Risk factors for bloodstream infection in COVID-19 patients in intensive care units: A systematic review and meta-analysis

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The purpose of this systematic review was to study the risk factors for BSI in patients admitted to ICUs for COVID-19. Methods: A systematic search was performed on PubMed, EMBASE, Cochrane Library, and Web of Science up to July 2024. Data were reported as combined odds ratio (OR) for categorical variables and weighted mean difference (WMD) for continuous variables. Results: 6914 studies were retrieved, of which 55 were included in the meta-analysis. Men (OR=1.28, 95% CI: 1.10-1.50, P=0.006), high SAPS II score (WMD=6.43, 95% CI: 0.23-12.63, P=0.042), diabetes (OR=1.34, 95% CI: 1.04-1.73, P=0.022), tracheal intubation (OR=8.68, 95% CI: 4.68-16.08, P<0.001), mechanical ventilation (OR=22.00, 95% CI: 3.77-128.328, P<0.001), ECMO (OR=2.70, 95% CI: 1.17-6.26, P=0.020), central venous cannulation (OR=9.33, 95% CI: 3.06-28.43, P<0.001), prolonged ICU stay (WMD=10.37, 95% CI: 9.29-11.44, P<0.001), methylprednisolone use (OR=2.24, 95% CI: 1.24-4.04, P=0.008), and the combination of methylprednisolone and Tocilizumab (OR=4.54, 95% CI: 1.09-18.88, P=0.037) were risk factors for ICU-BSI in COVID-19 patients. Conclusion: We identified 10 risk factors for ICU-BSI in COVID-19 patients. In future studies, these factors can be combined to establish a more comprehensive and accurate prediction model for ICU-BSI in COVID-19 patients. Targeted measures can be taken earlier to control BSI. Bloodstream infection COVID-19 ICU Meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background COVID-19 has had a severe impact on global public health systems and remains one of the most severe epidemics now over the world. As of January 2023, WHO reported about 750 million confirmed cases of COVID-19 globally, covering about 6.8 million deaths [ 1 ]. And the virus is constantly mutating, and the number of infected people continues to grow rapidly. According to statistics from outpatient and emergency clinics in various hospitals, about 40–50% of the patients are prone to serious illnesses and thus require hospital admission. Of these, 10–20% of patients are prone to critical illnesses and need to be admitted to intensive care units (ICUs) for supportive treatment. Consequently, the number of COVID-19 patients admitted to ICUs has markedly increased, and in some places, temporary ICUs have even been built in large numbers. Previous studies have shown that bloodstream infection (BSI) may occur in approximately 7% of COVID-19 inpatients [ 2 ]. The incidence of BSI in COVID-19 patients in ICUs is 10–50%, which is a significant increase and a new record and is four times higher than that in non-COVID-19 patients [ 3 ]. BSI can lead to bacterial or viral infections in various organs. In poorly treated or severe cases, it may lead to sepsis, resulting in fever and generalized pain, which gravely impacts the health. In severe cases, it can lead to the risk of death. ICU-BSI increases the risk of 30-day mortality by 40% [ 4 ]. In addition, resuscitation therapy such as mechanical ventilation and endotracheal intubation in ICU increases the risk of BSIand poses a great challenge to anti-infective treatment [ 5 – 7 ]. COVID-19 infection may become a small-scale recurrent epidemic pattern in the future. Thereby, it is necessary to stratify patients at risk of BSI and take timely measures to reduce the occurrence of BSI. Recent studies found that gender, SAPS II score, underlying medical complications [e.g., diabetes mellitus (DM), hypertension], treatment-related factors (e.g., mechanical ventilation, intubation, ECMO), and drug-related factors (e.g., Tocilizumab, Methylprednisolone) might be associated with an increased risk of BSI in critically ill patients with COVID-19 in ICUs. However, the conclusions of various studies about the risk factors for BSI are inconsistent. Regarding gender, most studies found that men had a higher risk of developing BSI, which reached 60%-70%, and men accounted for most COVID-19 admissions to ICUs [ 8 – 13 ]. Some studies concluded that gender was not associated with the risk of infection [ 14 – 18 ]. Interestingly, 2 studies found the risk of BSI was equal for males and females [ 19 , 20 ]. DM, as the most common underlying disease in human beings, is statistically associated with a higher risk of BSI in most studies [ 8 , 10 , 13 , 15 – 18 , 20 , 21 ]. However, other studies concluded that DM did not lead to a higher risk of BSI [ 9 , 11 , 12 ]. Additionally, the conclusions regarding hypertension were not clear and consistent. In most studies, statistical analyses suggested that hypertension caused a higher risk of BSI [ 10 – 13 , 16 , 18 , 19 ]; whereas some studies indicated no direct correlation between hypertension and the risk of BSI [ 8 , 17 , 20 ]. Therefore, in this study, we aimed to determine the risk factors for BSI in COVID-19 patients in ICUs through a systematic review and meta-analysis. 2. Methods This paper was designed and revised following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and was registered in the PROSPERO (CRD42023416813). 2.1 Search strategy PubMed, Embase, Web of Science, and Cochrane Library were comprehensively searched for English papers up to July 2024, while references in related literature were manually checked. The following keywords were utilized to screen published clinical information on risk factors for BSI in COVID-19 patients in ICUs: "intensive care unit", "covid-19", "sepsis", and "bloodstream infection". All literature retrieved was imported into EndnoteX9 for paper screening. The search process was undertaken by two reviewers and any disagreements were addressed through discussion. (Table S1 ) 2.2 Eligibility criteria Inclusion criteria covered: (1) study type: observational study; (2) patients: with COVID-19 in ICUs; (3) study content: risk factors for BSI in COVID-19 patients in ICUs. Exclusion criteria encompassed: (1) letters, reviews, conference proceedings, commentaries, and papers with unavailable full text and unsuitable types of publication; (2) with no available primary data; (3) published not in English. 2.3 Data extraction Based on the eligibility criteria, articles imported into EndnoteX9 were initially screened by reviewing the titles and abstracts. Ineligible articles were excluded, and the remaining articles were read through the full text to screen the eligible ones for meta-analysis. Relevant research data were extracted. All procedures were performed by two researchers (Ting Jiang and Jun Wang). The following data were extracted: (1) general information: authors, publication date, study area, study design, and period; (2) study characteristics: sample size, mean age, and gender distribution; (3) risk factors: SAPS II, hypertension, DM, chronic pulmonary disease, liver disease, immunosuppression, chronic kidney disease, heart disease, and tumors; and (4) treatment records: medication administration, treatment modalities, and duration of treatment. In studies where some information was lacking, we attempted to contact the authors by phone or email. In case of disagreement in literature screening and data extraction, a third researcher (Wei Wang) was consulted. 2.4 Quality assessment Two researchers independently assessed the study quality based on the Newcastle-Ottawa Scale (NOS) for cohort studies and case-control studies. The NOS scale covered three dimensions and eight items, with a maximum score of 9 points. A score < 4 was defined as low quality, 4–6 as moderate quality, and ≥ 7 as high quality. The higher score implied the lower risk of bias. The quality of cross-sectional studies was evaluated using a scale recommended by the Agency for Healthcare Research and Quality. The scale consisted of 11 items. Answers included yes, no, and unclear. For the answer of "yes", the item was assigned a score of 1. The higher the score, the higher the quality: low quality = 0–3; medium quality = 4–7; high quality = 8–11. If two researchers disagreed with quality assessment, a third researcher arbitrated. 2.5 Statistical analyses Statistical analyses were implemented using Stata 15.0 software. Categorical variables were analyzed using odds ratio (OR) while continuous variables using weighted mean difference (WMD). Data from the original studies were transformed before meta-analysis if they were not described as mean and standard deviation. All effect sizes were represented as 95% confidence intervals (CI). Heterogeneity was analyzed using I 2 . If there was no significant statistical heterogeneity between outcomes (P ≥ 0.1, I 2 ≤ 50%), a meta-analysis was performed using a fixed-effects model, otherwise, using a random-effects model. For highly heterogeneous results, sensitivity analyses were performed on the results to validate the stability and reliability of results. For risk factors that included ≥ 10 articles, the Egger test was adopted to determine whether there was publication bias. 3. Results 3.1 Screening results The database searches retrieved 9099 relevant English articles. 6914 articles were obtained after duplicates were removed. Subsequently, the titles and abstracts were read to exclude the ineligible studies, leaving 301 studies. Finally, after reading the full text, 55 studies were enrolled in the meta-analysis [ 3 , 5 – 58 ]. The screening process is represented in a PRISMA flowchart. (Fig. 1 ). 3.2 Characteristics The 55 included studies comprised 48 cross-sectional studies [ 5 – 9 , 11 , 12 , 14 , 15 , 17 , 18 , 20 – 23 , 26 – 58 ] (Table 1 ), 6 cohort studies [ 3 , 10 , 13 , 16 , 24 , 25 ], and 1 case-control study [ 19 ] (Table 2 ), including 25,939 patients ranging in age from 18 to 94 years across Italy, CHN, UK, US, Spain, France, Greece, Austria, Singapore, India, Germany, Turkey, Switzerland, Sweden, and Portugal. The meta-analysis results of the research indicators are shown in Table 3 . Among them, 48 cross-sectional studies had average AHRQ scale scores > 7, and 6 cohort studies and 1 case-control study had average NOS scores > 7, implying that these articles were all of high quality. Table 1 Studies characteristics and quality. (Cross-sectional study). Study Published time Country Study design Patients Age Gender(male) AHRQ grade Amit, M. et al. 2020 Israel Cross-sectional study 156 72(60–82) 108/48 9 Bonazzetti, C et al. 2020 Italy Cross-sectional study 89 61.5(53.1–68.7) 69/20 8 Fu, Guoping et al. 2020 CHN Cross-sectional study 51 60.94 ± 14.87(25–87) 27/14 8 Giacobbe, D. R et al. 2020 Italy Cross-sectional study 78 66 IQR 57–70 60/18 9 Anwar, Asad et al. 2021 UK Cross-sectional study 44 17–77 M59.5(IQR 50.5–64.5)/21–80 M59(IQR 49-67.5) 34/10 8 Bardi, Tommaso et al. 2021 Spain Cross-sectional study 140 61(57–67) 108/32 8 d’Humières, C et al. 2021 FRANCE Cross-sectional study 197 59(50–68) 148/49 8 Dupuis, Claire et al. 2021 FRANCE Cross-sectional study 303 61(53–70) 239/64 9 Grasselli, G. et al. 2021 Italy Cross-sectional study 774 62 (54–68) 597/177 9 Karruli, A. et al. 2021 Italy Cross-sectional study 32 68 [55.25–75] 23/9 9 Kokkoris, S. et al. 2021 Greece Cross-sectional study 50 Median age 64 36/14 8 Llitjos, Jean-Francois et al. 2021 FRANCE Cross-sectional study 176 63 (55–73) 134/42 8 Ong, C. C. H. et al. 2021 Singapore Cross-sectional study 71 52(39–66) 59/12 9 Ramos, Rafael et al. 2021 Spain Cross-sectional study 213 61(52–71) 110/103 8 Roedl, Kevin et al. 2021 Germany Cross-sectional study 223 69 (58-77.5) 163/60 8 Rollas, Kazim et al. 2021 Turkey Cross-sectional study 38 NR NR 9 Søgaard, K. K et al. 2021 Switzerland Cross-sectional study 41 64.8(54.7–72.1) 31/10 9 Suarez-de-la-Rica, A. et al. 2021 Spain Cross-sectional study 107 62.2 ± 10.6 76/31 8 Yakar, Mehmet Nuri et al. 2021 Turkey Cross-sectional study 249 71(61–80) 172/77 9 Yao, Ren-qi et al. 2021 CHN Cross-sectional study 35 64(59–67) 25/10 8 Zamora-Cintas, M. I. et al. 2021 Spain Cross-sectional study 54 NR NR 8 Zhang, J. et al. 2021 CHN Cross-sectional study 32 63.34 ± 12.48 20/12 9 Ahlstrom, Bjorn et al. 2022 Swedish Cross-sectional study 7382 63 (53–72) 5191/2191 9 Brücker, W. et al. 2022 Germany Cross-sectional study 61 66.4 ± 13.3 34/27 9 Caiazzo, L.et al. 2022 Italy Cross-sectional study 89 68.1 ± 9.3 66/23 8 Cidade, Jose Pedro et al. 2022 Portugal Cross-sectional study 118 63.3 ± 13.1 87/31 8 Ćurčić, M. et al. 2022 Croatia Cross-sectional study 692 NR NR 8 da Costa, R. L. et al. 2022 Brazil Cross-sectional study 191 69.66 ± 16.13 116/75 8 De Bruyn, A. et al. 2022 Belgium. Cross-sectional study 94 69.65 ± 11.29 55/39 9 DeVoe, C. et al. 2022 US Cross-sectional study 126 58.1 ± 17.9 85/41 8 Erbay, Kubra et al. 2022 Turkey Cross-sectional study 85 67.23 ± 13.05 54/31 8 Kozlowski, Bartosz et al. 2022 Poland Cross-sectional study 172 67.76 ± 11.16 112/60 8 Kurt, Ahmet Furkan et al. 2022 Turkey Cross-sectional study 470 66 ± 14.87 301/169 9 Lepape, Alain et al. 2022 FRANCE Cross-sectional study 4465 63.30 ± 11.68 3132/1333 8 Mantzarlis, K. et al. 2022 Greece Cross-sectional study 84 68.85 ± 12.17 56/28 9 Mustafa, Z. U. et al. 2022 Pakistan Cross-sectional study 636 NR 398/238 7 Pandey, M. et al. 2022 UK Cross-sectional study 299 NR 101/198 9 Roda, Silvia et al. 2022 Italy Cross-sectional study 22 61.36 ± 10.30 20/2 8 Routsi, C. et al. 2022 Greece Cross-sectional study 600 NR NR 8 Russo, A. et al. 2022 Italy Cross-sectional study 32 62.50 ± 10.99 21/11 8 Seitz, T. et al. 2022 Austria Cross-sectional study 117 57.2 ± 11.9 72/45 8 Torrecillas, Miriam et al. 2022 Spain Cross-sectional study 220 63.65 ± 12.69 169/51 8 Alenazi, T. A. et al. 2023 Saudi Arabia Cross-sectional study 118 60.97 ± 16.32 74/43 8 Alessandri, F. et al. 2023 Italy Cross-sectional study 138 62.20 ± 15.36 97/41 9 Bedenić, B. et al. 2023 Croatia Cross-sectional study 118 71 years (range 25–94) 78/40 8 Bonazzetti, C. t al. 2023 Italy Cross-sectional study 537 64.65 ± 11.15 402/135 9 Guanche Garcell, H. et al. 2023 Cuban Cross-sectional study 130 NR NR 8 Taysi, M. R. et al. 2023 Turkey Cross-sectional study 205 68.4 ± 13.1 119/86 8 AHRQ: Agency for Healthcare Research and Quality; NR: not reported. Table 2 Studies characteristics and quality. (Cohort study and Case-control study) Study Published time Country Study design Patients Age Gender (male) NOS grade Cataldo, M. A et al. 2020 Italy Cohort study 57 62 ± 13 41/16 7 Garcia, Pedro David Wendel et al 2020 European Cohort study 639 63 (53–71) 480/159 8 Zhang, H. et al. 2020 CHN Cohort study 38 64.76 ± 13.76 32/6 7 Massart, N. et al. 2021 France,Switzerland,Belgium Cohort study 4010 NR NR 8 Palanisamy, N. et al. 2021 India Cohort study 750 60 ± 17.71 562/188 8 Bartoszewicz, M. et al. 2023 Poland Cohort study 201 66.1 ± 12.1 114/87 8 Dupper, A. C. et al. 2022 US Case-control study 96 64.91 ± 9.51 57/39 8 NOS: Newcastle-Ottawa Scale; NR: not reported. Table 3 Outcomes of meta-analysis. Risk factors No. of studies Heterogeneity Analysis Statistical model statistical method Effect estimate P I² P (95%CI) Hypertension 10 70.4% 0.000 Random-effects OR 1.30(0.92,1.83) 0.131 Chronic pulmonary disease 11 23.4% 0.221 Fixed-effects OR 1.07(0.90,1.29) 0.443 Diabetes 12 50.2% 0.024 Random-effects OR 1.34(1.04,1.73) 0.022* Gender 14 0.0% 0.059 Fixed-effects OR 1.28(1.10,1.50) 0.006* Liver disease 6 2.3% 0.402 Fixed-effects OR 0.86(0.47,1.58) 0.635 Immunosuppressive diseases 5 29.9% 0.222 Fixed-effects OR 1.11(0.88,1.40) 0.375 Chronic kidney disease 6 0.0% 0.751 Fixed-effects OR 1.20(0.78,1.84) 0.411 Heart disease 10 0.0% 0.550 Fixed-effects OR 1.00(0.85,1.17) 0.957 Tocilizumab 9 34.3% 0.144 Fixed-effects OR 1.04(0.74,1.46) 0.815 Tumors 9 10.2% 0.350 Fixed-effects OR 1.04(0.78,1.37) 0.807 ECMO 4 74.1% 0.009 Random-effects OR 2.70(1.17,6.26) 0.020* Tracheal intubation 4 67.8% 0.025 Random-effects OR 8.68(4.68,16.08) < 0.001* Mechanical ventilation 2 0.0% 0.385 Fixed-effects OR 22.00(3.77,128.328) 0.001* Methylprednisolone 2 13.5% 0.282 Fixed-effects OR 2.24(1.24,4.04) 0.008* Methylprednisolone + Tocilizumab 2 71.0% 0.063 Random-effects OR 4.54(1.09,18.88) 0.037* Steroids 3 87.6% 0.000 Random-effects OR 1.17(0.15,9.23) 0.882 Remdesivir 2 54.4% 0.139 Random-effects OR 0.80(0.14,4.41) 0.794 Renal replacement therapy 2 97.9% 0.000 Random-effects OR 0.86(0.11,6.57) 0.882 Central venous catheterization 2 0.0% 0.559 Fixed-effects OR 9.33(3.06,28.43) < 0.001* Length of stay in ICUs 8 0.0% 0.712 Fixed-effects WMD 10.37(9.29,11.44) < 0.001* SAPS II score 2 58.3% 0.122 Random-effects WMD 6.43(0.23,12.63) 0.042* WMD: weight mean difference; OR: odds ratio; CI: confidence interval; *:P < 0.05. 3.3 Result synthesis 3.3.1 Patient-related factors Gender Fourteen studies [ 8 – 21 ] explored the correlation between gender and BSI in COVID-19 patients in ICUs. A pooled analysis showed that male COVID-19 patients in ICUs were 28% more likely to develop BSI (OR = 1.28, 95% CI: 1.10–1.50, P = 0.006, I 2 = 0.0%). (Fig. 2 ) SAPS II score Two studies [ 10 , 15 ] analyzed the correlation between SAPS II scores and ICU-BSI in COVID-19 patients. Meta-analysis showed that higher SAPS II scores were positively correlated with an increased incidence of BSI in COVID-19 patients in ICUs (WMD = 6.43, 95% CI: 0.23–12.63, P = 0.042, I 2 = 58.3%). (Fig. 2 ) DM Twelve studies [ 8 – 13 , 15 – 18 , 20 , 21 ] investigated the correlation between DM and BSI in COVID-19 patients in ICUs. Most of these articles did not indicate whether DM was a risk factor for BSI. Our pooled analysis unraveled that DM increased the occurrence of BSI in COVID-19 patients in ICUs by 34% (OR = 1.34, 95% CI: 1.04–1.73, P = 0.022, I 2 = 50.2%). (Fig. 2 ) Hypertension There were conflicting results about the association between hypertension and BSI in COVID-19 patients in ICUs. Ten studies [ 8 , 10 – 13 , 16 – 20 ] were involved with mixed results. Meta-analysis demonstrated no correlation between hypertension and BSI in COVID-19 patients in ICUs (OR = 1.30, 95% CI:0.92–1.83, P = 0.131, I 2 = 70.4%). (Fig. 2 ) Chronic pulmonary disease Because COVID-19 mainly attacked the respiratory system, we extensively investigated the correlation between chronic pulmonary disease and BSI in COVID-19 patients in ICUs through 11 studies [ 8 – 12 , 15 – 17 , 19 – 21 ]. Meta-analysis showed no correlation between chronic pulmonary disease and BSI in COVID-19 patients in ICUs (OR = 1.07, 95% CI: 0.90–1.29, P = 0.443, I 2 = 23.4%). (Fig. 2 ) Liver disease Six studies [ 8 , 9 , 12 , 15 , 16 , 21 ] investigated the correlation between liver disease and ICU-BSI in COVID-19 patients. Meta-analysis showed no correlation between liver disease and BSI in COVID-19 patients in ICUs (OR = 0.86, 95% CI: 0.47–1.58, P = 0.635, I 2 = 2.25%). (Fig. 3 ) Chronic kidney disease Seven studies [ 9 – 11 , 15 , 19 – 21 ] investigating the association between chronic kidney disease and BSI in COVID-19 patients in ICUs were included. One article was excluded by sensitivity analysis [ 10 ] and therefore six articles were included in the meta-analysis. It showed no correlation between chronic kidney disease and BSI in COVID-19 patients in ICUs (OR = 1.20, 95% CI: 0.78–1.84, P = 0.411, I 2 = 0.0%). (Fig. 3 ) Heart disease Ten studies [ 9 – 11 , 13 , 15 , 16 , 19 – 21 ] investigating the correlation between heart disease and BSI among COVID-19 patients in ICUs were included. Meta-analysis showed no correlation between heart disease and BSI in COVID-19 patients in ICUs (OR = 1.00, 95% CI: 0.85-1.17P = 0.957, I 2 = 0.0%). (Fig. 3 ) Immunosuppressive diseases All five studies [ 9 , 15 , 16 , 19 , 21 ] showed no correlation between immunosuppression and ICU-BSI in COVID-19 patients. Meta-analysis also showed no correlation between immunosuppression and BSI in COVID-19 patients in ICUs (OR = 1.11, 95% CI: 0.88–1.40, P = 0.375, I 2 = 29.9%). (Fig. 3 ) Tumors Nine studies [ 8 – 11 , 17 , 19 – 21 ] investigating the correlation between tumors and BSI in COVID-19 patients in ICUs were included. Meta-analysis showed no correlation between tumors and BSI in COVID-19 patients in ICUs (OR = 1.04, 95% CI: 0.78–1.37, P = 0.807, I 2 = 10.2%). (Fig. 3 ) 3.3.2 Treatment-related factors Tracheal intubation Four studies [ 10 , 15 , 16 , 21 ] were included to investigate the association between tracheal intubation and ICU-BSI in COVID-19 patients. Meta-analysis revealed that tracheal intubation increased the risk of BSI in COVID-19 patients in ICUs by nearly 9-fold (OR = 8.68, 95% CI: 4.68–16.08, P < 0.001, I 2 = 67.8%). (Fig. 4 ) Mechanical ventilation The correlation between mechanical ventilation and BSI in COVID-19 patients in ICUs was investigated by three studies [ 14 , 16 , 17 ]. Since no heterogeneity was found (P = 0.147, I 2 = 47.9%), a fixed-effects model was adopted and unraveled marked differences (OR = 4.98, 95% CI: 2.73–9.08, P < 0.001). After sensitivity analysis, the heterogeneity was greatly reduced (P = 0.385, I 2 = 0.0%) when the study of Palanisamy,N et al. [ 16 ] was excluded. The main source of heterogeneity might be the large sample size of their study, which tended to lead to unstable results compared to other studies with small sample sizes. Thus, this study was excluded because it led to a significant bias. The pooled analysis after exclusion using a fixed-effect model (OR = 22.00, 95% CI: 3.77-128.328, p < 0.001) showed statistically significant differences. The meta-analysis showcased that mechanical ventilation increased the risk of BSI by 22 times in COVID-19 patients in ICUs. The excluded study by Palanisamy,N et al. also showed that mechanical ventilation could increase the risk of BSI by 4-fold, in agreement with our results.(Fig. 4 ) ECMO Many critically ill patients have used ECMO for supportive care. Including four studies [ 10 , 12 , 18 , 55 ], we explored the correlation between ECMO and BSI among COVID-19 patients in ICUs. Meta-analysis manifested that ECMO increased the risk of BSI in COVID-19 patients in ICUs by nearly three times (OR = 2.70, 95% CI: 1.17–6.26, P = 0.020, I 2 = 74.1%). (Fig. 4 ) Central venous catheterization (CVC) Two studies[ 11 , 18 ] investigated the correlation between CVC and ICU-BSI in COVID-19 patients. Meta-analysis showed that CVC increase the Catheter-related BSI (OR = 9.33, 95% CI: 3.06–28.43, P < 0.001, I 2 = 0.0%). (Fig. 4 ) Renal replacement therapy (RRT) Two studies [ 10 , 12 ] investigating the correlation between RRT and BSI in COVID-19 patients in ICU were included. Meta-analysis showed no correlation between BSI and RRT in COVID-19 patients in ICUs (OR = 0.86, 95% CI: 0.11–6.57, P = 0.882, I 2 = 97.9%). (Fig. 4 ) Length of stay in ICUs Eightstudies were included [ 12 – 14 , 17 , 18 , 20 , 21 , 53 ], all of which showed a strong correlation between the length of stay in ICUs and the occurrence of BSI in COVID-19 patients in ICUs. A meta-analysis showed that the longer the ICU stay, the higher the risk of BSI in COVID-19 patients in ICU (WMD = 10.37, 95% CI:9.29–11.44, P < 0.001, I 2 = 0.0%). (Fig. 4 ) 3.3.3 Medication-related factors Tocilizumab The correlation between Tocilizumab and BSI in COVID-19 patients was investigated in 10 studies [ 8 – 12 , 16 , 17 , 20 , 21 , 55 ]. One article was excluded by sensitivity analysis [ 21 ] and therefore nine articles were enrolled in the meta-analysis. There was no correlation between Tocilizumab and BSI in COVID-19 patients in ICUs (OR = 1.04, 95% CI: 0.74–1.46, P = 0.815, I 2 = 34.3%). However, this may explain why Tocilizumab is widely used for severe and critically ill COVID-19 patients in ICUs under the guidance of guidelines. (Fig. 5 ) Methylprednisolone Two studies [ 8 , 12 ] investigated the association between Methylprednisolone and ICU-BSI in COVID-19 patients. Meta-analysis signified that Methylprednisolone was linked with BSI in COVID-19 patients in ICUs (OR = 2.24, 95% CI: 1.24–4.04, P = 0.008, I 2 = 13.5%). Meanwhile, we found that the combination of Methylprednisolone and Tocilizumab was associated with the occurrence of BSI in COVID-19 patients in ICUs (OR = 4.54, 95% CI: 1.09–18.88, P = 0.037, I 2 = 71%). This may imply that Methylprednisolone is widely used for treatment and that the combination of Methylprednisolone and Tocilizumab may be used for COVID-19 patients in ICUs with more critical conditions. (Fig. 5 ) Steroids The correlation between steroid use and ICU-BSI in COVID-19 patients was investigated in 3 studies [ 9 , 17 , 21 ]. Meta-analysis showed no correlation between steroid use and BSI in COVID-19 patients in ICUs (OR = 1.17, 95% CI: 0.15–9.23, P = 0.882, I 2 = 87.6%). This may be related to the fact that steroids are widely used as they are believed to improve the recovery of patients. (Fig. 5 ) Remdesivir The correlation between Remdesivir and ICU-BSI in COVID-19 patients was investigated in 2 studies [ 9 , 21 ]. Meta-analysis showed no correlation between Remdesivir and BSI in COVID-19 patients in ICUs (OR = 0.80, 95% CI: 0.14–4.41, P = 0.794, I2 = 54.4%). This may be related to the fact that Remdesivir is considered a potent drug for the treatment of COVID-19, with significant efficacy, and therefore is more widely used for severe and critically ill patients in ICUs. (Fig. 5 ) 3.4 Sensitivity analysis The stability of the results of the remaining articles was estimated by excluding each article in turn. Sensitivity analyses for gender, SAPS II score, DM, hypertension, chronic pulmonary disease, liver disease, heart disease, immunosuppressive disease, tumor, tracheal intubation, ECMO, CVC, RRT, length of stay in ICUs, and the use of Methylprednisolone, Steroids, and Remdesivir revealed that the results were relatively stable. In the sensitivity analysis of mechanical ventilation, the study by Palanisamy,N et al. [ 16 ] greatly impacted the results, so the results were pooled after the exclusion of that article, and the results were more stable. Similarly, in the sensitivity study of chronic kidney disease, it was found that Massart,N et al. [ 10 ] greatly influenced the results. After excluding the article and re-combining the results, the results were more stable. In the sensitivity study on the use of Tocilizumab, the study by Bonazzetti,C et al. [ 21 ] greatly influenced the results. The results were more stable when the article was excluded, and the results were re-combined. The sensitivity analyses of other factors implied stable and insignificant changes, so these studies were retained. (Figure S1 .S2.S3) 3.5 Publication bias Publication bias was examined using the Egger test for the risk factor containing ≥ 10 articles. There was no publication bias for Men (P = 0.187), DM (P = 0.142), Hypertension(P = 0.396), Heart disease (P = 0.592) and chronic pulmonary disease (P = 0.671). (Fig. 6 ) 4. Discussion In this meta-analysis, we aimed to identify risk factors for BSI in COVID-19 patients in ICUs. Among the studies with available data, 55 published English studies [ 3 , 5 – 58 ] investigating risk factors associated with BSI in COVID-19 patients in ICUs were included. Our findings showed that male, DM, tracheal intubation, mechanical ventilation, CVC, ECMO, Methylprednisolone use, higher SAPS II score, and longer ICU stay were risk factors for ICU-BSI in COVID-19 patients. In addition, hypertension, chronic pulmonary disease, liver disease, chronic kidney disease, heart disease, immunosuppression, tumor, RRT, and the use of Tocilizumab, Steroids, and Remdesivir neither increased nor decreased the risk of ICU-BSI in COVID-19 patients. 4.1 Patient-related factors Our analyses showed that male COVID-19 patients were at a higher risk of BSI in ICUs and that males also made up most of the ICU admission population. A previous study by Zamora-Cintas,M. et al. [ 37 ] highlighted that male patients had a higher risk of BSI, in line with our results. COVID-19 virus infection mainly affects pulmonary function, resulting in more patients with pulmonary dysfunction being admitted to the hospital, and more severe patients need to be admitted to ICUs, which makes them more susceptible to BSI. However, we did not study whether smoking was a risk factor for BSI in COVID-19 patients in ICUs and the proportion of men who smoke., which might require more data support. Our findings also showed that the SAPS II score directly reflected the risk of BSI in COVID-19 patients in ICUs, in agreement with the findings of Massart, N et al. [ 10 ]. SAPS II score, as an important evaluation component in ICUs, to some extent, also reflects infection indicators, which is directly related to our study. In short, a higher SAPS II score indicates a more severe condition and a worse prognosis [ 59 ]. DM is a common chronic underlying disease in clinical practice. Our results showed that DM was also an associated risk factor for ICU-BSI in COVID-19 patients. This may be related to diverse complications associated with DM and the poor resistance of diabetic patients, which makes them susceptible to a variety of related infections [ 60 ] and directly increases the risk of BSI[ 61 ]. 4.2 Treatment-related factors Our findings suggested that tracheal intubation substantially increased the risk of ICU-BSI in COVID-19 patients, in support with the findings of Bonazzetti,C et al. [ 14 ] and Rollas,Kazim et al. [ 17 ]. Tracheal intubation is a common resuscitation technique in ICUs and is essential to save patients in respiratory distress. It ensures that the patient receives an adequate supply of oxygen and provides mechanical ventilation support to maintain normal respiratory function [ 62 ]. This makes mechanical ventilation support also a possible risk factor for BSI in COVID-19 patients in ICUs. Invasive treatment is highly likely to cause airway damage, and tracheal intubation may introduce bacteria or other pathogens, increasing the risk of infection in patients. It is also easy for mis-aspiration to occur after tracheal intubation, leading to lung infections [ 63 ]. All these directly increase the risk of BSI. COVID-19 has become a specific infection that involves the pathophysiology of the lungs, including endothelial and epithelial changes, pulmonary embolism, and microvascular thrombosis. In addition, secondary infectious injury can cause acute lung injury and prolong mechanical ventilation [ 5 ]. Zhang,J et al. showed that multiple invasive treatments were important risk factors for BSI. Early extubation and regular assessment of infection should be done, therefore early anti-infective therapy is important [ 18 ]. CVC is widely used in the resuscitation of severe and critically ill patients, which is conducive to the measurement of central venous pressure, long-term medication, and large and rapid rehydration, thus preventing venous damage and repeated puncture. However, a common complication of CVC is deep vein thrombosis, which also leads to the invasion of external bacteria and infection. Patients present with persistent low-grade fever, and the simultaneous presence of bacteria and thrombus can exacerbate the infection [ 64 ]. Our study revealed that prolonged CVC substantially increased the risk of ICU-BSI in COVID-19 patients. Therefore, there is a need for timely monitoring of the situation and increased measures for infection control and nursing care for CVC. ECMO serves as an important therapeutic tool to provide continuous extracorporeal respiratory and circulatory function for critically ill patients presenting with cardiopulmonary failure. Some studies showed that infections were highly susceptible to occurring after the use of ECMO, which was related to the fact that patients with low immunity were susceptible to systemic hematogenous infections, thus dramatically increasing the risk of fungal infections [ 65 , 66 ]. This was in general agreement with the results of our study. Meanwhile, ECMO may result in renal failure in about 50% of patients, which may require RRT [ 67 ]. However, our study found that RRT was not a risk factor for ICU-BSI in COVID-19 patients, which may need to be supported by more data. In particular, intuitive data in our study pointed out that the longer the treatment duration in ICUs, the higher the risk of BSI in COVID-19 patients. The possible reasons are as follows: first, the treatment time reflects the severity of the patient's condition, and a longer ICU stay may mean that the patient's condition is more critical; second, the longer the treatment time, the higher the chance of nosocomial infections [ 11 ], which is closely related to the prolonged use of antibiotics and ward management; third, severe and critically ill patients in ICUs have low resistance and need to be left with various passages during treatment, and most COVID-19 patients have coughing symptoms, which is prone to aerosol dissemination and transmission of infectious disease between the patients, thus greatly increasing the risk of BSI [ 68 ]. 4.3 Medication-related factors In our study, the use of Tocilizumab, Remdesivir, and Steroids did not correlate with the risk of BSI in COVID-19 patients in ICUs, but the use of Methylprednisolone and the combination of Methylprednisolone and Tocilizumab directly increased the risk of ICU-BSI in COVID-19 patients. This is associated with the fact that patients receiving glucocorticoid therapy are more likely to require ventilatory support, vasopressors, and RRT [ 28 , 52 ]. Glucocorticoids are widely used and effective drugs during hospitalization, especially in ICU, due to their anti-inflammatory and immunosuppressive effects. Glucocorticoids inhibit the inflammatory chemotaxis of cells and the rate of phagocytosis to reach inflammation sites. In addition, glucocorticoids increase the stability of cells so that cell membranes are less likely to rupture, and cells are less likely to release lysosomal enzymes to phagocytose bacteria to destroy inflammatory foci, thus decreasing the body's immunity and making it susceptible to viral or bacterial infections [ 69 , 70 ]. Glucocorticoids will also directly inhibit the body's immune function, thus inhibiting the body's fever, so that the fever symptoms are not obvious, which in turn masks the severity of the disease and delays the diagnosis and treatment, leading to further deterioration of the condition [ 28 ]. Moreover, glucocorticoids also inhibit mucosal exudation and inflammatory exudation. For patients with respiratory tract infections, glucocorticoids inhibit the exudation of inflammatory secretions, so that patients reduce coughing, which is not conducive to discharging bacterial sputum out of the body through coughing. Additionally, it delays the detection and treatment, thus aggravating the infection and increasing the risk of BSI greatly [ 19 ]. Therefore, it is crucial to monitor the use of glucocorticoids rationally according to the condition [ 52 ]. 4.4 Incidence of ICU-BSI in COVID-19 patients Meta-analysis unraveled that the incidence of BSI in COVID-19 patients in ICUs was 19.9%, similar to the currently reported 10–50% incidence rate. Compared with previous studies, in which 7% of COVID-19 hospitalized patients may experience BSI [ 2 ], the incidence of BSI in COVID-19 patients in ICUs increased nearly threefold. The incidence of ICU-BSI in COVID-19 patients varied in different studies, mainly because the occurrence of BSI lies in the detection of blood cultures. Also, it is somewhat difficult to exclude sampling contamination and detection contamination, and most patients in ICUs receive various types of medications, which may affect the detection of BSI [ 15 , 71 ]. 4.5 Strengths and limitations This is the first systematic review analyzing risk factors for ICU-BSI in COVID-19 patients, and the data were reviewed by two investigators to ensure the accuracy. By incorporating an extensive array of paper with high quality, our findings provide an accurate and reliable framework for promptly identifying the risk of BSI occurrence in COVID-19 patients in ICUs. In addition, our findings provide more comprehensive references of risk factors for ICU-BSI in COVID-19 patients for clinical treatment, which is a guide for early prevention of BSI. However, some limitations need discussion. First, the studies covered diverse ethnicities, populations, methods, and periods of investigation, which is reflected in heterogeneity. However, this may be due to differences in study design rather than actual differences in outcome measures. Therefore, we used sensitivity analyses and random-effects models to verify the result stability in the presence of high heterogeneity. Second, the diagnosis of BSI in COVID-19 patients treated with Tocilizumab may be difficult because patients often do not have fever and have low serum levels of typical inflammatory markers, requiring further study. Third, Data on diseases such as diabetes and oncology did not have specific types of data, so specific rich data are needed to study their association with BSI. Fourth, few of the included studies analyzed the impact of post-invasive treatment care measures on the occurrence of BSI in COVID-19 patients in ICUs. Therefore, in the future, more assessments of the impact of treatment details on ICU-BSI in COVID-19 patients and randomized controlled trials are needed to enhance the reliability. 5. Conclusion Our findings showed that males, higher SAPS II scores, DM, tracheal intubation, mechanical ventilation, ECMO, CVC, longer ICU stays, and Methylprednisolone use increased the risk of ICU-BSI in COVID-19 patients. It is imperative for future research to integrate these factors into a comprehensive predictive assessment framework to identify and intervene promptly in COVID-19 patients at high risk for ICU-BSI to improve treatment outcomes and promote patient health recovery. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The datasets used during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors declare that they did not receive any funding from any source. Author Contributions All authors contributed to the study conception and design. Writing - original draft preparation: [Jun Wang ,Ting Jiang]; Writing - review and editing: [Jun Wang ,Ting Jiang]; Conceptualization: [Jun Wang ,Ting Jiang]; Methodology: [Jun Wang ,Ting Jiang]; Formal analysis and investigation: [Jun Wang ,Ting Jiang]; Resources: [Jun Wang ,Ting Jiang]; Supervision: [Jun Wang ,Ting Jiang],and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable Clinical Trial Number Not applicable References Pontali E, Filauro F: Repurposing an 'Old' Drug for the Treatment of COVID-19-Related Cytokine Storm . Journal of clinical medicine 2023, 12 (10). Ippolito M, Simone B, Filisina C, Catalanotto FR, Catalisano G, Marino C, Misseri G, Giarratano A, Cortegiani A: Bloodstream Infections in Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis . Microorganisms 2021, 9 (10). 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Selçuk Ü N, Sargın M, Baştopçu M, Mete EMT, Erdoğan SB, Öcalmaz Ş, Orhan G, Aka SA: Microbiological Spectrum of Nosocomial ECMO Infections in a Tertiary Care Center . Braz J Cardiovasc Surg 2021, 36 (3):338-345. Carelli S, Dell'Anna AM, Montini L, Bernardi G, Gozza M, Cutuli SL, Natalini D, Bongiovanni F, Tanzarella ES, Pintaudi G et al : Bloodstream infections in COVID-19 patients undergoing extracorporeal membrane oxygenation in ICU: An observational cohort study . Heart & lung : the journal of critical care 2023, 62 :193-199. Selewski DT, Wille KM: Continuous renal replacement therapy in patients treated with extracorporeal membrane oxygenation . Semin Dial 2021, 34 (6):537-549. Crawford C, Vanoli E, Decorde B, Lancelot M, Duprat C, Josserand C, Jilesen J, Bouadma L, Timsit JF: Modeling of aerosol transmission of airborne pathogens in ICU rooms of COVID-19 patients with acute respiratory failure . Sci Rep 2021, 11 (1):11778. Bruscoli S, Puzzovio PG, Zaimi M, Tiligada K, Levi-Schaffer F, Riccardi C: Glucocorticoids and COVID-19 . Pharmacol Res 2022, 185 :106511. Papic I, Bistrovic P, Cikara T, Busic N, Keres T, Ortner Hadziabdic M, Lucijanic M: Corticosteroid Dosing Level, Incidence and Profile of Bacterial Blood Stream Infections in Hospitalized COVID-19 Patients . Viruses 2024, 16 (1). Munro C, Zilberberg MD, Shorr AF: Bloodstream Infection in the Intensive Care Unit: Evolving Epidemiology and Microbiology . Antibiotics (Basel, Switzerland) 2024, 13 (2). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Supplementary materials Table S1: Search strategy. Figure S1: Sensitivity analysis of univariate data associating BSI risk with (A) gender; (B) diabetes; (C) hypertension and (D) chronic pulmonary disease for patients with COVID-19 in ICU. Figure S2: Sensitivity analysis of univariate data associating BSI risk with (A) liver disease; (B) chronic kidney disease; (C) heart disease; (D) immunosuppressive disease and (E) tumors for patients with COVID-19 in ICU. Figure S3: Sensitivity analysis of univariate data aassociating BSI risk with (A) tracheal intubation; (B) ECMO; (C) Length of stay in ICU (D) Tocilizumab and (E) Steroids for patients with COVID-19 in ICU. Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 25 Oct, 2024 Reviews received at journal 25 Oct, 2024 Reviews received at journal 07 Oct, 2024 Reviews received at journal 30 Sep, 2024 Reviewers agreed at journal 30 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviewers agreed at journal 23 Sep, 2024 Reviewers invited by journal 23 Sep, 2024 Editor invited by journal 27 Aug, 2024 Editor assigned by journal 27 Aug, 2024 Submission checks completed at journal 27 Aug, 2024 First submitted to journal 21 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4954620","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":358018352,"identity":"7262cc3f-c9f0-4afc-9031-1f9fe74e43a6","order_by":0,"name":"Jun Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYJCCDwwMbHJs7O0HiNbBOAOoxZiP50wCSVoYEudJOBgQp15+Ro5hM28bX3qbBEMCw4+KbcRYAdbCltsm3XiAsefMbcJamCVyzB/zbgNqkTmQwMzYRoQWNgmQLdvY0tkkEgyI08ID1ZJAvBYJnmeFjXP/sRm2AQP5IFF+kW9P3tjw5swxefn29oMPflQQoYVBIMOAiYfhGJh9gAj1QMB//AHjD4Ya4hSPglEwCkbByAQAyHo6T4PyLXAAAAAASUVORK5CYII=","orcid":"","institution":"Laboratory Department, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wang","suffix":""},{"id":358018353,"identity":"0550a118-cc64-4687-8d17-9cd297dd1049","order_by":1,"name":"Ting Jiang","email":"","orcid":"","institution":"Intensive Care Unit, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine.","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-08-22 02:49:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4954620/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4954620/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-024-10420-1","type":"published","date":"2025-01-03T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66793490,"identity":"3805de20-e9e0-4f00-a8f3-933beee4464e","added_by":"auto","created_at":"2024-10-16 13:51:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":129443,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the search strategy.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4954620/v1/c783b620a26501153a808105.png"},{"id":66793496,"identity":"88700ff7-9015-441e-a0eb-ecfa8892137e","added_by":"auto","created_at":"2024-10-16 13:51:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3718784,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of univariate data associating BSI risk with (A) gender; (B) SAPS II score; (C) diabetes; (D) hypertension and (E) chronic pulmonary disease for patients with COVID-19 in ICU.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4954620/v1/bb6364d2ef03009222899efa.png"},{"id":66793491,"identity":"14d92b16-467a-480f-8e4d-3db53fdc7016","added_by":"auto","created_at":"2024-10-16 13:51:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2766632,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of univariate data associating BSI risk with (A) liver disease; (B) chronic kidney disease; (C) heart disease; (D) immunosuppressive disease and (E) tumors for patients with COVID-19 in ICU.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4954620/v1/c245c711c702f2dadb603530.png"},{"id":66795696,"identity":"02675e90-e0a5-4864-8753-7f94fba1e09b","added_by":"auto","created_at":"2024-10-16 14:07:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1650873,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of univariate data associating BSI risk with (A) tracheal intubation; (B) mechanical ventilation; (C) ECMO; (D) CVC; (E) RRT and (F) Length of stay in ICU for patients with COVID-19 in ICU.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4954620/v1/4a50fcfd0cbe182d6ca9fa4d.png"},{"id":66795697,"identity":"54cbafdf-1c48-4e84-a504-6de960de2c57","added_by":"auto","created_at":"2024-10-16 14:07:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1875752,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of univariate data associating BSI risk with (A) Tocilizumab; (B) Methylprednisolone; (C) Methylprednisolone and Tocilizumab combination; (D) Steroids and (E) Remdesivir for patients with COVID-19 in ICU.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4954620/v1/2dc001a3188a107fb08be18a.png"},{"id":66793495,"identity":"5e2234f5-407b-47cc-b0ad-496a274370bf","added_by":"auto","created_at":"2024-10-16 13:51:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":676668,"visible":true,"origin":"","legend":"\u003cp\u003ePublication bias of univariate data associating BSI risk with (A) gender; (B) diabetes; (C) chronic pulmonary disease; (D) hypertension and (E) heart disease for patients with COVID-19 in ICU.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4954620/v1/ed307c1a67218e936803e0b5.png"},{"id":73093365,"identity":"28fee26a-f9b3-466c-9934-901194ffaaff","added_by":"auto","created_at":"2025-01-06 16:15:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15499864,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4954620/v1/7185cf42-a2b2-4d97-abe7-ffd7683982a8.pdf"},{"id":66795037,"identity":"16d4614f-b8ce-4093-8734-c4bd95940d3b","added_by":"auto","created_at":"2024-10-16 13:59:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":858169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1: \u003c/strong\u003eSearch strategy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1:\u003c/strong\u003e Sensitivity analysis of univariate data associating BSI risk with (A) gender; (B) diabetes; (C) hypertension and (D) chronic pulmonary disease for patients with COVID-19 in ICU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2:\u003c/strong\u003e Sensitivity analysis of univariate data associating BSI risk with (A) liver disease; (B) chronic kidney disease; (C) heart disease; (D) immunosuppressive disease and (E) tumors for patients with COVID-19 in ICU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3:\u003c/strong\u003e Sensitivity analysis of univariate data aassociating BSI risk with (A) tracheal intubation; (B) ECMO; (C) Length of stay in ICU (D) Tocilizumab and (E) Steroids for patients with COVID-19 in ICU.\u003c/p\u003e","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4954620/v1/f7268c736c092af9e8b917ca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk factors for bloodstream infection in COVID-19 patients in intensive care units: A systematic review and meta-analysis","fulltext":[{"header":"1. Background","content":"\u003cp\u003eCOVID-19 has had a severe impact on global public health systems and remains one of the most severe epidemics now over the world. As of January 2023, WHO reported about 750\u0026nbsp;million confirmed cases of COVID-19 globally, covering about 6.8\u0026nbsp;million deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. And the virus is constantly mutating, and the number of infected people continues to grow rapidly. According to statistics from outpatient and emergency clinics in various hospitals, about 40\u0026ndash;50% of the patients are prone to serious illnesses and thus require hospital admission. Of these, 10\u0026ndash;20% of patients are prone to critical illnesses and need to be admitted to intensive care units (ICUs) for supportive treatment. Consequently, the number of COVID-19 patients admitted to ICUs has markedly increased, and in some places, temporary ICUs have even been built in large numbers. Previous studies have shown that bloodstream infection (BSI) may occur in approximately 7% of COVID-19 inpatients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The incidence of BSI in COVID-19 patients in ICUs is 10\u0026ndash;50%, which is a significant increase and a new record and is four times higher than that in non-COVID-19 patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. BSI can lead to bacterial or viral infections in various organs. In poorly treated or severe cases, it may lead to sepsis, resulting in fever and generalized pain, which gravely impacts the health. In severe cases, it can lead to the risk of death. ICU-BSI increases the risk of 30-day mortality by 40% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, resuscitation therapy such as mechanical ventilation and endotracheal intubation in ICU increases the risk of BSIand poses a great challenge to anti-infective treatment [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. COVID-19 infection may become a small-scale recurrent epidemic pattern in the future. Thereby, it is necessary to stratify patients at risk of BSI and take timely measures to reduce the occurrence of BSI.\u003c/p\u003e \u003cp\u003eRecent studies found that gender, SAPS II score, underlying medical complications [e.g., diabetes mellitus (DM), hypertension], treatment-related factors (e.g., mechanical ventilation, intubation, ECMO), and drug-related factors (e.g., Tocilizumab, Methylprednisolone) might be associated with an increased risk of BSI in critically ill patients with COVID-19 in ICUs. However, the conclusions of various studies about the risk factors for BSI are inconsistent. Regarding gender, most studies found that men had a higher risk of developing BSI, which reached 60%-70%, and men accounted for most COVID-19 admissions to ICUs [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Some studies concluded that gender was not associated with the risk of infection [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Interestingly, 2 studies found the risk of BSI was equal for males and females [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. DM, as the most common underlying disease in human beings, is statistically associated with a higher risk of BSI in most studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, other studies concluded that DM did not lead to a higher risk of BSI [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the conclusions regarding hypertension were not clear and consistent. In most studies, statistical analyses suggested that hypertension caused a higher risk of BSI [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; whereas some studies indicated no direct correlation between hypertension and the risk of BSI [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, in this study, we aimed to determine the risk factors for BSI in COVID-19 patients in ICUs through a systematic review and meta-analysis.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis paper was designed and revised following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and was registered in the PROSPERO (CRD42023416813).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Search strategy\u003c/h2\u003e \u003cp\u003ePubMed, Embase, Web of Science, and Cochrane Library were comprehensively searched for English papers up to July 2024, while references in related literature were manually checked. The following keywords were utilized to screen published clinical information on risk factors for BSI in COVID-19 patients in ICUs: \"intensive care unit\", \"covid-19\", \"sepsis\", and \"bloodstream infection\". All literature retrieved was imported into EndnoteX9 for paper screening. The search process was undertaken by two reviewers and any disagreements were addressed through discussion. (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Eligibility criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria covered: (1) study type: observational study; (2) patients: with COVID-19 in ICUs; (3) study content: risk factors for BSI in COVID-19 patients in ICUs. Exclusion criteria encompassed: (1) letters, reviews, conference proceedings, commentaries, and papers with unavailable full text and unsuitable types of publication; (2) with no available primary data; (3) published not in English.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data extraction\u003c/h2\u003e \u003cp\u003eBased on the eligibility criteria, articles imported into EndnoteX9 were initially screened by reviewing the titles and abstracts. Ineligible articles were excluded, and the remaining articles were read through the full text to screen the eligible ones for meta-analysis. Relevant research data were extracted. All procedures were performed by two researchers (Ting Jiang and Jun Wang). The following data were extracted: (1) general information: authors, publication date, study area, study design, and period; (2) study characteristics: sample size, mean age, and gender distribution; (3) risk factors: SAPS II, hypertension, DM, chronic pulmonary disease, liver disease, immunosuppression, chronic kidney disease, heart disease, and tumors; and (4) treatment records: medication administration, treatment modalities, and duration of treatment. In studies where some information was lacking, we attempted to contact the authors by phone or email. In case of disagreement in literature screening and data extraction, a third researcher (Wei Wang) was consulted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Quality assessment\u003c/h2\u003e \u003cp\u003eTwo researchers independently assessed the study quality based on the Newcastle-Ottawa Scale (NOS) for cohort studies and case-control studies. The NOS scale covered three dimensions and eight items, with a maximum score of 9 points. A score\u0026thinsp;\u0026lt;\u0026thinsp;4 was defined as low quality, 4\u0026ndash;6 as moderate quality, and \u0026ge;\u0026thinsp;7 as high quality. The higher score implied the lower risk of bias. The quality of cross-sectional studies was evaluated using a scale recommended by the Agency for Healthcare Research and Quality. The scale consisted of 11 items. Answers included yes, no, and unclear. For the answer of \"yes\", the item was assigned a score of 1. The higher the score, the higher the quality: low quality\u0026thinsp;=\u0026thinsp;0\u0026ndash;3; medium quality\u0026thinsp;=\u0026thinsp;4\u0026ndash;7; high quality\u0026thinsp;=\u0026thinsp;8\u0026ndash;11. If two researchers disagreed with quality assessment, a third researcher arbitrated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were implemented using Stata 15.0 software. Categorical variables were analyzed using odds ratio (OR) while continuous variables using weighted mean difference (WMD). Data from the original studies were transformed before meta-analysis if they were not described as mean and standard deviation. All effect sizes were represented as 95% confidence intervals (CI). Heterogeneity was analyzed using I\u003csup\u003e2\u003c/sup\u003e. If there was no significant statistical heterogeneity between outcomes (P\u0026thinsp;\u0026ge;\u0026thinsp;0.1, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;50%), a meta-analysis was performed using a fixed-effects model, otherwise, using a random-effects model. For highly heterogeneous results, sensitivity analyses were performed on the results to validate the stability and reliability of results. For risk factors that included\u0026thinsp;\u0026ge;\u0026thinsp;10 articles, the Egger test was adopted to determine whether there was publication bias.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Screening results\u003c/h2\u003e \u003cp\u003eThe database searches retrieved 9099 relevant English articles. 6914 articles were obtained after duplicates were removed. Subsequently, the titles and abstracts were read to exclude the ineligible studies, leaving 301 studies. Finally, after reading the full text, 55 studies were enrolled in the meta-analysis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The screening process is represented in a PRISMA flowchart. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Characteristics\u003c/h2\u003e \u003cp\u003eThe 55 included studies comprised 48 cross-sectional studies [\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 6 cohort studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and 1 case-control study [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), including 25,939 patients ranging in age from 18 to 94 years across Italy, CHN, UK, US, Spain, France, Greece, Austria, Singapore, India, Germany, Turkey, Switzerland, Sweden, and Portugal. The meta-analysis results of the research indicators are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among them, 48 cross-sectional studies had average AHRQ scale scores\u0026thinsp;\u0026gt;\u0026thinsp;7, and 6 cohort studies and 1 case-control study had average NOS scores\u0026thinsp;\u0026gt;\u0026thinsp;7, implying that these articles were all of high quality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudies characteristics and quality. (Cross-sectional study).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublished time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGender(male)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAHRQ grade\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmit, M. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsrael\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72(60\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e108/48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBonazzetti, C et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.5(53.1\u0026ndash;68.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69/20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFu, Guoping et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.94\u0026thinsp;\u0026plusmn;\u0026thinsp;14.87(25\u0026ndash;87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiacobbe, D. R et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66 IQR 57\u0026ndash;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnwar, Asad et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17\u0026ndash;77 M59.5(IQR 50.5\u0026ndash;64.5)/21\u0026ndash;80 M59(IQR 49-67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBardi, Tommaso et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61(57\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e108/32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ed\u0026rsquo;Humi\u0026egrave;res, C et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59(50\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e148/49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDupuis, Claire et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61(53\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e239/64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrasselli, G. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62 (54\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e597/177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKarruli, A. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68 [55.25\u0026ndash;75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKokkoris, S. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian age 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLlitjos, Jean-Francois et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (55\u0026ndash;73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e134/42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOng, C. C. H. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingapore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52(39\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e59/12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRamos, Rafael et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61(52\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e110/103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoedl, Kevin et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69 (58-77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e163/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRollas, Kazim et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS\u0026oslash;gaard, K. K et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSwitzerland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.8(54.7\u0026ndash;72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuarez-de-la-Rica, A. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76/31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYakar, Mehmet Nuri et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71(61\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e172/77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYao, Ren-qi et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64(59\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZamora-Cintas, M. I. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang, J. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.34\u0026thinsp;\u0026plusmn;\u0026thinsp;12.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20/12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAhlstrom, Bjorn et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSwedish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (53\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5191/2191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBr\u0026uuml;cker, W. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34/27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaiazzo, L.et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66/23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCidade, Jose Pedro et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e87/31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eĆurčić, M. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCroatia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eda Costa, R. L. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.66\u0026thinsp;\u0026plusmn;\u0026thinsp;16.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e116/75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDe Bruyn, A. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBelgium.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55/39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeVoe, C. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85/41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErbay, Kubra et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.23\u0026thinsp;\u0026plusmn;\u0026thinsp;13.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54/31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKozlowski, Bartosz et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.76\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e112/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKurt, Ahmet Furkan et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66\u0026thinsp;\u0026plusmn;\u0026thinsp;14.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e301/169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLepape, Alain et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFRANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.30\u0026thinsp;\u0026plusmn;\u0026thinsp;11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3132/1333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMantzarlis, K. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.85\u0026thinsp;\u0026plusmn;\u0026thinsp;12.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56/28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMustafa, Z. U. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e398/238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePandey, M. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e101/198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoda, Silvia et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.36\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoutsi, C. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreece\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRusso, A. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.50\u0026thinsp;\u0026plusmn;\u0026thinsp;10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeitz, T. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAustria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e72/45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTorrecillas, Miriam et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.65\u0026thinsp;\u0026plusmn;\u0026thinsp;12.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e169/51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlenazi, T. A. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.97\u0026thinsp;\u0026plusmn;\u0026thinsp;16.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74/43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlessandri, F. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.20\u0026thinsp;\u0026plusmn;\u0026thinsp;15.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e97/41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBedenić, B. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCroatia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71 years (range 25\u0026ndash;94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78/40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBonazzetti, C. t al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e402/135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuanche Garcell, H. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCuban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaysi, M. R. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCross-sectional study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e119/86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAHRQ: Agency for Healthcare Research and Quality; NR: not reported.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudies characteristics and quality. (Cohort study and Case-control study)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublished time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStudy design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGender (male)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNOS grade\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCataldo, M. A et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62 \u0026plusmn; 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGarcia, Pedro David Wendel et al\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (53\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e480/159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang, H. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.76\u0026thinsp;\u0026plusmn;\u0026thinsp;13.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMassart, N. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrance,Switzerland,Belgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalanisamy, N. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u0026thinsp;\u0026plusmn;\u0026thinsp;17.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e562/188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBartoszewicz, M. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e114/87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDupper, A. C. et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase-control study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e64.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57/39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNOS: Newcastle-Ottawa Scale; NR: not reported.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcomes of meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. of studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHeterogeneity Analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatistical model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003estatistical method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEffect estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.30(0.92,1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.07(0.90,1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.34(1.04,1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.022*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.28(1.10,1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86(0.47,1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunosuppressive diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.11(0.88,1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.20(0.78,1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00(0.85,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTocilizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.04(0.74,1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.04(0.78,1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.70(1.17,6.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTracheal intubation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.68(4.68,16.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.00(3.77,128.328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylprednisolone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.24(1.24,4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethylprednisolone\u0026thinsp;+\u0026thinsp;Tocilizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.54(1.09,18.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteroids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.17(0.15,9.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemdesivir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.80(0.14,4.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal replacement therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86(0.11,6.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral venous catheterization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.33(3.06,28.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay in ICUs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFixed-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.37(9.29,11.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPS II score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom-effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.43(0.23,12.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eWMD: weight mean difference; OR: odds ratio; CI: confidence interval; *:P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Result synthesis\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Patient-related factors\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eGender\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eFourteen studies [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] explored the correlation between gender and BSI in COVID-19 patients in ICUs. A pooled analysis showed that male COVID-19 patients in ICUs were 28% more likely to develop BSI (OR\u0026thinsp;=\u0026thinsp;1.28, 95% CI: 1.10\u0026ndash;1.50, P\u0026thinsp;=\u0026thinsp;0.006, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eSAPS II score\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] analyzed the correlation between SAPS II scores and ICU-BSI in COVID-19 patients. Meta-analysis showed that higher SAPS II scores were positively correlated with an increased incidence of BSI in COVID-19 patients in ICUs (WMD\u0026thinsp;=\u0026thinsp;6.43, 95% CI: 0.23\u0026ndash;12.63, P\u0026thinsp;=\u0026thinsp;0.042, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;58.3%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eDM\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTwelve studies [\u003cspan additionalcitationids=\"CR9 CR10 CR11 CR12\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] investigated the correlation between DM and BSI in COVID-19 patients in ICUs. Most of these articles did not indicate whether DM was a risk factor for BSI. Our pooled analysis unraveled that DM increased the occurrence of BSI in COVID-19 patients in ICUs by 34% (OR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.04\u0026ndash;1.73, P\u0026thinsp;=\u0026thinsp;0.022, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;50.2%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eHypertension\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThere were conflicting results about the association between hypertension and BSI in COVID-19 patients in ICUs. Ten studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] were involved with mixed results. Meta-analysis demonstrated no correlation between hypertension and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;1.30, 95% CI:0.92\u0026ndash;1.83, P\u0026thinsp;=\u0026thinsp;0.131, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;70.4%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eChronic pulmonary disease\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBecause COVID-19 mainly attacked the respiratory system, we extensively investigated the correlation between chronic pulmonary disease and BSI in COVID-19 patients in ICUs through 11 studies [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Meta-analysis showed no correlation between chronic pulmonary disease and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 0.90\u0026ndash;1.29, P\u0026thinsp;=\u0026thinsp;0.443, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;23.4%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eLiver disease\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSix studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] investigated the correlation between liver disease and ICU-BSI in COVID-19 patients. Meta-analysis showed no correlation between liver disease and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;0.86, 95% CI: 0.47\u0026ndash;1.58, P\u0026thinsp;=\u0026thinsp;0.635, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.25%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eChronic kidney disease\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeven studies [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] investigating the association between chronic kidney disease and BSI in COVID-19 patients in ICUs were included. One article was excluded by sensitivity analysis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and therefore six articles were included in the meta-analysis. It showed no correlation between chronic kidney disease and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;1.20, 95% CI: 0.78\u0026ndash;1.84, P\u0026thinsp;=\u0026thinsp;0.411, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eHeart disease\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTen studies [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] investigating the correlation between heart disease and BSI among COVID-19 patients in ICUs were included. Meta-analysis showed no correlation between heart disease and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;1.00, 95% CI: 0.85-1.17P\u0026thinsp;=\u0026thinsp;0.957, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eImmunosuppressive diseases\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAll five studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] showed no correlation between immunosuppression and ICU-BSI in COVID-19 patients. Meta-analysis also showed no correlation between immunosuppression and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 0.88\u0026ndash;1.40, P\u0026thinsp;=\u0026thinsp;0.375, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;29.9%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eTumors\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNine studies [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] investigating the correlation between tumors and BSI in COVID-19 patients in ICUs were included. Meta-analysis showed no correlation between tumors and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 0.78\u0026ndash;1.37, P\u0026thinsp;=\u0026thinsp;0.807, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10.2%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Treatment-related factors\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eTracheal intubation\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eFour studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] were included to investigate the association between tracheal intubation and ICU-BSI in COVID-19 patients. Meta-analysis revealed that tracheal intubation increased the risk of BSI in COVID-19 patients in ICUs by nearly 9-fold (OR\u0026thinsp;=\u0026thinsp;8.68, 95% CI: 4.68\u0026ndash;16.08, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;67.8%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eMechanical ventilation\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe correlation between mechanical ventilation and BSI in COVID-19 patients in ICUs was investigated by three studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Since no heterogeneity was found (P\u0026thinsp;=\u0026thinsp;0.147, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;47.9%), a fixed-effects model was adopted and unraveled marked differences (OR\u0026thinsp;=\u0026thinsp;4.98, 95% CI: 2.73\u0026ndash;9.08, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After sensitivity analysis, the heterogeneity was greatly reduced (P\u0026thinsp;=\u0026thinsp;0.385, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0%) when the study of Palanisamy,N et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] was excluded. The main source of heterogeneity might be the large sample size of their study, which tended to lead to unstable results compared to other studies with small sample sizes. Thus, this study was excluded because it led to a significant bias. The pooled analysis after exclusion using a fixed-effect model (OR\u0026thinsp;=\u0026thinsp;22.00, 95% CI: 3.77-128.328, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed statistically significant differences. The meta-analysis showcased that mechanical ventilation increased the risk of BSI by 22 times in COVID-19 patients in ICUs. The excluded study by Palanisamy,N et al. also showed that mechanical ventilation could increase the risk of BSI by 4-fold, in agreement with our results.(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eECMO\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eMany critically ill patients have used ECMO for supportive care. Including four studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], we explored the correlation between ECMO and BSI among COVID-19 patients in ICUs. Meta-analysis manifested that ECMO increased the risk of BSI in COVID-19 patients in ICUs by nearly three times (OR\u0026thinsp;=\u0026thinsp;2.70, 95% CI: 1.17\u0026ndash;6.26, P\u0026thinsp;=\u0026thinsp;0.020, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;74.1%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eCentral venous catheterization (CVC)\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTwo studies[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] investigated the correlation between CVC and ICU-BSI in COVID-19 patients. Meta-analysis showed that CVC increase the Catheter-related BSI (OR\u0026thinsp;=\u0026thinsp;9.33, 95% CI: 3.06\u0026ndash;28.43, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eRenal replacement therapy (RRT)\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTwo studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] investigating the correlation between RRT and BSI in COVID-19 patients in ICU were included. Meta-analysis showed no correlation between BSI and RRT in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;0.86, 95% CI: 0.11\u0026ndash;6.57, P\u0026thinsp;=\u0026thinsp;0.882, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;97.9%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eLength of stay in ICUs\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEightstudies were included [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], all of which showed a strong correlation between the length of stay in ICUs and the occurrence of BSI in COVID-19 patients in ICUs. A meta-analysis showed that the longer the ICU stay, the higher the risk of BSI in COVID-19 patients in ICU (WMD\u0026thinsp;=\u0026thinsp;10.37, 95% CI:9.29\u0026ndash;11.44, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Medication-related factors\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eTocilizumab\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe correlation between Tocilizumab and BSI in COVID-19 patients was investigated in 10 studies [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. One article was excluded by sensitivity analysis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and therefore nine articles were enrolled in the meta-analysis. There was no correlation between Tocilizumab and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI: 0.74\u0026ndash;1.46, P\u0026thinsp;=\u0026thinsp;0.815, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;34.3%). However, this may explain why Tocilizumab is widely used for severe and critically ill COVID-19 patients in ICUs under the guidance of guidelines. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eMethylprednisolone\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eTwo studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] investigated the association between Methylprednisolone and ICU-BSI in COVID-19 patients. Meta-analysis signified that Methylprednisolone was linked with BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;2.24, 95% CI: 1.24\u0026ndash;4.04, P\u0026thinsp;=\u0026thinsp;0.008, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.5%). Meanwhile, we found that the combination of Methylprednisolone and Tocilizumab was associated with the occurrence of BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;4.54, 95% CI: 1.09\u0026ndash;18.88, P\u0026thinsp;=\u0026thinsp;0.037, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;71%). This may imply that Methylprednisolone is widely used for treatment and that the combination of Methylprednisolone and Tocilizumab may be used for COVID-19 patients in ICUs with more critical conditions. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eSteroids\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe correlation between steroid use and ICU-BSI in COVID-19 patients was investigated in 3 studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Meta-analysis showed no correlation between steroid use and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;1.17, 95% CI: 0.15\u0026ndash;9.23, P\u0026thinsp;=\u0026thinsp;0.882, I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;87.6%). This may be related to the fact that steroids are widely used as they are believed to improve the recovery of patients. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eRemdesivir\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe correlation between Remdesivir and ICU-BSI in COVID-19 patients was investigated in 2 studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Meta-analysis showed no correlation between Remdesivir and BSI in COVID-19 patients in ICUs (OR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.14\u0026ndash;4.41, P\u0026thinsp;=\u0026thinsp;0.794, I2\u0026thinsp;=\u0026thinsp;54.4%). This may be related to the fact that Remdesivir is considered a potent drug for the treatment of COVID-19, with significant efficacy, and therefore is more widely used for severe and critically ill patients in ICUs. (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe stability of the results of the remaining articles was estimated by excluding each article in turn. Sensitivity analyses for gender, SAPS II score, DM, hypertension, chronic pulmonary disease, liver disease, heart disease, immunosuppressive disease, tumor, tracheal intubation, ECMO, CVC, RRT, length of stay in ICUs, and the use of Methylprednisolone, Steroids, and Remdesivir revealed that the results were relatively stable. In the sensitivity analysis of mechanical ventilation, the study by Palanisamy,N et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] greatly impacted the results, so the results were pooled after the exclusion of that article, and the results were more stable. Similarly, in the sensitivity study of chronic kidney disease, it was found that Massart,N et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] greatly influenced the results. After excluding the article and re-combining the results, the results were more stable. In the sensitivity study on the use of Tocilizumab, the study by Bonazzetti,C et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] greatly influenced the results. The results were more stable when the article was excluded, and the results were re-combined. The sensitivity analyses of other factors implied stable and insignificant changes, so these studies were retained. (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.S2.S3)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Publication bias\u003c/h2\u003e \u003cp\u003ePublication bias was examined using the Egger test for the risk factor containing\u0026thinsp;\u0026ge;\u0026thinsp;10 articles. There was no publication bias for Men (P\u0026thinsp;=\u0026thinsp;0.187), DM (P\u0026thinsp;=\u0026thinsp;0.142), Hypertension(P\u0026thinsp;=\u0026thinsp;0.396), Heart disease (P\u0026thinsp;=\u0026thinsp;0.592) and chronic pulmonary disease (P\u0026thinsp;=\u0026thinsp;0.671). (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this meta-analysis, we aimed to identify risk factors for BSI in COVID-19 patients in ICUs. Among the studies with available data, 55 published English studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] investigating risk factors associated with BSI in COVID-19 patients in ICUs were included. Our findings showed that male, DM, tracheal intubation, mechanical ventilation, CVC, ECMO, Methylprednisolone use, higher SAPS II score, and longer ICU stay were risk factors for ICU-BSI in COVID-19 patients. In addition, hypertension, chronic pulmonary disease, liver disease, chronic kidney disease, heart disease, immunosuppression, tumor, RRT, and the use of Tocilizumab, Steroids, and Remdesivir neither increased nor decreased the risk of ICU-BSI in COVID-19 patients.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Patient-related factors\u003c/h2\u003e \u003cp\u003eOur analyses showed that male COVID-19 patients were at a higher risk of BSI in ICUs and that males also made up most of the ICU admission population. A previous study by Zamora-Cintas,M. et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] highlighted that male patients had a higher risk of BSI, in line with our results. COVID-19 virus infection mainly affects pulmonary function, resulting in more patients with pulmonary dysfunction being admitted to the hospital, and more severe patients need to be admitted to ICUs, which makes them more susceptible to BSI. However, we did not study whether smoking was a risk factor for BSI in COVID-19 patients in ICUs and the proportion of men who smoke., which might require more data support. Our findings also showed that the SAPS II score directly reflected the risk of BSI in COVID-19 patients in ICUs, in agreement with the findings of Massart, N et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. SAPS II score, as an important evaluation component in ICUs, to some extent, also reflects infection indicators, which is directly related to our study. In short, a higher SAPS II score indicates a more severe condition and a worse prognosis [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. DM is a common chronic underlying disease in clinical practice. Our results showed that DM was also an associated risk factor for ICU-BSI in COVID-19 patients. This may be related to diverse complications associated with DM and the poor resistance of diabetic patients, which makes them susceptible to a variety of related infections [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] and directly increases the risk of BSI[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Treatment-related factors\u003c/h2\u003e \u003cp\u003eOur findings suggested that tracheal intubation substantially increased the risk of ICU-BSI in COVID-19 patients, in support with the findings of Bonazzetti,C et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and Rollas,Kazim et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Tracheal intubation is a common resuscitation technique in ICUs and is essential to save patients in respiratory distress. It ensures that the patient receives an adequate supply of oxygen and provides mechanical ventilation support to maintain normal respiratory function [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This makes mechanical ventilation support also a possible risk factor for BSI in COVID-19 patients in ICUs. Invasive treatment is highly likely to cause airway damage, and tracheal intubation may introduce bacteria or other pathogens, increasing the risk of infection in patients. It is also easy for mis-aspiration to occur after tracheal intubation, leading to lung infections [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. All these directly increase the risk of BSI. COVID-19 has become a specific infection that involves the pathophysiology of the lungs, including endothelial and epithelial changes, pulmonary embolism, and microvascular thrombosis. In addition, secondary infectious injury can cause acute lung injury and prolong mechanical ventilation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Zhang,J et al. showed that multiple invasive treatments were important risk factors for BSI. Early extubation and regular assessment of infection should be done, therefore early anti-infective therapy is important [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. CVC is widely used in the resuscitation of severe and critically ill patients, which is conducive to the measurement of central venous pressure, long-term medication, and large and rapid rehydration, thus preventing venous damage and repeated puncture. However, a common complication of CVC is deep vein thrombosis, which also leads to the invasion of external bacteria and infection. Patients present with persistent low-grade fever, and the simultaneous presence of bacteria and thrombus can exacerbate the infection [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Our study revealed that prolonged CVC substantially increased the risk of ICU-BSI in COVID-19 patients. Therefore, there is a need for timely monitoring of the situation and increased measures for infection control and nursing care for CVC. ECMO serves as an important therapeutic tool to provide continuous extracorporeal respiratory and circulatory function for critically ill patients presenting with cardiopulmonary failure. Some studies showed that infections were highly susceptible to occurring after the use of ECMO, which was related to the fact that patients with low immunity were susceptible to systemic hematogenous infections, thus dramatically increasing the risk of fungal infections [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This was in general agreement with the results of our study. Meanwhile, ECMO may result in renal failure in about 50% of patients, which may require RRT [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. However, our study found that RRT was not a risk factor for ICU-BSI in COVID-19 patients, which may need to be supported by more data. In particular, intuitive data in our study pointed out that the longer the treatment duration in ICUs, the higher the risk of BSI in COVID-19 patients. The possible reasons are as follows: first, the treatment time reflects the severity of the patient's condition, and a longer ICU stay may mean that the patient's condition is more critical; second, the longer the treatment time, the higher the chance of nosocomial infections [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which is closely related to the prolonged use of antibiotics and ward management; third, severe and critically ill patients in ICUs have low resistance and need to be left with various passages during treatment, and most COVID-19 patients have coughing symptoms, which is prone to aerosol dissemination and transmission of infectious disease between the patients, thus greatly increasing the risk of BSI [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Medication-related factors\u003c/h2\u003e \u003cp\u003eIn our study, the use of Tocilizumab, Remdesivir, and Steroids did not correlate with the risk of BSI in COVID-19 patients in ICUs, but the use of Methylprednisolone and the combination of Methylprednisolone and Tocilizumab directly increased the risk of ICU-BSI in COVID-19 patients. This is associated with the fact that patients receiving glucocorticoid therapy are more likely to require ventilatory support, vasopressors, and RRT [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Glucocorticoids are widely used and effective drugs during hospitalization, especially in ICU, due to their anti-inflammatory and immunosuppressive effects. Glucocorticoids inhibit the inflammatory chemotaxis of cells and the rate of phagocytosis to reach inflammation sites. In addition, glucocorticoids increase the stability of cells so that cell membranes are less likely to rupture, and cells are less likely to release lysosomal enzymes to phagocytose bacteria to destroy inflammatory foci, thus decreasing the body's immunity and making it susceptible to viral or bacterial infections [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Glucocorticoids will also directly inhibit the body's immune function, thus inhibiting the body's fever, so that the fever symptoms are not obvious, which in turn masks the severity of the disease and delays the diagnosis and treatment, leading to further deterioration of the condition [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, glucocorticoids also inhibit mucosal exudation and inflammatory exudation. For patients with respiratory tract infections, glucocorticoids inhibit the exudation of inflammatory secretions, so that patients reduce coughing, which is not conducive to discharging bacterial sputum out of the body through coughing. Additionally, it delays the detection and treatment, thus aggravating the infection and increasing the risk of BSI greatly [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, it is crucial to monitor the use of glucocorticoids rationally according to the condition [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Incidence of ICU-BSI in COVID-19 patients\u003c/h2\u003e \u003cp\u003eMeta-analysis unraveled that the incidence of BSI in COVID-19 patients in ICUs was 19.9%, similar to the currently reported 10\u0026ndash;50% incidence rate. Compared with previous studies, in which 7% of COVID-19 hospitalized patients may experience BSI [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], the incidence of BSI in COVID-19 patients in ICUs increased nearly threefold. The incidence of ICU-BSI in COVID-19 patients varied in different studies, mainly because the occurrence of BSI lies in the detection of blood cultures. Also, it is somewhat difficult to exclude sampling contamination and detection contamination, and most patients in ICUs receive various types of medications, which may affect the detection of BSI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Strengths and limitations\u003c/h2\u003e \u003cp\u003eThis is the first systematic review analyzing risk factors for ICU-BSI in COVID-19 patients, and the data were reviewed by two investigators to ensure the accuracy. By incorporating an extensive array of paper with high quality, our findings provide an accurate and reliable framework for promptly identifying the risk of BSI occurrence in COVID-19 patients in ICUs. In addition, our findings provide more comprehensive references of risk factors for ICU-BSI in COVID-19 patients for clinical treatment, which is a guide for early prevention of BSI.\u003c/p\u003e \u003cp\u003eHowever, some limitations need discussion. First, the studies covered diverse ethnicities, populations, methods, and periods of investigation, which is reflected in heterogeneity. However, this may be due to differences in study design rather than actual differences in outcome measures. Therefore, we used sensitivity analyses and random-effects models to verify the result stability in the presence of high heterogeneity. Second, the diagnosis of BSI in COVID-19 patients treated with Tocilizumab may be difficult because patients often do not have fever and have low serum levels of typical inflammatory markers, requiring further study. Third, Data on diseases such as diabetes and oncology did not have specific types of data, so specific rich data are needed to study their association with BSI. Fourth, few of the included studies analyzed the impact of post-invasive treatment care measures on the occurrence of BSI in COVID-19 patients in ICUs. Therefore, in the future, more assessments of the impact of treatment details on ICU-BSI in COVID-19 patients and randomized controlled trials are needed to enhance the reliability.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur findings showed that males, higher SAPS II scores, DM, tracheal intubation, mechanical ventilation, ECMO, CVC, longer ICU stays, and Methylprednisolone use increased the risk of ICU-BSI in COVID-19 patients. It is imperative for future research to integrate these factors into a comprehensive predictive assessment framework to identify and intervene promptly in COVID-19 patients at high risk for ICU-BSI to improve treatment outcomes and promote patient health recovery.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they did not receive any funding from any source.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Writing - original draft preparation: [Jun Wang ,Ting Jiang]; Writing - review and editing: [Jun Wang ,Ting Jiang]; Conceptualization: [Jun Wang ,Ting Jiang]; Methodology: [Jun Wang ,Ting Jiang]; Formal analysis and investigation: [Jun Wang ,Ting Jiang]; Resources: [Jun Wang ,Ting Jiang]; Supervision: [Jun Wang ,Ting Jiang],and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePontali E, Filauro F: \u003cstrong\u003eRepurposing an \u0026apos;Old\u0026apos; Drug for the Treatment of COVID-19-Related Cytokine Storm\u003c/strong\u003e. \u003cem\u003eJournal of clinical medicine \u003c/em\u003e2023, \u003cstrong\u003e12\u003c/strong\u003e(10).\u003c/li\u003e\n\u003cli\u003eIppolito M, Simone B, Filisina C, Catalanotto FR, Catalisano G, Marino C, Misseri G, Giarratano A, Cortegiani A: \u003cstrong\u003eBloodstream Infections in Hospitalized Patients with COVID-19: A Systematic Review and Meta-Analysis\u003c/strong\u003e. \u003cem\u003eMicroorganisms \u003c/em\u003e2021, 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Update on knowledge and devices\u003c/strong\u003e. \u003cem\u003eIntensive Care Med \u003c/em\u003e2022, \u003cstrong\u003e48\u003c/strong\u003e(10):1287-1298.\u003c/li\u003e\n\u003cli\u003eAquino-Martinez R, Hern\u0026aacute;ndez-Vigueras S: \u003cstrong\u003eSevere COVID-19 Lung Infection in Older People and Periodontitis\u003c/strong\u003e. \u003cem\u003eJournal of clinical medicine \u003c/em\u003e2021, \u003cstrong\u003e10\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eMayer S, Bonhag C, Jenkins P, Cornett B, Watts P, Scherbak D: \u003cstrong\u003eProbiotic-Associated Central Venous Catheter Bloodstream Infections Lead to Increased Mortality in the ICU\u003c/strong\u003e. \u003cem\u003eCrit Care Med \u003c/em\u003e2023, \u003cstrong\u003e51\u003c/strong\u003e(11):1469-1478.\u003c/li\u003e\n\u003cli\u003eSel\u0026ccedil;uk \u0026Uuml; N, Sargın M, Baştop\u0026ccedil;u M, Mete EMT, Erdoğan SB, \u0026Ouml;calmaz Ş, Orhan G, Aka SA: \u003cstrong\u003eMicrobiological Spectrum of Nosocomial ECMO Infections in a Tertiary Care Center\u003c/strong\u003e. \u003cem\u003eBraz J Cardiovasc Surg \u003c/em\u003e2021, \u003cstrong\u003e36\u003c/strong\u003e(3):338-345.\u003c/li\u003e\n\u003cli\u003eCarelli S, Dell\u0026apos;Anna AM, Montini L, Bernardi G, Gozza M, Cutuli SL, Natalini D, Bongiovanni F, Tanzarella ES, Pintaudi G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eBloodstream infections in COVID-19 patients undergoing extracorporeal membrane oxygenation in ICU: An observational cohort study\u003c/strong\u003e. \u003cem\u003eHeart \u0026amp; lung : the journal of critical care \u003c/em\u003e2023, \u003cstrong\u003e62\u003c/strong\u003e:193-199.\u003c/li\u003e\n\u003cli\u003eSelewski DT, Wille KM: \u003cstrong\u003eContinuous renal replacement therapy in patients treated with extracorporeal membrane oxygenation\u003c/strong\u003e. \u003cem\u003eSemin Dial \u003c/em\u003e2021, \u003cstrong\u003e34\u003c/strong\u003e(6):537-549.\u003c/li\u003e\n\u003cli\u003eCrawford C, Vanoli E, Decorde B, Lancelot M, Duprat C, Josserand C, Jilesen J, Bouadma L, Timsit JF: \u003cstrong\u003eModeling of aerosol transmission of airborne pathogens in ICU rooms of COVID-19 patients with acute respiratory failure\u003c/strong\u003e. \u003cem\u003eSci Rep \u003c/em\u003e2021, \u003cstrong\u003e11\u003c/strong\u003e(1):11778.\u003c/li\u003e\n\u003cli\u003eBruscoli S, Puzzovio PG, Zaimi M, Tiligada K, Levi-Schaffer F, Riccardi C: \u003cstrong\u003eGlucocorticoids and COVID-19\u003c/strong\u003e. \u003cem\u003ePharmacol Res \u003c/em\u003e2022, \u003cstrong\u003e185\u003c/strong\u003e:106511.\u003c/li\u003e\n\u003cli\u003ePapic I, Bistrovic P, Cikara T, Busic N, Keres T, Ortner Hadziabdic M, Lucijanic M: \u003cstrong\u003eCorticosteroid Dosing Level, Incidence and Profile of Bacterial Blood Stream Infections in Hospitalized COVID-19 Patients\u003c/strong\u003e. \u003cem\u003eViruses \u003c/em\u003e2024, \u003cstrong\u003e16\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eMunro C, Zilberberg MD, Shorr AF: \u003cstrong\u003eBloodstream Infection in the Intensive Care Unit: Evolving Epidemiology and Microbiology\u003c/strong\u003e. \u003cem\u003eAntibiotics (Basel, Switzerland) \u003c/em\u003e2024, \u003cstrong\u003e13\u003c/strong\u003e(2).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bloodstream infection, COVID-19, ICU, Meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-4954620/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4954620/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eRisk factors for bloodstream infection in patients with COVID-19 in the intensive care unit (icu) remain unclear. The purpose of this systematic review was to study the risk factors for BSI in patients admitted to ICUs for COVID-19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA systematic search was performed on PubMed, EMBASE, Cochrane Library, and Web of Science up to July 2024. Data were reported as combined odds ratio (OR) for categorical variables and weighted mean difference (WMD) for continuous variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e 6914 studies were retrieved, of which 55 were included in the meta-analysis. Men (OR=1.28, 95% CI: 1.10-1.50, P=0.006), high SAPS II score (WMD=6.43, 95% CI: 0.23-12.63, P=0.042), diabetes (OR=1.34, 95% CI: 1.04-1.73, P=0.022), tracheal intubation (OR=8.68, 95% CI: 4.68-16.08, P\u0026lt;0.001), mechanical ventilation (OR=22.00, 95% CI: 3.77-128.328, P\u0026lt;0.001), ECMO (OR=2.70, 95% CI: 1.17-6.26, P=0.020), central venous cannulation (OR=9.33, 95% CI: 3.06-28.43, P\u0026lt;0.001), prolonged ICU stay (WMD=10.37, 95% CI: 9.29-11.44, P\u0026lt;0.001), methylprednisolone use (OR=2.24, 95% CI: 1.24-4.04, P=0.008), and the combination of methylprednisolone and Tocilizumab (OR=4.54, 95% CI: 1.09-18.88, P=0.037) were risk factors for ICU-BSI in COVID-19 patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eWe identified 10 risk factors for ICU-BSI in COVID-19 patients. In future studies, these factors can be combined to establish a more comprehensive and accurate prediction model for ICU-BSI in COVID-19 patients. Targeted measures can be taken earlier to control BSI.\u003c/p\u003e","manuscriptTitle":"Risk factors for bloodstream infection in COVID-19 patients in intensive care units: A systematic review and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-16 13:51:35","doi":"10.21203/rs.3.rs-4954620/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-25T13:52:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-25T13:22:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-07T10:37:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-01T00:42:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200427866956382485701039566092645469438","date":"2024-09-30T08:51:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68685910829095173085612140615155377465","date":"2024-09-23T14:23:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78406791350279856354284861218472802596","date":"2024-09-23T12:09:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-23T11:39:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-27T14:49:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-27T04:02:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-27T04:01:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2024-08-22T02:47:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7f10c6c3-d5f4-44bf-b66d-920cc5c2d92d","owner":[],"postedDate":"October 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-06T16:02:45+00:00","versionOfRecord":{"articleIdentity":"rs-4954620","link":"https://doi.org/10.1186/s12879-024-10420-1","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-01-03 15:57:36","publishedOnDateReadable":"January 3rd, 2025"},"versionCreatedAt":"2024-10-16 13:51:35","video":"","vorDoi":"10.1186/s12879-024-10420-1","vorDoiUrl":"https://doi.org/10.1186/s12879-024-10420-1","workflowStages":[]},"version":"v1","identity":"rs-4954620","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4954620","identity":"rs-4954620","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}