A Tool to Early Predict Severe Corona Virus Disease 2019 (COVID-19) : A Multicenter Study using the Risk Nomogram in Wuhan and Guangdong, China

Abstract

Background

Due to no reliable risk stratification tool for severe corona virus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identifying cases at high risk of progression to severe COVID-19.

Methods

In this retrospective three-centers study, 372 non-severe COVID-19 patients during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. Based on baseline data of the two groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluate its performance.

Results

The train cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19. We found that old age, and higher serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width, blood urea nitrogen, direct bilirubin, lower albumin, are associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the train cohort (AUC 0.912 [95% CI 0.846-0.978], sensitivity 85.71%, specificity 87.58%); in validation cohort (0.853 [0.790-0.916], 77.5%, 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that nomogram conferred high clinical net benefit.

Conclusion

Our nomogram could help clinicians to early identify patients who will exacerbate to severe COVID-19, which will enable better centralized management and early treatment of severe patients.

Summary

Older age; higher LDH, CRP, RDW, DBIL, BUN; lower ALB on admission correlated with higher odds of severe COVID-19. An effective prognostic nomogram composed of 7 features could allow early identification of patients at risk of exacerbation to severe COVID-19.

SciScore for 10.1101/2020.03.17.20037515: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Institutional Review Board Statement	Consent: Written informed consent was waived by the Ethics Commission of each hospital for emerging infectious diseases. IRB: The study was approved by the Ethics Committee of the Eighth People’s Hospital of Guangzhou, the Ethics Commission of the Third Affiliated Hospital of Sun Yat-sen University and the Ethics Commission of Zhongnan Hospital.
Randomization	not detected.
Blinding	not detected.
Power Analysis	not detected.
Sex as a biological variable	not detected.

Table 2: Resources

Software and Algorithms
Sentences	Resources
The missing values were imputed by expectation-maximization (EM) method using SPSS statistical software, version 25 (SPSS, Inc., Chicago, IL, USA).	SP…

SciScore for 10.1101/2020.03.17.20037515: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Institutional Review Board Statement	Consent: Written informed consent was waived by the Ethics Commission of each hospital for emerging infectious diseases. IRB: The study was approved by the Ethics Committee of the Eighth People’s Hospital of Guangzhou, the Ethics Commission of the Third Affiliated Hospital of Sun Yat-sen University and the Ethics Commission of Zhongnan Hospital.
Randomization	not detected.
Blinding	not detected.
Power Analysis	not detected.
Sex as a biological variable	not detected.

Table 2: Resources

Software and Algorithms
Sentences	Resources
The missing values were imputed by expectation-maximization (EM) method using SPSS statistical software, version 25 (SPSS, Inc., Chicago, IL, USA).	SPSS suggested: (SPSS, RRID:SCR_002865)

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

There were some limitations in the study. First, this is a retrospective study, including 372 patients with non-severe COVID-19 on admission. ACE2, the receptor for SARS-COV-2, has been reported to be differentially expressed in different populations[21]. The differences in patient profiles and healthcare might have effect on the performance of nomogram in other populations outside of China. Further studies on different populations with larger patient cohorts are required to verify our findings. Second, some patients are still in hospital and their condition may change with follow-up. The final survival outcome is lacking. Third, the study has not included IgM and IgG antibodies detection. More comprehensive investigations need to be conducted to explain the characteric of the 7 features. In summary, our data suggest that our nomogram could early identify the severe COVID-19 patients, and RDW was vaulable for prediction of severe diseases. Our nomogram is especially valuable for risk stratification management, which will be helpful for alleviating insufficient medical resources and reducing mortality.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
Thank you for including a protocol registration statement.

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