Literature DB >> 34761532

Comparison of comorbidities among severe and non-severe COVID-19 patients in Asian versus non-Asian populations: A systematic review and meta-analysis.

Anju Puri1, Lin He2, Mohan Giri3, Chengfei Wu1, Qinghua Zhao1.   

Abstract

OBJECTIVES: This study aimed to evaluate the comorbidities among severe and non-severe COVID-19 patients in Asian versus non-Asian populations.
DESIGN: Systemic review and Meta-analysis.
METHODS: A systematic literature search was conducted using PubMed, Embase, Scopus and the web of science Database up to 24 March 2021. Odds ratios were calculated using a random-effects model.
RESULTS: We identified 66 studies including 39 Asian and 27 non-Asian studies. This study demonstrated that the proportion of hypertension was significantly higher in severe group than in non-severe group for Asian (OR = 2.46) and non-Asian (OR = 1.60, 95% CI: 1.37-1.86, I2  = 84%; p < .00001) patients. Similarly, the proportion of diabetes, cardiovascular disease and chronic kidney disease was significantly higher in severe group than in non-severe group for both Asian and non-Asian studies. We found no statistically significant difference between the severe versus non-severe group for cancer (OR = 1.26) and chronic obstructive pulmonary disease (OR = 1.32) among non-Asian patients.
© 2021 The Authors. Nursing Open published by John Wiley & Sons Ltd.

Entities:  

Keywords:  COVID-19; SARS-CoV-2; comorbidity; meta-analysis; severe

Mesh：

Year:  2021        PMID： 34761532      PMCID： PMC8661719          DOI： 10.1002/nop2.1126

Source DB:  PubMed          Journal:  Nurs Open        ISSN： 2054-1058


INTRODUCTION

The outbreak of Coronavirus disease (COVID‐19), which was first reported in early December 2019 in Wuhan, China has emerged as one of the most serious global pandemic and global health hazard (Huang, Wang, et al., 2020). The cases of COVID‐19 are still rapidly increasing with higher morbidity and mortality. Globally, there have been 146,067,511 confirmed COVID‐19 patients by 25 April 2021 and among them 3,092,497 lost their lives (World Health Organization, 2021). The clinical manifestations highly range from asymptomatic to symptomatic and shows clusters of flu like symptoms such as fever, fatigue, myalgia, dry cough, dyspnoea, anorexia and so on (Hong et al., 2020; Huang, Wang, et al., 2020; Huang, Lian, et al., 2020). Patients are classified into four type, that is, mild, moderate, severe and critical based on clinical manifestation and laboratory findings. Some studies have documented that COVID‐19‐infected patients who already have pre‐existing comorbidities such as hypertension, diabetes, congestive heart failure, cardiovascular diseases, cerebrovascular disease, chronic kidney disease (CKD), chronic liver disease, cancer, chronic obstructive pulmonary disease and asthma leads to poor prognosis or even fatal outcomes (Giri et al., 2020; Gregoriano et al., 2020; Yang, Zheng, et al., 2020; Zhou, Yang, et al., 2020). In addition, the older people who already have above listed underlying chronic conditions are more susceptible to COVID‐19. Severe cases admitted in intensive care unit with pre‐existing comorbidities yield poorer clinical outcomes than those without (Abohamr et al., 2020; Guan et al., 2020; Huang, Wang, et al., 2020; Tabata et al., 2020). Thus, it is critical to thoroughly understand and identify the actual high‐risk comorbidities, which are closely associated with COVID‐19 in order to do prompt management and prevent the deterioration from mild and moderate conditions to the severe ones. Thus far, most of published meta‐analysis about the comorbidity in severe COVID‐19 patients included limited studies and most studies included in these meta‐analysis were conducted in China. Therefore, it is necessary to carry out a meta‐analysis to give systematic evaluation of common comorbidities in severe and non‐severe COVID‐19 patients around the globe. To the best of our knowledge, this is the first study to compare comorbidities among severe and non‐severe COVID‐19 patients in Asian versus non‐Asian populations.

METHODS

Eligibility criteria

For research article selection the inclusion criteria were as follows: (1) Study population: Studies with patients diagnosed with COVID‐19; (2) Comparative studies: Studies that compared severe or ICU (elevated troponin T (TnT) level as the second choice if severe or ICU data were not given) and non‐sever or non‐ICU (normal TnT level as the second choice if non‐severe or non‐ICU data was not given) cases of COVID‐19; and (3) The studies reporting parameters of comorbidities such as hypertension, diabetes, cardiovascular disease, cancer, chronic obstructive pulmonary disease and chronic kidney disease. Non‐English studies, letters, case studies, editorials, conference abstract, vaccination trials studies and articles with only abstract were excluded. Studies with fewer than 20 cases were also excluded.

Information sources and Searching strategies

This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) Statement guidelines (Moher et al., 2009). PRISMA checklist was followed step by step (Electronic Supplementary information Appendix S1). We used PubMed, Embase, Scopus and Web of Science online database to conduct a comprehensive search up to 24 March 2021, with following search terms: “COVID‐19,” “Novel coronavirus,” “SARS‐CoV‐2,” “Coronavirus disease‐19,” “Epidemiological character,” “Clinical features,” “Clinical character,” “Clinical Presentation,” “comorbidities,” “Comorbidities” and “Complications.” Full electronic search strategy in PubMed database can be found here (Electronic Supplementary information Appendix S2). We applied search filters to include English language studies. We screened reference lists of included studies to ensure literature saturation.

Data extraction

Microsoft Excel database was used to record all available information. Two authors (AP and LH) who performed the literature search also independently extracted following items from each article: first author, publication year, country, study design, age, gender, sample size and number of people in severe and non‐severe group. In case of missing data, we also contacted the authors of an article to obtain more precise data about the comorbidities of the patients evaluated. Disagreement occurred during research period were resolved by consensus with third author. The primary outcome measure was to compare the proportion of comorbidities such as hypertension, diabetes, cardiovascular disease, and chronic kidney disease in severe group versus non‐severe group for both Asian and non‐Asian studies.

Risk of bias assessment

Methodological index for non‐randomized studies (MINORS) (Slim et al., 2003) was used to assess methodological quality of included studies by two independent researchers. Each item in the MINORS has three scores: 0, unreported; 1, reported but inadequately or partially; and 2, adequately reported. The total score is 24. The detailed risk of bias for all the included studies using MINORS criteria score is presented in Table 1. According to MINORS criteria score the studies were classified as very low quality (0–6), low quality (7–12), moderate quality (13–18) and high quality (19–24). Two reviewers independently assessed the quality of included studies and disagreements were resolved through discussion with third reviewer. Publication bias among included studies was assessed by funnel plots and a symmetrical plot indicated low‐risk publication bias.
TABLE 1

MINORS rating scale for quality of included studies

Study①②③④⑤⑥⑦⑧⑨⑩⑪⑫Score
Asian studies
Abohamr SI22222010222219
Alqahtani AM22222100222219
Bastug A22222020222220
Cao J22222100222219
Cao Z22222010222219
Du RH22222020222220
Guan WJ22222000222218
Güner R22222010222219
Guo T22222000222218
Hong KS22222010222219
Huang C22222120222221
Huang R22222110222220
Khamis F22222020222220
Khan A22222110222220
Lee JY22222010222219
Lee SG22222000222218
Li C22222110222220
LI K22222100222219
Li X22222010222219
Lv Z22222020222220
Omrani A22222110222220
Shabrawishi M22222010222219
Shahriarirad R22222010222219
Shi S22222120222221
Tabata S22222000222218
Tian S22222110222220
Wan S22222110222220
Wang D22222120222221
Wang W22222020222220
Wang Y22222100222219
Wang Z22222120222221
Wei Y22222110222220
Wu J22222110222220
Xiong F22222000222218
Xiong S22222110222220
Yang L22222000222218
Zhang G22222010222219
Zhang JJ22222000222218
Zhou J22222000222218
Non‐Asian studies
Argenziano MG22222120222221
Buckner FS22222010222219
Cattelan AM22222000222218
Ferguson J22222110222220
Filardo TD22222010222219
Garibaldi BT22222000222218
Giustino G22222110222220
Gregoriano C22222100222219
Israelsen SB22222000222218
Jourdes A22222110222220
Kaeuffer C22222000222218
Lombardi CM22222010222219
Matangila JR22222020222220
Ortiz‐Brizuela E22222000222218
Oud L22222010222219
Pellaud C22222110222220
Petrilli CM22222000222218
Popov GT22222000222218
Raad M22222010222219
Reilev M22222010222219
Samuels S22222020222220
Schönfeld D22222100222219
Stefan, G.22222010222219
Sulejmani A22222000222218
Suleyman G22222010222219
Turcotte JJ22222010222219
Yazdanpanah Y22222110222220

① A clearly stated aim; ② Inclusion of consecutive patients; ③ Prospective collection of data; ④ Endpoints appropriate to the aim of the study; ⑤ Unbiased assessment of the study endpoint; ⑥ Follow‐up period appropriate to the aim of the study; ⑦ Loss to follow up less than 5%; ⑧ Prospective calculation of the study size. ⑨ Appropriate selection of control group; ⑩ Synchronization of control group; ⑪ Baseline comparable between groups; ⑫ Appropriately statistical analysis. The global ideal score being 24 for comparative studies.

MINORS rating scale for quality of included studies ① A clearly stated aim; ② Inclusion of consecutive patients; ③ Prospective collection of data; ④ Endpoints appropriate to the aim of the study; ⑤ Unbiased assessment of the study endpoint; ⑥ Follow‐up period appropriate to the aim of the study; ⑦ Loss to follow up less than 5%; ⑧ Prospective calculation of the study size. ⑨ Appropriate selection of control group; ⑩ Synchronization of control group; ⑪ Baseline comparable between groups; ⑫ Appropriately statistical analysis. The global ideal score being 24 for comparative studies.

Statistical analysis

Meta‐analysis was performed using RevMan software version 5.3. We calculated pooled odds ratio (OR) and 95% CI for comorbidities, in severe versus non‐severe Asian and non‐Asian studies. Heterogeneity between studies was assessed using the Cochran Q test and I 2 statistics. Generally, in cases of I 2 being larger than 50%, a random‐effect model is used, otherwise a fixed‐effect model is used. However, owing to the clinical heterogeneity inherent in the data and the different effect sizes of included studies we used random‐effects analysis for all meta‐analyses. The I 2 values of <25%, 25%–50%, 50%–75% and 75%–100% were regarded as homogeneous, low, moderate and high heterogeneous levels, respectively. The p‐value less than 0.05 was used to indicate statistical significance.

RESULTS

Searches in electronic databases found 11,874 articles. After excluding duplicates (N = 7318), 4,556 citation records remained. Thereafter, 4,556 articles were screened in terms of title and abstract. 4,442 ineligible studies were excluded. The full text of 114 studies was assessed to determine their eligibility. We excluded 48 full texts, comprising 24 review articles, 15 non‐comparative studies, five meta‐analysis and four editorials. Ultimately, out of 114 full text article finally 66 articles, which met the inclusion criteria were included in the final analysis. Figure 1 shows a flow chart of studies selection process.
FIGURE 1

Flow diagram of study selection process

Flow diagram of study selection process

Study characteristics and quality

A total of 66 studies were included among them 39 studies were Asian and 27 were non‐Asian. Out of 39 Asian studies, most of them were carried out in China (N = 26) (Cao, Li, et al., 2020; Cao, Zheng, et al., 2020; Du et al., 2020; Guan et al., 2020; Guo et al., 2020; Huang, Wang, et al., 2020; Huang, Zhu, et al., 2020; Li, Jiang, et al., 2020; Li, Wu, et al., 2020; Li, Xu, et al., 2020; Lv et al., 2020; Shi et al., 2020; Tian et al., 2020; Wan et al., 2020; Wang, Hu, et al., 2020; Wang, Xin, et al., 2020; Wang, Yang, et al., 2020; Wang, Zhen, et al., 2020; Wei et al., 2020; Wu et al., 2020; Xiong, Liu, et al., 2020; Xiong, Tang, et al., 2020; Yang, Liu, et al., 2020; Zhang, Dong, et al., 2020; Zhang, Hu, et al., 2020; Zhou, Sun, et al., 2020), followed by Saudi Arabia (N = 4) (Abohamr et al., 2020; Alqahtani et al., 2020; Khan et al., 2020; Shabrawishi et al., 2020), South Korea (N = 3) (Hong et al., 2020; Lee, Hong, et al., 2020; Lee, Park, et al., 2020), Turkey (N = 2) (Bastug et al., 2020; Güner et al., 2020), Oman (N = 1) (Khamis et al., 2020), Qatar (N = 1) (Omrani et al., 2020), Iran (N = 1) (Shahriarirad et al., 2020), and Japan (N = 1) (Tab ata et al., 2020). Most of the non‐Asian studies were conducted in United States (N = 12) (Argenziano et al., 2020; Buckner et al., 2020; Ferguson et al., 2020; Filardo et al., 2020; Garibaldi et al., 2021; Giustino et al., 2020; Oud & Garza, 2021; Petrilli et al., 2020; Raad et al., 2020; Samuels et al., 2021; Suleyman et al., 2020; Turcotte et al., 2020), followed by Italy (N = 3) (Cattelan et al., 2020; Lombardi et al., 2020; Sulejmani et al., 2021), France (N = 3) (Jourdes et al., 2020; Kaeuffer et al., 2020; Yazdanpanah, 2021), Switzerland (N = 2) (Gregoriano et al., 2020; Pellaud et al., 2020), Denmark (N = 2) (Israelsen et al., 2020; Reilev et al., 2020), Congo (N = 1) (Matangila et al., 2020), Mexico (N = 1) (Ortiz‐Brizuela et al., 2020), Bulgaria (N = 1) (Popov et al., 2020), Argentina (N = 1) (Schönfeld et al., 2021) and Romania (N = 1) (Stefan et al., 2021). All included studies were published in 2020 and 2021 with varying sample size that ranged from 37 to 207,079 patients. The characteristics of the included studies are depicted in Table 2. We performed assessments of risk of bias for all the included studies using MINORS rating scale and reported in Table 1. The mean MINORS score was 19.23 ± 0.91 (range: 18–21) out of a possible 24 for comparative studies (Table 1). All of the included studies were moderate‐to‐high quality.
TABLE 2

Characteristics of the included studies

StudyType of study designCountryTotal patientsSevere patientsNon‐sever patients
Age, years a MaleAge, years a Male
Asian studies
Abohamr SIRetrospectiveSaudi Arabia76847.4 ± 13.828445.5 ± 13.5305
Alqahtani AMRetrospectiveSaudi Arabia458NA37NA361
Bastug ARetrospectiveTurkey19171 (28–91)2643 (18–83)81
Cao JRetrospectiveChina24462.20 ± 13.436359.79 ± 13.4944
Cao ZRetrospectiveChina8071 ± 151644 ± 1622
Du RHRetrospectiveChina10968.4 ± 9.73672.7 ± 11.638
Guan WJRetrospectiveChina109952 (40–65)10045 (34–57)537
Güner RCohortTurkey22262.2 ± 11.93347.7 ± 16.199
Guo TRetrospectiveChina18771.4 ± 9.433453.53 ± 13.2257
Hong KSRetrospectiveSouth Korea9863.2 ± 10.1654.2 ± 17.732
Huang CProspectiveChina4149 (41–61)1149 (41–57.5)19
Huang RRetrospectiveChina20249 (35–59)1744 (33–53)99
Khamis FRetrospectiveOman6350 ± 172147 ± 1632
Khan ARetrospectiveSaudi Arabia64837 (27)5233 (18)290
Lee JYRetrospectiveSouth Korea694NA57NA155
Lee SGRetrospectiveSouth Korea733966.8 ± 15.244144.2 ± 17.82529
Li CRetrospectiveChina206869 (60–78)28261 (49–68)723
LI KRetrospectiveChina8353.7 ± 12.31541.9 ± 10.629
Li XRetrospectiveChina54865 (54–72)15356 (44–66)126
Lv ZRetrospectiveChina35462 (25–89)7761 (23–79)58
Omrani ASRetrospectiveQatar500049.5 (39.5–60)10038 (30–49)1067
Shabrawishi MRetrospectiveSaudi Arabia15049.8 ± 15.71345.4 ± 1658
Shahriarirad RRetrospectiveIran113NA7NA64
Shi SCohortChina41674 (34–95)4460 (21–90)161
Tabata SRetrospectiveJapan10473 (55–77)1760 (40–71)22
Tian SRetrospectiveChina26261.4 (1–94)2644.5 (1–93)101
Wan SRetrospectiveChina13556 (52–73)2144 (33–49)52
Wang DRetrospectiveChina13866 (57–78)2251 (37–62)53
Wang WRetrospectiveChina42156 (45–63)2851 (38–60)186
Wang YRetrospectiveChina22270 (65.5–80)1260.5 (48–67)96
Wang ZRetrospectiveChina6970.5 (62–77)737 (32–51)25
Wei YRetrospectiveChina27665 (60–72.8)1050 (39–57)145
Wu JRetrospectiveChina28063.04 ± 10.204537.55 ± 17.10106
Xiong FRetrospectiveChina13163.3 ± 12.41763.1 ± 13.458
Xiong SRetrospectiveChina11664 (53–76)3856 (37–64)42
Yang LRetrospectiveChina20071 ± 13.41652 ± 16.282
Zhang GRetrospectiveChina22162 (52–74)3551 (36–64.3)73
Zhang JJRetrospectiveChina14064 (25–87)3351.5 (26–78)38
Zhou JRetrospectiveChina20157 (46–66)2740 (31–53)75
Non‐Asian studies
Argenziano MGRetrospectiveUSA100062 (52–72)15864 (51–77)353
Buckner FSRetrospectiveUSA10570 (23–97)3067 (25–96)23
Cattelan AMRetrospectiveItaly30368 (56–77)5360 (47–72)129
Ferguson JRetrospectiveUSA72NANANANA
Filardo TDRetrospectiveUSA27060 (51–68)9557 (48–67)87
Garibaldi BTCohortUSA83258 (51–70)9660 (45–72)266
Giustino GRetrospectiveUSA30566 (56–74)13258 (47–70)73
Gregoriano CRetrospectiveSwitzerland9969 (57–75)2863.5 (56–76)34
Israelsen SBRetrospectiveDenmark17568 (60–72)1673 (55–83)69
Jourdes ACohortFrance26367 (56–73)3364 (53–76)122
Kaeuffer CProspectiveFrance104567.3 ± 13.430365.6 ± 17.4309
Lombardi CMRetrospectiveItaly61471.3 ± 1220164 ± 13.6234
Matangila JRRetrospectiveCongo16058 (50–70)3151 (35–61)41
Ortiz‐Brizuela EProspectiveMexico30953 (40–64)2048 (29–60.5)65
Oud LCohortUSA136,728NA79,184NA2665
Pellaud CRetrospectiveSwitzerland19665 (56–71)3074 (61–83)89
Petrilli CMCohortUSA272968 (58–78)65660 (48–71)1016
Popov GTRetrospectiveBulgaria13863 ± 12.83348.3 ± 15.754
Raad MRetrospectiveUSA102070 (51–89)22959 (39–79)280
Reilev MCohortDenmark11,12268 (58–75)22872 (55–81)984
Samuels SRetrospectiveUSA169265 ± 16.19062 ± 19.1166
Schönfeld DCohortArgentina207,07966 (54–76)349955 (37–72)22,183
Stefan GCohortRomania3767 (60–72)562 (52–67)14
Sulejmani ARetrospectiveItaly17574 (60–81)9061 (57–72)15
Suleyman GRetrospectiveUSA46363.8 ± 5.48059.8 ± 15.285
Turcotte JJRetrospectiveUSA11770.2 ± 12.12662.6 ± 16.936
Yazdanpanah YCohortFrance24668 (53–76)5160 (49–72)88

Age is presented as median (IQR) or mean ± SD.

Characteristics of the included studies Age is presented as median (IQR) or mean ± SD.

Hypertension in Asian and non‐Asian population

Fifty eight studies reported data on hypertension in severe and non‐severe COVID‐19 patients. The overall pooled incidence of hypertension was significantly higher in severe patients (50.90%) compared to non‐severe patients (30.71%). In subgroup analysis, the proportion of hypertension was significantly higher in severe group than in non‐severe group for Asian studies (OR = 2.46, 95% CI: 1.94–3.11; p < .00001) (Table 3 and Figure 2). There was high heterogeneity among the included studies (I 2 = 82%). Similarly, non‐Asian studies also showed statistically significant difference in hypertension incidence in severe and non‐severe patients (OR = 1.60, 95% CI: 1.37–1.86, I 2 = 84%; p < .00001) (Table 3 and Figure 2).
TABLE 3

Analysis of severe and non‐severe patients of COVID‐19 by using Mantel‐Haenszel test

VariableNumber of studiesOR95% CISevereNon‐severeχ2 a I 2b Z c p
Overall studies
Hypertension582.011.75–2.32674521,542354.50849.73<.00001
Diabetes621.951.71–2.2251,81612,662367.68839.95<.00001
Cancer471.631.29–2.0615,4673829258.72824.07<.0001
COPD392.041.60–2.6110092996104.82645.77<.00001
Cardiovascular disease482.472.00–3.0614772182228.96798.31<.00001
Chronic kidney disease382.231.77–2.8135,9853388273.27866.81<.00001
Asian studies
Hypertension342.461.94–3.1114252827181.46827.5<.00001
Diabetes362.702.16–3.3710111802121.19718.70<.00001
Cancer292.311.68–3.1816227539.27295.17<.00001
COPD244.043.05–5.3411613623.2219.76<.00001
Cardiovascular disease293.722.87–4.8156379073.33629.97<.00001
Chronic kidney disease203.242.01–5.2316815548.92614.81<.00001
Non‐Asian studies
Hypertension241.601.37–1.86532018,71594.99765.97<.00001
Diabetes261.441.27–1.6350,80510,86098.98755.75<.00001
Cancer181.260.96–1.6415,3053554134.88871.65.10
COPD151.321.02–1.70893286041.16662.15.03
Cardiovascular disease191.521.20–1.92914139261.64713.46.0005
Chronic kidney disease181.971.39–2.3035,8173233172.20904.52<.00001

Abbreviations: 95% CI, 95% confidence interval; COPD, Chronic obstructive pulmonary disease; OR, odds ratio.

Chi‐squared test for heterogeneity.

I 2 index to quantify the degree of heterogeneity.

Z‐statistics.

FIGURE 2

Forest plot for the ORs for comparing hypertension between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Analysis of severe and non‐severe patients of COVID‐19 by using Mantel‐Haenszel test Abbreviations: 95% CI, 95% confidence interval; COPD, Chronic obstructive pulmonary disease; OR, odds ratio. Chi‐squared test for heterogeneity. I 2 index to quantify the degree of heterogeneity. Z‐statistics. Forest plot for the ORs for comparing hypertension between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Diabetes in Asian and non‐Asian population

Data on the diabetes were reported in the 62studies. The overall pooled estimate showed significantly higher incidence of diabetes in severe patients than non‐severe patients (OR = 1.95, 95% CI: 1.71–2.22, I 2 = 83%; p < .001) (Table 3 and Figure 3). In Asian studies, the proportion of diabetes was statistically significant higher in severe patients compared with non‐severe patients (OR = 2.70, 95% CI: 2.16–3.37, I 2 = 71%; p < .00001). In non‐Asian studies, the pooled odds of diabetes was also significantly higher in patients with severe disease than in those without (OR = 1.44, 95% CI: 1.27–1.63, I 2 = 75%; p < .00001) (Table 3 and Figure 3).
FIGURE 3

Forest plots depict the comparison of diabetes between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Forest plots depict the comparison of diabetes between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Cardiovascular disease in Asian and non‐Asian population

Pooled findings of 48studies revealed significantly higher incidence of cardiovascular disease in severe patients compared to non‐severe patients (OR = 2.47, 95% CI: 2.00–3.06, I 2 = 79%; p < .00001) (Table 3 and Figure 4). The subgroup analysis of both Asian (OR = 3.72, 95% CI: 2.87–4.81, I 2 = 62%; p < .00001) and non‐Asian (OR = 1.52, 95% CI: 1.20–1.92, I 2 = 71%; p = .0005) (Table 3 and Figure 4) studies demonstrated statistically significant differences in cardiovascular disease incidence between severe and non‐severe patients.
FIGURE 4

Forest plot for the ORs for comparing cardiovascular disease between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Forest plot for the ORs for comparing cardiovascular disease between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Cancer in Asian and non‐Asian population

Data on cancer were reported by forty seven studies and pooled analysis revealed significantly higher incidence of cancer in severe patients than non‐severe patients (OR = 1.63, 95% CI: 1.29–2.06, I 2 = 82%; p < .0001) (Table 3 and Figure 5). Furthermore, Asian studies showed statistically significant difference in cancer incidence between severe and non‐severe patients (OR = 2.31, 95% CI: 1.68–3.18, I 2 = 29%; p < .00001), while no statistically significant differences in cancer incidence were noted for non‐Asian patients with COVID‐19 (OR = 1.26, 95% CI: 0.96–1.64, I 2 = 87%; p =.10) (Table 3 and Figure 5) in subgroup analysis.
FIGURE 5

Forest plots depict the ORs for comparing cancer between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Forest plots depict the ORs for comparing cancer between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Chronic obstructive pulmonary disease (COPD) in Asian and non‐Asian population

About the COPD thirty nine studies reported data in severe and non‐severe COVID‐19 patients. Pooled summary revealed significantly higher incidence of COPD in severe patients compared to non‐severe patients (OR = 2.04, 95% CI: 1.60–2.6, I 2 = 64%; p < .00001) (Table 3 and Figure 6). In subgroup analysis Asian studies showed statistically significant difference in COPD incidence between severe and non‐severe patients (OR = 4.04, 95% CI: 3.05–5.34, I 2 = 1%; p <.00001) (Table 3 and Figure 6). However, no statistically significant differences in COPD incidence were observed between severe versus non‐severe for non‐Asian patients (OR = 1.32, 95% CI: 1.02–1.70, I 2 = 66%; p = .03).
FIGURE 6

Forest plots depict the ORs for comparing COPD between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Forest plots depict the ORs for comparing COPD between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Chronic kidney disease in Asian and non‐Asian population

In terms of chronic kidney disease, thirty eight studies reported data in severe and non‐severe COVID‐19 patients. Compared with non‐severe, severe patients revealed significantly higher incidence of chronic kidney disease in pooled analysis (OR = 2.23, 95% CI: 1.77–2.8, I 2 = 86%; p < .00001) (Table 3 and Figure 7). Additionally, there was statistically significant differences in both Asian (OR = 3.24, 95% CI: 2.01–5.23, I 2 = 61%; p < .00001) and non‐Asian (OR = 1.79, 95% CI: 1.39–2.30, I 2 = 90%; p < .00001) (Table 3 and Figure 7) studies in terms of chronic kidney disease severity in subgroup analysis.
FIGURE 7

Forest plots depict the ORs for comparing chronic kidney disease between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Forest plots depict the ORs for comparing chronic kidney disease between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients

Publication bias

Funnel plots for all six comorbidities are included in supplementary information (Figures S1–S6). Nearly symmetrical graphical funnel plots were obtained from all included studies evaluating comorbidities between severe and non‐severe cases in SARS‐CoV‐2 infected Asian versus non‐Asian patients. This visual symmetry and funnel shape suggested a low risk of publication bias.

DISCUSSION

The rapid increase in the number of COVID‐19 cases and death toll is having devastating social and economic consequences around the world. Early identification and timely treatment of severe cases are vitally important in resource‐limited countries to save more lives with limited healthcare facilities. This systematic review and meta‐analysis of comparative studies suggested that the severity of patients with COVID‐19 was significantly associated with pre‐existing comorbidities. To the best of our knowledge, this study is the first meta‐analysis to compare the comorbidities between severe versus non‐severe COVID‐19 patients in Asian and non‐Asian populations. We found that the incidence of hypertension, diabetes, cardiovascular disease and chronic kidney disease was significantly higher in severe compared to non‐severe patients in both Asian and non‐Asian group in terms of subgroup analysis. Our findings are consistent with previous studies that showed a statistically significant association of pre‐existing comorbidities with severe COVID‐19 cases (Del Sole et al., 2020; Yang, Zheng, et al., 2020; Zhang, Lee, et al., 2020; Zhou, Yang, et al., 2020). Additionally, among Asian studies, there was a statistically significant difference between cancer and COPD incidence between severe and non‐severe COVID‐19 patients. However, the incidence of cancer and COPD in severe and non‐severe non‐Asian patients demonstrated no statistically significant difference. Meta‐analysis by Yin et al. (2021) assessed the role of comorbidity in COVID‐19 progression in Chinese patients and indicated that chronic kidney disease, cardiovascular disease, cancer, diabetes and hypertension were the strongest risk factor in disease exacerbation. Besides, Yang, Zheng, et al. (2020) showed that the pooled odds ratio of hypertension, respiratory system disease and cardiovascular disease were 2.36, 2.46 and 3.42, respectively, between severe and non‐severe patients. Another meta‐analysis by Giri et al. (2020) concluded that incidence of hypertension, cardiovascular disease, diabetes and cancer in the severe group was statistically significant higher than non‐severe group. However, in their meta‐analysis, all included studies were from China. Although we could not find any meta‐analysis that compared comorbidities between severe and non‐severe COVID‐19 patients for the non‐Asian studies only; however, the result of individual studies published in non‐Asian countries showed that hypertension, cardiovascular disease, diabetes and cancer incidences were higher in severe or ICU groups (Argenziano et al., 2020; Buckner et al., 2020; Cattelan et al., 2020; Ferguson et al., 2020; Filardo et al., 2020; Pellaud et al., 2020; Schönfeld et al., 2021). Our findings are in line with current knowledge that patients with comorbidities are more susceptible to severe infection. Pre‐existing cardiovascular disease and cardiovascular risk factors such as hypertension and diabetes enhance vulnerability to COVID‐19 as the SARS‐CoV‐2 enters lung cells via the ACE2 receptor (Ni et al., 2020). Furthermore, COVID‐19 may induce direct myocardial injury by upregulation of angiotensin‐converting enzyme (Zheng et al., 2020). Additionally, renin–aldosterone–angiotensin system (RAAS) plays a vital role in the pathogenesis of COVID‐19 and Tignanelli et al. (2020) revealed that hypertensive patients have hyperactive RAAS activation through angiotensin‐2, which may lead to acute lung injury during SARS‐CoV‐2 virus infection. Previous studies (Al‐Salameh et al., 2021; Zhou et al., 2021) have demonstrated that patients with diabetes were associated with significantly higher risk of suffering from severe COVID‐19 confirming that inflammation is important in the pathogenesis of severe COVID‐19. Due to weakened immune systems, people with cancer are considered as a highly vulnerable group for COVID‐19. This was further supported by study by Liang et al. (2020) as people with cancer were at increased risk of severe clinical events in a nationwide cohort study in China. A recent meta‐analysis that evaluated the effects of cancer on patients with COVID‐19 also showed that people with cancer were more susceptible to COVID‐19 especially for those who had lung cancer than those without lung cancer (Yang, Chai et al., 2021). A meta‐analysis that was performed to evaluate the association of chronic kidney disease demonstrated that COVID‐19 patients with pre‐existing chronic kidney disease had significantly increased risks of progression to a severe condition and even death (Wang, Luo et al., 2021). Another study that examined the clinical courses of critically ill COVID‐19 patients with and without pre‐existing chronic kidney disease suggested that underlying kidney disease confers higher risk for individuals with COVID‐19 with poorer COVID‐19 outcomes (Flythe et al., 2021). Therefore, clinicians should closely monitor CKD patients with suspected COVID‐19 to prevent disease progression. People with specific comorbid and underlying conditions are at high risk for COVID‐19 severity and mortality. Hence, these population groups should be prioritized for access to COVID‐19 vaccination regardless of their geographical location. There are several limitations to this systematic review and meta‐analysis. First, most of the studies included in the meta‐analysis were retrospective and conducted in different countries, settings and variation in reporting of medical conditions may be present. Second, high heterogeneity among included studies might be due to the large variation among studies in the sample size. Third, there was heterogeneity in the definition of moderate and severe cases of COVID‐19 patients, which might have contributed to the high heterogeneity of the meta‐analysis. Fourth, as our topic was related to current pandemic and we already initiated literature searches hence we failed to register in the PROSPERO. However, during systematic processes involved in our literature review we strictly followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement guidelines. Despite these limitations, to the best of our knowledge, our study is the first systematic review and meta‐analysis that examined comorbidities among severe and non‐severe COVID‐19 patients by including a large number of high‐quality studies from Asian and non‐Asian countries with large sample sizes.

CONCLUSION

In conclusion, this systemic review and meta‐analysis showed that the incidence of hypertension, diabetes, cardiovascular disease and chronic kidney disease was significantly higher in severe compared to non‐severe patients in both Asian and non‐Asian population. Despite the continuous efforts to prevent and reduce severity of the disease the COVID‐19 pandemic is exacting enormous medical and economic tolls on human life. Timely identification of comorbidities predictive for severe disease and ICU admission, can help frontline health workers such as doctors and nurses to effectively prioritize individual at risk in countries with limited resources. Patients with comorbidities have a tendency to develop severe or critical disease and have a poor disease outcome. More attention should be given to the care of patients with pre‐existing comorbidities. More well designed and high‐quality randomized‐control studies that use standardized patient selection are needed to confirm our findings.

CONFLICTS OF INTEREST

The authors have no conflicts of interest to declare.

AUTHOR CONTRIBUTIONS

AP, LH, MG and QHZ: conceptualization. AP, LH, MG and CFW: methodology. AP, LH, MG: statistical analysis. AP, LH, MG: data extraction and management. AP, LH, MG and QHZ: writing—original draft preparation. AP, LH, MG, QHZ and CFW: writing—review and editing. QHZ: supervision. All authors contributed to the article and approved the submitted version.

ETHICAL APPROVAL

Ethical review and approval is not required as this is systemic review and meta‐analysis. Supplementary Material Click here for additional data file.
  85 in total

1.  Characteristics of patients with COVID-19 pneumonia at Hvidovre Hospital, March-April 2020.

Authors:  Simone Bastrup Israelsen; Klaus Tjelle Kristiansen; Bettina Hindsberger; Charlotte Suppli Ulrik; Ove Andersen; Magnus Jensen; Sten Andersen; Christian Rasmussen; Henrik L Jørgensen; Christian Østergaard; Bjarne Ørskov Lindhardt; Gitte Kronborg; Thomas Benfield
Journal:  Dan Med J       Date:  2020-05-15       Impact factor: 1.240

2.  Clinical Characteristics of and Medical Interventions for COVID-19 in Hemodialysis Patients in Wuhan, China.

Authors:  Fei Xiong; Hui Tang; Li Liu; Can Tu; Jian-Bo Tian; Chun-Tao Lei; Jing Liu; Jun-Wu Dong; Wen-Li Chen; Xiao-Hui Wang; Dan Luo; Ming Shi; Xiao-Ping Miao; Chun Zhang
Journal:  J Am Soc Nephrol       Date:  2020-05-08       Impact factor: 10.121

3.  Epidemiology and outcomes of invasive mechanical ventilation without ICU admission in acute care hospitals in Texas: A population-based cohort study.

Authors:  Lavi Oud; John Garza
Journal:  J Crit Care       Date:  2020-10-24       Impact factor: 3.425

4.  CLINICAL AND EPIDEMIOLOGICAL CHARACTERISTICS OF PATIENTS DIAGNOSED WITH COVID-19 IN A TERTIARY CARE CENTER IN MEXICO CITY: A PROSPECTIVE COHORT STUDY.

Authors:  Edgar Ortiz-Brizuela; Marco Villanueva-Reza; María F González-Lara; Karla M Tamez-Torres; Carla M Román-Montes; Bruno A Díaz-Mejía; Esteban Pérez-García; Antonio Olivas-Martínez; Sandra Rajme-López; Bernardo A Martinez-Guerra; Nereyda A de-León-Cividanes; Oscar A Fernández-García; Lorena Guerrero-Torres; Lorena Torres-González; Fabián A Carrera-Patiño; Ever A Corral-Herrera; Aldo N Hernández-Alemón; Maria de Los Á Tovar-Vargas; Yamile G Serrano-Pinto; Cristian E Espejo-Ortiz; María de la L Morales-Ortega; Óscar A Lozano-Cruz; José L Cárdenas-Fragoso; José de J Vidal-Mayo; Thierry Hernández-Gilsoul; Eduardo Rivero-Sigarroa; Guillermo Domínguez-Cherit; Luz E Cervantes-Villar; Maria Del P Ramos-Cervantes; Violeta Ibarra-González; Juan J Calva-Mercado; Juan G Sierra-Madero; Álvaro López-Íñiguez; Eric Ochoa-Hein; Brenda E Crabtree-Ramírez; Arturo Galindo-Fraga; María de L Guerrero-Almeida; Guillermo M Ruiz-Palacios; Alfonso Gulías-Herrero; José Sifuentes-Osornio; David Kershenobich-Stalnikowitz; Alfredo Ponce-de-León
Journal:  Rev Invest Clin       Date:  2020       Impact factor: 1.451

5.  Clinical Characteristics and Outcomes of 421 Patients With Coronavirus Disease 2019 Treated in a Mobile Cabin Hospital.

Authors:  Wei Wang; Can Xin; Zhongwei Xiong; Xixi Yan; Yuankun Cai; Keyao Zhou; Chuanshun Xie; Tingbao Zhang; Xiaohui Wu; Kui Liu; Zhiqiang Li; Jincao Chen
Journal:  Chest       Date:  2020-05-08       Impact factor: 9.410

6.  Clinical characteristics and co-infections of 354 hospitalized patients with COVID-19 in Wuhan, China: a retrospective cohort study.

Authors:  Zhihua Lv; Shaohua Cheng; Juan Le; Jingtao Huang; Lina Feng; Binghong Zhang; Yan Li
Journal:  Microbes Infect       Date:  2020-05-18       Impact factor: 2.700

7.  Characteristics and Outcomes of Coronavirus Disease Patients under Nonsurge Conditions, Northern California, USA, March-April 2020.

Authors:  Jessica Ferguson; Joelle I Rosser; Orlando Quintero; Jake Scott; Aruna Subramanian; Mohammad Gumma; Angela Rogers; Shanthi Kappagoda
Journal:  Emerg Infect Dis       Date:  2020-05-14       Impact factor: 6.883

8.  Clinical presentation and outcomes of the first patients with COVID-19 in Argentina: Results of 207079 cases from a national database.

Authors:  Daniel Schönfeld; Sergio Arias; Juan Carlos Bossio; Hugo Fernández; David Gozal; Daniel Pérez-Chada
Journal:  PLoS One       Date:  2021-02-11       Impact factor: 3.240

9.  Clinical characteristics of COVID-19 patients hospitalized at Clinique Ngaliema, a public hospital in Kinshasa, in the Democratic Republic of Congo: A retrospective cohort study.

Authors:  Junior Rika Matangila; Ritha Kibambe Nyembu; Gloire Mosala Telo; Christian Djuba Ngoy; Taty Msueun Sakobo; Joel Mbembo Massolo; Blandine Mbo Muyembe; Richard Kapela Mvwala; Cedric Kabemba Ilunga; Emmanuel Bakilo Limbole; Jeff Mukengeshay Ntalaja; Roger Minga Kongo
Journal:  PLoS One       Date:  2020-12-18       Impact factor: 3.240

10.  Risk factors for severe illness in hospitalized Covid-19 patients at a regional hospital.

Authors:  Justin J Turcotte; Barry R Meisenberg; James H MacDonald; Nandakumar Menon; Marcia B Fowler; Michaline West; Jane Rhule; Sadaf S Qureshi; Eileen B MacDonald
Journal:  PLoS One       Date:  2020-08-12       Impact factor: 3.240
View more
  3 in total

1.  Cancers and COVID-19 Risk: A Mendelian Randomization Study.

Authors:  Zengbin Li; Yudong Wei; Guixian Zhu; Mengjie Wang; Lei Zhang
Journal:  Cancers (Basel)       Date:  2022-04-22       Impact factor: 6.575

2.  Comparison of comorbidities among severe and non-severe COVID-19 patients in Asian versus non-Asian populations: A systematic review and meta-analysis.

Authors:  Anju Puri; Lin He; Mohan Giri; Chengfei Wu; Qinghua Zhao
Journal:  Nurs Open       Date:  2021-11-10

3.  Diabetes Does Not Increase the Risk of Hospitalization Due to COVID-19 in Patients Aged 50 Years or Older in Primary Care-APHOSDIAB-COVID-19 Multicenter Study.

Authors:  Domingo Orozco-Beltrán; Juan Francisco Merino-Torres; Antonio Pérez; Ana M Cebrián-Cuenca; Ignacio Párraga-Martínez; Luis Ávila-Lachica; Gemma Rojo-Martínez; Francisco J Pomares-Gómez; Fernando Álvarez-Guisasola; Manuel Sánchez-Molla; Felix Gutiérrez; Francisco J Ortega; Manel Mata-Cases; Enrique Carretero-Anibarro; Josep Maria Vilaseca; Jose A Quesada
Journal:  J Clin Med       Date:  2022-04-08       Impact factor: 4.964
  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.