Risk factors for critical outcome of COVID-19 differ according to location: a systematic review and meta-analysis

doi:10.21203/rs.3.rs-133535/v1

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Risk factors for critical outcome of COVID-19 differ according to location: a systematic review and meta-analysis

https://doi.org/10.21203/rs.3.rs-133535/v1

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Version 1

posted 23 Dec, 2020

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Background

The mortality rates of the coronavirus disease 2019 (COVID-19) differ across the globe. While some risk factors for poor prognosis of the disease are known, regional differences are suspected. We reviewed the risk factors for critical outcomes in extensive number of studies according to the study location.

Methods

We searched the PubMed, Embase, Cochrane Library, and Web of Science literature databases from January 1, 2020 to June 8, 2020. We defined the critical outcome as death, admission to the intensive care unit, or critical type of COVID-19. Candidate variables to predict the critical outcome included patient demographics, underlying medical condition, symptoms, and laboratory findings. Pooled relative risks (RRs) and standardized mean differences were calculated for each variable and were also determined according to the study’s continent.

Results

A total of 80 studies were included from Asia (n = 48), Europe (n = 22), and North America (n = 10). The risk factors for the critical outcome in the overall population included male sex, age, and all inspected underlying medical conditions. Symptoms of dyspnea, anorexia, dizziness, fatigue, and certain laboratory findings were also indicators of the critical outcome. Subgroup analysis was performed according to study location, and we found several discrepancies. Underlying respiratory disease was associated higher risk of the critical outcome in studies from Asia (pooled RR 2.16 [1.60–2.92] and Europe (pooled RR 1.50 [1.32–1.69]), but not North America. Underlying hepatic disease was associated with a higher risk of the critical outcome from Europe (pooled RR 1.34 [1.15–1.56]), but not from Asia and North America. Symptoms of vomiting (pooled RR 2.43 [1.60–3.69]), anorexia (pooled RR 2.38 [1.45–3.91]), dizziness (pooled RR 2.23 [1.51–3.28]), and fatigue (pooled RR 1.92 [1.23–3.02]) were significantly associated with the critical outcome in studies from Asia, but not from Europe and North America. Hemoglobin and platelet count affected patients differently in Asia compared to those in Europe and North America.

Conclusions

There are several discrepancies among risk factors for critical outcomes among patients with COVID-19 according to the location of the infected patient.

Critical Care & Emergency Medicine

Pulmonology

Internal Medicine

COVID-19

prognostic factor

location

meta-analysis

systematic review

Coronavirus disease 2019 (COVID-19) is an acute respiratory illness caused by the novel severe acute respiratory syndrome virus 2, which was first reported in Wuhan, China [1, 2]. Because the virus is highly contagious, it has caused a global pandemic [3]. Disparities exist across the globe. According to the World Health Organization Dashboard as of December 10, 2020 [4], 29.1 million cases were confirmed with 760.9 thousand deaths in the Americas, and 20.9 million cases with 462.6 thousand deaths in Europe, and 11.2 million confirmed cases and 170.9 thousand deaths in South-East Asia. This corresponds to calculated mortality rates of 2.61%, 2.22%, and 1.52%, respectively.

The COVID-19 outbreak has rapidly overloaded healthcare facilities [5]. Since the availability of these resources is crucial for patient survival, areas with sudden upsurges in patients showed higher mortality rates [6, 7]. Recognizing the patient-at-risk characteristics is important for the distribution of patients to appropriate levels of care. In addition, prioritizing people as candidates for potential vaccines is also necessary [8].

The risk factors for poor outcomes of COVID-19 have been reviewed. A previous systematic review of 13 studies reported male sex, older age, smoking, underlying comorbidities, symptoms of dyspnea, and several laboratory findings as significant factors for poor prognoses [9]. Another review including 14 studies reported similar results [10]. However, most of the included studies in these meta-analyses were from China because COVID-19 was mostly spread in China during the early phase of the pandemic. Summarized results from other parts of the world have not yet been reported.

This study systematically reviewed the differences in risk factors for critical outcomes of patients with COVID-19 according to the continent on which the studies were performed. In addition, we aimed to update the risk factors based on a wider range of studies.

Search strategy and study protocol

We searched PubMed, Embase, Cochrane Library, and Web of Science literature databases using keywords related to COVID-19, hospitalized adult patients, and critical outcomes to identify studies published from January 1 to June 8, 2020. The critical outcome was defined as death, admission to the intensive care unit (ICU), or critical type of COVID-19. The critical type of COVID-19 was defined as COVID-19 with respiratory failure, septic shock, or multiple organ dysfunction [11]. We followed the Peer Review of Electronic Search Strategies to design a structural search strategy (see Additional file 1) [12]. We also conducted a manual search using study identifiers or references from previous studies.

Our systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [13] and Meta-analysis of Observational Studies in Epidemiology [14]. The PRISMA checklist is available from Additional file 2, and the protocol for this systematic review was registered on International Prospective Register of Systematic Reviews (www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42020181062).

Study selection

Studies were selected by following the PRISMA flow diagram [13]. After removing duplicates, the titles and abstracts were screened to identify eligible studies for full-text review. When different outcome data were found in the same study population with similar study periods, the data with the larger population were selected. Studies with ≤ 5 patients with critical outcomes were excluded because the calculations of mean and standard deviations (SDs) were considered unreliable in these studies. When data were not presented according to the critical outcome, the authors were contacted to provide organized results. Studies performed in the ICU, or those including patients with negative COVID-19 polymerase chain reaction results were also excluded.

Data extraction

From each study, we collected article information including the authors, study design, location, period, restriction in patient selection, and study outcome. Patient characteristics were collected including sex, age, ethnicity, underlying medical condition, symptoms, and laboratory findings. The underlying medical condition included smoking history, hypertension, diabetes, cardiac disease, renal disease, respiratory disease, hepatic disease, cerebral disease, malignancy. The comorbidities were defined differently in each study, as shown in Additional file 3: Table E1. The symptoms included fever, fatigue, myalgia, dizziness, headache, dyspnea, chest tightness, cough, sputum, sore throat, rhinorrhea, anorexia, nausea, vomiting, abdominal pain, and diarrhea. The laboratory findings included white blood cell count, neutrophil count, lymphocyte count, monocyte count, hemoglobin, platelet count, creatinine, blood urea nitrogen, aspartate transaminase (AST), alanine transaminase (ALT), total bilirubin, creatine kinase, lactate dehydrogenase, and prothrombin time.

The characteristics were organized according to the critical outcome defined in each study. For categorical variables, the input variables were organized as a two-by-two table. For continuous variables, means and SDs were organized as recommended by the Cochrane handbook [15]. Google Translate was used to translate the articles published in other languages to English.

Statistical considerations and assessment of bias

Forest plots with a random-effects model were used to explore the baseline characteristics and the impact of each variable on the critical outcome. I² statistics were used to assess the heterogeneity [16]. Pooled relative risks (RRs) were calculated for categorical variables. For continuous variables, standardized mean differences (SMDs) were calculated for most variables because of the differences in scale, except for age for which weighted mean difference (WMD) was calculated. The 95% confidence intervals were calculated for each pooled value and are presented in square brackets throughout the manuscript.

Quality assessment of each study was performed according to the recommended six areas of potential study biases: study participation, study attrition, outcome measurement, confounding measurement and account, and analysis [17]. Egger’s regression tests were performed to assess publication bias [18].

Analyses were performed in the overall population and in subgroups according to the continent. The impact of ethnicity on the critical outcome was inspected separately with studies specifying the race according to the four categories: non-Hispanic white, non-Hispanic black, Hispanic, and Asian. To reduce the heterogeneity of the results, sensitivity analyses were performed among studies without any restriction in patient selection, critical outcome confined to death, and at least partly achieving every standard of the six areas of potential study biases.

The process of study screening, data extraction, and assessment of quality and risk of bias were performed by two independent reviewers, and an agreement was reached through group discussion. All statistical analyses were performed using Stata version 16 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX, StataCorp LLC).

Search findings and study characteristics

The initial search revealed 3,071 studies, which narrowed to 2,151 studies after duplicate removal. After screening, 1,578 studies were removed and 573 articles were assessed with full-text review. After removing 493 non-relevant studies, our systematic review included a total of 80 studies (Additional file 3: Figure E1). The full list of the included studies is available in Additional file 4 [19–98].

The 80 studies included 43,248 patients, with a median of 130 patients per study (interquartile range 73–317). The studies were conducted in Asia (n = 48), Europe (n = 22), and North America (n = 10). The countries included China (n = 43), the United States of America (n = 10), Italy (n = 9), Spain (n = 5), Iran (n = 3), the United Kingdom (n = 3), South Korea (n = 1), India (n = 1), Denmark (n = 1), France (n = 1), Greece (n = 1), Norway (n = 1), and Poland (n = 1). The study outcomes were death (n = 41, 51.3%), admission to the ICU (n = 15, 18.8%), admission to the ICU or death (n = 9, 11.3%), and critical type of COVID-19 (n = 15, 18.8%). A median of 23.7% of patients had suffered the critical outcome in the overall population and was highest in studies from North America (34.8%), followed by Europe (26.5%) and Asia (17.3%) (P < 0.001). Most of the studies were retrospective observational studies (n = 68, 85.0%). Of the 80 studies, 50 (62.5%) did not specify any restriction in patient selection, while 13 studies included patients with certain comorbidities, seven with certain COVID-19 severity, and four with computed tomography findings (Table 1).

Table 1

Characteristics of studies included in analysis
Variables		Total	Asia	Europe	North America	P
Variables		N = 80	n = 48	n = 22	n = 10	P
Number of patients		130 (73– 317)	136 (99–323)	114 (36–233)	104 (72– 1000)	0.328
Study outcome						< 0.001
	Death	41 (51.3)	24 (50.0)	15 (68.2)	2 (20.0)
	Admission to the ICU	15 (18.8)	6 (12.5)	5 (22.7)	4 (40.0)
	Admission to the ICU or death	9 (11.3)	3 (6.3)	2 (9.1)	4 (40.0)
	Critical type COVID-19^*	15 (18.8)	15 (31.3)	0 (0.0)	0 (0.0)
Proportion of patients with critical outcome, %		23.7 (14.8– 34.4)	17.3 (13.6–27.9)	26.4 (19.0–45.8)	34.8 (30.0– 42.7)	< 0.001
Country						NA
	China	43 (53.8)	43 (89.6)	0	0
	United States of America	10 (12.5)	0	0	10 (100.0)
	Italy	9 (11.3)	0	9 (40.9)	0
	Spain	5 (6.3)	0	5 (22.7)	0
	Iran	3 (3.8)	3 (6.3)	0	0
	United Kingdom	3 (3.8)	0	3 (13.6)	0
	South Korea	1 (1.3)	1 (2.1)	0	0
	India	1 (1.3)	1 (2.1)	0	0
	Denmark	1 (1.3)	0	1 (4.6)	0
	France	1 (1.3)	0	1 (4.6)	0
	Greece	1 (1.3)	0	1 (4.6)	0
	Norway	1 (1.3)	0	1 (4.6)	0
	Poland	1 (1.3)	0	1 (4.6)	0
Study design						0.004
	Retrospective observational	68 (85.0)	45 (93.8)	14 (63.6)	9 (90.9)
	Prospective cohort	12 (15.0)	3 (6.3)	8 (36.4)	1 (10.0)
Restriction in patient selection						0.856
	None	50 (62.5)	30 (62.5)	12 (54.6)	8 (80.0)
	Certain comorbidity	13 (16.3)	6 (12.5)	5 (22.7)	2 (20.0)
	Certain severity	7 (8.8)	5 (10.4)	2 (9.1)	0 (0.0)
	Patients with CT results	4 (5.0)	3 (6.3)	1 (4.6)	0 (0.0)
	Certain age group	2 (2.5)	2 (4.2)	0 (0.0)	0 (0.0)
	Certain symptom	1 (1.3)	1 (2.1)	0 (0.0)	0 (0.0)
	Multiple PCR tests	1 (1.3)	1 (2.1)	0 (0.0)	0 (0.0)
	Hospitalized via ER	1 (1.3)	0 (0.0)	1 (4.6)	0 (0.0)
	Certain race	1 (1.3)	0 (0.0)	1 (4.6)	0 (0.0)
Numbers are presented as number (percentage) or median (interquartile range). P-values are calculated from chi-square test, Fisher’s exact test, or Kruskal–Wallis test.
^*Critical type COVID-19 refers to disease extent with respiratory failure, septic shock, and/or multiple organ dysfunction
Abbreviations: ICU, intensive care unit; NA, not applicable; CT, computed tomography; PCR, polymerase chain reaction; ER, emergency room.

Baseline patient characteristics and symptoms

In the overall population, a proportion of 0.57 [0.55–0.59] were male, with a median age of 66.00 [61.14–68.85] years. The common underlying medical conditions were hypertension (pooled proportion 0.41 [0.35–0.47]), smoking history (pooled proportion 0.23 [0.19–0.27]), and diabetes (pooled proportion 0.21 [0.17–0.25]). The common symptoms were fever (pooled proportion 0.79 [0.70–0.86]), cough (pooled proportion 0.65 [0.60–0.70]), and anorexia (pooled proportion 0.58 [0.43–0.72]) (Table 2).

Table 2

Summary of patient characteristics according to the continents of the studies performed.
Variables		Total	n	Asia	n	Europe	n	North America	n
Male sex		0.57 (0.55–0.59)	51	0.52 (0.50–0.55)	24	0.64 (0.61–0.68)	19	0.57 (0.53–0.60)	8
Age, years		66.00 (63.14–68.85)	77	58.98 (54.93–63.02)	46	74.71 (70.38–79.04)	21	61.53 (50.53–72.54)	10
Ethnicity
	Non-Hispanic White	0.30 (0.13–0.47)	4	–	0	–	0	0.30 (0.13–0.47)	4
	Hispanic	0.27 (0.24–0.29)	4	–	0	–	0	0.27 (0.24–0.29)	4
	Non-Hispanic black	0.15 (0.10–0.19)	4	–	0	–	0	0.15 (0.10–0.19)	4
	Asian	0.06 (0.02–0.10)	4	–	0	–	0	0.06 (0.02–0.10)	4
	Unknown/Others	0.21 (0.00–0.42)	4	–	0	–	0	0.21 (0.00–0.42)	4
Underlying medical condition
	Hypertension	0.41 (0.35–0.47)	45	0.27 (0.23–0.31)	21	0.51 (0.44–0.59)	16	0.62 (0.58–0.66)	8
	Smoking history	0.23 (0.19–0.27)	23	0.13 (0.04–0.26)	8	0.29 (0.21–0.39)	8	0.30 (0.25–0.35)	7
	Diabetes	0.21 (0.17–0.25)	46	0.15 (0.12–0.17)	20	0.21 (0.17–0.24)	18	0.38 (0.32–0.43)	8
	Cardiac disease	0.18 (0.15–0.22)	46	0.13 (0.08–0.20)	21	0.24 (0.18–0.31)	17	0.20 (0.16–0.24)	8
	Renal disease	0.12 (0.09–0.15)	33	0.04 (0.02–0.06)	11	0.18 (0.12–0.25)	14	0.21 (0.16–0.27)	8
	Malignancy	0.10 (0.08–0.12)	38	0.03 (0.02–0.04)	16	0.23 (0.16–0.32)	15	0.11 (0.08–0.15)	7
	Respiratory disease	0.09 (0.07–0.11)	44	0.04 (0.03–0.06)	20	0.12 (0.10–0.15)	17	0.17 (0.13–0.21)	7
	Hepatic disease	0.04 (0.02–0.06)	18	0.06 (0.01–0.12)	13	0.01 (0.01–0.01)	3	0.01 (0.01–0.02)	2
	Cerebral disease	0.06 (0.05–0.08)	22	0.05 (0.03–0.07)	12	0.09 (0.06–0.11)	7	0.06 (0.02–0.11)	3
Symptoms
	Fever	0.79 (0.70–0.86)	32	0.82 (0.76–0.88)	17	0.80 (0.64–0.92)	9	0.65 (0.42–0.85)	6
	Cough	0.65 (0.60–0.70)	31	0.66 (0.59–0.73)	17	0.60 (0.50–0.70)	10	0.71 (0.63–0.79)	4
	Anorexia	0.58 (0.43–0.72)	8	0.62 (0.47–0.75)	7	–	0	0.31 (0.23–0.41)	1
	Fatigue	0.44 (0.32–0.55)	17	0.50 (0.34–0.67)	10	0.26 (0.17–0.37)	3	0.39 (0.28–0.51)	4
	Dyspnea	0.43 (0.34–0.52)	28	0.31 (0.21–0.42)	14	0.50 (0.39–0.60)	9	0.64 (0.57–0.70)	5
	Sputum	0.27 (0.20–0.35)	16	0.35 (0.30–0.40)	10	0.16 (0.08–0.27)	3	0.15 (0.06–0.25)	3
	Myalgia	0.22 (0.17–0.27)	22	0.22 (0.16–0.30)	12	0.17 (0.09–0.28)	6	0.30 (0.22–0.38)	4
	Chest tightness	0.21 (0.11–0.33)	10	0.26 (0.13–0.41)	7	–	0	0.12 (0.07–0.17)	3
	Dizziness	0.14 (0.04–0.28)	5	0.13 (0.03–0.28)	4	–	0	0.19 (0.04–0.46)	1
	Diarrhea	0.14 (0.10–0.19)	25	0.11 (0.10–0.19)	14	0.17 (0.09–0.28)	6	0.23 (0.21–0.26)	5
	Headache	0.12 (0.08–0.16)	19	0.10 (0.04–0.17)	10	0.17 (0.05–0.32)	4	0.12 (0.08–0.16)	5
	Nausea	0.12 (0.07–0.17)	12	0.08 (0.04–0.13)	5	0.09 (0.02–0.21)	3	0.18 (0.16–0.20)	4
	Sore throat	0.09 (0.06–0.12)	15	0.09 (0.05–0.14)	8	0.09 (0.02–0.19)	3	0.08 (0.06–0.10)	4
	Vomiting	0.08 (0.02–0.18)	7	0.08 (0.01–0.22)	5	–	0	0.09 (0.05–0.14)	2
	Rhinorrhea	0.08 (0.04–0.12)	5	0.04 (0.02–0.07)	2	–	0	0.10 (0.06–0.14)	3
	Abdominal pain	0.04 (0.01–0.09)	5	0.04 (0.01–0.09)	5	–	0	–	0
Numbers are presented as pooled value with 95% confidence intervals. Values represent proportions unless specified otherwise.
Abbreviation: n, numbers of studies included in calculating the pooled value in the previous column

Impacts of baseline demographics and underlying medical condition on the critical outcome

Of the 43,248 patients, 10,652 suffered the critical outcome. A meta-analysis of 51 studies revealed that male sex was associated with an increased risk of the critical outcome (pooled RR 1.26 [1.17–1.36], I² = 36.7%). The results remained consistent in subgroup analyses of each continent: 24 studies from Asia showed a pooled RR of 1.42 [1.26–1.61] (I² = 0.0%), 19 studies from Europe showed a pooled RR of 1.19 [1.02–1.40] (I² = 48.2%), and 8 studies from North America showed a pooled RR of 1.23 [1.07–1.42] (I² = 61.2%) (Additional file 3: Figure E2). Older age was consistently associated with an increased risk of the critical outcome across the three continents, with a pooled WMD of 10.61 [10.28–10.94] (I² = 89.9%) (Additional file 3: Figure E3).

Underlying medical condition including cerebral disease (pooled RR 2.12 [1.67–2.70], I² = 82.8%), hepatic disease (pooled RR 1.84 [1.22–2.77], I² = 64.5%), cardiac disease (pooled RR 1.80 [1.60–2.03], I² = 74.7%), renal disease (pooled RR 1.76 [1.53–2.04], I² = 78.0%), hypertension (pooled RR 1.70 [1.49–1.93], I² = 71.2%), malignancy (pooled RR 1.64 [1.46–1.83], I² = 51.6%), respiratory disease (pooled RR 1.55 [1.36–1.78], I² = 64.9%), diabetes (pooled RR 1.54 [1.40–1.69], I² = 58.0%), and smoking history (pooled RR 1.23 [1.18–1.28], I² = 0.1%) were risk factors for the critical outcome. Subgroup analyses across the three continents showed largely similar results; however, several differences were noted. First, the presence of respiratory disease was associated with a higher risk of the critical outcome in studies from Asia (pooled RR 2.16 [1.60–2.92], I² = 57.8%) and Europe (pooled RR 1.50 [1.32–1.69], I² = 16.6%), but not North America (pooled RR 1.07 [0.96–1.19], I² = 0.0%). Second, the presence of hepatic disease was associated with a higher risk of the critical outcome from Europe (pooled RR 1.34 [1.15–1.56], I² = 0.0%), but not from Asia (pooled RR 1.94 [0.90–4.16], I² = 68.1%) and North America (pooled RR 0.97 [0.22–4.25], I² = 39.4%) (Fig. 1).

Associations between patient symptoms and the critical outcome

The results of the meta-analysis showed that dyspnea (pooled RR 2.90 [2.10–4.03], I² = 85.7%), anorexia (pooled RR 2.07 [1.21–3.52], I² = 69.8%), dizziness (pooled RR 2.06 [1.39–3.06], I² = 5.1%), and fatigue (pooled RR 1.43 [1.08–1.89], I² = 62.8%) were significantly associated with the critical outcome. In the subgroup analysis, dyspnea was the only symptom that was consistently associated with the poor outcome in Asia (pooled RR 5.60 [3.24–9.65], I² = 84.0%), Europe (pooled RR 1.45 [1.10–1.91], I² = 28.7%), and North America (pooled RR 1.52 [1.27–1.81], I² = 0.0%). Vomiting (pooled RR 2.43 [1.60–3.69], I² = 0.0%), anorexia (pooled RR 2.38 [1.45–3.91], I² = 49.5%), dizziness (pooled RR 2.23 [1.51–3.28], I² = 0.0%), and fatigue (pooled RR 1.92 [1.23–3.02], I² = 72.3%) were significantly associated with the critical outcome in studies from Asia, but not from Europe and North America (Fig. 2).

Associations between laboratory findings and the critical outcome

Higher levels of lactate dehydrogenase (pooled SMD 1.13 [1.06–1.20], I² = 91.0%), blood urea nitrogen (pooled SMD 1.07 [0.97–1.17], I² = 93.6%), neutrophil count (pooled SMD [0.83–0.99], I² = 92.7%), white blood cell count (pooled SMD 0.72 [0.66–0.78], I² = 92.6%), creatine kinase (pooled SMD 0.62 [0.53–0.72], I² = 89.5%), AST (pooled SMD 0.61 [0.56–0.66], I² = 87.4%), creatinine (pooled SMD 0.47 [0.42–0.52], I² = 84.3%), prothrombin time (pooled SMD 0.43 [0.32–0.53], I² = 32.2%), total bilirubin (pooled SMD 0.39 [0.30–0.48], I² = 54.8%), and ALT (pooled SMD 0.20 [0.15–0.25], I² = 30.2%) were associated with the critical outcome. In contrast, levels of hemoglobin (pooled SMD − 0.16 [-0.25–-0.08], I² = 67.8%), platelet count (pooled SMD − 0.24 [-0.31–-0.17], I² = 72.5%), and lymphocyte count (pooled SMD − 0.43 [-0.48–-0.38], I² = 76.8%) were inversely related to the critical outcome.

While many findings were consistent across the three continents, platelet count and hemoglobin levels showed different results. While platelet count was inversely associated with the critical outcome in studies from Asia (pooled SMD − 0.447 [-0.539–-0.354], I² = 62.7%), this association was not observed in studies from Europe (pooled SMD 0.084 [-0.045–0.213], I² = 39.5%) or North America (pooled SMD 0.076 [-0.170–0.322], I² = 16.2%). In contrast, while lower hemoglobin levels were associated with the critical outcome in studies from Europe (pooled SMD − 0.389 [-0.569–-0.209], I² = 34.5%) and North America (pooled SMD − 0.385 [-0.668–-0.101], I² = 57.6%), this association was not identified in studies from Asia (pooled SMD − 0.061 [-0.163–0.041], I² = 72.0%) (Fig. 3).

Impact of ethnicity on the critical outcome

Out of a total of 80 studies, only four studies from North America reported patients’ ethnicity according to the four categories (non-Hispanic white, non-Hispanic black, Hispanic, and Asian). The pooled proportions of each ethnicity were non-Hispanic white (0.30 [0.13–0.47]), Hispanic (0.27 [0.24–0.29]), non-Hispanic black (0.15 [0.10–0.19]), Asian (0.06 [0.02–0.10]), and others/unknown (0.21 [0.00–0.42]) (Table 2).

Compared with non-Hispanic white, Hispanic ethnicity (pooled RR 0.83 [0.71–.96], I² = 8.4%) was associated with a lower risk of the critical outcome, while non-Hispanic black (pooled RR 0.84 [0.67–1.06], I² = 28.3%) and Asian ethnicity (pooled RR 1.33 [0.86–2.06], I² = 51.8%) was not (Additional file 3: Figure E4). Publication biases were not observed in these analyses (Egger’s P = 0.388, 0.282, and 0.557, respectively)

Assessment of study quality and publication bias

While most studies at least partly met the quality standards of each area, several studies did not. The two studies did not represent the population of interest (study participation), two studies did not adequately measure the prognostic factor of interest (prognostic factor measurement), and nine studies did not account for important potential confounders (confounding measurement and account). (Additional file 3: Table E2).

Most of the variables did not show any publication bias; however, male sex (P = 0.042), underlying diabetes (P = 0.001), malignancy (P = 0.020), cerebral disease (P = 0.042), symptoms of dyspnea (P = 0.019), and vomiting (P = 0.045) revealed significant publication bias. Laboratory findings of lymphocyte count (P = 0.003), ALT (P = 0.012), and AST (P = 0.023) also revealed publication biases (Additional file 3: Table E3).

Sensitivity analysis

A sensitivity analysis was performed in 17 studies without any restriction in patient selection, outcome confined to death, and at least partly achieving every standard of the six areas of potential study biases. The results were largely consistent with the main analyses. Male sex (pooled RR 1.17 [1.02–1.34], I² = 44.8%) and older age (pooled WMD 11.94 [11.56–12.33], I² = 79.0%) were risk factors for death. Most of the underlying comorbidities remained significant risk factors for death except for hepatic disease (pooled RR 1.99 [1.00–3.98], I² = 52.0%). The pooled RRs for the comorbidities were: hypertension, 2.76 [1.95–3.90], I² = 65.1%; cerebral disease, 2.72 [1.41–5.26], I² = 87.2%; cardiac disease, 2.45 [1.97–3.05], I² = 82.3%; respiratory disease, 2.11 [1.52–2.92], I² = 56.1%; malignancy, 1.75 [1.37–2.23], I² = 70.4%; renal disease, 1.62 [1.55–1.70], I² = 0.0%; and diabetes, 1.42 [1.21–1.67], I² = 58.8%. Symptoms of dyspnea (pooled RR 2.86 [1.35–6.07], I² = 82.9%), fatigue (pooled RR 1.62 [1.05–2.51], I² = 0.0%), and anorexia (pooled RR 2.16 [1.02–4.61], I² = 5.8%) were associated with higher risk of death. The extent of heterogeneity in these categorical variables was largely reduced when further analyses were performed according to each continent. The association between laboratory findings and death was consistent with the main analyses, except that lower monocyte counts were associated with a higher risk of death (pooled SMD − 0.33 [-0.57–-0.09], I² = 92.2%). The results of the sensitivity analysis are summarized in Additional file 3: Figure E5, and the details are described in Additional file 3: Table E4.

This is the first study to summarize the risk factors for the critical outcomes (death, admission to the ICU, or critical type of COVID-19) of COVID-19 according to the study location. It is also the largest updated systematic review regarding risk factors for the poor prognosis of patients with COVID-19. While most risk factors were largely similar across the three continents, several differences were noted. The presence of respiratory disease was associated with a higher risk of the critical outcome in Asia and Europe, but not North America. The presence of hepatic disease was associated with a higher risk of the critical outcome in Europe, but not in Asia and North America. Symptoms of vomiting, anorexia, dizziness, and fatigue were significantly associated with the critical outcome in Asia, but not Europe and North America. While platelet count was inversely associated with the critical outcome in Asia, it was not in Europe and North America. In contrast, lower hemoglobin levels were associated with the poor outcome in Europe and North America, but not in Asia.

Our findings of the overall population are consistent with those of previous reviews. Male sex, older age, underlying comorbidities, and several laboratory parameters have been repeatedly emphasized as risk factors for poor outcomes in patients with COVID-19 [9, 10, 99–103]. This disease is well-known for male-sex predominant deterioration. A nationwide study from Denmark reported that male sex was an independent risk factor for death even after adjusting for age and comorbidities [104]. The underlying mechanism for this observation has not yet been elucidated but may be explained by the immune regulatory genes encoded by the X chromosome, which makes men more susceptible to viral infections as compared to women [105]. In addition, sex hormones may act directly in innate immune cells to regulate their function, and indirectly via non-immune cells resulting in immune cell actions [106]. Older age was also a risk factor for grave prognosis among COVID-19 patients in previous systematic reviews [9, 10]. This is easily understandable as old age is also a well-known risk factor for death among patients with community-acquired pneumonia and influenza [107–109]. Among many symptoms, dyspnea was the only symptom that was consistently associated with a higher risk of the critical outcome in all three continents, a finding concordant with those in previous reviews [9, 102]. Dyspnea is relatively uncommon among COVID-19 patients despite typical lung involvement [2, 110]. Therefore, the presence of dyspnea could imply extensive involvement of the lung and lead to poor outcomes.

The results of our study not only confirm previous knowledge regarding the risk factors for the deterioration of COVID-19 patients but also reveal some novel findings. First, some risk factors revealed inter-continental differences. Recognizing such differences can inform the development of proper guidelines for the management of patients according to their region and ethnicity. As noted, underlying respiratory disease was associated with the critical outcomes in Asia and Europe, but not in North America. Although the exact reason for this disparity is beyond the scope of our review, it may be partly explained by the differences in therapies for the treatment of these chronic respiratory diseases [111]. In China, only about 56% of patients with chronic obstructive pulmonary disease receive treatments that are standard in Western countries, while 23% receive Chinese traditional treatments [111]. Considering the protective effect of corticosteroids in the treatment of COVID-19 [112], such a gap in the treatment of chronic respiratory disease may have led to different outcomes among continents. Among the symptoms of COVID-19, vomiting, anorexia, dizziness, and fatigue were risk factors in Asia, but not in Europe and North America. These symptoms can be associated with weight loss and poor nutritional status during the course of the disease, while body mass index is mostly higher for individuals living in Europe and North America, compared to those in Eastern Asian countries [113]. Although our meta-analysis suggested that Hispanic patients may have better prognosis compared to non-Hispanic white, the impact of ethnicity on the prognosis of COVID-19 is yet to be explained. While a regional study from the United States reported that ethnicity may be a factor for diverse outcomes [114], other studies denied these findings after adjusting for risk factors [115, 116]. A recent meta-analysis of has suggested that, after adjusting patient characteristics, ethnicity may not be an independent prognostic factor [117].

Second, with enough pooled analysis, various comorbidities are proven to be risk factors. Because viral infections can cause a systemic inflammatory response and localized vascular inflammation [118], studies have focused on diseases associated with cardiovascular outcomes as risk factors. In previous systematic reviews including 13, 16, 25, and 36 studies [9, 99, 101, 119], underlying cardiovascular disease, hypertension, diabetes, congestive heart failure, cerebrovascular disease, chronic kidney disease, respiratory disease, and cancer were identified as risk factors for poor patient outcome. The reviews did not find or mention any significant impact of underlying liver disease. However, several studies have inferred the impact of liver disease on the prognosis of COVID-19. For example, laboratory abnormalities associated with hepatic dysfunction were frequently observed in patients with COVID-19, and were more common in severe forms of COVID-19 [120]. Furthermore, a pooled analysis showed a higher incidence of acute hepatic injury in severe COVID-19 compared to that in non-severe disease [121].

To correctly acknowledge our study findings, several limitations should be noted. First, most of the included studies had retrospective design. This was inevitable because COVID-19 is a novel disease that caused a sudden pandemic. Second, residual heterogeneity was observed in the analyses of continuous variables. The residual extent of heterogeneity may be partially explained by differences in the reported forms of the variables (i.e., mean and SD, median and range, median, and interquartile range).

Our extensive systematic review summarized the risk factors associated with the critical outcome (death, admission to the ICU, and critical type of COVID-19) of COVID-19 patients according to location of infected patients (Asia, Europe, and North America). Although the risk factors were mostly consistent across the three continents, underlying diseases, patient symptoms, and laboratory findings posed different impact on patient prognosis in each location. Future studies are required to understand the reasons for such discrepancy.

ALT: alanine transaminase; AST:aspartate transaminase; COVID-19:Coronavirus disease 2019; ICU:intensive care unit; PRISMA:Preferred Reporting Items for Systematic Reviews and Meta-analysis; RR:relative risk; SD:standard deviation; SMD:standardized mean difference; WMD:weighted mean difference

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon a reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was not funded by any organization.

Authors' contributions

HJK, JJY, and JL contributed to the conception and design of the work. HJK and H Hwang contributed to the data acquisition. HJK, H Hwang, H Hong, JJY, and JL contributed to the analysis and interpretation of the data for the work. HJK drafted the article. H Hwang, H Hong, JJY, and JL revised it critically for important intellectual content. All authors approved the final version of the manuscript to be published, and agreed to be accountable for all aspects of the work, in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Acknowledgements

Not applicable

Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020;395:1054-62.
Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX et al. Clinical characteristics of Coronavirus Disease 2019 in China. N Engl J Med 2020;382:1708-20.
Wiersinga WJ, Rhodes A, Cheng AC, Peacock SJ, Prescott HC. Pathophysiology, transmission, diagnosis, and treatment of Coronavirus Disease 2019 (COVID-19): A Review. JAMA 2020;324:782-93.
WHO Coronavirus Disease (COVID-19) dashboard. 2020. https://covid19.who.int/. Accessed 10 Dec 2020.
Ranney ML, Griffeth V, Jha AK. Critical supply shortages - the need for ventilators and personal protective equipment during the Covid-19 pandemic. N Engl J Med 2020;382:e41.
Zhang Z, Yao W, Wang Y, Long C, Fu X. Wuhan and Hubei COVID-19 mortality analysis reveals the critical role of timely supply of medical resources. J Infect 2020;81:147-78.
Shim E, Mizumoto K, Choi W, Chowell G. Estimating the risk of COVID-19 death during the course of the outbreak in Korea, February-May 2020. J Clin Med 2020;9:1641.
Graham BS. Rapid COVID-19 vaccine development. Science 2020;368:945-6.
Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J et al. Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect 2020;81:e16-e25.
Tian W, Jiang W, Yao J, Nicholson CJ, Li RH, Sigurslid HH et al. Predictors of mortality in hospitalized COVID-19 patients: A systematic review and meta-analysis. J Med Virol 2020;92:1875-83.
Wu Z, McGoogan JM. Characteristics of and important lessons from the Coronavirus Disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020;323:1239-42.
McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. PRESS Peer Review of Electronic Search Strategies: 2015 guideline statement. J Clin Epidemiol 2016;75:40-6.
Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009;6:e1000097.
Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000;283:2008-12.
Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M et al. Cochrane Handbook for Systematic Reviews of Interventions, version 6.1. 2020. https://www.training.cochrane.org/handbook. Accessed 28 Oct 2020.
DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177-88.
Hayden JA, Cote P, Bombardier C. Evaluation of the quality of prognosis studies in systematic reviews. Ann Intern Med 2006;144:427-37.
Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315:629-34.
Aggarwal S, Garcia-Telles N, Aggarwal G, Lavie C, Lippi G, Henry BM. Clinical features, laboratory characteristics, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19): Early report from the United States. Diagnosis (Berl) 2020;7:91-6.
Antinori S, Cossu MV, Ridolfo AL, Rech R, Bonazzetti C, Pagani G et al. Compassionate remdesivir treatment of severe Covid-19 pneumonia in intensive care unit (ICU) and Non-ICU patients: Clinical outcome and differences in post-treatment hospitalisation status. Pharmacol Res 2020;158:104899.
Argenziano MG, Bruce SL, Slater CL, Tiao JR, Baldwin MR, Barr RG et al. Characterization and clinical course of 1000 patients with coronavirus disease 2019 in New York: retrospective case series. BMJ 2020;369:m1996.
Bonetti G, Manelli F, Patroni A, Bettinardi A, Borrelli G, Fiordalisi G et al. Laboratory predictors of death from coronavirus disease 2019 (COVID-19) in the area of Valcamonica, Italy. Clin Chem Lab Med 2020;58:1100-5.
Borghesi A, Zigliani A, Golemi S, Carapella N, Maculotti P, Farina D et al. Chest X-ray severity index as a predictor of in-hospital mortality in coronavirus disease 2019: A study of 302 patients from Italy. Int J Infect Dis 2020;96:291-3.
Buckner FS, McCulloch DJ, Atluri V, Blain M, McGuffin SA, Nalla AK et al. Clinical Features and Outcomes of 105 Hospitalized patients with COVID-19 in Seattle, Washington. Clin Infect Dis 2020;71:2167-73
Campochiaro C, Della-Torre E, Cavalli G, De Luca G, Ripa M, Boffini N et al. Efficacy and safety of tocilizumab in severe COVID-19 patients: a single-centre retrospective cohort study. Eur J Intern Med 2020;76:43-9.
Chen C, Chen C, Yan JT, Zhou N, Zhao JP, Wang DW. Analysis of myocardial injury in patients with COVID-19 and association between concomitant cardiovascular diseases and severity of COVID-19. Zhonghua Xin Xue Guan Bing Za Zhi 2020;48:567-71.
Chen X, Zhao B, Qu Y, Chen Y, Xiong J, Feng Y et al. Detectable serum Severe Acute Respiratory Syndrome Coronavirus 2 viral load (RNAemia) is closely correlated with drastically elevated Interleukin 6 level in critically ill patients with Coronavirus Disease 2019. Clin Infect Dis 2020;71:1937-42.
Colombi D, Bodini FC, Petrini M, Maffi G, Morelli N, Milanese G et al. Well-aerated lung on admitting chest CT to predict adverse outcome in COVID-19 pneumonia. Radiology 2020;296:E86-E96.
Crespo M, Perez-Saez MJ, Redondo-Pachon D, Llinas-Mallol L, Montero MM, Villar-Garcia J et al. COVID-19 in elderly kidney transplant recipients. Am J Transplant 2020;20:2883-9.
de Abajo FJ, Rodríguez-Martín S, Lerma V, Mejía-Abril G, Aguilar M, García-Luque A et al. Use of renin–angiotensin–aldosterone system inhibitors and risk of COVID-19 requiring admission to hospital: a case-population study. The Lancet 2020;395:1705-14.
Deng Q, Hu B, Zhang Y, Wang H, Zhou X, Hu W et al. Suspected myocardial injury in patients with COVID-19: Evidence from front-line clinical observation in Wuhan, China. Int J Cardiol 2020;311:116-21.
Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L et al. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ 2020;369:m1985.
Du RH, Liang LR, Yang CQ, Wang W, Cao TZ, Li M et al. Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study. Eur Respir J 2020;55.
Feng Y, Ling Y, Bai T, Xie Y, Huang J, Li J et al. COVID-19 with different severities: A multicenter study of clinical features. Am J Respir Crit Care Med 2020;201:1380-8.
Ferguson J, Rosser JI, Quintero O, Scott J, Subramanian A, Gumma M et al. Characteristics and outcomes of Coronavirus Disease patients under nonsurge conditions, Northern California, USA, March-April 2020. Emerg Infect Dis 2020;26:1679-85.
Giacomelli A, Ridolfo AL, Milazzo L, Oreni L, Bernacchia D, Siano M et al. 30-day mortality in patients hospitalized with COVID-19 during the first wave of the Italian epidemic: A prospective cohort study. Pharmacol Res 2020;158:104931.
Goicoechea M, Sanchez Camara LA, Macias N, Munoz de Morales A, Rojas AG, Bascunana A et al. COVID-19: clinical course and outcomes of 36 hemodialysis patients in Spain. Kidney Int 2020;98:27-34.
Guo F, Zhu L, Xu H, Qin L, Liang X, Deng X. Correlation between clinical classification of COVID-19 and imaging characteristics of MSCT volume scanning of the lungs. Nan Fang Yi Ke Da Xue Xue Bao 2020;40:321-6.
He W, Chen L, Chen L, Yuan G, Fang Y, Chen W et al. COVID-19 in persons with haematological cancers. Leukemia 2020;34:1637-45.
Hong KS, Lee KH, Chung JH, Shin KC, Choi EY, Jin HJ et al. Clinical features and outcomes of 98 patients hospitalized with SARS-CoV-2 infection in Daegu, South Korea: a brief descriptive study. Yonsei Med J 2020;61:431-7.
Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497-506.
Huang J, Cheng A, Kumar R, Fang Y, Chen G, Zhu Y et al. Hypoalbuminemia predicts the outcome of COVID-19 independent of age and co-morbidity. J Med Virol 2020;92:2152-58
Huang JT, Ran RX, Lv ZH, Feng LN, Ran CY, Tong YQ et al. Chronological changes of viral shedding in adult inpatients with COVID-19 in Wuhan, China. Clin Infect Dis 2020;71:2158-66
Ihle-Hansen H, Berge T, Tveita A, Ronning EJ, Erno PE, Andersen EL et al. COVID-19: Symptoms, course of illness and use of clinical scoring systems for the first 42 patients admitted to a Norwegian local hospital. Tidsskr Nor Laegeforen 2020; doi: 10.4045/tidsskr.20.0301.
Israelsen SB, Kristiansen KT, Hindsberger B, Ulrik CS, Andersen O, Jensen M et al. Characteristics of patients with COVID-19 pneumonia at Hvidovre Hospital, March-April 2020. Dan Med J 2020;67.
Javanian M, Bayani M, Shokri M, Sadeghi-Haddad-Zavareh M, Babazadeh A, Yeganeh B et al. Clinical and laboratory findings from patients with COVID-19 pneumonia in Babol North of Iran: a retrospective cohort study. Rom J Intern Med 2020;58:161-7.
Ji M, Yuan L, Shen W, Lv J, Li Y, Li M et al. Characteristics of disease progress in patients with coronavirus disease 2019 in Wuhan, China. Epidemiol Infect 2020;148:e94.
Kalligeros M, Shehadeh F, Mylona EK, Benitez G, Beckwith CG, Chan PA et al. Association of obesity with disease severity among patients with Coronavirus Disease 2019. Obesity (Silver Spring) 2020;28:1200-4.
Klang E, Kassim G, Soffer S, Freeman R, Levin MA, Reich DL. Severe obesity as an independent risk factor for COVID-19 mortality in hospitalized patients younger than 50. Obesity (Silver Spring) 2020;28:1595-9.
Lagi F, Piccica M, Graziani L, Vellere I, Botta A, Tilli M et al. Early experience of an infectious and tropical diseases unit during the coronavirus disease (COVID-19) pandemic, Florence, Italy, February to March 2020. Euro Surveill 2020; doi: 10.2807/1560-7917.ES.2020.25.17.2000556.
Lee LY, Cazier JB, Angelis V, Arnold R, Bisht V, Campton NA et al. COVID-19 mortality in patients with cancer on chemotherapy or other anticancer treatments: a prospective cohort study. Lancet 2020;395:1919-26.
Li J, Li M, Zheng S, Li M, Zhang M, Sun M et al. Plasma albumin levels predict risk for nonsurvivors in critically ill patients with COVID-19. Biomark Med 2020;14:827-37.
Li J, Wang X, Chen J, Zhang H, Deng A. Association of renin-angiotensin system inhibitors with severity or risk of death in patients with hypertension hospitalized for Coronavirus Disease 2019 (COVID-19) infection in Wuhan, China. JAMA Cardiol 2020;5:825-30.
Li L, Yang L, Gui S, Pan F, Ye T, Liang B et al. Association of clinical and radiographic findings with the outcomes of 93 patients with COVID-19 in Wuhan, China. Theranostics 2020;10:6113-21.
Luo X, Zhou W, Yan X, Guo T, Wang B, Xia H et al. Prognostic value of C-reactive protein in patients with COVID-19. Clin Infect Dis 2020;71:2174-79
Lv Z, Cheng S, Le J, Huang J, Feng L, Zhang B et al. Clinical characteristics and co-infections of 354 hospitalized patients with COVID-19 in Wuhan, China: a retrospective cohort study. Microbes Infect 2020;22:195-9.
Nowak B, Szymanski P, Pankowski I, Szarowska A, Zycinska K, Rogowski W et al. Clinical characteristics and short-term outcomes of patients with coronavirus disease 2019: a retrospective single-center experience of a designated hospital in Poland. Pol Arch Intern Med 2020;130:407-11.
Omrani-Nava V, Maleki I, Ahmadi A, Moosazadeh M, Hedayatizadeh-Omran A, Roozbeh F et al. Evaluation of hepatic enzymes changes and association with prognosis in COVID-19 patients. Hepatitis Monthly 2020; doi:10.5812/hepatmon.103179.
Pan F, Yang L, Li Y, Liang B, Li L, Ye T et al. Factors associated with death outcome in patients with severe coronavirus disease-19 (COVID-19): a case-control study. Int J Med Sci 2020;17:1281-92.
Pan L, Mu M, Yang P, Sun Y, Wang R, Yan J et al. Clinical Characteristics of COVID-19 patients with digestive symptoms in Hubei, China: a descriptive, cross-sectional, multicenter study. Am J Gastroenterol 2020;115:766-73.
Pei G, Zhang Z, Peng J, Liu L, Zhang C, Yu C et al. Renal involvement and early prognosis in patients with COVID-19 pneumonia. J Am Soc Nephrol 2020;31:1157-65.
Peng YD, Meng K, Guan HQ, Leng L, Zhu RR, Wang BY et al. Clinical characteristics and outcomes of 112 cardiovascular disease patients infected by 2019-nCoV. Zhonghua Xin Xue Guan Bing Za Zhi 2020;48:450-5.
Pereira MR, Mohan S, Cohen DJ, Husain SA, Dube GK, Ratner LE et al. COVID-19 in solid organ transplant recipients: Initial report from the US epicenter. Am J Transplant 2020;20:1800-8.
Petrilli CM, Jones SA, Yang J, Rajagopalan H, O'Donnell L, Chernyak Y et al. Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study. BMJ 2020;369:m1966.
Renieris G, Katrini K, Damoulari C, Akinosoglou K, Psarrakis C, Kyriakopoulou M et al. Serum hydrogen sulfide and outcome association in pneumonia by the SARS-CoV-2 Coronavirus. Shock 2020;54:633-7.
Rogado J, Obispo B, Pangua C, Serrano-Montero G, Martin Marino A, Perez-Perez M et al. Covid-19 transmission, outcome and associated risk factors in cancer patients at the first month of the pandemic in a Spanish hospital in Madrid. Clin Transl Oncol 2020;22:2364-8.
Russo V, Di Maio M, Attena E, Silverio A, Scudiero F, Celentani D et al. Clinical impact of pre-admission antithrombotic therapy in hospitalized patients with COVID-19: A multicenter observational study. Pharmacol Res 2020;159:104965.
Sabri A, Davarpanah AH, Mahdavi A, Abrishami A, Khazaei M, Heydari S et al. Novel coronavirus disease 2019: predicting prognosis with a computed tomography-based disease severity score and clinical laboratory data. Pol Arch Intern Med 2020;130:629-34.
Shi Q, Zhang X, Jiang F, Zhang X, Hu N, Bimu C et al. Clinical Characteristics and Risk Factors for Mortality of COVID-19 Patients With Diabetes in Wuhan, China: A Two-Center, Retrospective Study. Diabetes Care 2020;43:1382-91.
Shi S, Qin M, Cai Y, Liu T, Shen B, Yang F et al. Characteristics and clinical significance of myocardial injury in patients with severe coronavirus disease 2019. Eur Heart J 2020;41:2070-9.
Smadja DM, Guerin CL, Chocron R, Yatim N, Boussier J, Gendron N et al. Angiopoietin-2 as a marker of endothelial activation is a good predictor factor for intensive care unit admission of COVID-19 patients. Angiogenesis 2020;23:611-20.
Stroppa EM, Toscani I, Citterio C, Anselmi E, Zaffignani E, Codeluppi M et al. Coronavirus disease-2019 in cancer patients. A report of the first 25 cancer patients in a western country (Italy). Future Oncol 2020;16:1425-32.
Suleyman G, Fadel RA, Malette KM, Hammond C, Abdulla H, Entz A et al. Clinical characteristics and morbidity associated with Coronavirus Disease 2019 in a series of patients in metropolitan Detroit. JAMA Netw Open 2020;3:e2012270.
Sun H, Ning R, Tao Y, Yu C, Deng X, Zhao C et al. Risk factors for mortality in 244 older adults with COVID-19 in Wuhan, China: a retrospective study. J Am Geriatr Soc 2020;68:E19-E23.
Sun S, Cai X, Wang H, He G, Lin Y, Lu B et al. Abnormalities of peripheral blood system in patients with COVID-19 in Wenzhou, China. Clin Chim Acta 2020;507:174-80.
Sze S, Pan D, Williams CML, Wong N, Sahota A, Bell D et al. Letter to the Editor: Variability but not admission or trends in NEWS2 score predicts clinical outcome in elderly hospitalised patients with COVID-19. J Infect 2020; doi: 10.1016/j.jinf.2020.05.063.
Tambe MP, Parande MA, Tapare VS, Borle PS, Lakde RN, Shelke SC et al. An epidemiological study of laboratory confirmed COVID-19 cases admitted in a tertiary care hospital of Pune, Maharashtra. Indian J Public Health 2020;64:S183-S7.
Tang N, Bai H, Chen X, Gong J, Li D, Sun Z. Anticoagulant treatment is associated with decreased mortality in severe coronavirus disease 2019 patients with coagulopathy. J Thromb Haemost 2020;18:1094-9.
Urra JM, Cabrera CM, Porras L, Rodenas I. Selective CD8 cell reduction by SARS-CoV-2 is associated with a worse prognosis and systemic inflammation in COVID-19 patients. Clin Immunol 2020;217:108486.
Valeri AM, Robbins-Juarez SY, Stevens JS, Ahn W, Rao MK, Radhakrishnan J et al. Presentation and outcomes of patients with ESKD and COVID-19. J Am Soc Nephrol 2020;31:1409-15.
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J et al. Clinical characteristics of 138 hospitalized patients with 2019 novel Coronavirus-infected pneumonia in Wuhan, China. JAMA 2020;323:1061-9.
Wang D, Yin Y, Hu C, Liu X, Zhang X, Zhou S et al. Clinical course and outcome of 107 patients infected with the novel coronavirus, SARS-CoV-2, discharged from two hospitals in Wuhan, China. Crit Care 2020;24:188.
Wang F, Yang Y, Dong K, Yan Y, Zhang S, Ren H et al. Clinical characteristics of 28 patients with diabetes and Covid-19 in Wuhan, China. Endocr Pract 2020;26:668-74.
Wang K, Zuo P, Liu Y, Zhang M, Zhao X, Xie S et al. Clinical and laboratory predictors of in-hospital mortality in patients With Coronavirus Disease-2019: a cohort study in Wuhan, China. Clin Infect Dis 2020;19;71:2079-88
Wang R, Pan M, Zhang X, Han M, Fan X, Zhao F et al. Epidemiological and clinical features of 125 Hospitalized Patients with COVID-19 in Fuyang, Anhui, China. Int J Infect Dis 2020;95:421-8.
Wei X, Zeng W, Su J, Wan H, Yu X, Cao X et al. Hypolipidemia is associated with the severity of COVID-19. J Clin Lipidol 2020;14:297-304.
Xu B, Fan CY, Wang AL, Zou YL, Yu YH, He C et al. Suppressed T cell-mediated immunity in patients with COVID-19: A clinical retrospective study in Wuhan, China. J Infect 2020;81:e51-e60.
Yan X, Li F, Wang X, Yan J, Zhu F, Tang S et al. Neutrophil to lymphocyte ratio as prognostic and predictive factor in patients with coronavirus disease 2019: A retrospective cross-sectional study. J Med Virol 2020;92:2573-81
Yang L, Liu J, Zhang R, Li M, Li Z, Zhou X et al. Epidemiological and clinical features of 200 hospitalized patients with corona virus disease 2019 outside Wuhan, China: A descriptive study. J Clin Virol 2020;129:104475.
Yao Q, Wang P, Wang X, Qie G, Meng M, Tong X et al. A retrospective study of risk factors for severe acute respiratory syndrome coronavirus 2 infections in hospitalized adult patients. Pol Arch Intern Med 2020;130:390-9.
Yuan M, Yin W, Tao Z, Tan W, Hu Y. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PLoS One 2020;15:e0230548.
Zhang F, Xiong Y, Wei Y, Hu Y, Wang F, Li G et al. Obesity predisposes to the risk of higher mortality in young COVID-19 patients. J Med Virol 2020;92:2536-42
Zhang G, Zhang J, Wang B, Zhu X, Wang Q, Qiu S. Analysis of clinical characteristics and laboratory findings of 95 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a retrospective analysis. Respir Res 2020;21:74.
Zhang J, Lu S, Wang X, Jia X, Li J, Lei H et al. Do underlying cardiovascular diseases have any impact on hospitalised patients with COVID-19? Heart 2020;106:1148-53.
Zhang J, Wang X, Jia X, Li J, Hu K, Chen G et al. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clin Microbiol Infect 2020;26:767-72.
Zhang J, Yu M, Tong S, Liu LY, Tang LV. Predictive factors for disease progression in hospitalized patients with coronavirus disease 2019 in Wuhan, China. J Clin Virol 2020;127:104392.
Zhang X, Tan Y, Ling Y, Lu G, Liu F, Yi Z et al. Viral and host factors related to the clinical outcome of COVID-19. Nature 2020;583:437-40.
Zhao X, Wang K, Zuo P, Liu Y, Zhang M, Xie S et al. Early decrease in blood platelet count is associated with poor prognosis in COVID-19 patients-indications for predictive, preventive, and personalized medical approach. EPMA J 2020;11:1-7.
Ssentongo P, Ssentongo AE, Heilbrunn ES, Ba DM, Chinchilli VM. Association of cardiovascular disease and 10 other pre-existing comorbidities with COVID-19 mortality: A systematic review and meta-analysis. PLoS One 2020;15:e0238215.
Huang I, Lim MA, Pranata R. Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia - A systematic review, meta-analysis, and meta-regression. Diabetes Metab Syndr 2020;14:395-403.
Pranata R, Huang I, Lim MA, Wahjoepramono EJ, July J. Impact of cerebrovascular and cardiovascular diseases on mortality and severity of COVID-19-systematic review, meta-analysis, and meta-regression. J Stroke Cerebrovasc Dis 2020; doi: 10.1016/j.jstrokecerebrovasdis.2020.104949.
Jain V, Yuan JM. Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: a systematic review and meta-analysis. Int J Public Health 2020;65:533-46.
Ou M, Zhu J, Ji P, Li H, Zhong Z, Li B et al. Risk factors of severe cases with COVID-19: a meta-analysis. Epidemiol Infect 2020;148:e175.
Kragholm K, Andersen MP, Gerds TA, Butt JH, Ostergaard L, Polcwiartek C et al. Association between male sex and outcomes of Coronavirus Disease 2019 (Covid-19) - a Danish nationwide, register-based study. Clin Infect Dis 2020; doi: 10.1093/cid/ciaa924.
Conti P, Younes A. Coronavirus COV-19/SARS-CoV-2 affects women less than men: clinical response to viral infection. J Biol Regul Homeost Agents 2020;34:339-43.
Kadel S, Kovats S. Sex hormones regulate innate immune cells and promote sex differences in respiratory virus infection. Front Immunol 2018;9:1653.
Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax 2003;58:377-82.
Fine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, Singer DE et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med 1997;336:243-50.
Van Kerkhove MD, Vandemaele KA, Shinde V, Jaramillo-Gutierrez G, Koukounari A, Donnelly CA et al. Risk factors for severe outcomes following 2009 influenza A (H1N1) infection: a global pooled analysis. PLoS Med 2011;8:e1001053.
Fu L, Wang B, Yuan T, Chen X, Ao Y, Fitzpatrick T et al. Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: A systematic review and meta-analysis. J Infect 2020;80:656-65.
Halpin DMG, Faner R, Sibila O, Badia JR, Agusti A. Do chronic respiratory diseases or their treatment affect the risk of SARS-CoV-2 infection? Lancet Respir Med 2020;8:436-8.
Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, Linsell L et al. Dexamethasone in hospitalized patients with Covid-19 - preliminary report. N Engl J Med 2020; doi:10.1056/NEJMoa2021436
Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9· 1 million participants. Lancet 2011;377:557-67.
Hsu HE, Ashe EM, Silverstein M, Hofman M, Lange SJ, Razzaghi H et al. Race/ethnicity, underlying medical conditions, homelessness, and hospitalization status of adult patients with COVID-19 at an Urban Safety-Net Medical Center—Boston, Massachusetts, 2020. MMWR Morb Mortal Wkly Rep 2020;69:864-9.
Kabarriti R, Brodin NP, Maron MI, Guha C, Kalnicki S, Garg MK et al. Association of race and ethnicity with comorbidities and survival among patients With COVID-19 at an urban medical center in New York. JAMA Netw Open 2020;3:e2019795.
Rentsch CT, Kidwai-Khan F, Tate JP, Park LS, King JT, Jr., Skanderson M et al. Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: A nationwide cohort study. PLoS Med 2020;17:e1003379.
Raharja A, Tamara A, Kok LT. Association between ethnicity and severe COVID-19 disease: a systematic review and meta-analysis. J Racial Ethn Health Disparities 2020; doi: 10.1007/s40615-020-00921-5.
Madjid M, Safavi-Naeini P, Solomon SD, Vardeny O. Potential Effects of Coronaviruses on the Cardiovascular System: A Review. JAMA Cardiol 2020;5:831-40.
Tian Y, Qiu X, Wang C, Zhao J, Jiang X, Niu W et al. Cancer associates with risk and severe events of COVID-19: A systematic review and meta-analysis. Int J Cancer 2020;148:363-74.
Wu Y, Li H, Guo X, Yoshida EM, Mendez-Sanchez N, Levi Sandri GB et al. Incidence, risk factors, and prognosis of abnormal liver biochemical tests in COVID-19 patients: a systematic review and meta-analysis. Hepatol Int 2020;14:621-37.
Kumar MP, Mishra S, Jha DK, Shukla J, Choudhury A, Mohindra R et al. Coronavirus disease (COVID-19) and the liver: a comprehensive systematic review and meta-analysis. Hepatol Int 2020;14:711-22.

Additionalfile1.docx
This file includes the detailed search strategy used for our systematic review and meta-analysis.
Additionalfile2.doc
This file includes the checklist recommended by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines
Additionalfile3.docx
This file includes Figures S1 to S5, and Tables S1 to S4. The titles of each figure and table are as follows: Figure E1. Flow diagram of the systematic literature review Figure E2. Impact of male sex on the critical outcome including death, intensive care unit admission, and critical type of COVID-19. Figure E3. Impact of patient age on the critical outcome including death, intensive care unit admission, and critical type of COVID-19. Figure E4. Association between ethnicity and the critical outcome including intensive care unit admission and death compared to non-Hispanic White ethnicity with a random effects model. a) Hispanic ethnicity. b) non-Hispanic black ethnicity. c) Asian ethnicity. Table E1. Definition of underlying disease mentioned in each study Table E2. Evaluation of the quality of studies included in the systematic review Table E3. Degree of heterogeneity and publication bias according to each analysis Figure E5. Impact of (a) underlying medical condition, (b) patient symptom, and (c) laboratory findings on death of patients with COVID-19. Table E4. Detailed results from the sensitivity analysis with selected studies
Additionalfile4.xlsx
This file includes the full list of the included articles used in our systematic review and meta-analysis.

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Risk factors for critical outcome of COVID-19 differ according to location: a systematic review and meta-analysis

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials And Methods

Search strategy and study protocol

Study selection

Data extraction

Statistical considerations and assessment of bias

Results

Search findings and study characteristics

Baseline patient characteristics and symptoms

Impacts of baseline demographics and underlying medical condition on the critical outcome

Associations between patient symptoms and the critical outcome

Associations between laboratory findings and the critical outcome

Impact of ethnicity on the critical outcome

Assessment of study quality and publication bias

Sensitivity analysis

Discussion

Conclusions

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1