Introduction
Materials and methods
Literature search and literature screening
Literature inclusion and exclusion criteria
Literature quality assessment methods
Data extraction
Statistical analysis
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(1) Selection of effect sizes. For dichotomous variables, the dominance ratio (OR) was used as the effect size; for continuous variables, continuous data were expressed as the median (minimum to maximum), and 95% confidence intervals (CI) were calculated for both. Values of P < 0.05 were considered to be statistically significant.
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(2) Evaluation of literature quality. The Newcastle–Ottawa Scale (NOS) was used to evaluate the quality of the literature. The NOS consists of 3 parts: study population selection, comparability, and exposure or outcome evaluation, with eight entries and a total score of 9. The higher the score, the better the quality.
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(3) Heterogeneity test. Inter-study heterogeneity was quantified by the I2 statistic, where I2 > 50% was evidence of substantial heterogeneity. If there was no heterogeneity, a fixed-effects model was used for analysis; if there was heterogeneity, sensitivity analysis is used to explore whether the results are robust. Sensitivity analysis uses a case-by-case rejection method. Study was excluded sequentially, and the remaining articles (n-1) were combined in a meta-analysis, and the changes in the combined results were observed to assess whether the original meta-analysis results changed significantly due to the influence of some studies.
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(4) Other analyses: the presence of publication bias was determined by a funnel plot combined with Egger's test.
Results
Literature screening results
Risk of bias in included studies
No | Author | Year | Country | Study design | Statistical methods | Selection | Comparability | Outcome | Score | Ending variables |
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1 | S.Shafeek [16] | 2018 | Egypt | Forward-looking queues | Multiple logistic regression | ✰✰✰ | ✰ | ✰✰ | 6 | ①,⑤ |
2 | Jaffray [17] | 2022 | United States | Case–control | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰✰ | 8 | ②,③ |
3 | Chojnacka [18] | 2022 | Poland | Case–control retrospective | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰ | 8 | ② |
4 | El-Naggar [19] | 2020 | Canada | Retrospective Matching Queue | Multiple logistic regression | ✰✰✰✰ | ✰ | ✰✰ | 7 | ① |
5 | Bhat [20] | 2022 | United States | Case–control | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰✰✰ | 9 | ②,④,⑤,⑦ |
6 | Amankwah [21] | 2014 | United States | Case–control | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰✰ | 8 | ③ |
7 | Bhat [22] | 2015 | United States | Case–control | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰ | 8 | ④ |
8 | Bhatia [23] | 2022 | Canada | Case–control | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰✰ | 8 | ②,⑧ |
9 | Bhat [24] | 2018 | United States | Case–control | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰✰ | 8 | ② |
10 | Lambert [25] | 2019 | United States | Retrospective cohort | Multiple Cox ratios | ✰✰✰ | ✰✰ | ✰✰ | 7 | ① |
11 | Cabannes [26] | 2018 | France | Forward-looking queues | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰✰ | 8 | ①,③ |
12 | AlTassan [27] | 2014 | Saudi Arabia | Forward-looking queues | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰✰ | 8 | ① |
13 | Robinson [28] | 2021 | United States | Retrospective cohort | Multiple logistic regression | ✰✰ | ✰✰ | ✰✰ | 6 | ①,②, ③,④, ⑤,⑥,⑦ |
14 | Ulloa-Ricardez [29] | 2016 | Mexico | Case–control retrospective | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰✰ | 8 | ⑦ |
15 | Bhatia [30] | 2021 | Canada | Retrospective cohort | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰ | 6 | ① |
16 | Sirachainan [31] | 2018 | Thailand | Retrospective cohort | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰ | 7 | ① |
17 | Giordano [32] | 2018 | Italy | Case–control retrospective | Multiple logistic regression | ✰✰✰ | ✰✰ | ✰✰ | 7 | ⑧ |
18 | Boo [33] | 1999 | Malaysia | Forward-looking observations | Multiple logistic regression | ✰✰ | ✰✰ | ✰✰ | ③,⑥,⑧ |
Type of thrombosis (No. of Studies) | Patients/Control Subjects, n | OR/95% CI (Fixed-Effects or Random-Effects Model) | I2, %; P | Bias Indicator, P |
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① Incidence of disease | 1,210,689, 8 | 0.02[0.01,0.02] | 99% (p < 0.001) | 0.032 |
② Infection and sepsis | 1,278,550, 5 | 1.96[1.45–2.65] | 74% (p = 0.04) | 0.152 |
③ CVC | 1,164,297, 6 | 5.75[4.32–7.67] | 0% (p = 0.49) | 0.85 |
④ Mechanical Ventilation | 1,281,829, 3 | 2.23[2.00–2.49] | 80% (p < 0.05) | - |
⑤ Gestational age | 1,277,757, 2 | 1.5[1.34–1.68] | 0% (p = 0.65) | - |
⑥ Ethnicity | 1,158,854, 3 | 0.88[0.78–0.98] | 0% (p = 0.78) | - |
⑦ Surgery | 12,777,933, 3 | 2.25[1.2–4.22] | 98% (p < 0.01) | - |
⑧ Respiratory distress | 218/4741, 2 | 1.39[0.42–4.63] | 84% (p < 0.01) | - |