Risk Factors For Adverse Pregnancy Outcomes in Systemic Lupus Erythematosus: A Meta Analysis and Systemic review

Risk Factors For Adverse Pregnancy Outcomes in Systemic Lupus Erythematosus: A Meta Analysis and Systemic review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk Factors For Adverse Pregnancy Outcomes in Systemic Lupus Erythematosus: A Meta Analysis and Systemic review Chen Sun, Xia Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4871546/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Systemic lupus erythematosus (SLE) is a prominent autoimmune disease highly linked to adverse pregnancy outcomes (APOs). Previous research on the risk factors for APOs in SLE pregnancies has been limited by regional constraints or inadequate sample sizes. There is currently a dearth of comprehensive systemic reviews on this topic. To address these research gaps, we conducted a rigorous meta-analysis and systematic review to elucidate the risk factors for APOs in SLE pregnancies. Methods: PubMed, Embase, Web of Science, and the Cochrane Library systematically searched for articles on risk factors for APOs in SLE pregnancy from initiation to December 31, 2023. The pooled Odds Ratio (OR) was estimated using a random-effects or fixed-effects model for each investigated factor. Egger's P value was calculated to assess publication bias and heterogeneity was evaluated by the I 2 test. Results: 42 unique studies were enrolled. Patients with hypertension (OR, 5.23; 95% CI, 2.76–9.91), lupus nephritis (LN) (OR, 3.02; 95% CI, 2.10–4.34), high disease activity (OR, 2.51; 95% CI, 1.39–4.50), low complements (OR, 1.94; 95% CI, 1.39–2.72), antiphospholipid syndrome (APS)/positive antiphospholipid antibody (aPL) (OR, 7.93; 95% CI, 4.35–14.44) were at higher risk for APOs. The risk factors for preterm birth included LN (OR, 3.69; 95% CI, 2.31–5.89), hypertension (OR, 4.50; 95% CI, 1.86–10.87), disease flares (OR, 4.02; 95% CI, 2.24–7.19), disease activity (OR, 3.92; 95% CI, 2.52–6.10), preeclampsia/eclampsia (OR, 8.85;95% CI, 4.72–16.58), and APS (OR, 3.95; 95% CI, 2.20–7.07). The risk factors for pregnancy loss were APS/aPL (OR, 3.46; 95% CI, 2.44–4.91), low complements (OR, 2.60; 95% CI, 1.08–6.27), disease flares (OR, 2.72; 95% CI, 1.36–5.46), LN (OR, 3.47; 95% CI, 1.74–6.89), hypertension (OR, 1.33; 95% CI, 0.71–1.94), thrombocytopenia (OR, 8.85; 95% CI, 4.72–16.58), and disease activity (OR, 9.19; 95% CI). LN also predicted intrauterine growth restriction (OR, 3.51; 95% CI, 1.30–9.51) and low birth weight (OR, 5.55; 95% CI, 1.29–23.86). Conclusions: This study identified risk factors for APOs in SLE pregnancies, enhancing clinician awareness and enabling early intervention for high-risk patients. Systemic lupus erythematosus pregnancy outcome risk factor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Background Systemic lupus erythematosus (SLE), commonly known as lupus, is a prevalent chronic multisystemic autoimmune disease characterized by the presence of pathogenic autoantibodies and immune complexes, contributing to tissue damage. The estimated global incidence is 5.14 (from 1.4 to 15.3) per 100,000 person-years, with a corresponding prevalence estimated at 43.7 (from 15.87 to 108.92) per 100,000 individuals [ 1 ]. The chronic disease progression and the use of immunosuppressive therapy are significant contributors to life-threatening systemic organ damage. The mortality among SLE patients remains significantly higher, with rates two to three times greater than that among the general population [ 2 ]. Certain ethnic populations, like Black, Asian, and Hispanic, exhibit a pronounced predisposition [ 2 ]. SLE presents diverse phenotypes, spanning from mild skin symptoms to severe organ failures, affecting virtually any organ in the body. It has a distinct gender bias, predominantly affecting women of reproductive age, with a female/male ratio of roughly 9:1. The average age at which SLE is diagnosed is around 35 years [ 3 ]. SLE ranks among the top causes of mortality in young females [ 4 ]. The high susceptibility of reproductive-age women to SLE raises significant concerns for clinicians regarding childbearing. Despite comparable anti-Müllerian hormone levels and fertility potential as healthy controls [ 5 ], SLE women have heightened vulnerability to adverse pregnancy outcomes (APOs) [ 6 ]. Meta-analysis revealed that SLE subgroups exhibited significantly elevated rates of spontaneous abortion, stillbirth, premature birth, "small for gestational age” (SGA) infants, and infants with low birth weight [ 7 , 8 ]. SLE-associated pregnancies are deemed high-risk pregnancies. Early identification of high-risk factors for APOs is of utmost importance. The careful monitoring of predictors and effective management of pregnancies in SLE women can improve outcomes. The risk factors for APOs in SLE pregnancies have been extensively studied. However, these reports had limitations including a small sample size and limited diversity in terms of ethnicity and geographical regions. A systemic meta-analysis on this topic is currently absent. Therefore, our study aims to conduct a systemic review and meta-analysis to identify a set of risk factors that could potentially be targeted for interventions. Methods Search Strategies PubMed, Cochrane Library, Web of Science, and EMBASE were comprehensively searched for relevant articles published in English and Chinese from initiation up to December 31, 2023. The search strategies encompassed Mesh terms and free words. The detailed retrieval strategies used are displayed in Supplementary Table 1. Study Selection After removal of duplicates, two investigators (Sun C, Li X) independently evaluated the potentially eligible articles by skimming through titles and abstracts. The inclusion criteria covered: (1) with SLE pregnant women as study subjects; (2) with specific diagnostic criteria for identifying APOs; (3) multivariate analysis was employed to determine risk factors; (4) cohort or case-control study. The articles were excluded for the following criteria: (1) guidelines, reviews, editorials, case reports, abstracts, letters, meeting summaries, or recalled articles; (2) incomplete or unavailable data; (3) inconsistent definition of APOs; (4) limited to univariate analysis; (5) publications not in English or Chinese languages. Quality evaluation Each eligible study underwent independent evaluation by the two authors using the Newcastle-Ottawa Scale (NOS) in three domains: patient representation, exposure and outcome determination, and follow-up adequacy. A comprehensive assessment led to the overall NOS score for each study was 9, with scores of 0-5 indicating low quality, scores of 6-7 indicating moderate quality, and scores of 8-9 indicating high quality, signifying a low risk of bias. Data Extraction Two independent reviewers (Sun C, Li X) extracted all the data, including first author, publication year, study location, sample size, mean age of pregnancy, diagnostic criteria, and documented risk factors. Terms Definition APOs were defined to include at least one of the following complications: -Pregnancy loss: abortion, stillbirth, or neonatal death. -Preterm birth: delivery occurring before 37 weeks of gestation. -Intrauterine growth restriction (IUGR): impaired fetal growth resulting in a smaller than expected size. -Small for gestational age (SGA): a neonate with a birthweight below the fifth percentile for their gestational age. -Low birth weight (LBW): infants with a birthweight below a certain threshold, typically 2500 grams. Data Analysis A meta-analysis was conducted to assess the connection between potential risk factors and APOs in SLE patients. The odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were computed by Stata 15.1 software. Forest plots were employed to present the ORs from individual studies and the pooled OR estimate. Given the variability in baseline data across the included studies, heterogeneity was observed. To evaluate this heterogeneity, the I² test was utilized. Heterogeneity was considered statistically significant if I²≥ 50% or p-value < 0.05. Based on heterogeneity (I²), a fixed-effects (I²50%) statistical model was employed. The results’ robustness was appraised using sensitivity analysis, while publication bias was judged to evaluate the potential impact of selective reporting. Result Article Selection The initial research on four databases retrieved 1123 records. After the removal of duplicates, 647 records remained. After carefully reviewing the titles and abstracts, 569 articles were excluded, resulting in 78 articles for further comprehensive evaluation in full-text format. Through this rigorous selection process, 42 articles (1 in Chinese and 41 in English) were utilized for review, as depicted in Fig. 1 . Study Characteristics Table 1 lists the key characteristics of 42 included articles, providing information on study type, geographical regions, and sample sizes. As for study types, 4 were retrospective case-control studies, 8 were retrospective cohort studies, and 30 were prospective cohort studies. The patient population was geographically diverse, spanning regions such as Asia, North America, Europe, the Middle East, and others. Overall, the analysis involved a substantial sample size of 8723 pregnancies, ensuring reliable and generalizable findings. Table 1 Key characteristics of included articles Study Year Country Study Design Sample Size Mean Age of pregnancy Andrade 2008 USA prospective cohort 102 - Arfaj 2010 Saudi Arabia retrospective cohort 383 36.4 ± 7.4 Al-Riyami 2021 Oman retrospective cohort 149 30.6 ± 5 Buyon 2015 USA prospective cohort 385 30.93 ± 4.90 Chen 2018 China retrospective cohort 243 28.9 ± 3.9 Chen 2021 China retrospective cohort 85 27.4 Chen 2015 China retrospective cohort 83 Clowse 2006 USA prospective cohort 166 30.2 ± 4.9 Deguchi 2018 Japan prospective cohort 56 33.9 ± 4.6 Hiramatsu 2021 Japan retrospective case-control 108 33 Hamijoyo 2019 Indonesia retrospective case-control 109 26 ± 6 He 2021 China retrospective cohort 223 27.8 ± 3.9 Irino 2021 Japan retrospective cohort 64 31.2 ± 4.9 Jiang 2021 China retrospective cohort 513 29.7 ± 4.0 Kim 2021 Korea retrospective case-control 163 31.9 ± 4.3 Kalok 2019 Malaysia retrospective case-control 71 30.5 ± 3.9 Ko 2011 Korea retrospective cohort 183 30.4 ± 3.2 Kwok 2011 Hong Kong prospective cohort 55 30.2 Laíno-Pineiro 2023 Spain retrospective cohort 1869 - Larosa 2022 France prospective cohort 238 31.6 + 4.5 Liu 2017 China retrospective cohort 131 24.3 ± 2.8 Liu 2012 China retrospective cohort 111 29.2 ± 4.2 Louthrenoo 2021 Thailand retrospective cohort 90 26.94 ± 4.80 Lu 2021 China retrospective cohort 55 - Lv 2015 China retrospective cohort 52 29.0 ± 3.7 Madrazo 2022 Spain retrospective cohort 64 32.1 ± 5.04 Miranda 2021 Mexico prospective cohort 351 28.3 ± 5.5 Mokbel 2023 Egypt prospective cohort 201 27.16 ± 4.8 Natli 2022 Greece prospective cohort 84 33.5 (6.8) Oishi 2021 Japan retrospective cohort 98 30 Palma dos Reis 2020 Portugal retrospective cohort 157 29.6 (4.7) Park 2014 Korea retrospective cohort 62 - Shaharir 2020 Malaysia retrospective cohort 240 29.9 ± 4.8 Song 2016 China retrospective cohort 69 26.4 ± 3.9 Sugawara 2019 Japan retrospective cohort 57 30 Tani 2021 Germany retrospective cohort 281 31.9 ± 4.5 Tian 2015 China retrospective cohort 347 31.9 ± 4.5 Zamani 2020 Iran retrospective cohort 121 33.74 ± 3.80 Zhan 2018 China retrospective cohort 180 29.4 ± 3.5 Zhan 2017 China retrospective cohort 263 28.6 ± 3.9 Zhang 2022 China retrospective cohort 123 27.1 ± 4.1 Wu 2019 China retrospective cohort 338 29.5 ± 4.0 Quality Evaluation The risk of bias was evaluated using NOS scores, and the results are presented in Supplementary Tables 2–3. Among the 41 studies analyzed, a significant majority of 75.60% (31/41) attained 8 scores or higher, indicating high quality. Additionally, 10 studies were rated as moderate quality. Analysis Result Lupus Nephritis 11 studies revealed a significant association between lupus nephritis (LN) and APOs. The analysis, employing a fixed-effects model, indicated low heterogeneity (I 2 = 21.7, P = 0.250) and demonstrated that patients with LN exhibited a 3.02-fold higher risk of APO than those without LN (OR, 3.02; 95% CI, 2.10–4.34). P < 0.001) (Fig. 2 ). In 7 studies examining the association between LN and preterm birth, a fixed-effects model was utilized, showing low heterogeneity (I 2 = 3.3, P = 0.401). The analysis revealed LN as a prominent high-risk factor for preterm birth, with individuals with LN having a 3.69-fold increased risk compared to those without LN (OR, 3.69; 95% CI, 2.31–5.89, P = 0.001) (Fig. 3 ). In 7 studies on pregnancy loss, a random-effects model was employed, indicating moderate heterogeneity (I 2 = 50.1, P = 0.051). The analysis identified LN as a high-risk factor for pregnancy loss (OR, 3.47; 95% CI, 1.74–6.89, P < 0.001) (Fig. 4 ). In 3 studies on IUGR, a heterogeneity test showed high heterogeneity (I 2 = 69.4, P = 0.038). The analysis revealed that LN was connected with a 3.51-fold elevated risk of IUGR (OR, 3.51; 95% CI, 1.30–9.51, P = 0.013) (Fig. 5 ). In 3 studies on LBW, moderate heterogeneity was observed (I 2 = 41.8, P = 0.179). LN was significantly linked to a 5.55-fold elevated risk of LBW (OR, 5.55; 95% CI, 1.29–23.86, P = 0.021) (Fig. 6 ). Hypertension In the analysis of 6 studies, a heterogeneity test revealed moderate heterogeneity (I 2 = 57.4, P = 0.029). Hypertension was strongly linked to a 5.23-fold enhanced risk of APOs (OR, 5.23; 95% CI, 2.76–9.91, P < 0.001) (Fig. 7 ). Similarly, in the analysis of 5 studies, moderate heterogeneity was observed (I 2 = 58.6, P = 0.046). Hypertension was uncovered as a high-risk factor for preterm birth, with a 4.50-fold raised risk (OR, 4.50; 95% CI, 1.86–10.87, P < 0.001) (Fig. 8 ). However, the role of high blood pressure in pregnancy loss remains inconclusive (OR, 1.33; 95% CI, 0.71–1.94, P < 0.001) (Fig. 9 ). Disease activity Based on 7 studies, active lupus during pregnancy substantially increased the risk of APOs, with high heterogeneity (I 2 = 76.0, P < 0.000). The analysis revealed a 2.51-fold higher risk of APOs in patients with active lupus during pregnancy (OR, 2.51; 95% CI, 1.39–4.50, P = 0.002). Furthermore, active lupus related to a 3.92-fold higher risk of preterm birth (OR, 3.92; 95% CI, 2.52–6.10, P < 0.001) and a 9.19-fold higher risk of pregnancy loss (OR, 9.19; 95% CI, 3.14–26.89, P < 0.001). In 3 studies evaluating the impact of low disease activity state (LLDAS) during pregnancy on APOs, low heterogeneity was observed (I 2 = 0.00, P = 0.616). LLDAS was considered a protective factor against APOs (OR, 0.26; 95% CI, 0.12–0.57, P < 0.001). Based on 6 studies, disease flare during pregnancy markedly enhanced the risk of preterm birth (OR, 4.02; 95% CI, 2.24–7.19, P < 0.001), with moderate heterogeneity (I 2 = 56.7, P = 0.042). Additionally, in the analysis of 4 studies, disease flare was connected with a 2.72-fold raised risk of pregnancy loss (OR, 2.72; 95% CI, 1.36–5.46, P = 0.005), with moderate heterogeneity (I 2 = 54.2, P = 0.087) (Supplementary Figs. 1–6). Antiphospholipid syndrome (APS)/Positive antiphospholipid antibody (aPL) APS or positive aPL significantly elevated the risk of APOs by 4.97 times (OR, 4.97; 95% CI, 1.87–13.17, P < 0.001), based on 5 studies with high heterogeneity (I 2 = 71.6, P = 0.007) (Supplementary Fig. 7). APS was also a risk factor for preterm birth, with a 3.95-fold raised risk (OR, 3.95; 95% CI, 2.20–7.07, P < 0.001), according to 5 studies with low heterogeneity (I 2 = 0, P = 0.457). Additionally, APS or positive aPL elevated the risk of pregnancy loss by 3.46 times (OR, 3.46; 95% CI, 2.44–4.91, P < 0.001), based on 10 articles with low heterogeneity (I 2 = 0, P = 0.822) (Supplementary Figs. 8–9). Hypocomplementemia Based on the analysis of 5 studies, there was a reported relationship between low complement levels and APOs (OR, 1.94; 95% CI, 1.39–2.72, P < 0.001), with moderate heterogeneity (I 2 = 45.7, P = 0.118). Additionally, low complement levels were linked to an intensified risk of pregnancy loss (OR, 2.60; 95% CI, 1.08–6.27, P = 0.033) (Supplementary Figs. 10–11). Thrombocytopenia Based on the analysis of 4 studies, a relationship was found between low platelet count and pregnancy loss (OR, 2.20; 95% CI, 1.00–4.81, P = 0.049), with high heterogeneity (I 2 = 81.8, P < 0.000), indicating significant variability in the results (Supplementary Fig. 12). Preeclampsia/eclampsia Based on the analysis of four studies, a relationship was found between preeclampsia/eclampsia and preterm birth (OR, 8.85; 95% CI, 4.72–16.58, P < 0.001), with low heterogeneity (I 2 = 0, P = 0.732), indicating consistent findings across the studies (Supplementary Fig. 13). Publication bias As mentioned in the previous statistical analysis, we assessed the potential publication bias. The results of Egger's tests can be found in Table 2 . Table 2 Summarized results Adverse Pregnancy Outcomes Risk factors Study (n) Heterogeneity OR (95%CI) P Egger’s test I 2 (%) P APO Hypertension 6 57.4 0.029 5.23(2.76,9.91) 0.001 0.038 Lupus nephritis 11 48.2 0.037 3.02(2.10,4.34) 0.001 0.014 Hypocomplementemia 5 45.7 0.118 1.94(1.39,2.72) 0.001 0.219 LLDAS 3 0.00 0.616 0.26 (0.12,0.57) 0.001 0.351 Active disease 7 76.0 0 2.51(1.39,4.50) 0.002 0.003 APS/aPL 5 71.6 0.007 4.97(1.87,13.17) 0.001 0.753 Preterm Birth Lupus nephritis 7 3.3 0.401 3.69(2.31, 5.89) 0.001 0.392 Hypertension 5 58.6 0.046 4.50(1.86,10.87) 0.001 0.093 Preeclampsia/eclampsia 4 0.0 0.732 8.8(4.72, 16.58) 0.001 0.151 Flare 6 56.7 0.042 4.02(2.24,7.19) 0.001 0.001 Active disease 7 21.0 0.276 3.92(2.52, 6.10) 0.001 0.252 APS 5 0.00 0.457 3.95(2.20, 7.07) 0.001 0.515 Pregnancy Loss Hypocomplementemia 5 86.4 0 2.60(1.08,6.27) 0.033 0.179 Flare 4 54.2 0.087 2.72(1.36,5.46) 0.005 0.381 Thrombocytopenia 4 81.8 0 2.20(1.00, 4.81) 0.049 0.004 Lupus nephritis 7 50.1 0.051 3.47(1.74,6.89) 0.001 0.660 Hypertension 5 0.0 0.480 1.33(0.71,1.94) 0.001 0.232 APS/aPL 10 40.9 0.085 3.46(2.44,4.91) 0.001 0.244 Active 3 0.00 0.822 9.19(3.14,26.89) 0.001 0.182 Low Birth Weight Lupus nephritis 3 69.4 0.038 5.55(1.29,23.86) 0.021 0.594 Intrauterine Growth Restriction Lupus nephritis 3 41.8 0.179 3.51(1.30,9.51) 0.013 0.984 Discussion Pregnancy in SLE patients is commonly regarded as a high-risk condition due to its well-documented connection with APOs [ 9 ]. SLE pregnancies are more susceptible to pregnancy loss, preterm births, and IUGR than the general population [ 10 , 11 ]. A meta-analysis of 2751 pregnancies showed a 23.4% rate of unsuccessful pregnancy and a 39.4% rate of premature birth [ 12 ]. Our meta-analysis of 41 papers investigated the risk factors for APOs. Pregnancy and autoimmunity influence each other. The concept of Th2 phenomenon in successful pregnancy, proposed in 1993, refers to the suppression of CD4 + T helper 1 (Th1) cells and a shift towards Th2 anti-inflammatory cytokines [ 13 ]. Extensive research has revealed the dynamic balance of cytokine expression during different stages of pregnancy [ 14 ]. Proinflammatory Th17 cells are associated with APOs, while regulatory T (Treg) cells increase in healthy pregnant women and are linked to immune tolerance towards the fetus [ 14 ]. In the third trimester of SLE pregnancy, the lower-than-anticipated decrease of Th1 cytokines was detected [ 15 ]. Both the quantity and functionality of Treg cells are reduced in SLE patients, suggesting impairment in placental development and fetal tolerance [ 16 ]. They also display increased secretions of activin A, IL-6, IL-17, IL-10, and TNF-α during pregnancy, indicating a hyper-reactive immune system [ 17 ]. A 16-year cohort study by Clowse ME et al. revealed that high-activity lupus during pregnancy augmented the risk of preterm birth, decreased live births, and brought about a significant rate of fetal loss [ 18 ]. Active lupus may impact the uteroplacental unit, leading to preterm labor and membrane rupture [ 18 ]. The activity indices in SLE pregnancy can be assessed using the SLE in Pregnancy Activity Index, Modified Lupus Activity Measurement, and Lupus Activity Index in Pregnancy [ 19 ]. American College of Rheumatology’s Reproductive Health Guideline 2020 recommends SLE women to conceive during a period of clinically mild or inactive disease activity in the 6 months before pregnancy and to continue taking pregnancy-compatible lupus medications throughout pregnancy [ 20 ]. Active LN during pregnancy is linked to a high risk of fetal loss, ranging from 35–50% [ 21 ]. Smyth A et al. have confirmed LN raises the risk of preterm delivery and unsuccessful pregnancy, while IUGR incidence is estimated to be 12.7% [ 12 ], consistent with the findings of another meta-analysis [ 22 ]. Patients with class III/IV LN exhibit a lower mean birthweight [ 23 ]. In patients with renal involvement, baseline renal function, proteinuria levels, and new onset have been associated with APOs [ 24 ]. European League Against Rheumatism recommends ongoing monitoring during pregnancy, including urine protein excretion, urine sediment analysis (assessing glomerular hematuria and urinary casts), serum creatinine levels, and glomerular filtration rate [ 25 ]. SLE women have noticeably higher rates of hypertension than healthy individuals [ 26 ]. It has been firmly established that hypertension has a significant impact on pregnancy outcomes [ 27 ]. Inadequate blood pressure regulation during pregnancy is linked to decreased gestational age at delivery [ 28 ], consistent with our results. Hypertension during pregnancy can trigger an imbalance of Th cells and the release of cytokines, leading to the reactive flare of lupus [ 29 ]. Conversely, active SLE can also serve as a predictor of hypertension [ 29 ]. The meta-analysis confirmed that aPL was connected with higher rates of APOs, including elevated fetal loss risk for Antiβ2GP1 and anticardiolipin antibody (aCL), and higher risk of preterm birth for lupus anticoagulant (LAC) [ 30 ]. LAC and aCL are predictors of APOs in aPL-positive patients [ 30 – 32 ]. In addition to causing thrombosis in the uteroplacental vasculature, aPL directly impairs the production of chorionic gonadotropin by influencing anionic phospholipids and β2GP1 in trophoblast cells during early pregnancy [ 33 ]. Despite aspirin and low molecular weight heparin treatment, APS still results in a 20–30% rate of pregnancy loss, suggesting that hydroxychloroquine and pravastatin should be explored as potential alternative options [ 28 ]. Complement activation contributes to abnormal placental development, as evidenced by the significant association between increased Bb and sC5b-9 levels in early pregnancy and APOs [ 34 ]. Complement fragments are detected in placental samples from complicated pregnancies, and C4d emerges as the prominent biomarker for complement activation in the placenta [ 35 ]. Physiological fluctuations in complement levels during gestation can be indicative of APOs in SLE pregnancy if there is a lack of anticipated increase in C3 and C4 levels [ 36 , 37 ]. Thrombocytopenia during SLE pregnancy is linked to heightened disease activity, early onset preeclampsia, and elevated rates of pregnancy loss [ 38 ]. Plateletcrit is a valuable marker for predicting the risk of stillbirth in SLE pregnancies [ 39 ]. The decrease in platelet count could potentially signify early signs of preeclampsia, microthrombosis, or lupus activity. Based on our findings, eclampsia or preeclampsia is a high-risk factor for preterm birth in SLE. SLE pregnancies carry an elevated risk of preeclampsia, which is influenced by SLE circulating immune complexes and dysregulated cytokine expression [ 40 ]. This research is constrained by several limitations, including moderate heterogeneity during subgroup analysis, differences in the definition of pregnancy outcomes and risk factors, and variations in medications used during pregnancy and treatment strategies across hospitals. These limitations should be considered when interpreting and applying the obtained findings. Further research may be necessary to address these limitations and provide more precise references. Conclusion The current systematic review and meta-analysis examined the risk factors for APOs in SLE pregnancies and identified LN, hypertension, disease activity, aPL, hypocomplementemia, thrombocytopenia, and preeclampsia/eclampsia as significant risk factors. These findings enhance comprehension regarding risk factors for APOs in SLE pregnancies and allow healthcare providers to take appropriate measures to optimize maternal and fetal health during pregnancy. Abbreviations aCL anticardiolipin antibody APL antiphospholipid antibody APO adverse pregnancy outcomes APS antiphospholipid syndrome IGR intrauterine growth restriction LAC lupus anticoagulant LBW low birth weight LN lupus nephritis NOS Newcastle-Ottawa Scale OR odds ratio SGA small for gestational age SLE systemic lupus erythematosus Th cells T helper cells Treg cells regulatory T cells Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable. Availability of data and materials The datasets generated during and/or analyzed 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. Authors' contributions All authors contributed to the study conception and design. Writing - original draft preparation: Sun Chen; Writing - review and editing: Sun Chen; Conceptualization: Sun Chen; Methodology: Sun Chen; Formal analysis and investigation: Sun Chen; Resources: Li Xia; Supervision: Li Xia, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Acknowledgments Not applicable. References Tian J, Zhang D, Yao X, Huang Y, Lu Q. Global epidemiology of systemic lupus erythematosus: a comprehensive systematic analysis and modelling study. Ann Rheum Dis. 2023;82(3):351-6. Barber MRW, Drenkard C, Falasinnu T, Hoi A, Mak A, Kow NY, et al. Global epidemiology of systemic lupus erythematosus. Nat Rev Rheumatol. 2021;17(9):515-32. Nusbaum JS, Mirza I, Shum J, Freilich RW, Cohen RE, Pillinger MH, et al. Sex Differences in Systemic Lupus Erythematosus: Epidemiology, Clinical Considerations, and Disease Pathogenesis. Mayo Clin Proc. 2020;95(2):384-94. Yen EY, Singh RR. 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EULAR recommendations for women's health and the management of family planning, assisted reproduction, pregnancy and menopause in patients with systemic lupus erythematosus and/or antiphospholipid syndrome. Ann Rheum Dis. 2017;76(3):476-85. Chakravarty EF, Nelson L, Krishnan E. Obstetric hospitalizations in the United States for women with systemic lupus erythematosus and rheumatoid arthritis. Arthritis Rheum. 2006;54(3):899-907. Bramham K, Parnell B, Nelson-Piercy C, Seed PT, Poston L, Chappell LC. Chronic hypertension and pregnancy outcomes: systematic review and meta-analysis. Bmj. 2014;348:g2301. Eudy AM, Siega-Riz AM, Engel SM, Franceschini N, Howard AG, Clowse MEB, et al. Preconceptional Cardiovascular Health and Pregnancy Outcomes in Women with Systemic Lupus Erythematosus. J Rheumatol. 2019;46(1):70-7. Chen D, Lao M, Cai X, Li H, Zhan Y, Wang X, et al. Hypertensive disorders of pregnancy associated with adverse pregnant outcomes in patients with systemic lupus erythematosus: a multicenter retrospective study. Clin Rheumatol. 2019;38(12):3501-9. Huang J, Zhu Q, Wang B, Wang H, Xie Z, Zhu X, et al. Antiphospholipid antibodies and the risk of adverse pregnancy outcomes in patients with systemic lupus erythematosus: a systematic review and meta-analysis. Expert Rev Clin Immunol. 2024:1-9. Yelnik CM, Laskin CA, Porter TF, Branch DW, Buyon JP, Guerra MM, et al. Lupus anticoagulant is the main predictor of adverse pregnancy outcomes in aPL-positive patients: validation of PROMISSE study results. Lupus Sci Med. 2016;3(1):e000131. Lockshin MD, Kim M, Laskin CA, Guerra M, Branch DW, Merrill J, et al. Prediction of adverse pregnancy outcome by the presence of lupus anticoagulant, but not anticardiolipin antibody, in patients with antiphospholipid antibodies. Arthritis Rheum. 2012;64(7):2311-8. Ulcova-Gallova Z, Mockova A, Cedikova M. Screening tests of reproductive immunology in systemic lupus erythematosus. Autoimmune Dis. 2012;2012:812138. Kim MY, Guerra MM, Kaplowitz E, Laskin CA, Petri M, Branch DW, et al. Complement activation predicts adverse pregnancy outcome in patients with systemic lupus erythematosus and/or antiphospholipid antibodies. Ann Rheum Dis. 2018;77(4):549-55. Chighizola CB, Lonati PA, Trespidi L, Meroni PL, Tedesco F. The Complement System in the Pathophysiology of Pregnancy and in Systemic Autoimmune Rheumatic Diseases During Pregnancy. Front Immunol. 2020;11:2084. Radin M, Cecchi I, Crisafulli F, Klumb EM, de Jesús GR, Lacerda MI, et al. Complement levels during the first trimester predict disease flare and adverse pregnancy outcomes in systemic lupus erythematosus: A network meta-analysis on 532 pregnancies. Autoimmun Rev. 2023;22(12):103467. Crisafulli F, Andreoli L, Zucchi D, Reggia R, Gerardi MC, Lini D, et al. Variations of C3 and C4 Before and During Pregnancy in Systemic Lupus Erythematosus: Association With Disease Flares and Obstetric Outcomes. J Rheumatol. 2023;50(10):1296-301. Xu X, Liang MY, Wang JL, Chen S. Clinical features and outcome of pregnancy with SLE-associated thrombocytopenia. J Matern Fetal Neonatal Med. 2016;29(5):789-94. Akkuş F, Doğru Ş. Platelet ındices as potential biomarkers of perinatal outcomes in women with SLE during pregnancy. Arch Gynecol Obstet. 2024. Dong Y, Yuan F, Dai Z, Wang Z, Zhu Y, Wang B. Preeclampsia in systemic lupus erythematosus pregnancy: a systematic review and meta-analysis. Clin Rheumatol. 2020;39(2):319-25. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Supplementary Materials Supplementary Table 1 Search strategy Supplementary Table 2 Assessment of case-control study quality-Newcastle Ottawa Scale Supplementary Table 3 Assessment of corhort study quality-Newcastle Ottawa Scale Supplementary Figure 1 OR for association between active lupus during pregnancy and APOs Supplementary Figure 2 OR for association between active lupus during pregnancy and preterm birth Supplementary Figure 3 OR for association between active lupus during pregnancy and pregnancy loss Supplementary Figure 4 OR for association between LLDAS and APOs Supplementary Figure 5 OR for association between disease flare and preterm birth Supplementary Figure 6 OR for association between disease flare and pregnancy loss Supplementary Figure 7 OR for association between APS/positive aPL and APOs Supplementary Figure 8 OR for association between APS and preterm birth Supplementary Figure 9 OR for association between APS/positive aPL and pregnancy loss Supplementary Figure 10 OR for association between hypocomplementemia and APOs Supplementary Figure 11 OR for association between hypocomplementemia and pregnancy loss Supplementary Figure 12 OR for association between low platelet count and pregnancy loss Supplementary Figure 13 OR for association between preeclampsia/eclampsia and preterm birth Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4871546","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339355836,"identity":"967c09b8-6ef2-48c7-84b7-9aa07c5fe2ad","order_by":0,"name":"Chen Sun","email":"","orcid":"","institution":"Hangzhou Normal University Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Sun","suffix":""},{"id":339355837,"identity":"82aaf6ea-8d37-4ecf-85a6-5c2149360e72","order_by":1,"name":"Xia Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3RMQrCMBSA4SeBdIl2bVHxCpGCOkjPEil4ARcHkYCDi7gLHqJHeCVgFw/RqS4ubi6iSUHHmFEw/9IS3kcSAuDz/WAdAoAAEaGBBK4XWvIboR/C0JV8/iLRfBxIwIZ4X02CTnypFgym/RxJXdkPRkWxO+mDdYVIGMyTHOmY2wlBbMuGoCZqliOjkZ20ZPEwJC6kJk8XQlA1u0QENEEXQoXqmbuwOQyPPEsOio6sJAzPye26WmeDbVnz6zLt78tNbSXvMrMjbx6TuMzrUjNbOQ77fD7fn/UCM+M7Bspmgo4AAAAASUVORK5CYII=","orcid":"","institution":"Sir Run Run Shaw Hospital, Zhejiang University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xia","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-08-07 03:21:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4871546/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4871546/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64714208,"identity":"f66db995-001a-4d98-99c9-e23c13240371","added_by":"auto","created_at":"2024-09-18 02:19:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":504892,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram representing the study selection\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/5fe7377b2ab62b13d18d5211.png"},{"id":64714206,"identity":"7ee825e0-4d60-41b6-94de-c23de889c64a","added_by":"auto","created_at":"2024-09-18 02:19:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":480825,"visible":true,"origin":"","legend":"\u003cp\u003eOR for association between LN and APOs\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/8eeb283bbd9297eb4136030c.png"},{"id":64714812,"identity":"a11bc8e0-b133-4ea8-a2de-e70c1381c5fa","added_by":"auto","created_at":"2024-09-18 02:27:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":304404,"visible":true,"origin":"","legend":"\u003cp\u003eOR for association between LN and preterm birth\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/8e7d50dc42f8da2508d02b95.png"},{"id":64714813,"identity":"9c83be1a-da66-47b0-a6ad-7a0617a1fc7a","added_by":"auto","created_at":"2024-09-18 02:27:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":425595,"visible":true,"origin":"","legend":"\u003cp\u003eOR for association between LN and pregnancy loss\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/11ada1498b435bcb85b5c503.png"},{"id":64715194,"identity":"8c0f81da-3063-4add-8888-9cbfa38d4227","added_by":"auto","created_at":"2024-09-18 02:35:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":208879,"visible":true,"origin":"","legend":"\u003cp\u003eOR for association between LN and IGUR\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/7296dbd518c1c01e402a05ed.png"},{"id":64714211,"identity":"8caeb78b-1e8c-44c0-9e0a-31eac4d74226","added_by":"auto","created_at":"2024-09-18 02:19:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":203043,"visible":true,"origin":"","legend":"\u003cp\u003eOR for association between LN and LBW\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/db8ebfa8035f29408f25a84e.png"},{"id":64714818,"identity":"eac3c330-9c28-4307-b877-01de2547e626","added_by":"auto","created_at":"2024-09-18 02:27:43","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":394110,"visible":true,"origin":"","legend":"\u003cp\u003eOR for association between hypertension and APOs\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/8a599170e11ab060df14c0c2.png"},{"id":64714215,"identity":"2e124e7b-1878-436e-b585-ba6940bc0b10","added_by":"auto","created_at":"2024-09-18 02:19:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":300664,"visible":true,"origin":"","legend":"\u003cp\u003eOR for association between hypertension and preterm birth\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/64773c943432148cc9a463db.png"},{"id":64714212,"identity":"4ab8b6b7-e20f-46e3-9e1a-1ea4488fc7ce","added_by":"auto","created_at":"2024-09-18 02:19:43","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":309089,"visible":true,"origin":"","legend":"\u003cp\u003eOR for association between hypertension and pregnancy loss\u003c/p\u003e","description":"","filename":"Fig.9.png","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/a197d830fa5ae1f7f476752e.png"},{"id":79077019,"identity":"a8718955-d64c-4803-bade-9858079c42cb","added_by":"auto","created_at":"2025-03-24 07:32:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4119258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/dcae3b83-feba-4b73-bc73-1a15a4858710.pdf"},{"id":64714814,"identity":"3090f4d9-e28f-413c-bcfa-c1f58abda5c2","added_by":"auto","created_at":"2024-09-18 02:27:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5093474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 1 \u003c/strong\u003eSearch strategy\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 2 \u003c/strong\u003eAssessment of case-control study quality-Newcastle Ottawa Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e Assessment of corhort study quality-Newcastle Ottawa Scale\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e OR for association between active lupus during pregnancy and APOs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 2\u003c/strong\u003e OR for association between active lupus during pregnancy and preterm birth\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 3\u003c/strong\u003e OR for association between active lupus during pregnancy and pregnancy loss\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 4\u003c/strong\u003e OR for association between LLDAS and APOs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5\u003c/strong\u003e OR for association between disease flare and preterm birth\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 6\u003c/strong\u003e OR for association between disease flare and pregnancy loss\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 7 \u003c/strong\u003eOR for association between APS/positive aPL and APOs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 8\u003c/strong\u003e OR for association between APS and preterm birth\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 9\u003c/strong\u003e OR for association between APS/positive aPL and pregnancy loss\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 10\u003c/strong\u003e OR for association between hypocomplementemia and APOs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 11\u003c/strong\u003e OR for association between hypocomplementemia and pregnancy loss\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 12\u003c/strong\u003e OR for association between low platelet count and pregnancy loss\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary\u003c/strong\u003e \u003cstrong\u003eFigure 13 \u003c/strong\u003eOR for association between preeclampsia/eclampsia and preterm birth\u003c/p\u003e","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4871546/v1/f5afc70f874ac8061f5f7e08.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRisk Factors For Adverse Pregnancy Outcomes in Systemic Lupus Erythematosus: A Meta Analysis and Systemic review\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eSystemic lupus erythematosus (SLE), commonly known as lupus, is a prevalent chronic multisystemic autoimmune disease characterized by the presence of pathogenic autoantibodies and immune complexes, contributing to tissue damage. The estimated global incidence is 5.14 (from 1.4 to 15.3) per 100,000 person-years, with a corresponding prevalence estimated at 43.7 (from 15.87 to 108.92) per 100,000 individuals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The chronic disease progression and the use of immunosuppressive therapy are significant contributors to life-threatening systemic organ damage. The mortality among SLE patients remains significantly higher, with rates two to three times greater than that among the general population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Certain ethnic populations, like Black, Asian, and Hispanic, exhibit a pronounced predisposition [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSLE presents diverse phenotypes, spanning from mild skin symptoms to severe organ failures, affecting virtually any organ in the body. It has a distinct gender bias, predominantly affecting women of reproductive age, with a female/male ratio of roughly 9:1. The average age at which SLE is diagnosed is around 35 years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. SLE ranks among the top causes of mortality in young females [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The high susceptibility of reproductive-age women to SLE raises significant concerns for clinicians regarding childbearing. Despite comparable anti-M\u0026uuml;llerian hormone levels and fertility potential as healthy controls [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], SLE women have heightened vulnerability to adverse pregnancy outcomes (APOs) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Meta-analysis revealed that SLE subgroups exhibited significantly elevated rates of spontaneous abortion, stillbirth, premature birth, \"small for gestational age\u0026rdquo; (SGA) infants, and infants with low birth weight [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. SLE-associated pregnancies are deemed high-risk pregnancies. Early identification of high-risk factors for APOs is of utmost importance. The careful monitoring of predictors and effective management of pregnancies in SLE women can improve outcomes.\u003c/p\u003e \u003cp\u003eThe risk factors for APOs in SLE pregnancies have been extensively studied. However, these reports had limitations including a small sample size and limited diversity in terms of ethnicity and geographical regions. A systemic meta-analysis on this topic is currently absent. Therefore, our study aims to conduct a systemic review and meta-analysis to identify a set of risk factors that could potentially be targeted for interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSearch Strategies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePubMed, Cochrane Library, Web of Science, and EMBASE were comprehensively searched for relevant articles published in English and Chinese from initiation up to December 31, 2023. The search strategies encompassed Mesh terms and free words. The detailed retrieval strategies used are displayed in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Selection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter removal of duplicates, two investigators (Sun C, Li X) independently evaluated the potentially eligible articles by skimming through titles and abstracts.\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria covered: (1) with SLE pregnant women as study subjects; (2) with specific diagnostic criteria for identifying APOs; (3) multivariate analysis was employed to determine risk factors; (4) cohort or case-control study.\u003c/p\u003e\n\u003cp\u003eThe articles were excluded for the following criteria: (1) guidelines, reviews, editorials, case reports, abstracts, letters, meeting summaries, or recalled articles; (2) incomplete or unavailable data; (3) inconsistent definition of APOs; (4) limited to univariate analysis; (5) publications not in English or Chinese languages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach eligible study underwent independent evaluation by the two authors using the Newcastle-Ottawa Scale (NOS) in three domains: patient representation, exposure and outcome determination, and follow-up adequacy. A comprehensive assessment led to the overall NOS score for each study was 9, with scores of 0-5 indicating low quality, scores of 6-7 indicating moderate quality, and scores of 8-9 indicating high quality, signifying a low risk of bias.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Extraction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo independent reviewers (Sun C, Li X) extracted all the data, including first author, publication year, study location, sample size, mean age of pregnancy, diagnostic criteria, and documented risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTerms Definition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAPOs were defined to include at least one of the following complications:\u003c/p\u003e\n\u003cp\u003e-Pregnancy loss: abortion, stillbirth, or neonatal death.\u003c/p\u003e\n\u003cp\u003e-Preterm birth: delivery occurring before 37 weeks of gestation.\u003c/p\u003e\n\u003cp\u003e-Intrauterine growth restriction (IUGR): impaired fetal growth resulting in a smaller than expected size.\u003c/p\u003e\n\u003cp\u003e-Small\u0026nbsp;for\u0026nbsp;gestational\u0026nbsp;age\u0026nbsp;(SGA): a neonate with a birthweight below the fifth percentile for their gestational age.\u003c/p\u003e\n\u003cp\u003e-Low birth weight (LBW): infants with a birthweight below a certain threshold, typically 2500 grams.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA meta-analysis was conducted to assess the connection between potential risk factors and APOs in SLE patients. The odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were computed by Stata 15.1 software. Forest plots were employed to present the ORs from individual studies and the pooled OR estimate.\u003c/p\u003e\n\u003cp\u003eGiven the variability in baseline data across the included studies, heterogeneity was observed. To evaluate this heterogeneity, the I² test was utilized. Heterogeneity was considered statistically significant if I²≥ 50% or p-value \u0026lt; 0.05. Based on heterogeneity (I²), a fixed-effects (I²\u0026lt;50%) or random-effects (I²\u0026gt;50%) statistical model was employed.\u003c/p\u003e\n\u003cp\u003eThe results’ robustness was appraised using sensitivity analysis, while publication bias was judged to evaluate the potential impact of selective reporting.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eArticle Selection\u003c/h2\u003e \u003cp\u003eThe initial research on four databases retrieved 1123 records. After the removal of duplicates, 647 records remained. After carefully reviewing the titles and abstracts, 569 articles were excluded, resulting in 78 articles for further comprehensive evaluation in full-text format. Through this rigorous selection process, 42 articles (1 in Chinese and 41 in English) were utilized for review, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy Characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the key characteristics of 42 included articles, providing information on study type, geographical regions, and sample sizes. As for study types, 4 were retrospective case-control studies, 8 were retrospective cohort studies, and 30 were prospective cohort studies. The patient population was geographically diverse, spanning regions such as Asia, North America, Europe, the Middle East, and others. Overall, the analysis involved a substantial sample size of 8723 pregnancies, ensuring reliable and generalizable findings.\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\u003eKey characteristics of included articles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \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\u003eYear\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\u003eSample Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean Age of pregnancy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndrade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArfaj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2010\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\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl-Riyami\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\u003eOman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuyon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen\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\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\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\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClowse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeguchi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHiramatsu\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\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective case-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHamijoyo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective case-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHe\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\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\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\u003e27.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrino\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\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJiang\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\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKim\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\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective case-control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKalok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective case-control\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\u003e30.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKwok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHong Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLa\u0026iacute;no-Pineiro\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\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarosa\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\u003eprospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.6\u0026thinsp;+\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLouthrenoo\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\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.94\u0026thinsp;\u0026plusmn;\u0026thinsp;4.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLu\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\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMadrazo\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\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiranda\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\u003eMexico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMokbel\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\u003eEgypt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eprospective cohort\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\u003e27.16\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatli\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\u003eprospective cohort\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\u003e33.5 (6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOishi\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\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalma dos Reis\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\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.6 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKorea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShaharir\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\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugawara\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\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\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTani\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\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZamani\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\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.74\u0026thinsp;\u0026plusmn;\u0026thinsp;3.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang\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\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eretrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQuality Evaluation\u003c/h2\u003e \u003cp\u003eThe risk of bias was evaluated using NOS scores, and the results are presented in Supplementary Tables\u0026nbsp;2\u0026ndash;3. Among the 41 studies analyzed, a significant majority of 75.60% (31/41) attained 8 scores or higher, indicating high quality. Additionally, 10 studies were rated as moderate quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis Result\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eLupus Nephritis\u003c/h2\u003e \u003cp\u003e11 studies revealed a significant association between lupus nephritis (LN) and APOs. The analysis, employing a fixed-effects model, indicated low heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;21.7, P\u0026thinsp;=\u0026thinsp;0.250) and demonstrated that patients with LN exhibited a 3.02-fold higher risk of APO than those without LN (OR, 3.02; 95% CI, 2.10\u0026ndash;4.34). P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 7 studies examining the association between LN and preterm birth, a fixed-effects model was utilized, showing low heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;3.3, P\u0026thinsp;=\u0026thinsp;0.401). The analysis revealed LN as a prominent high-risk factor for preterm birth, with individuals with LN having a 3.69-fold increased risk compared to those without LN (OR, 3.69; 95% CI, 2.31\u0026ndash;5.89, P\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 7 studies on pregnancy loss, a random-effects model was employed, indicating moderate heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;50.1, P\u0026thinsp;=\u0026thinsp;0.051). The analysis identified LN as a high-risk factor for pregnancy loss (OR, 3.47; 95% CI, 1.74\u0026ndash;6.89, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 3 studies on IUGR, a heterogeneity test showed high heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;69.4, P\u0026thinsp;=\u0026thinsp;0.038). The analysis revealed that LN was connected with a 3.51-fold elevated risk of IUGR (OR, 3.51; 95% CI, 1.30\u0026ndash;9.51, P\u0026thinsp;=\u0026thinsp;0.013) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn 3 studies on LBW, moderate heterogeneity was observed (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;41.8, P\u0026thinsp;=\u0026thinsp;0.179). LN was significantly linked to a 5.55-fold elevated risk of LBW (OR, 5.55; 95% CI, 1.29\u0026ndash;23.86, P\u0026thinsp;=\u0026thinsp;0.021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHypertension\u003c/h2\u003e \u003cp\u003eIn the analysis of 6 studies, a heterogeneity test revealed moderate heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;57.4, P\u0026thinsp;=\u0026thinsp;0.029). Hypertension was strongly linked to a 5.23-fold enhanced risk of APOs (OR, 5.23; 95% CI, 2.76\u0026ndash;9.91, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, in the analysis of 5 studies, moderate heterogeneity was observed (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;58.6, P\u0026thinsp;=\u0026thinsp;0.046). Hypertension was uncovered as a high-risk factor for preterm birth, with a 4.50-fold raised risk (OR, 4.50; 95% CI, 1.86\u0026ndash;10.87, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, the role of high blood pressure in pregnancy loss remains inconclusive (OR, 1.33; 95% CI, 0.71\u0026ndash;1.94, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDisease activity\u003c/h2\u003e \u003cp\u003eBased on 7 studies, active lupus during pregnancy substantially increased the risk of APOs, with high heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;76.0, P\u0026thinsp;\u0026lt;\u0026thinsp;0.000). The analysis revealed a 2.51-fold higher risk of APOs in patients with active lupus during pregnancy (OR, 2.51; 95% CI, 1.39\u0026ndash;4.50, P\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eFurthermore, active lupus related to a 3.92-fold higher risk of preterm birth (OR, 3.92; 95% CI, 2.52\u0026ndash;6.10, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a 9.19-fold higher risk of pregnancy loss (OR, 9.19; 95% CI, 3.14\u0026ndash;26.89, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn 3 studies evaluating the impact of low disease activity state (LLDAS) during pregnancy on APOs, low heterogeneity was observed (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.00, P\u0026thinsp;=\u0026thinsp;0.616). LLDAS was considered a protective factor against APOs (OR, 0.26; 95% CI, 0.12\u0026ndash;0.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eBased on 6 studies, disease flare during pregnancy markedly enhanced the risk of preterm birth (OR, 4.02; 95% CI, 2.24\u0026ndash;7.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with moderate heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;56.7, P\u0026thinsp;=\u0026thinsp;0.042).\u003c/p\u003e \u003cp\u003eAdditionally, in the analysis of 4 studies, disease flare was connected with a 2.72-fold raised risk of pregnancy loss (OR, 2.72; 95% CI, 1.36\u0026ndash;5.46, P\u0026thinsp;=\u0026thinsp;0.005), with moderate heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;54.2, P\u0026thinsp;=\u0026thinsp;0.087) (Supplementary Figs.\u0026nbsp;1\u0026ndash;6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAntiphospholipid syndrome (APS)/Positive antiphospholipid antibody (aPL)\u003c/h2\u003e \u003cp\u003eAPS or positive aPL significantly elevated the risk of APOs by 4.97 times (OR, 4.97; 95% CI, 1.87\u0026ndash;13.17, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), based on 5 studies with high heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;71.6, P\u0026thinsp;=\u0026thinsp;0.007) (Supplementary Fig.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eAPS was also a risk factor for preterm birth, with a 3.95-fold raised risk (OR, 3.95; 95% CI, 2.20\u0026ndash;7.07, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), according to 5 studies with low heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0, P\u0026thinsp;=\u0026thinsp;0.457). Additionally, APS or positive aPL elevated the risk of pregnancy loss by 3.46 times (OR, 3.46; 95% CI, 2.44\u0026ndash;4.91, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), based on 10 articles with low heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0, P\u0026thinsp;=\u0026thinsp;0.822) (Supplementary Figs.\u0026nbsp;8\u0026ndash;9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eHypocomplementemia\u003c/h2\u003e \u003cp\u003eBased on the analysis of 5 studies, there was a reported relationship between low complement levels and APOs (OR, 1.94; 95% CI, 1.39\u0026ndash;2.72, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with moderate heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;45.7, P\u0026thinsp;=\u0026thinsp;0.118). Additionally, low complement levels were linked to an intensified risk of pregnancy loss (OR, 2.60; 95% CI, 1.08\u0026ndash;6.27, P\u0026thinsp;=\u0026thinsp;0.033) (Supplementary Figs.\u0026nbsp;10\u0026ndash;11).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThrombocytopenia\u003c/h2\u003e \u003cp\u003eBased on the analysis of 4 studies, a relationship was found between low platelet count and pregnancy loss (OR, 2.20; 95% CI, 1.00\u0026ndash;4.81, P\u0026thinsp;=\u0026thinsp;0.049), with high heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;81.8, P\u0026thinsp;\u0026lt;\u0026thinsp;0.000), indicating significant variability in the results (Supplementary Fig.\u0026nbsp;12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePreeclampsia/eclampsia\u003c/h2\u003e \u003cp\u003eBased on the analysis of four studies, a relationship was found between preeclampsia/eclampsia and preterm birth (OR, 8.85; 95% CI, 4.72\u0026ndash;16.58, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with low heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0, P\u0026thinsp;=\u0026thinsp;0.732), indicating consistent findings across the studies (Supplementary Fig.\u0026nbsp;13).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePublication bias\u003c/h2\u003e \u003cp\u003eAs mentioned in the previous statistical analysis, we assessed the potential publication bias. The results of Egger's tests can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eSummarized results\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=\"left\" 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=\"char\" char=\".\" 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\u003eAdverse Pregnancy Outcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHeterogeneity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEgger\u0026rsquo;s test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eAPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.23(2.76,9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLupus nephritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.02(2.10,4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypocomplementemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.94(1.39,2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLLDAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26 (0.12,0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.51(1.39,4.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPS/aPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.97(1.87,13.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePreterm Birth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLupus nephritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.69(2.31, 5.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.50(1.86,10.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreeclampsia/eclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.8(4.72, 16.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.02(2.24,7.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\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=\"c2\"\u003e \u003cp\u003eActive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.92(2.52, 6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.95(2.20, 7.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ePregnancy Loss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypocomplementemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.60(1.08,6.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.72(1.36,5.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThrombocytopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.20(1.00, 4.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLupus nephritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.47(1.74,6.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.33(0.71,1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAPS/aPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.46(2.44,4.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.19(3.14,26.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Birth Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLupus nephritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.55(1.29,23.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrauterine Growth Restriction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLupus nephritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.51(1.30,9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePregnancy in SLE patients is commonly regarded as a high-risk condition due to its well-documented connection with APOs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. SLE pregnancies are more susceptible to pregnancy loss, preterm births, and IUGR than the general population [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A meta-analysis of 2751 pregnancies showed a 23.4% rate of unsuccessful pregnancy and a 39.4% rate of premature birth [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our meta-analysis of 41 papers investigated the risk factors for APOs.\u003c/p\u003e \u003cp\u003ePregnancy and autoimmunity influence each other. The concept of Th2 phenomenon in successful pregnancy, proposed in 1993, refers to the suppression of CD4\u0026thinsp;+\u0026thinsp;T helper 1 (Th1) cells and a shift towards Th2 anti-inflammatory cytokines [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Extensive research has revealed the dynamic balance of cytokine expression during different stages of pregnancy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Proinflammatory Th17 cells are associated with APOs, while regulatory T (Treg) cells increase in healthy pregnant women and are linked to immune tolerance towards the fetus [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In the third trimester of SLE pregnancy, the lower-than-anticipated decrease of Th1 cytokines was detected [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Both the quantity and functionality of Treg cells are reduced in SLE patients, suggesting impairment in placental development and fetal tolerance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. They also display increased secretions of activin A, IL-6, IL-17, IL-10, and TNF-α during pregnancy, indicating a hyper-reactive immune system [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A 16-year cohort study by Clowse ME et al. revealed that high-activity lupus during pregnancy augmented the risk of preterm birth, decreased live births, and brought about a significant rate of fetal loss [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Active lupus may impact the uteroplacental unit, leading to preterm labor and membrane rupture [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The activity indices in SLE pregnancy can be assessed using the SLE in Pregnancy Activity Index, Modified Lupus Activity Measurement, and Lupus Activity Index in Pregnancy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. American College of Rheumatology\u0026rsquo;s Reproductive Health Guideline 2020 recommends SLE women to conceive during a period of clinically mild or inactive disease activity in the 6 months before pregnancy and to continue taking pregnancy-compatible lupus medications throughout pregnancy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eActive LN during pregnancy is linked to a high risk of fetal loss, ranging from 35\u0026ndash;50% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Smyth A et al. have confirmed LN raises the risk of preterm delivery and unsuccessful pregnancy, while IUGR incidence is estimated to be 12.7% [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], consistent with the findings of another meta-analysis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Patients with class III/IV LN exhibit a lower mean birthweight [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In patients with renal involvement, baseline renal function, proteinuria levels, and new onset have been associated with APOs [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. European League Against Rheumatism recommends ongoing monitoring during pregnancy, including urine protein excretion, urine sediment analysis (assessing glomerular hematuria and urinary casts), serum creatinine levels, and glomerular filtration rate [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSLE women have noticeably higher rates of hypertension than healthy individuals [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It has been firmly established that hypertension has a significant impact on pregnancy outcomes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Inadequate blood pressure regulation during pregnancy is linked to decreased gestational age at delivery [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], consistent with our results. Hypertension during pregnancy can trigger an imbalance of Th cells and the release of cytokines, leading to the reactive flare of lupus [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Conversely, active SLE can also serve as a predictor of hypertension [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe meta-analysis confirmed that aPL was connected with higher rates of APOs, including elevated fetal loss risk for Antiβ2GP1 and anticardiolipin antibody (aCL), and higher risk of preterm birth for lupus anticoagulant (LAC) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. LAC and aCL are predictors of APOs in aPL-positive patients [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition to causing thrombosis in the uteroplacental vasculature, aPL directly impairs the production of chorionic gonadotropin by influencing anionic phospholipids and β2GP1 in trophoblast cells during early pregnancy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Despite aspirin and low molecular weight heparin treatment, APS still results in a 20\u0026ndash;30% rate of pregnancy loss, suggesting that hydroxychloroquine and pravastatin should be explored as potential alternative options [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComplement activation contributes to abnormal placental development, as evidenced by the significant association between increased Bb and sC5b-9 levels in early pregnancy and APOs [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Complement fragments are detected in placental samples from complicated pregnancies, and C4d emerges as the prominent biomarker for complement activation in the placenta [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Physiological fluctuations in complement levels during gestation can be indicative of APOs in SLE pregnancy if there is a lack of anticipated increase in C3 and C4 levels [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThrombocytopenia during SLE pregnancy is linked to heightened disease activity, early onset preeclampsia, and elevated rates of pregnancy loss [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Plateletcrit is a valuable marker for predicting the risk of stillbirth in SLE pregnancies [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The decrease in platelet count could potentially signify early signs of preeclampsia, microthrombosis, or lupus activity. Based on our findings, eclampsia or preeclampsia is a high-risk factor for preterm birth in SLE. SLE pregnancies carry an elevated risk of preeclampsia, which is influenced by SLE circulating immune complexes and dysregulated cytokine expression [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis research is constrained by several limitations, including moderate heterogeneity during subgroup analysis, differences in the definition of pregnancy outcomes and risk factors, and variations in medications used during pregnancy and treatment strategies across hospitals. These limitations should be considered when interpreting and applying the obtained findings. Further research may be necessary to address these limitations and provide more precise references.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current systematic review and meta-analysis examined the risk factors for APOs in SLE pregnancies and identified LN, hypertension, disease activity, aPL, hypocomplementemia, thrombocytopenia, and preeclampsia/eclampsia as significant risk factors. These findings enhance comprehension regarding risk factors for APOs in SLE pregnancies and allow healthcare providers to take appropriate measures to optimize maternal and fetal health during pregnancy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaCL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eanticardiolipin antibody\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantiphospholipid antibody\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eadverse pregnancy outcomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eantiphospholipid syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIGR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintrauterine growth restriction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elupus anticoagulant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLBW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow birth weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elupus nephritis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNewcastle-Ottawa Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esmall for gestational age\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSLE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystemic lupus erythematosus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTh cells\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT helper cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTreg cells\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eregulatory T cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\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 generated during and/or analyzed 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\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Writing - original draft preparation: Sun Chen; Writing - review and editing: Sun Chen; Conceptualization: Sun Chen; Methodology: Sun Chen; Formal analysis and investigation: Sun Chen; Resources: Li Xia; Supervision: Li Xia, 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\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eTian J, Zhang D, Yao X, Huang Y, Lu Q. Global epidemiology of systemic lupus erythematosus: a comprehensive systematic analysis and modelling study. Ann Rheum Dis. 2023;82(3):351-6.\u003c/li\u003e\n \u003cli\u003eBarber MRW, Drenkard C, Falasinnu T, Hoi A, Mak A, Kow NY, et al. Global epidemiology of systemic lupus erythematosus. Nat Rev Rheumatol. 2021;17(9):515-32.\u003c/li\u003e\n \u003cli\u003eNusbaum JS, Mirza I, Shum J, Freilich RW, Cohen RE, Pillinger MH, et al. Sex Differences in Systemic Lupus Erythematosus: Epidemiology, Clinical Considerations, and Disease Pathogenesis. Mayo Clin Proc. 2020;95(2):384-94.\u003c/li\u003e\n \u003cli\u003eYen EY, Singh RR. Brief Report: Lupus-An Unrecognized Leading Cause of Death in Young Females: A Population-Based Study Using Nationwide Death Certificates, 2000-2015. Arthritis Rheumatol. 2018;70(8):1251-5.\u003c/li\u003e\n \u003cli\u003eGasparin AA, Souza L, Siebert M, Xavier RM, Chakr RM, Palominos PE, et al. Assessment of anti-M\u0026uuml;llerian hormone levels in premenopausal patients with systemic lupus erythematosus. Lupus. 2016;25(3):227-32.\u003c/li\u003e\n \u003cli\u003ePolić A, Običan SG. Pregnancy in systemic lupus erythematosus. Birth Defects Res. 2020;112(15):1115-25.\u003c/li\u003e\n \u003cli\u003eBundhun PK, Soogund MZ, Huang F. Impact of systemic lupus erythematosus on maternal and fetal outcomes following pregnancy: A meta-analysis of studies published between years 2001-2016. J Autoimmun. 2017;79:17-27.\u003c/li\u003e\n \u003cli\u003eHe WR, Wei H. Maternal and fetal complications associated with systemic lupus erythematosus: An updated meta-analysis of the most recent studies (2017-2019). Medicine (Baltimore). 2020;99(16):e19797.\u003c/li\u003e\n \u003cli\u003eZhang S, Han X, Liu W, Wen Q, Wang J. Pregnancy in patients with systemic lupus erythematosus: a systematic review. Arch Gynecol Obstet. 2023;308(1):63-71.\u003c/li\u003e\n \u003cli\u003eYan Yuen S, Krizova A, Ouimet JM, Pope JE. Pregnancy outcome in systemic lupus erythematosus (SLE) is improving: Results from a case control study and literature review. Open Rheumatol J. 2008;2:89-98.\u003c/li\u003e\n \u003cli\u003eDhar JP, Essenmacher LM, Ager JW, Sokol RJ. Pregnancy outcomes before and after a diagnosis of systemic lupus erythematosus. Am J Obstet Gynecol. 2005;193(4):1444-55.\u003c/li\u003e\n \u003cli\u003eSmyth A, Oliveira GH, Lahr BD, Bailey KR, Norby SM, Garovic VD. A systematic review and meta-analysis of pregnancy outcomes in patients with systemic lupus erythematosus and lupus nephritis. Clin J Am Soc Nephrol. 2010;5(11):2060-8.\u003c/li\u003e\n \u003cli\u003eWegmann TG, Lin H, Guilbert L, Mosmann TR. Bidirectional cytokine interactions in the maternal-fetal relationship: is successful pregnancy a TH2 phenomenon? Immunol Today. 1993;14(7):353-6.\u003c/li\u003e\n \u003cli\u003e\u0026Oslash;stensen M, Villiger PM, F\u0026ouml;rger F. Interaction of pregnancy and autoimmune rheumatic disease. Autoimmun Rev. 2012;11(6-7):A437-46.\u003c/li\u003e\n \u003cli\u003eIaccarino L, Ghirardello A, Zen M, Villalta D, Tincani A, Punzi L, et al. Polarization of TH2 response is decreased during pregnancy in systemic lupus erythematosus. Reumatismo. 2012;64(5):314-20.\u003c/li\u003e\n \u003cli\u003eTower C, Crocker I, Chirico D, Baker P, Bruce I. SLE and pregnancy: the potential role for regulatory T cells. Nat Rev Rheumatol. 2011;7(2):124-8.\u003c/li\u003e\n \u003cli\u003eTorricelli M, Bellisai F, Novembri R, Galeazzi LR, Iuliano A, Voltolini C, et al. High levels of maternal serum IL-17 and activin A in pregnant women affected by systemic lupus erythematosus. Am J Reprod Immunol. 2011;66(2):84-9.\u003c/li\u003e\n \u003cli\u003eClowse ME, Magder LS, Witter F, Petri M. The impact of increased lupus activity on obstetric outcomes. Arthritis Rheum. 2005;52(2):514-21.\u003c/li\u003e\n \u003cli\u003eRuiz-Irastorza G, Khamashta MA. Evaluation of systemic lupus erythematosus activity during pregnancy. Lupus. 2004;13(9):679-82.\u003c/li\u003e\n \u003cli\u003eSammaritano LR, Bermas BL, Chakravarty EE, Chambers C, Clowse MEB, Lockshin MD, et al. 2020 American College of Rheumatology Guideline for the Management of Reproductive Health in Rheumatic and Musculoskeletal Diseases. Arthritis Rheumatol. 2020;72(4):529-56.\u003c/li\u003e\n \u003cli\u003eKattah AG, Garovic VD. Pregnancy and Lupus Nephritis. Semin Nephrol. 2015;35(5):487-99.\u003c/li\u003e\n \u003cli\u003eWu J, Ma J, Zhang WH, Di W. Management and outcomes of pregnancy with or without lupus nephritis: a systematic review and meta-analysis. Ther Clin Risk Manag. 2018;14:885-901.\u003c/li\u003e\n \u003cli\u003eCarmona F, Font J, Moga I, L\u0026agrave;zaro I, Cervera R, Pac V, et al. Class III-IV proliferative lupus nephritis and pregnancy: a study of 42 cases. Am J Reprod Immunol. 2005;53(4):182-8.\u003c/li\u003e\n \u003cli\u003eBramham K, Soh MC, Nelson-Piercy C. Pregnancy and renal outcomes in lupus nephritis: an update and guide to management. Lupus. 2012;21(12):1271-83.\u003c/li\u003e\n \u003cli\u003eAndreoli L, Bertsias GK, Agmon-Levin N, Brown S, Cervera R, Costedoat-Chalumeau N, et al. EULAR recommendations for women\u0026apos;s health and the management of family planning, assisted reproduction, pregnancy and menopause in patients with systemic lupus erythematosus and/or antiphospholipid syndrome. Ann Rheum Dis. 2017;76(3):476-85.\u003c/li\u003e\n \u003cli\u003eChakravarty EF, Nelson L, Krishnan E. Obstetric hospitalizations in the United States for women with systemic lupus erythematosus and rheumatoid arthritis. Arthritis Rheum. 2006;54(3):899-907.\u003c/li\u003e\n \u003cli\u003eBramham K, Parnell B, Nelson-Piercy C, Seed PT, Poston L, Chappell LC. Chronic hypertension and pregnancy outcomes: systematic review and meta-analysis. 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Clinical features and outcome of pregnancy with SLE-associated thrombocytopenia. J Matern Fetal Neonatal Med. 2016;29(5):789-94.\u003c/li\u003e\n \u003cli\u003eAkkuş F, Doğru Ş. Platelet ındices as potential biomarkers of perinatal outcomes in women with SLE during pregnancy. Arch Gynecol Obstet. 2024.\u003c/li\u003e\n \u003cli\u003eDong Y, Yuan F, Dai Z, Wang Z, Zhu Y, Wang B. Preeclampsia in systemic lupus erythematosus pregnancy: a systematic review and meta-analysis. Clin Rheumatol. 2020;39(2):319-25.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Systemic lupus erythematosus, pregnancy outcome, risk factor","lastPublishedDoi":"10.21203/rs.3.rs-4871546/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4871546/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSystemic lupus erythematosus (SLE) is a prominent autoimmune disease highly linked to adverse pregnancy outcomes (APOs). Previous research on the risk factors for APOs in SLE pregnancies has been limited by regional constraints or inadequate sample sizes. There is currently a dearth of comprehensive systemic reviews on this topic. To address these research gaps, we conducted a rigorous meta-analysis and systematic review to elucidate the risk factors for APOs in SLE pregnancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003ePubMed, Embase, Web of Science, and the Cochrane Library systematically searched for articles on risk factors for APOs in SLE pregnancy from initiation to December 31, 2023. The pooled Odds Ratio (OR) was estimated using a random-effects or fixed-effects model for each investigated factor. Egger's P value was calculated to assess publication bias and heterogeneity was evaluated by the I\u003csup\u003e2 \u003c/sup\u003etest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e42 unique studies were enrolled. Patients with hypertension (OR, 5.23; 95% CI, 2.76–9.91), lupus nephritis (LN) (OR, 3.02; 95% CI, 2.10–4.34), high disease activity (OR, 2.51; 95% CI, 1.39–4.50), low complements (OR, 1.94; 95% CI, 1.39–2.72), antiphospholipid syndrome (APS)/positive antiphospholipid antibody (aPL) (OR, 7.93; 95% CI, 4.35–14.44) were at higher risk for APOs. The risk factors for preterm birth included LN (OR, 3.69; 95% CI, 2.31–5.89), hypertension (OR, 4.50; 95% CI, 1.86–10.87), disease flares (OR, 4.02; 95% CI, 2.24–7.19), disease activity (OR, 3.92; 95% CI, 2.52–6.10), preeclampsia/eclampsia (OR, 8.85;95% CI, 4.72–16.58), and APS (OR, 3.95; 95% CI, 2.20–7.07). The risk factors for pregnancy loss were APS/aPL (OR, 3.46; 95% CI, 2.44–4.91), low complements (OR, 2.60; 95% CI, 1.08–6.27), disease flares (OR, 2.72; 95% CI, 1.36–5.46), LN (OR, 3.47; 95% CI, 1.74–6.89), hypertension (OR, 1.33; 95% CI, 0.71–1.94), thrombocytopenia (OR, 8.85; 95% CI, 4.72–16.58), and disease activity (OR, 9.19; 95% CI). LN also predicted intrauterine growth restriction (OR, 3.51; 95% CI, 1.30–9.51) and low birth weight (OR, 5.55; 95% CI, 1.29–23.86).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study identified risk factors for APOs in SLE pregnancies, enhancing clinician awareness and enabling early intervention for high-risk patients.\u003c/p\u003e","manuscriptTitle":"Risk Factors For Adverse Pregnancy Outcomes in Systemic Lupus Erythematosus: A Meta Analysis and Systemic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-18 02:19:37","doi":"10.21203/rs.3.rs-4871546/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f520b21-cdcb-469a-95dc-bbc5f084e7b8","owner":[],"postedDate":"September 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T07:24:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-18 02:19:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4871546","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4871546","identity":"rs-4871546","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}