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Revista de Saúde Pública

Print version ISSN 0034-8910On-line version ISSN 1518-8787

Rev. Saúde Pública vol.54  São Paulo  2020  Epub June 01, 2020

https://doi.org/10.11606/s1518-8787.2020054002481 

Brief communication

A brief-review of the risk factors for covid-19 severity

J.E. RodI  II  III 
http://orcid.org/0000-0002-1584-1983

Oscar Oviedo-TrespalaciosII  III  IV 
http://orcid.org/0000-0001-5916-3996

Javier Cortes-RamirezI 
http://orcid.org/0000-0002-5876-165X

IQueensland University of Technology (QUT). School of Public Health and Social Work. Brisbane, Queensland, Australia

IIQueensland University of Technology (QUT). Institute of Health and Biomedical Innovation. Brisbane, Queensland, Australia

IIIQueensland University of Technology (QUT). Centre for Accident Research and Road Safety Queensland (CARRS-Q). Brisbane, Queensland, Australia

IVUniversidad del Norte. Departamento de ingeniería. Barranquila, Atlántico, Colombia


ABSTRACT

The World Health Organization has emphasized that one of the most important questions to address regarding the covid-19 pandemic is to understand risk factors for disease severity. We conducted a brief review that synthesizes the available evidence and provides a judgment on the consistency of the association between risk factors and a composite end-point of severe-fatal covid-19. Additionally, we also conducted a comparability analysis of risk factors across 17 studies. We found evidence supporting a total of 60 predictors for disease severity, of which seven were deemed of high consistency, 40 of medium and 13 of low. Among the factors with high consistency of association, we found age, C-reactive protein, D-dimer, albumin, body temperature, SOFA score and diabetes. The results suggest that diabetes might be the most consistent comorbidity predicting disease severity and that future research should carefully consider the comparability of reporting cases, factors, and outcomes along the different stages of the natural history of covid-19.

Key words: Coronavirus Infections, epidemiology; Fatal Outcome; Risk Factors; Review

INTRODUCTION

More than 200 countries and territories have reported confirmed cases of the novel coronavirus disease covid-19, characterized as a pandemic by the World Health Organisation on April 7, 20201. As this global health emergency tests the resilience of health systems around the world, health care and public health practitioners are required to have high quality evidence to identify its most significant risks and prioritize resources where they are most needed.

One of the most important questions to address the currently unfolding pandemic is what are the risk factors for severe illness or death?”1,2. Systematic reviews and meta-analysis paired with a standardised method to assess the quality of evidence are deemed to provide the best evidence by current standards3. For example, the GRADE evidence assessment focuses on comparing factors or outcomes across studies to provide an evidence synthesis. If researchers conduct a GRADE evaluation for covid-19 severity in the future, the time investment and internal validity of the assessment will be heavily influenced by the consistency of categorizations and reporting of cases, factors, and outcomes across studies.

Since scientific publications addressing the pandemic are being produced rapidly, including risk factor studies, summarizing and sharing such information is of paramount importance to support an efficient and rapid response. An early review of risk factor studies could also provide some insight on the undesirable heterogeneity of definitions and reporting that might affect later evidence evaluations. Therefore, we compiled a brief summary of the literature evaluating the risk factors for covid-19 disease severity with a two-fold purpose: (i) to provide healthcare and public health professionals with a reference list of the consistency of risk factors for covid-19 severity, and (ii) to inform researchers about the consistency of reporting in the available literature.

METHODS

We conducted a review to assess studies looking for risk factors of severity or death for covid-19, using a composite outcome of disease severity-fatality (CSF)4. Unstructured searches using the terms: “disease attributes,” “clinical findings,” “clinical features,” “clinical characteristic,” “novel coronavirus,” “covid-19,” “SARS-Cov-2,” “fatality,” “fatal,” “death,” “mortality,” “severity,” “disease severity,” “predictor,” and “risk factor” were performed to identify articles written in English available on PubMed, Scopus and MedRxiv. Articles were selected for the review if they included a comparison between non-severe and CSF cases according to the categorization of severity in each article. After completing article selection, we assessed the consistence of statistically significant associations for a particular risk factor by classifying it as high, medium, and low, following two criteria: (i) a positive difference in the total number of studies reporting statistical significance dissimilarities between non-severe and CSF, minus the number of studies without statistical significance for the same factor, and, (ii) the reporting of statistically significant estimates when performing multivariate statistics. The consistency of association was categorized as high (both criteria were met), medium (one criterion), low (none). Additionally, we assessed heterogeneity by comparing the terminology, units, statistical descriptions, and cut-off points of each risk factor and reported the highest comparable (hc) number across studies. We then subtracted the total of studies including the risk factor by the hc number across the sample (Table).

Table Summary of risk factors associated with covid-19 severity and evaluation of reporting consistency 

RISK FACTORS Consistency computation for each factor Studies not reporting p values (n) Total (p < 0.05) - total (p > 0.05) Results of (p < 0.05) multivariate statistics Consistency Heterogeneity


All studies (n) with p < 0.05 for differences non-severe vs CSF % of p < 0.05 studies supporting a value direction or the presence of the risk factor Studies p > 0.05 (n)
  • ●●● High

  • ●●○ Medium

  • ●○○ Low

  • hc# (Total - hc#/total)

  • hc-statistic hc-definition, unit

Age
  • Zhang et al.6 (19)

  • Wang et al.7 (138)

  • Tian et al.8 (262)

  • Liu et al.9 (109)

  • Liu et al.10 (78)

  • Li et al.11 (84)

  • Caramelo et al.12 (72314)

  • Tang et al.13 (183)

  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

100% - ↑
  • Huang et al.17 (41)

  • Guan et al.18 (1099)

  • Yang et al.19 (52)

  • Young et al.20 (18)

  • Ji et al.21 (NA)

  • Zhonghua22 (44672)

10
  • Liu et al.10 ≥ 60: OR 8.5 95% CI 1.6 - 44.8

  • Caramelo et al.12 50-59: OR 6.7 95% CI 2.9 - 15.2

  • ≥ 80: OR 86.6 95% CI 32.6 - 202.4

  • Zhou et al.15 NA: OR 1.1 95% CI 1.0 - 1.2

●●●
  • 12 (5/17) = 29%

  • Median (IQR) Age, years

C- reactive protein
  • Zhang et al.6 (19)

  • Li et al.11 (84)

  • Liu et al.10 (78)

  • Liu et al.9 (109)

  • Ruan et al.14 (150)

  • Wu et al.16 (201)

100% - ↑
  • 0

  • Young et al.20 (18)

  • Guan et al.18 (1099)

6
  • Liu et al.10 > 8.2 mg/L: OR 10.5 95% CI 1.2 - 34.7

●●●
  • 6 (2/8) = 25%

  • Median (IQR) C-reactive protein, mg/L

D- Dimer
  • Tang et al.13 (183)

  • Huang et al.17 (41)

  • Zhang et al.6 (19)

  • Wang et al.7 (138)

  • Liu et al.9 (109)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

100% - ↑
  • Liu et al.10 (78)

  • Guan et al.18 (1099)

6
  • Zhou et al.15 > 1 μg/mL: OR 18.42 95% CI 2.6 - 128.5

●●●
  • 7 (2/9) = 22%

  • Median (IQR) D-dimer, μg/mL

Albumin
  • Liu et al.10 (78)

  • Huang et al.17 (41)

  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

100% - ↓
  • 0

  • 0

5
  • Liu et al.10 < 40 g/L: OR 7.4 95% CI 1.1 - 50.0

●●●
  • 4 (1/5) = 20%

  • Median (IQR) Albumin, g/L

Body temperature
  • Huang et al.17 (41)

  • Li et al.11 (84)

  • Liu et al.10 (78)

  • Wu et al.16 (201)

75% - ↑
  • Tian et al.8 (262)

  • Guan et al.18 (1099)

  • Young et al.20 (18)

3
  • Liu et al.10 ≥ 37.3°: OR 8.9 95% CI 1.0 - 78.1

●●●
  • 4 (3/7) = 43%

  • Median (IQR) Highest temperature, C°

SOFA score
  • Liu et al.9 (109)

  • Zhou et al.15 (191)

100% - ↑
  • 0

  • 0

2
  • Zhou et al.15 NA: OR 5.7 95% CI 2.6 - 12.2

●●●
  • 2 (0/2) = 0%

  • Median (IQR) SOFA score, NA

Diabetes
  • Caramelo et al.12 (72314)

  • Li et al.11 (84)

  • Wang et al.7 (138)

  • Liu et al.9 (109)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

100% - Presence
  • Huang et al.17 (41)

  • Zhang et al.6 (19)

  • Liu et al.10 (78)

  • Ruan et al.14 (150)

  • Guan et al.18 (1099)

  • Zhonghua22 (44672)

  • Yang et al.19 (52)

2
  • Caramelo et al.12 Diabetes: OR 9.0 95% CI 7.4 - 11.3

●●●
  • 13 (0/13) = 0%

  • Percentages Diabetes, NA

Lymphocyte count
  • Zhang et al.6 (19)

  • Li et al.11 (84)

  • Wang et al.7 (138)

  • Huang et al.17 (41)

  • Liu et al.9 (109)

  • Ruan et al.14 (150)

  • Wu et al.16 (201)

  • Zhou et al.15 (191)

100% - ↓
  • Yang et al.19 (52)

  • Liu et al.10 (78)

  • Young et al.20 (18)

  • Guan et al.18 (1099)

6
  • NA

●●○
  • 9 (3/12) = 25%

  • Median (IQR) Lymphocyte count, ×109/L

Dyspnea
  • Li et al.11 (84)

  • Huang et al.17 (41)

  • Tian et al.8 (262)

  • Wang et al.7 (138)

  • Ruan et al.14 (150)

  • Wu et al.16 (201)

100% - Presence
  • Zhang et al.6 (19)

  • Yang et al.19 (52)

  • Guan et al.18 (1099)

  • Young et al.20 (18)

5
  • NA

●●○
  • 10 (0/10) = 0%

  • Percentages Dyspnea, NA

White blood Cell count
  • Huang et al.17 (41)

  • Wang et al.7 (138)

  • Liu et al.9 (109)

  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

  • Zhang et al.6 (19)

100% - ↑
  • Li et al.11 (84)

  • Liu et al.10 (78)

  • Guan et al.18 (1099)

  • Young et al.20 (18)

5
  • NA

●●○
  • 8 (3/11) = 27%

  • Median (IQR) WBC count, ×109/L

Procalcitonin
  • Li et al.11 (84)

  • Wang et al.7 (138)

  • Huang et al.17 (41)

  • Liu et al.9 (109)

  • Zhou et al.15 (191)

100% - ↑
  • Liu et al.10 (78)

  • Guan et al.18 (1099)

4
  • NA

●●○
  • 6 (1/7) = 14%

  • Median (IQR) Procalcitonin, ng/ml

Lactate dehydrogenase
  • Wang et al.7 (138)

  • Huang et al.17 (41)

  • Liu et al.9 (109)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

100% - ↑
  • Ruan et al.14 (150)

  • Young et al.20 (18)

  • Guan et al.18 (1099)

4
  • NA

●●○
  • 7 (1/8) = 13%

  • Median (IQR) LDH, U/L

Cardiac troponins
  • Wang et al.7 (138)

  • Huang et al.17 (41)

  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

100% - ↑
  • 0

  • 0

4
  • NA

●●○
  • 3 (1/4) = 25%

  • Median (IQR) Hypersensitive troponin I, pg/ml

Prothrombin time
  • Huang et al.17 (41)

  • Tang et al.13 (183)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

100% - ↑
  • Wang et al.7 (138)

  • Yang et al.19 (52)

3
  • NA

●●○
  • 5 (1/6) = 17%

  • Median (IQR) Prothrombin time, s,

Blood urea nitrogen
  • Ruan et al.14 (150)

  • Liu et al.9 (109)

  • Wang et al.7 (138)

100% - ↑
  • 0

  • 0

3
  • NA

●●○
  • 3 (0/3) = 0%

  • Blood urea nitrogen, mmol/L

Total bilirubin
  • Ruan et al.14 (150)

  • Wang et al.7 (138)

  • Huang et al.17 (41)

  • Wu et al.16 (201)

100% - ↑
  • Liu et al.9 (109)

  • Yang et al.19 (52)

  • Guan et al.18 (1099)

3
  • NA

●●○
  • 4 (3/7) = 43%

  • Total bilirubin, μmol/L

Interleukin-6
  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

100% - ↑
  • 0

  • 0

3
  • NA

●●○
  • 3 (0/3) = 0%

  • Median (IQR) IL-6, pg/mL

Serum ferritin
  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

100% - ↑
  • 0

  • 0

3
  • NA

●●○
  • 3 (0/3) = 0%

  • Median (IQR) Serum ferritin, ng/mL

Comorbidity
  • Li et al.11 (84)

  • Wang et al.7 (138)

  • Zhang et al.6 (19)

  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

100% - Presence
  • Huang et al.17 (41)

  • Tang et al.13 (183)

  • Guan et al.18 (1099)

  • Yang et al.19 (52)

3
  • NA

●●○
  • 8 (0/8) = 0%

  • Percentages Comorbidity, NA

Neutrophil count
  • Huang et al.17 (41)

  • Wang et al.7 (138)

  • Liu et al.9 (109)

  • Wu et al.16 (201)

75% - ↑
  • Liu et al.10 (78)

  • Li et al.11 (84)

  • Young et al.20 (18)

2
  • NA

●●○
  • 7 (0/7) = 0%

  • Median (IQR) Neutrophil count, ×109/L

Creatine kinase MB
  • Wang et al.7 (138)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

100% - ↑
  • Liu et al.9 (109)

  • 0

2
  • NA

●●○
  • 4 (0/4) = 0%

  • Median (IQR) CK-MB, U/L

CURB-65
  • Liu et al.9 (109) Zhou et al.15 (191)

100% - ↑
  • 0

  • 0

2
  • NA

●●○
  • 2 (0/2) = 0%

  • Median (IQR) CURB-65 score, NA

Respiratory rate
  • Huang et al.17 (41)

  • Liu et al.10 (78)

  • Tian et al.8 (262)

  • Zhou et al.15 (191)

100% - ↑
  • Li et al.11 (84)

  • Wang et al.7 (138)

  • Young et al.20 (18)

2
  • NA

●●○
  • 3 (4/7) = 57%

  • Median (IQR) Respiratory rate, breaths*min

Lymphocyte ratio
  • Li et al.11 (84)

100% - ↓
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Mean (SD) Lymphocyte ratio, %

Chronic kidney disease
  • Liu et al.9 (109)

  • Zhou et al.15 (191)

100% - Presence
  • Ruan et al.14 (150)

  • 0

1
  • NA

●●○
  • 3 (0/3) = 0%

  • Percentages Chronic kidney dis ease, NA

Chest pain
  • Li et al.11 (84)

100% - Presence
  • 0

  • Yang et al.19 (52)

1
  • NA

●●○
  • 2 (0/2) = 0%

  • Percentages Chest pain, NA

Neutrophil ratio
  • Li et al.11 (84)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Percentage Neutrophil ratio, %

Fibrinogen degradation product
  • Tang et al.13 (183)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) FDP, ug/mL

Myoglobin
  • Ruan et al.14 (150)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Mean (SD) Myoglobin, ng/mL

APACHE II
  • Liu et al.9 (109)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) APACHE II score, NA

PaO2:FiO2
  • Liu et al.9 (109)

100% - ↓
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) PaO2:FiO2, mmHg

Globulin
  • Wu et al.16 (201)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) Globulin, g/L

Prealbumin
  • Wu et al.16 (201)

100% - ↓
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) prealbumin, mg/L

Urea
  • Wu et al.16 (201)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) prealbumin, mM

Glucose
  • Wu et al.16 (201)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) Glucose, mM

Cholinesterase
  • Wu et al.16 (201)

100% - ↓
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) Cholinesterase, U/L

Cystatin C
  • Wu et al.16 (201)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) Cystatin C, mg/L

α-HBDH
  • Wu et al.16 (201)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) α-HBDH 100 U/L

LDL
  • Wu et al.16 (201)

100% - ↓
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Median (IQR) LDL, mM

Heart Rate
  • Zhou et al.15 (191)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0%

  • Percentages Heart Rate ≥125, beats/min

Health system burden in Hubei
  • Ji et al.21 (NA)

100% - ↑
  • 0

  • 0

1
  • NA

●●○
  • 1 (0/1) = 0% NA NA

Days from onset of symptoms to hospital
  • Li et al.11 (84)

  • Wang et al.7 (138)

  • Tian et al.8 (262)*

100% - ↑
  • Zhang et al.6 (19)

  • Zhou et al.15 (191)

  • Huang et al.17 (41)

1
  • NA

●●○
  • 5 (1/6) = 17%

  • Median (IQR) Symptom onset to admission, days

O2 saturation
  • Li et al.11 (84)

100% - ↓
  • Liu et al.10 (78)

  • Young et al.20 (18)

0
  • NA

●●○
  • 3 (0/3) = 0%

  • Median (IQR) Oxygen saturation, %

Fibrinogen
  • Liu et al.9 (109)

100% - ↑
  • Tang et al.13 (183)

  • 0

0
  • NA

●●○
  • 2 (0/2) = 0%

  • Median (IQR) Fibrinogen, g/L

Smoking
  • Liu et al.10 (78)

100% - Presence
  • Huang et al.17 (41)

  • Zhang et al.6 (19)

  • Zhou et al.15 (191)

  • Guan et al.18 (1099)

  • Yang et al.19 (52)

-2
  • Liu et al.10 Past use: OR 14.3 95% CI 1.6 - 25.0

●●○
  • 5 (1/6 = 17%)

  • Percentages Current smoker, NA

Chronic respiratory disease
  • Caramelo et al.12 (72314)

  • Li et al.11 (84)

  • Zhou et al.15 (191)

100% - Presence
  • Huang et al.17 (41)

  • Zhang et al.6 (19)

  • Wang et al.7 (138)

  • Liu et al.10 (78)

  • Liu et al.9 (109)

  • Ruan et al.14 (150)

  • Yang et al.19 (52)

  • Zhonghua22 (44672)

  • Guan et al.18 (1099)

-3
  • Caramelo et al.12 CRD: OR 7.8 95% CI 5.5 - 10.4

●●○
  • 10 (2/12 = 17%)

  • Percentages COPD, NA

Cancer
  • Caramelo et al.12 (72314)

100% - Presence
  • Huang et al.17 (41)

  • Liu et al.10 (78)

  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

  • Zhonghua22 (44672)

  • Guan et al.18 (1099)

-3
  • Liu et al.10 Cancer: OR 6.9 95% CI 3.4 - 12.5

●●○
  • 7 (0/7) = 0%

  • Percentages Malignancy, NA

Cardiovascular disease
  • Caramelo et al.12 (72314)

  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

100% - Presence
  • Huang et al.17 (41)

  • Wang et al.7 (138)

  • Liu et al.9 (109)

  • Zhang et al.6 (19)

  • Liu et al.10 (78)

  • Li et al.11 (84)

  • Wu et al.16 (201)

  • Guan et al.18 (1099)

  • Yang et al.19 (52)

  • Zhonghua22 (44672)

-4
  • Caramelo et al.12 HTA: OR 7.4 95% CI 6.3 - 8.8 Cardiac: OR 12.8 95% CI 10.3 - 15.9

●●○
  • 12 (1/13) =8%

  • Percentages Cardiac disease, NA

Lactate
  • Liu et al.9 (109)

100% - ↑
  • 0

  • Yang et al.19 (52)

  • Wang et al.7 (138)

-1
  • NA

●○○
  • 3 (0/3) = 0%

  • Median (IQR) Lactate, mmol/L

Monocyte count
  • Li et al.11 (84)

100% - ↓
  • Wang et al.7 (138)

  • Wu et al.16 (201)

  • 0

-1
  • NA

●○○
  • 3 (0/3) = 0%

  • Median (IQR) Lymphocite count / ×109/L

Creatinine
  • Ruan et al.14 (150)

  • Wang et al.7 (138)

  • Zhou et al.15 (191)

100% - ↑
  • Liu et al.10 (78)

  • Huang et al.17 (41)

  • Liu et al.9 (109)

  • Wu et al.16 (201)

  • Yang et al.19 (52)

  • Guan et al.18 (1099)

-1
  • NA

●○○
  • 5 (4/9) = 44%

  • Median (IQR) Creatinin, μmol

Composite abnormal radiological findings (CT-RX)
  • Li et al.11 (84)

  • Zhou et al.15 (191)

100% - ↑
  • Wang et al.7 (138)

  • Zhang et al.6 (19)

  • Huang et al.17 (41)

  • Young et al.20 (18)

  • Guan et al.18 (1099)

-1
  • NA

●○○
  • 0 (6/6) = 100% NA NA

Systolic blood pressure
  • Huang et al.17 (41)

100% - ↑
  • Tian et al.8 (262)*

  • Zhou et al.15 (191)

  • Yang et al.19 (52)

  • Young et al.20 (18)

-1
  • NA

●○○
  • 2 (2/5) = 40% Mean (SD)

  • Median (IQR) Systolic blood pressure, mmHg

Platelet count
  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

100% - ↓
  • Wang et al.7 (138)

  • Liu et al.10 (78)

  • Wu et al.16 (201)

  • Yang et al.19 (52)

  • Tang et al.13 (183)

  • Young et al.20 (18)

  • Guan et al.18 (1099)

  • Huang et al.17 (41)

-1
  • NA

●○○
  • 5 (5/10) = 50%

  • Median (IQR) Platelet count / ×109/L

AST
  • Wang et al.7 (138)

  • Wu et al.16 (201)

100% - ↑
  • Liu et al.10 (78) Liu et al.9 (109) Ruan et al.14 (150)

  • Guan et al.18 (1099) Huang et al.17 (41)

-1
  • NA

●○○
  • 6 (1/7) = 14%

  • Median (IQR) Aspartate aminotransferase, UL

ALT
  • Wang et al.7 (138)

  • Huang et al.17 (41)

  • Zhou et al.15 (191)

100% - ↑
  • Liu et al.10 (78)

  • Liu et al.9 (109)

  • Ruan et al.14 (150)

  • Wu et al.16 (201)

  • Guan et al.18 (1099)

-1
  • NA

●○○
  • 6 (2/8) = 25%

  • Median (IQR) Alanine aminotransferase, UL

Expectoration
  • Li et al.11 (84)

100% - Presence
  • Wang et al.7 (138)

  • Huang et al.17 (41)

  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

  • Guan et al.18 (1099)

-3
  • NA

●○○
  • 6 (0/6) = 0%

  • Percentages Sputum, NA

Cough
  • Li et al.11 (84)

  • Zhang et al.6 (19)

100% - Presence
  • Huang et al.17 (41)

  • Tian et al.8 (262)

  • Wang et al.7 (138)

  • Liu et al.10 (78)

  • Liu et al.9 (109)

  • Ruan et al.14 (150)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

  • Yang et al.19 (52)

  • Guan et al.18 (1099)

  • Young et al.20 (18)

-6
  • NA

●○○
  • 11 (2/13) = 15%

  • Percentages Cough, NA

Fatigue
  • Liu et al.9 (109)

100% - Presence
  • Huang et al.17 (41)

  • Zhang et al.6 (19)

  • Tian et al.8 (262)

  • Wang et al.7 (138)

  • Ruan et al.14 (150)

  • Wu et al.16 (201)

  • Zhou et al.15 (191)

  • Guan et al.18 (1099)

-6
  • NA

●○○
  • 5 (2/7) = 29%

  • Percentages Fatigue, NA

Male gender
  • Tang et al.13 (183)

  • Caramelo et al.12 (72314)

100% - Presence
  • Huang et al.17 (41)

  • Zhang et al.6 (19)

  • Wang et al.7 (138)

  • Tian et al.8 (262)

  • Liu et al.9 (109)

  • Li et al.11 (84)

  • Ruan et al.14 (150)

  • Liu et al.10 (78)

  • Zhou et al.15 (191)

  • Wu et al.16 (201)

  • Guan et al.18 (1099)

  • Yang et al.19 (52)

  • Zhonghua22 (44672)

  • Young et al.20 (18)

-8
  • NA

●○○
  • 15 (1/16) = 6%

  • Percentages Male, NA

HTA: hypertension; CRD: chronic respiratory disease

* Data provided directly by the authors of the publication.

RESULTS

We identified a total of 17 studies, with most of them relying on a retrospective cross-sectional design and reporting data using descriptive statistics. Only three studies performed multivariate analysis adjusting for confounding factors. Sixteen of the studies reported laboratory-confirmed cases of covid-19 and one reported clinically diagnosed cases. There were 60 risk factors identified for COVID-19 severity (Table). Of these, 7 were considered of high, 40 of medium and 13 of low consistency. Increasing values of age, D-dimer, C-reactive protein, sequential organ failure assessment (SOFA) score and body temperature while decreasing albumin, and a history of diabetes were the risk factors with the highest consistency as predictors for covid-19 severity. Additionally, elevated values of white blood cells count, procalcitonin, lactate dehydrogenase, cardiac troponins, prothrombin time, interleukin-6, serum ferritin, neutrophils count, creatine kinase MB, CURB-65 score with decreased lymphocyte count, and dyspnea were classified as medium consistency risk factors with at least a positive difference of two studies reporting a statistically significant difference between non-severe and CSF groups. There was high heterogeneity in the definition of CSF, ranging from the need for supplemental oxygen to the development of acute respiratory distress syndrome (ARDS), ICU admission and death. In terms of risk factor heterogeneity, 40% of factors presented a value of zero with an overall median of 14% (IQR = 0–25). Nevertheless, when considering only the remaining 60% variables, the mean heterogeneity value was 28.5% (SD = 19.6).

DISCUSSION

The results from this review are consistent with current analyses considering age and comorbidities the most important risk factors for covid-19 disease severity. However, our findings also suggest that diabetes is one of the most critical comorbidities in terms of disease severity. Diabetes has been previously associated with other respiratory virus disease severities in cross national samples5. This might be explained by the immunosuppressive effects of hyperglycaemia5 and could also explain why patients that develop ARDS due to covid-19 were found to have statistically significant higher glucose levels (Table). This finding has important implications given the high global prevalence of diabetes. When considering the heterogeneity of reported factors across studies, 60% presented some level of heterogeneity, which indicates that there is a need for higher reporting consistency in future research looking at the risk factors for covid-19 disease severity.

Some limitations should be considered when interpreting these findings. Most of the selected studies were conducted in China (and one in Singapore), limiting the external validity of risk factors for other countries. As we used a composite index of severity, the relevance of these factors varies according to the natural history of the disease with some factors, such as body temperature and neutrophils count (and their value directions -higher/lower) being more relevant for different stages. A limitation of this study is the rapid growth of knowledge about covid-19. Therefore, the results of the present review might vary as the scientific understanding of covid-19 progresses. Additionally, two of the 17 studies reviewed were pre-prints (neither published, nor peer-reviewed). Despite this, the wide range of risk factors identified across 17 publications can guide future research to rapidly validate the present results on cross-national samples.

Given that the burden to the health system due to covid-19 is a determinant of the disease severity (Table), the results from this review can support clinical and public health initiatives to target populations and patients that are at most risk while further evidence is generated. Additionally, the present review provides researchers with a rapid reference on reported clinical and demographical factors in order to increase the comparability of results and further decrease uncertainties regarding the covid-19 severity. This can support clinical management decisions and the design of strategies to inform the general public about important risk factors for covid-19 severity, For instance, when communicating who is most at risk for the disease, instead of making a broad generalization such as “increasing age and underlying health conditions,” messaging that communicates risk factors should at a minimum include: age > 50, diabetes, smoking, respiratory disease, cancer and cardiovascular disease. Additionally, for patients that are isolated outside a healthcare institution either due to clinical suspicion or confirmed mild case, specific factors such as shortness of breath and chest pain could be communicated as triggers for seeking care. Factors such as fatigue, cough and expectoration have low consistency for predicting disease severity and by themselves should not be relied upon for clinical assessment. We expect that the results of this brief review can support government and medical strategies in response to the pandemic.

Acknowledgements

To the contribution of a team of medical professionals that conducted part of the data extraction: Laura Acosta Camargo, Moises Molina Fuentes, John David Rodriguez and Juan Carlos Pantoja.

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Funding. J.E. Rod is the recipient of an ATN-LATAM QUT Postgraduate Research Scholarship and a QUT HDR Tuition Fee Sponsorship. Dr Oscar Oviedo-Trespalacios is the recipient of an Australian Research Council Discovery Early Career Researcher Award [DE200101079] funded by the Australian Government

Received: March 25, 2020; Accepted: April 14, 2020

Correspondence: CARRS-Q, K Block, 130 Victoria Park Road, 4059, Kelvin Grove, Brisbane, QLD, Australia. E-mail: j.rodriguezzabala@qut.edu.au

Authors’ Contribution: Study design: JER, OOT, JCR. Data collection, analysis and interpretation: JER, OOT, JCR. Writing of the manuscript and approval of the final version: JER, OOT, JCR. Public responsibility for the content of the article: JER, OOT, JCR.

Conflict of Interest: The authors declare no conflict of interest.

Creative Commons License  This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.