Risk of severe disease

Clinical Question

What are the risk factors for severe COVID-19 disease?

Key Findings

  • Average rates of intubation per total number of known COVID19 cases appear to be in the range of 4-11%.
  • In multiple studies, patients requiring intubation were significantly more likely to be older (60s v. 50s), male gender, have dyspnea, co-infection, longer time between symptom onset and hospital admission, and have underlying disease, including: COPD, CVD, HTN, cerebrovascular disease, diabetes and malignancy.
  • One study of deceased and severely ill pts found lymphopenia to be an independent and significant predictor of poor outcomes.13

Summary of Information

Reference #

Country

Description of study

Ventilation rate

Indicators of illness severity or predictors of intubation

Comments

1 - Wang et al.

Wuhan, China138 hospitalized patients

As summarized above -

Of 138 hospitalized patients:36 (26.0%) were transferred to ICU.

Of these:4 (11.1%) high-flow O215 (41.7%) noninvasive ventilation17 (47.2%) invasive ventilation - including 4 extracorporeal membrane oxygenation

See table below

Statistically significant differences between ICU (n=36) and non-ICU (n=102) cases:

  • Age (66 v 51 yrs)
  • Underlyingcomorbidities (72 v 37%)
    • Hypertension
    • Cardiovascular dz
    • Cerebrovascular dz
    • Diabetes
  • Later onset of dyspnea (5 v 6.5 days)
  • Longer time to hospital admission (7 vs. 8 days)

The median time from first symptom to dyspnea was 5.0 days, to hospital admission was 7.0 days, and to ARDS was 8.0 days.ICU pts were also more likely to report:

  • Anorexia 
  • Dyspnea 
  • Sore throat 
  • Dizziness 
  • Abdominal pain 

2 - Guan et al. (NEJM)

Wuhan, China

O2 therapy: 41.3% of all patients and 71% of those with severe disease

Mech ventilation: 6.1% of all patients (all pts who were mechanically ventilated had severe dz)

Of those with severe disease, 14% had invasive ventilation, and 32% had non-invasive ventilation (with some overlap between these groups)

See table 2 and 3 (no P values)

Refer to Table 2 and 3 below for clinical and lab values distinguishing between severe and nonsevere disease.

Systemic glucocorticoids were given to 204 (18.6%) of patients, including 44% of severe and 13% of non-severe patients.

Of these 204 patients, 33 (16.2%) were admitted to the ICU, 17 (8.3%) underwent invasive ventilation, and 5 (2.5%) died. Extracorporeal membrane oxygenation was performed in 5 patients (0.5%) with severe disease.

3 - ICNARC Report

England, Wales and Northern Ireland

Comparison of 775 COVID+ patients to 5755 with non-COVID viral pneumonia

78.7% of pts in critical care units in the UK require mechanical ventilation (this is data from ICUs alone, not hospitals overall)

Of patients in the ICU - 70.5% male, mean age 60.2. For non-covid viral pneumonia ICU - 54.3% male, mean age 58.

Patients receive intensive respiratory support for an average of 5 days (4, 8). On average survivors in critical care units are staying (in ICU) for 3 days (non-survivors 4).

Outcomes of ICU stays for known COVID19 are 51% alive, 47.9% dead.

4 - Ruan et al.

Wuhan, China

N/A

Death vs discharge:

  • Cardiovascular disease (p< 0.001)
  • Age (p< 0.001)
  • Gender (p=0.43)

Overall - presence of secondary infection and elevated inflammatory indicators in the blood related to increased risk.

Results obtained from this study also suggest that COVID-19 mortality might be due to virus-activated “cytokine storm syndrome” or fulminant myocarditis.

5 - European Centre for Disease Prevention and Control (ECDC)

Italy

N/A

Risk factors for severe disease/ICU include:

  • COPD
  • Dyspnea
  • Cardiovascular disease
  • Hypertension
  • Age
  • Male gender

6 - Chen et al.

Wuhan, China

N/A

Deceased patients were more likely than recovered pts to have:

  • Hypertension
  • Cardiovascular disease
  • Cerebrovascular disease

No statistical analysis done, frequency data only.

7 - Guan et al. (European Respiratory Journal)

Across mainland China

1590 cases

N/A

25.1% of patients reported at least one comorbidity (32% of severe and 10% of non-severe cases)

Compared to non-severe cases, severe cases were more likely to have:

  • Hypertension (32.7 v 12.6%)
  • Cardiovascular diseases (33.9 v 15.3%)
  • Cerebrovascular diseases (50.0 v 15.3%)
  • Diabetes (34.6 v 14.3%)
  • Hepatitis B infection (32.1 v 15.7%)
  • COPD (62.5 v 15.3%)
  • Chronic Kidney Disease (38.1 v 15.7%)
  • Malignancy (50.0 v 15.6%)

8 - Jain et al.

Meta-analysis

Across mainland China

1813 cases

N/A

“Dyspnoea was the only symptom strongly predictive for both severe disease and ICU admission, and could be useful in guiding clinical management decisions early in the course of illness”

“The most prevalent symptoms in the severe group were cough (70.5%), fever (64.1%) and fatigue (44.5%); in the ICU group these were cough (67.2%), fever (62.9%) and dyspnoea (61.2%). The most prevalent comorbidities in the severe group were hypertension (25.4%) and diabetes (16.8%) and in the ICU group were hypertension (40.5%) and Cardiovascular Disease (24.1%).”

“Although no more likely to be in the severe group, men were 1.55 times more likely than women to be admitted to ICU (95% CI 1.02 – 2.36).”

“Cough was associated with severe disease (pOR 1.63, 95% CI 1.03 – 2.60), but not ICU admission”“ Chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and hypertension were the comorbidities significantly predictive for both severe disease and ICU admission. pORs: COPD (6.42, 95% CI 2.44- 16.9), CVD (2.70, 95% CI 1.52 – 4.80) and hypertension (1.97, 95% CI 1.40 – 2.77).”

COVID-19 severity was not consistently defined across included studies

Supplementary Information


(1) Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y, Li Y, Wang X, Peng Z. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020 Feb 7;. doi: 10.1001/jama.2020.1585. [Epub ahead of print] PubMed PMID: 32031570; PubMed Central PMCID: PMC7042881

Descriptive text is not available for this image

(2) Guan, WJ, Ni, ZY, Hu, Y, Liang, WH, Ou, CQ, He, JX, Liu, L, Shan, H, Lei, CL, Hui, DSC, Du, B, Li, LJ, Zeng, G, Yuen, KY, Chen, RC, Tang, CL, Wang, T, Chen, PY, Xiang, J, Li, SY, Wang, JL, Liang, ZJ, Peng, YX, Wei, L, Liu, Y, Hu, YH, Peng, P, Wang, JM, Liu, JY, Chen, Z, Li, G, Zheng, ZJ, Qiu, SQ, Luo, J, Ye, CJ, Zhu, SY, Zhong, NS. Clinical characteristics of coronavirus disease 2019 in China N Engl J Med 2020[Epub ahead of print]. DOI: 10.1056/NEJMoa2002032

Descriptive text is not available for this image

Descriptive text is not available for this image

(3) “ICNARC report on COVID-19 in Critical Care 27 March 2020”. Intensive Care National Audit and Resource Centre COVID-19 Study Case Mix Programme Database.

Descriptive text is not available for this image
Descriptive text is not available for this image

(7). Guan WJ, Liang WH, Zhao Y, et al. Comorbidity and its impact on 1590 patients with Covid-19 in China: A Nationwide Analysis [published online ahead of print, 2020 Mar 26]. Eur Respir J. 2020;2000547. doi:10.1183/13993003.00547-2020.

Risk factors associated with ICU admission, invasive ventilation or death

Descriptive text is not available for this image

Predictors of the composite endpoints in the proportional hazards model. Shown in the figure are the hazards ratio (HR) and the 95% confidence interval (95%CI) for the risk factors associated with the composite endpoints (admission to intensive care unit, invasive ventilation, or death). The comorbidities were classified according to the organ systems as well as the number. The scale bar indicates the HR. Cox proportional hazard regression models were applied to determine the potential risk factors associated with the composite endpoints, with the hazards ratio (HR) and 95% confidence interval (95%CI) being reported. The model has been adjusted with age and smoking status.

Descriptive text is not available for this image

Comparison of the time-dependent risk of reaching to the composite endpoints. a) The time-dependent risk of reaching to the composite endpoints between patients with (orange curve) or without any comorbidity (dark blue curve). b) The time-dependent risk of reaching to the composite endpoints between patients without any comorbidity (orange curve), patients with a single comorbidity (dark blue curve), and patients with two or more comorbidities (green curve). Cox proportional hazard regression models were applied to determine the potential risk factors associated with the composite endpoints, with the hazards ratio (HR) and 95% confidence interval (95%CI) being reported.

(8) Jain V., Yuan J-M. “Systematic review and meta-analysis of predictive symptoms and comorbidities for severe COVID-19 infection.” March 16, 2020. Medrxiv: doi.org/10.1101/2020.03.15.20035360.

Descriptive text is not available for this image
Descriptive text is not available for this image
Descriptive text is not available for this image

Author Information

Authors: Lucas Keyt MS4, Zoey ZoBell MS4, Peter Clements MS4, UC San Diego School of Medicine
Completed on: March 29, 2020
Last updated on: Not yet updated

Reviewed by: Sara Baird MD
Reviewed on: April 7, 2020

This CoRESPOND summary was written as part of Earth 2.0 COVID-19 Rapid Response at UC San Diego. For information about the project, please visit https://earth2-covid.ucsd.edu/

References

  1. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y, Li Y, Wang X, Peng Z. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020 Feb 7;. doi: 10.1001/jama.2020.1585. [Epub ahead of print] PubMed PMID: 32031570; PubMed Central PMCID: PMC7042881
  2. Guan, WJ, Ni, ZY, Hu, Y, Liang, WH, Ou, CQ, He, JX, Liu, L, Shan, H, Lei, CL, Hui, DSC, Du, B, Li, LJ, Zeng, G, Yuen, KY, Chen, RC, Tang, CL, Wang, T, Chen, PY, Xiang, J, Li, SY, Wang, JL, Liang, ZJ, Peng, YX, Wei, L, Liu, Y, Hu, YH, Peng, P, Wang, JM, Liu, JY, Chen, Z, Li, G, Zheng, ZJ, Qiu, SQ, Luo, J, Ye, CJ, Zhu, SY, Zhong, NS. Clinical characteristics of coronavirus disease 2019 in China N Engl J Med 2020[Epub ahead of print]. DOI: 10.1056/NEJMoa2002032
  3. “ICNARC report on COVID-19 in Critical Care 27 March 2020”. Intensive Care National Audit and Resource Centre COVID-19 Study Case Mix Programme Database.
  4. Ruan, Q., Yang, K., Wang, W. et al. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med (2020). https://doi.org/10.1007/s00134-020-05991-x
  5. Eurosurveillance Editorial Team. Updated rapid risk assessment from ECDC on the novel coronavirus disease 2019 (COVID-19) pandemic: increased transmission in the EU/EEA and the UK. Euro Surveill. 2020;25(10):2003121. doi:10.2807/1560-7917.ES.2020.25.10.2003121
  6. Chen T, Wu D, Chen H, Yan W, Yang D, Chen G, Ma K, Xu D, Yu H, Wang H, Wang T, Guo W, Chen J, Ding C, Zhang X, Huang J, Han M, Li S, Luo X, Zhao J, Ning Q. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ. 2020 Mar 26;368:m1091. doi: 10.1136/bmj.m1091. PubMed PMID: 32217556.
  7. Guan WJ, Liang WH, Zhao Y, et al. Comorbidity and its impact on 1590 patients with Covid-19 in China: A Nationwide Analysis [published online ahead of print, 2020 Mar 26]. Eur Respir J. 2020;2000547. doi:10.1183/13993003.00547-2020.
  8. Jain V., Yuan J-M. “Systematic review and meta-analysis of predictive symptoms and comorbidities for severe COVID-19 infection.” March 16, 2020. Medrxiv: doi.org/10.1101/2020.03.15.20035360.
  9. https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html
  10. https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
  11. “Severe Outcomes Among Patients with Coronavirus Disease 2019 (COVID-19) — United States, February 12–March 16, 2020.” Morbidity and Mortality Weekly Report. CDC Covid-19 Response Team.
    Coronavirus disease 2019 (COVID-19) pandemic: increased transmission in the EU/EEA and the UK – seventh update, 25 March 2020. Stockholm: ECDC; 2020.
  12. The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team. The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) — China, 2020[J]. China CDC Weekly, 2020, 2(8): 113-122.
  13. [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za Zhi. 2020 Feb 17;41(2):145-151. doi: 10.3760/cma.j.issn.0254-6450.2020.02.003. [Epub ahead of print] PubMed PMID: 32064853.
  14. Real estimates of mortality following COVID-19 infection. Baud, David et al. The Lancet Infectious Diseases, Volume 0, Issue 0. https://doi.org/10.1016/S1473-3099(20)30195-X
  15. Mizumoto Kenji, Kagaya Katsushi, Zarebski Alexander, Chowell Gerardo. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Euro Surveill. 2020;25(10):pii=2000180. https://doi.org/10.2807/1560-7917.ES.2020.25.10.2000180
  16. Nishiura H, Kobayashi T, Miyama T, Suzuki A, Jung S, Hayashi K, et al. Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19). medRxiv. 17 Feb 2020. https://doi.org/http://dx.doi.org/10.1101/2020.02.03.20020248
  17. Mizumoto K Chowell G. Estimating the risk of 2019 novel coronavirus death during the course of the outbreak in China, 2020.medRxiv. 2020; (published online Feb 23.) (preprint). DOI:10.1101/2020.02.19.20025163
  18. Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis; published online Feb 19. https://doi.org/10.1016/S1473-3099(20)30120-1. Johns Hopkins University.
  19. https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6
  20. Timothy W Russell, Joel Hellewell, Christopher I Jarvis, Kevin van-Zandvoort, Sam Abbott, Ruwan Ratnayake, CMMID nCov working group, Stefan Flasche, Rosalind M Eggo, Adam J Kucharski. Estimating the infection and case fatality ratio for COVID-19 using age-adjusted data from the outbreak on the Diamond Princess cruise ship. medRxiv 2020.03.05.20031773; doi:https://doi.org/10.1101/2020.03.05.20031773.
  21. Ministry of Health, Labour and Welfare. About new coronavirus infections [Japanese]. Tokyo, Japan: Ministry of Health, Labour and Welfare; 2020. https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000164708_00001.htmlexternal icon