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02 August 2021: Editorial

Editorial: The National COVID Cohort Collaborative Consortium Combines Population Data with Machine Learning to Evaluate and Predict Risk Factors for the Severity of COVID-19

Dinah V. Parums *

DOI: 10.12659/MSM.934171

Med Sci Monit 2021; 27:e934171

Abstract

ABSTRACT: Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pneumonia. However, COVID-19 is now recognized to involve multiple organ systems with varying severity and duration. In July 2021, the findings from a retrospective population study from the National COVID Cohort Collaborative (N3C) Consortium were published that included analysis by machine learning methods of 174,568 adults with SARS-CoV-2 infection from 34 medical centers in the US. The study stratified patients for COVID-19 according to the World Health Organization (WHO) Clinical Progression Scale (CPS). Severe clinical outcomes were identified as the requirement for invasive ventilatory support, or extracorporeal membrane oxygenation (ECMO), and patient mortality. Machine learning analysis showed that the factor most strongly associated with severity of clinical course in patients with COVID-19 was pH. A separate multivariable logistic regression model showed that independent factors associated with more severe clinical outcomes included age, dementia, male gender, liver disease, and obesity. This Editorial aims to present the rationale and findings of the largest population cohort of adult patients with COVID-19 to date and highlights the importance of using large population studies with sophisticated analytical methods, including machine learning.

Keywords: Editorial, Population Characteristics, machine learning, Epidemiology, severe acute respiratory syndrome, COVID-19, severe acute respiratory syndrome coronavirus 2

Conclusions

Machine learning analysis of a national multicenter population database from the US the National COVID Cohort Collaborative (N3C) Consortium analyzed clinical and demographic data on 174,568 SARS-CoV-2-positive adults admitted to hospital. This study confirmed that patient mortality from COVID-19 decreased during 2020. Patient demographic characteristics of age, male gender, and comorbidities, including obesity, were associated with increased clinical severity.

References

1. Parums DV: Editorial: Long COVID, or post-COVID syndrome, and the global impact on health care: Med Sci Monit, 2021; 27; e933446

2. Li L, Sun W, Han M: A study on the predictors of disease severity of COVID-19: Med Sci Monit, 2020; 26; e927167

3. Hochhegger B, Zanon M, Altmayer S: COVID-19 mimics on chest CT: A pictorial review and radiologic guide: Br J Radiol, 2021; 94(1118); 20200703

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5. Collins J, Stern EJ: COVID-19 Treatment Guidelines Panel: Clinical spectrum of SARS-CoV-2 infection, 2021

6. Collins J, Stern EJ: A minimal common outcome measure set for COVID-19 clinical research: Lancet Infect Dis, 2020; 20(8); e192-97

7. Wang Y, Yao S, Liu X: Risk factors of coronavirus disease 2019-related mortality and optimal treatment regimens: A retrospective study: Med Sci Monit, 2021; 27; e926751

8. Parums DV: Editorial: Registries and population databases in clinical research and practice: Med Sci Monit, 2021; 27; e933554

9. Parums DV: Editorial: Artificial intelligence (AI) in clinical medicine and the 2020 CONSORT-AI study guidelines: Med Sci Monit, 2021; 27; e933675

10. Bennett TD, Moffitt RA, Hajagos JG: Clinical characterization and prediction of clinical severity of SARS-CoV-2 infection among US adults using data from the US National COVID Cohort Collaborative: JAMA Netw Open, 2021; 4(7); e2116901

11. Haendel MA, Chute CG, Bennett TD: The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment: J Am Med Inform Assoc, 2021; 28(3); 427-43

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750