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

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(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

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(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.

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(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

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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.

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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.

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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

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  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
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Last updated: April 27, 2020