A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study

Dai, Yi-Ning; Zheng, Wei; Wu, Qing-Qing; Hui, Tian-Chen; Sun, Nan-Nan; Chen, Guo-Bo; Tong, Yong-**; Bao, Su-**a; Wu, Wen-Hao; Huang, Yi-Cheng; Yin, Qiao-Qiao; Wu, Li-Juan; Yu, Li-**a; Shi, Ji-Chan; Fang, Nian; Shen, Yue-Fei; **e, **n-Sheng; Ma, Chun-Lian; Yu, Wan-Jun; Tu, Wen-Hui; Yan, Rong; Wang, Ming-Shan; Chen, Mei-Juan; Zhang, Jia-Jie; Ju, Bin; Gao, Hai-Nv; Huang, Hai-Jun; Li, Lan-Juan; Pan, Hong-Ying

doi:10.1038/s41598-021-83054-x

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study

Article
Open access
Published: 16 February 2021

Volume 11, article number 3863, (2021)
Cite this article

Download PDF

You have full access to this open access article

Scientific Reports

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study

Download PDF

Yi-Ning Dai¹^na1,
Wei Zheng¹^na1,
Qing-Qing Wu^1,2^na1,
Tian-Chen Hui^1,3^na1,
Nan-Nan Sun⁴,
Guo-Bo Chen^5,6,
Yong-** Tong¹,
Su-**a Bao¹,
Wen-Hao Wu^1,7,
Yi-Cheng Huang¹,
Qiao-Qiao Yin^1,3,
Li-Juan Wu⁸,
Li-**a Yu⁹,
Ji-Chan Shi¹⁰,
Nian Fang¹¹,
Yue-Fei Shen¹²,
**n-Sheng **e¹³,
Chun-Lian Ma¹⁴,
Wan-Jun Yu¹⁵,
Wen-Hui Tu¹⁶,
Rong Yan¹,
Ming-Shan Wang¹,
Mei-Juan Chen¹,
Jia-Jie Zhang¹,
Bin Ju⁴,
Hai-Nv Gao¹⁷,
Hai-Jun Huang¹,
Lan-Juan Li¹⁸ &
…
Hong-Ying Pan¹

1301 Accesses
4 Altmetric
1 Mention
Explore all metrics

Abstract

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

A predictive score at admission for respiratory failure among hospitalized patients with confirmed 2019 Coronavirus Disease: a simple tool for a complex problem

Article 29 April 2021

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

Article Open access 16 April 2020

Predictive Score of Risk Associated with Progression of Patients with COVID-19 Pneumonia in Wuhan, China: the ALA Score

Article 28 June 2021

Introduction

Since December 2019, novel coronavirus pneumonia (NCP) emerged in Wuhan, Hubei, which was well known as the largest transportation hub in China. The pathogen has been proved to be a novel betacoronavirus that is currently named 2019 novel coronavirus (2019-nCoV)¹. The disease has swept across China rapidly through human-to-human transmission^2,3,4. Since February 27, 2020, more than 78,000 people were confirmed to be infected and more than 2700 were died in China⁵.

As the number of patients soaring, scholars have summarized the clinical characteristics of NCP^6,7,8. Symptoms at onset of disease included fever, cough, headache, vomiting, diarrhea and so on. Normal or decreased leukocyte count was common. Radiologic abnormalities like ground-glass opacity and patchy shadowing on chest X-ray or computed tomography (CT) were marked characteristics. Acute respiratory distress syndrome, arrhythmia and shock could also occur in severe cases. Until now, to detect 2019-nCoV by the accurate real-time reverse transcription polymerase chain amplification (RT-PCR) assessment has been regarded as the golden diagnostic standard⁹.

Nevertheless, false negative results in initial RT-PCR examination existed in a number of cases^{Full size table}

Model score without epidemiological history (model 3) = 0.6 (if having coexisting diseases) + 0.8 (if having fatigue) + 1.2 (if having dyspnea) + 2.4 (if feeling muscle soreness) − 0.3 * WBC count − 0.3 * Lymphocyte count + 1.6 * pulmonary imaging score. The AUROC was 0.859 (95% CI 0.833–0.884) (Fig. 2), with the optimal cutoff value of − 1, a sensitivity of 83.5% (95% CI 79.1–87.1%), a specificity of 76.0% (95% CI 72.1–79.4%), a diagnostic accuracy of 78.9% (95% CI 76.2–81.7%), and a Youden index of 0.595. Repeated fivefold Cross-Validation showed the average AUROC was 0.854, with the standard deviation of 0.027. The Hosmer–Lemeshow χ² was 12.218 (P = 0.142), indicating no statistical difference from a perfect fit.

Discussion

In this study, we compared the characteristics between the NCP patients and the suspected individuals who were finally ruled out of NCP. Having analyzed the clinical and epidemiological features, we developed a rapid screening model for predicting NCP in a Zhejiang population. The model included four epidemiological features: travel or residence history within 14 days in Wuhan, contacting patients with fever or respiratory symptoms within 14 days from Wuhan, contacting patients from other areas with persistent local transmission or community with definite cases, relationship with a cluster outbreak; and five clinical manifestations: fatigue, dyspnea, muscle soreness, decreased WBC count, and imaging changes in chest X-ray or CT. The diagnostic performance of the established scale was excellent with an AUROC of 0.920.

At a cut-off value of > 1.0, the model could detect NCP with a sensitivity of 85% and a specificity of 82.3%. Due to the nature of a communicable disease, the associated costs of a false negative are huge, therefore it is essential to avoid missed diagnoses, in particular given its surging outbreak. When the score was higher than 4.0, subjects were more likely to suffer from NCP (with a specificity of 98.3%) and they should be immediately isolated and further tests are highly recommended. In contrast, during the outbreak, a great quantity of patients with flu-like symptoms were scared and crowded into hospitals, giving clinicians great pressure. A model score of < −0.5 demonstrated a very small probability to be infected by 2019-nCoV (with a sensitivity of 97.9%). Clinicians can set the best cut-off value based on actual demands.

Under the circumstance of continuing spread of 2019-nCoV, Zhejiang model established in this study, as the first rapid screening diagnostic model for NCP, is of great significance in this battle. Unlike virus isolation or RT-PCR testing, the screening scale is economical, uncomplicated and fast, which can be used to select potential patients for further RT-PCR examinations.

Nevertheless, there were several limitations based on the model. Firstly, the enrolled participants were limited to Zhejiang Province, leading to certain regional limitations in the application of the screening model, in particular the epidemiological characteristics of possible compromise in another area. Secondly, our research was confined to early and rapid screening, without adequate information on disease progression and prognosis. Last but not least, with the development of epidemic situation, weight of certain characteristics, especially epidemiological characteristics, should be modified to increase the scope and accuracy of the diagnostic model.

In the purpose of eliminating the effects of location-specific factors like Wuhan-related criteria, which might be no longer applicable with the evolution of the pandemic, a simplified model was developed by combining all the epidemic regions. Furthermore, we repeated the analysis by drop** the epidemiological data. Both of the subsequent models were proved to be effective. In addition, fivefold Cross-Validations were repeated in each model during internal validation to quantify any optimism in the predictive performance, and Hosmer–Lemeshow χ² test was utilized to measure calibration. Further nationwide even worldwide studies are needed to access the utility of this model, and subject to further adjustment and calibration if necessary.

According to the recent literatures, most patients with NCP are characterized by fever, cough, fatigue, and myalgia in the initial stage¹¹. Atypical symptoms include diarrhea, nausea, headache, sore throat and so on. As the illness progressed, a proportion of patients gradually presented with dyspnea, especially in the populations with low immune functions¹². Complications like acute respiratory distress syndrome (ARDS), arrhythmia and shock, is probably associated with a poor prognosis⁷.

The most common laboratory abnormalities observed are leukopenia and lymphocytopenia. Moreover, it is reported that hypoalbuminemia, elevated CRP and lactate dehydrogenase (LDH), and decreased CD8 count can be seen in part of cases^6,13. The most frequent imaging manifestation is patchy/punctate ground glass opacities involved in single or multiple pulmonary lobes¹⁴. Alterations on chest CT can reflect the severity and progress of NCP¹⁵. However, 2019-nCoV infection can also present with normal pulmonary imaging, particularly in early stage, suggesting the necessity to combine epidemiological information, clinical manifestations and imaging in the screening and diagnosis¹⁶.

At present, RT-PCR remains the confirmation criteria for the diagnosis of 2019-nCoV infection. RT-PCR is a technology combining RNA reverse transcription (RT) with polymerase chain amplification (PCR) of cDNA. It has been widely used in detecting different coronavirus (such as SARS-CoV and MERS-CoV) in laboratory, because of its high specificity and sensitivity. Besides that the RT-PCR test can be time-consuming, a shortage of test kits supply may not meet the needs of a growing infected population. Furthermore, RT-PCR of 2019-nCoV may be false negative due to unstable kits or unstandardized sampling¹⁷. **ao et al. reported that some patients who met the diagnosis of NCP based on clinical and imaging findings, had negative results for viral RNA¹⁸. In another study, initial negative RT-PCR results turned positive in repeated testing in a number of patients¹⁰. With the purpose of timely isolation and early treatment, it is necessary to establish a rapid screening diagnostic model for distinguishing highly suspicious patients with NCP. Actually, the authors are trying to develop a procedure for fast scoring in clinical application based on this model.

In conclusion, the study established a rapid screening model for predicting NCP in a Zhejiang population. What’s more, we developed a simplified model by combining the epidemic regions and rounding the coefficients, as well as a model without any epidemiological factor. The models can be used as a simple, fast, and cost-effective tool for screening NCP with significant clinical value.

Methods

Patients

From January 17 to February 19, 2020, a total of 880 patients who were suspected of 2019-nCoV infection were recruited from hospitals in Hangzhou, Wenzhou, Shaoxing, Taizhou, Ningbo and Jiaxing in Zhejiang Province. The study was approved by the Ethics Committee of Zhejiang Provincial People’s Hospital (2020KY006); in addition, all research was performed in accordance with relevant guidelines¹⁹. Exempt informed consent was approved by the Ethics Committee of Zhejiang Provincial People’s Hospital because the subjects would not be exposed to any risk in this observational study, and the information of subjects was anonymized at collection and prior to analysis.

Epidemiological history and clinical manifestations were collected in each individual. Age, gender, region, coexisting diseases, body temperature, results of blood routine test and chest X-ray or CT were recorded for all participants. Throat swab, sputum, blood, or stool samples were collected to examine the 2019-nCoV nucleic acid using real-time RT-PCR. If the first-time RT-PCR test revealed negative, samples should be collected after 24 h for a repeated test.

Eligibility

Patients admitted to the fever clinics who were initially suspected of NCP were included in the study. Suspected or confirmed cases were diagnosed according to the 5th edition of the Chinese recommendations for diagnosis and treatment of pneumonia caused by 2019-nCoV¹⁹.

Suspected cases

NCP should be suspected if subjects conform to any one of the criteria in the epidemiological history and any two of the standards in clinical presentations. If there is no epidemiological history, suspected cases should meet three of the criteria in clinical presentations.

Epidemiological history: (1) Subjects with a travel or residence history in Wuhan or its neighboring areas, or other areas with persistent local transmission, or communities with definite cases within 14 days; (2) Subjects with a history of contacting confirmed cases with 2019-nCoV infections (positive nucleic acid detection) within 14 days; (3) Subjects with a history of contacting patients with fever or respiratory symptoms who have a travel or residence history in Wuhan or its neighboring areas, or in other areas with persistent local transmission, or communities with definite cases within 14 days; (4) Subjects who are associated with a cluster outbreak, which is defined as one definite case with NCP in family or place of work within 14 days, along with other patients with fever or respiratory symptoms.

Clinical presentations: (1) Fever and/or respiratory symptoms; (2) Typical chest imaging features of NCP, such as ground-glass opacity, infiltrating shadows, and pulmonary consolidation. (3) Normal or decreased white blood cell (WBC) count, or decreased lymphocyte count in the early stage of the disease.

Confirmed cases

Suspected cases who accord with any one of the following criteria: (1) Positive 2019-nCoV nucleic acid in throat swab, sputum, blood samples, or stool by using real-time RT-PCR; (2) Genetic sequencing of samples being highly homologous with the known 2019-nCoV.

Establishment of the rapid screening scale

Based on the epidemic situation in Zhejiang Province, we included age, gender, co-existing diseases, the epidemiological parameters, clinical symptoms, body temperature, WBC count, lymphocyte count, neutrophil count, and chest imaging to establish a novel diagnostic model of NCP. The epidemiological features and symptoms were considered as binary variables, and were scored as “1” if “yes”, and “0” if “no”. As to chest radiologic changes, they were simply classified as “normal”, “unilateral local patchy shadowing”, “bilateral multiple ground glass opacity”, “bilateral diffuse ground glass shadowing with pulmonary consolidation”, and “Other imaging alterations such as pulmonary nodule or pleural effusion”, and were scored as “0”, “0.5”, “1”, “2” and “0.3”, respectively.

The samples were classified to NCP, 339 individuals, and non-NCP, 520 individuals, according to their real-time PT-PCR outcomes since the detection of the 2019-nCoV nucleic acid using real-time RT-PCR was considered the golden standard. Consequently, after the derivation of the screening model, the diagnostic performance of the established scale was also verified. We further combined all the geographical regions into one parameter, that is “epidemic areas with persistent local transmission or communities with definite cases”, and developed a simplified model by rounding the coefficients. Moreover, we dropped the epidemiological parameters that are likely to become outdated given the evolution of the pandemic, and repeated the analysis.

Statistical analysis

Statistical analyses were conducted using SPSS software (version 22.0) for Windows (SPSS, Chicago, IL). Continuous variables were presented as mean ± standard deviation. Continuous variables were compared using the Student’s t-test, and categorical variables were compared using the chi-squared test. For multiple comparisons, the one-way analysis of variance (ANOVA) was performed. Univariate logistic regression analyses were conducted to assess the factors associated with NCP. The parameters with statistical significance were loaded to a multivariate logistic regression model to further identify independent predictors for NCP.

To identify candidate predictors, we performed a stepwise logistic regression analysis (P value to enter = 0.05 and P value to remove = 0.10). A model based on the results of multiple logistic regression analysis was established to screen NCP; furthermore, fivefold cross-validation was employed repeatedly for 10 times to evaluate the performance of the model and examine whether the model was over fitted. Model calibration was evaluated using the Hosmer–Lemeshow χ² test. Area under receiver operating characteristic curve (AUROC) with 95% CI was used to assess the predictive accuracy of the screening model for determining NCP²⁰. Bootstraps with 500 resample were applied to overplot the point-wise 95% CIs of the ROC curves by the R software (version 4.0.3) (not to re-estimate the regression coefficients). Optimal cut-off values were set, and the corresponding sensitivities, specificities, diagnostic accuracies, positive likelihood ratios, and negative likelihood ratios of the model were calculated. A two-sided P value cutoff < 0.05 was considered to be statistically significant.

References

Lu, R. et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395, 565–574 (2020).
Article CAS Google Scholar
Chan, J. F. et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 395, 514–523 (2020).
Article CAS Google Scholar
Li, Q. et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N. Engl. J. Med. 382, 1199–1207 (2020).
Article CAS Google Scholar
Tuite, A. R. & Fisman, D. N. Reporting, epidemic growth, and reproduction numbers for the 2019 novel coronavirus (2019-nCoV) epidemic. Ann. Intern. Med. 172, 567–568 (2020).
Article Google Scholar
Update on the outbreak of new coronavirus pneumonia as at 24 February 27. http://www.nhc.gov.cn/xcs/yqtb/202002/d5e15557ee534fcbb5aaa9301ea5235f.shtml. (assessed on February 27th, 2020).
Huang, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395, 497–506 (2020).
Article CAS Google Scholar
Wang, D. et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323, 1061–1069 (2020).
Article CAS Google Scholar
Chen, Z. et al. Diagnosis and treatment recommendations for pediatric respiratory infection caused by the 2019 novel coronavirus. World J. Pediatr. 16, 240–246 (2020).
Article CAS Google Scholar
Corman, V. et al. Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR. Euro. Surveill. 25, 2000045 (2020).
PubMed Central Google Scholar
**e, X. et al. Chest CT for typical 2019-nCoV pneumonia: relationship to negative RT-PCR testing. Radiology 296, E41–E45 (2020).
Article Google Scholar
Xu, X. W. et al. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ 368, m606 (2020).
Article Google Scholar
Chen, N. et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 395, 507–513 (2020).
Article CAS Google Scholar
Liu, Y. et al. Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci. China Life Sci. 63, 364–374 (2020).
Article ADS CAS Google Scholar
Pan, Y. et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China. Eur. Radiol. 30, 3306–3309 (2020).
Article CAS Google Scholar
Pan, F. et al. Time course of lung changes on chest CT during recovery from 2019 Novel Coronavirus (COVID-19) Pneumonia. Radiology 295, 715–721 (2020).
Article Google Scholar
Chung, M. et al. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 295, 202–207 (2020).
Article Google Scholar
Hao, W. & Li, M. Clinical features of atypical 2019 novel coronavirus pneumonia with an initially negative RT-PCR assay. J. Infect. 80, 671–693 (2020).
Article MathSciNet CAS Google Scholar
**ao, S. Y., Wu, Y. & Liu, H. Evolving status of the 2019 novel coronavirus infection: proposal of conventional serologic assays for disease diagnosis and infection monitoring. J. Med. Virol. 92, 464–467 (2020).
Article CAS Google Scholar
China National Health Commission. Diagnosis and treatment of pneumonitis caused by new coronavirus (trial version 5). Bei**g: China National Health Commission, 2020. http://www.nhc.gov.cn/yzygj/s7653p/202002/d4b895337e19445f8d728fcaf1e3e13a.shtml (accessed February 8, 2020).
Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).
Article ADS MathSciNet CAS Google Scholar

Download references

Acknowledgements

We thank all the persons fighting in this outbreak. This work was supported by the grant from the emergency project of key research and development plan in Zhejiang Province, China (2020C03123).

Author information

These authors contributed equally: Yi-Ning Dai, Wei Zheng, Qing-Qing Wu and Tian-Chen Hui.

Authors and Affiliations

Department of Infectious Diseases, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310014, Zhejiang, China
Yi-Ning Dai, Wei Zheng, Qing-Qing Wu, Tian-Chen Hui, Yong-** Tong, Su-**a Bao, Wen-Hao Wu, Yi-Cheng Huang, Qiao-Qiao Yin, Rong Yan, Ming-Shan Wang, Mei-Juan Chen, Jia-Jie Zhang, Hai-Jun Huang & Hong-Ying Pan
The Second Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
Qing-Qing Wu
Bengbu Medical College, Bengbu, China
Tian-Chen Hui & Qiao-Qiao Yin
Hangzhou Wowjoy Information Technology Co., Ltd, Hangzhou, China
Nan-Nan Sun & Bin Ju
Clinical Research Institute, Zhejiang Provincial People’s Hospital, People’s Hospital of Hangzhou Medical College, Hangzhou, China
Guo-Bo Chen
Key Laboratory of Endocrine Gland Diseases of Zhejiang Province, Hangzhou, China
Guo-Bo Chen
Medical College of Qingdao University, Qingdao, China
Wen-Hao Wu
Department of Infectious Diseases, First People’s Hospital of Tongxiang, Jiaxing, China
Li-Juan Wu
Department of Infectious Diseases, People’s Hospital of Shaoxing, Shaoxing, China
Li-**a Yu
Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China
Ji-Chan Shi
Department of Infectious Diseases, First Hospital of Taizhou, Taizhou, China
Nian Fang
Department of Infectious Diseases, First People’s Hospital of **aoshan, Hangzhou, China
Yue-Fei Shen
Department of Infectious Diseases, First Hospital of Jiaxing, Jiaxing, China
**n-Sheng **e
Department of Infectious Diseases, First People’s Hospital of Wenling, Taizhou, China
Chun-Lian Ma
Department of Respiratory and Critical Care Medicine, Yinzhou People’s Hospital, Affiliated Yinzhou Hospital, College of Medicine, Ningbo University, Ningbo, China
Wan-Jun Yu
Department of Infectious Diseases, Taizhou Municipal Hospital, Taizhou, China
Wen-Hui Tu
Department of Infectious Diseases, ShuLan (Hangzhou) Hospital Affiliated to Zhejiang Shuren University Shulan International Medical College, Hangzhou, China
Hai-Nv Gao
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Centre for Infectious Diseases, Collaborative Innovation Centre for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang Province, China
Lan-Juan Li

Authors

Yi-Ning Dai
View author publications
You can also search for this author in PubMed Google Scholar
Wei Zheng
View author publications
You can also search for this author in PubMed Google Scholar
Qing-Qing Wu
View author publications
You can also search for this author in PubMed Google Scholar
Tian-Chen Hui
View author publications
You can also search for this author in PubMed Google Scholar
Nan-Nan Sun
View author publications
You can also search for this author in PubMed Google Scholar
Guo-Bo Chen
View author publications
You can also search for this author in PubMed Google Scholar
Yong-** Tong
View author publications
You can also search for this author in PubMed Google Scholar
Su-**a Bao
View author publications
You can also search for this author in PubMed Google Scholar
Wen-Hao Wu
View author publications
You can also search for this author in PubMed Google Scholar
Yi-Cheng Huang
View author publications
You can also search for this author in PubMed Google Scholar
Qiao-Qiao Yin
View author publications
You can also search for this author in PubMed Google Scholar
Li-Juan Wu
View author publications
You can also search for this author in PubMed Google Scholar
Li-**a Yu
View author publications
You can also search for this author in PubMed Google Scholar
Ji-Chan Shi
View author publications
You can also search for this author in PubMed Google Scholar
Nian Fang
View author publications
You can also search for this author in PubMed Google Scholar
Yue-Fei Shen
View author publications
You can also search for this author in PubMed Google Scholar
**n-Sheng **e
View author publications
You can also search for this author in PubMed Google Scholar
Chun-Lian Ma
View author publications
You can also search for this author in PubMed Google Scholar
Wan-Jun Yu
View author publications
You can also search for this author in PubMed Google Scholar
Wen-Hui Tu
View author publications
You can also search for this author in PubMed Google Scholar
Rong Yan
View author publications
You can also search for this author in PubMed Google Scholar
Ming-Shan Wang
View author publications
You can also search for this author in PubMed Google Scholar
Mei-Juan Chen
View author publications
You can also search for this author in PubMed Google Scholar
Jia-Jie Zhang
View author publications
You can also search for this author in PubMed Google Scholar
Bin Ju
View author publications
You can also search for this author in PubMed Google Scholar
Hai-Nv Gao
View author publications
You can also search for this author in PubMed Google Scholar
Hai-Jun Huang
View author publications
You can also search for this author in PubMed Google Scholar
Lan-Juan Li
View author publications
You can also search for this author in PubMed Google Scholar
Hong-Ying Pan
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

H.Y. P., L.J.L. and H.J. H.: Conception and design of the study. Y.N.D., W.Z., Q.Q.W., T.C.H., S.X.B., Y.X.T., W.H.W., Y.C.H., Q.Q.Y., L.J.W., L.X.Y., J.C.S., N.F., Y.F.S., X.S.X., C.L.M., W.J.Y., W.H.T., R.Y., M.S.W., M.J.C., J.J.Z., and H.V.G.: Data collection. Y.N.D., G.B.C., N.N.S., and B.J.: Data analysis. Y.N.D., W. Z. and Q.Q.W.: Writing of the article. We thank all the persons fighting in this outbreak.

Corresponding authors

Correspondence to Hai-Jun Huang, Lan-Juan Li or Hong-Ying Pan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Dai, YN., Zheng, W., Wu, QQ. et al. A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study. Sci Rep 11, 3863 (2021). https://doi.org/10.1038/s41598-021-83054-x

Download citation

Received: 31 March 2020
Accepted: 25 January 2021
Published: 16 February 2021
DOI: https://doi.org/10.1038/s41598-021-83054-x
Springer Nature Limited

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study

Abstract

Similar content being viewed by others

A predictive score at admission for respiratory failure among hospitalized patients with confirmed 2019 Coronavirus Disease: a simple tool for a complex problem

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

Predictive Score of Risk Associated with Progression of Patients with COVID-19 Pneumonia in Wuhan, China: the ALA Score

Introduction

Discussion

Methods

Patients

Eligibility

Suspected cases

Confirmed cases

Establishment of the rapid screening scale

Statistical analysis

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Competing interests

Additional information

Publisher's note

Rights and permissions

About this article

Cite this article

Navigation

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study

Abstract

Similar content being viewed by others

A predictive score at admission for respiratory failure among hospitalized patients with confirmed 2019 Coronavirus Disease: a simple tool for a complex problem

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

Predictive Score of Risk Associated with Progression of Patients with COVID-19 Pneumonia in Wuhan, China: the ALA Score

Introduction

Discussion

Methods

Patients

Eligibility

Suspected cases

Confirmed cases

Establishment of the rapid screening scale

Statistical analysis

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Competing interests

Additional information

Publisher's note

Rights and permissions

About this article

Cite this article

Share this article

Search

Navigation