the features of chest CT imaging in Chinese patients with COVID-19. All data were analyzed utilizing R i386 4.0.0 software. Random-effects models were employed to calculate pooled mean differences. Results 19 retrospective studies (1332 cases) were included. The results demonstrated that the combined proportion of ground-glass opacities (GGO) was 0.79 (95% CI 0.68, 0.89), consolidation was 0.34 (95% CI 0.23, 0.47); mixed GGO and consolidation was 0.46 (95% CI 0.37; 0.56); air bronchogram sign was 0.41 (95% CI 0.26; 0.55); crazy paving pattern was 0.32 (95% CI 0.17, 0.47); interlobular septal thickening was 0.55 (95% CI 0.42, 0.67); reticulation was 0.30 (95% CI 0.12, 0.48); bronchial wall thickening was 0.24 (95% CI 0.11, 0.40); vascular enlargement was 0.74 (95% CI 0.64, 0.86); subpleural linear opacity was 0.28 (95% CI 0.12, 0.48); intrathoracic lymph node enlargement was 0.03 (95% CI 0.00, 0.07); pleural effusions was 0.03 (95% CI 0.02, 0.06). The distribution in lung: the combined proportion of central was 0.05 (95% CI 0.01, 0.11); peripheral was 0.74 (95% CI 0.62, 0.84); peripheral involving central was 0.38 (95% CI 0.19, 0.75); diffuse was 0.19 (95% CI 0.06, 0.32); unifocal involvement was 0.09 (95% CI 0.05, 0.14); multifocal involvement was 0.57 (95% CI 0.48, 0.68); unilateral was 0.16 (95% CI 0.10, 0.23); bilateral was 0.83 (95% CI 0.78, 0.89); The combined proportion of lobes involved (> 2) was 0.70 (95% CI 0.61, 0.78); lobes involved (≦ 2) was 0.35 (95% CI 0.26, 0.44). Conclusion GGO, vascular enlargement, interlobular septal thickening more frequently occurred in patients with COVID-19, which distribution features were peripheral, bilateral, involved lobes > 2. Therefore, based on chest CT features of COVID-19 mentioned, it might be a promising means for identifying COVID-19.

Objective: To evaluate the diagnostic efficiency of different methods in detecting COVID-19 to provide preliminary evidence on choosing favourable method for COVID-19 detection. Methods: PubMed, Web of Science and Embase databases were searched for identifing eligible articles. All data were calculated utilizing Meta Disc 1.4, Revman 5.3.2 and Stata 12. The diagnostic efficiency was assessed via these indicators including summary sensitivity and specificity, positive likelihood ratio (PLR), negative LR (NLR), diagnostic odds ratio (DOR), summary receiver operating characteristic curve (sROC) and calculate the AUC. Results: 18 articles (3648 cases) were included. The results showed no significant threshold exist. EPlex: pooled sensitivity was 0.94; specificity was 1.0; PLR was 90.91; NLR was 0.07; DOR was 1409.49; AUC=0.9979, Q*=0.9840. Panther Fusion: pooled sensitivity was 0.99; specificity was 0.98; PLR was 42.46; NLR was 0.02; DOR was 2300.38; AUC=0.9970, Q*=0.9799. Simplexa: pooled sensitivity was 1.0; specificity was 0.97; PLR was 26.67; NLR was 0.01; DOR was 3100.93; AUC=0.9970, Q*=0.9800. Cobas: pooled sensitivity was 0.99; specificity was 0.96; PLR was 37.82; NLR was 0.02; DOR was 3754.05; AUC=0.9973, Q*=0.9810. RT-LAMP: pooled sensitivity was 0.98; specificity was 0.99; PLR was 36.22; NLR was 0.04; DOR was 751.24; AUC=0.9905, Q*=0.9596. Xpert Xpress: pooled sensitivity was 0.99; specificity was 0.97; PLR was 27.44; NLR was 0.01; DOR was 3488.15; AUC=0.9977, Q*=0.9829. Conclusions: These methods (ePlex, Panther Fusion, Simplexa, Cobas, RT-LAMP and Xpert Xpress) bear higher sensitivity and specificity, and might be efficient methods complement to the gold standard.

Objective: To evaluate the diagnostic efficiency of different methods in detecting COVID-19.Methods: PubMed, Web of Science and Embase databases were searched for identifing eligible articles. All data were calculated utilizing Meta Disc 1.4, Revman 5.3.2 and Stata 12. The diagnostic efficiency was assessed via these indicators including summary sensitivity and specificity, positive likelihood ratio (PLR), negative LR (NLR), diagnostic odds ratio (DOR), summary receiver operating characteristic curve (sROC) and calculate the AUC. Results: 18 articles (3648 cases) were included. EPlex: pooled sensitivity was 0.94; specificity 1.0; PLR 90.91; NLR 0.07; DOR 1409.49; AUC=0.9979, Q*=0.9840. Panther Fusion: pooled sensitivity was 0.99; specificity 0.98; PLR 42.46; NLR 0.02; DOR 2300.38; AUC=0.9970, Q*=0.9799. Simplexa: pooled sensitivity was 1.0; specificity 0.97; PLR 26.67; NLR 0.01; DOR 3100.93; AUC=0.9970, Q*=0.9800. Cobas®: pooled sensitivity was 0.99; specificity 0.96; PLR 37.82; NLR 0.02; DOR 3754.05; AUC=0.9973, Q*=0.9810. RT-LAMP: pooled sensitivity was 0.98; specificity 0.99; PLR 36.22; NLR 0.04; DOR 751.24; AUC=0.9905, Q*=0.9596. Xpert Xpress: pooled sensitivity was 0.99; specificity 0.97; PLR 27.44; NLR 0.01; DOR 3488.15; AUC=0.9977, Q*=0.9829.Conclusions: These methods (ePlex, Panther Fusion, Simplexa, Cobas®, RT-LAMP and Xpert Xpress) bear higher sensitivity and specificity, and might be efficient methods complement to the gold standard.