A model to predict the risk of adverse ocular outcomes in pregnant women

A model to predict the risk of adverse ocular outcomes in pregnant women | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Article A model to predict the risk of adverse ocular outcomes in pregnant women Xintian Liu, Yiyi Wen, Haiqing Zou, Shuangyong Y. Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4454924/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 Purpose This study aims to analyze common clinical data obtained during pregnancy, disease history, and maternal characteristics to determine ocular parameters and develop a risk prediction model for adverse ocular outcomes. Methods We retrospectively analyzed the medical records of 760 pregnant women (1,520 eyes) from September 2020 to September 2022 at the Third Affiliated Hospital of Guangzhou Medical University. The maternal variables that could influence adverse ocular outcomes were identified, including maternal age, pregnancy-induced hypertension (PIH), gestational diabetes mellitus (GDM), eclampsia and pre-eclampsia, uterine disease, fetal abnormalities, in vitro fertilization with embryo transfer, hypoproteinemia, and major comorbidities during pregnancy. Univariate and multivariate logistic regression analyses were performed to evaluate the effects of the independent predictors on adverse ocular outcomes. The receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off probability with optimum sensitivity and specificity. Results Eclampsia and pre-eclampsia, GDM, history of chronic hypertension, and hypoproteinemia were independent predictors of adverse ocular outcomes during pregnancy (P < 0.05). Maternal age, PIH, intrauterine growth retardation (IUGR), obesity, and pregnancy with immunoglobulin A nephropathy were predictors of moderate and severe retinal arteriole sclerosis during pregnancy (P < 0.05). Moreover, hemolysis, elevated liver enzymes, and low platelet (HELLP) syndrome was a predictor of retinal hemorrhage and exudate during pregnancy (P < 0.05). Adverse ocular outcomes showed area under the ROC curve values of 0.75 and 0.74. Conclusion Our predictive model could effectively predict adverse ocular outcomes during pregnancy, with the risk factors including maternal age, eclampsia and pre-eclampsia, GDM, obesity, history of chronic hypertension, hypoproteinemia, IUGR, pregnancy with immunoglobulin A nephropathy, and HELLP syndrome. Health sciences/Risk factors Health sciences/Health care/Public health Health sciences/Medical research/Study design Health sciences/Diseases/Eye diseases risk prediction model adverse ocular outcome pregnancy maternal health Figures Figure 1 Figure 2 Figure 3 1. Introduction With the development of assisted reproductive technology, the incidence of complications in pregnant women is increasing[1]. Pregnancy can influence multiple bodily functions, including metabolism, cardiovascular function, and eyesight[20]. As an end organ, the eyes may directly demonstrate changes in the terminal microvasculature. Pregnancy affects the eyes in several ways, both physiologically and pathologically[25; 28]. Alterations in corneal shape and sensation, as well as intraocular pressure, have been documented[31]. Moreover, changes related to the exacerbation of preexisting ocular disease may occur. Glaucoma[11], diabetic chorioretinopathy[4], uveitis[26], and retinal changes[8] in patients with pregnancy-induced hypertension (PIH) are some of the pregnancy features that have health implications. Many predictive models have been utilized to evaluate the associations between risk factors and adverse pregnancy outcomes[16; 36; 13]. The most common independent predictive factors are pre-eclampsia, GDM, obesity, cardiac disease, and method of delivery, amongst others[15; 38; 36]. Adverse maternal and fetal outcomes can be assessed by evaluating the maternal disease history, lifestyle, disease onset, and biochemical markers, amongst other factors. For instance, the fullPIERS model can predict patients with pre-eclampsia within 48 hours and shows that 59% of high-risk patients have an adverse outcome up to 7 days[34]. The predictors in the fullPIERS model include gestational age, chest pain or dyspnea, oxygen saturation, platelet count, and creatinine and aspartate transaminase concentrations. Utilizing this model, clinicians can improve maternal and fetal outcomes by implementing timely clinical intervention in high-risk patients with these predictors. Predictive models in ophthalmology have often focused on glaucoma, age-related macular regeneration, and cataract surgery[32; 23]. However, few studies have evaluated ocular changes in system disease. In this study, we aim to identify the independent predictors of adverse ocular outcomes during pregnancy and develop a risk prediction model, which may enable earlier intervention in pregnant women who are at risk of adverse ocular outcomes. We performed this retrospective study of common clinical data, disease history, and maternal characteristics to determine ocular parameters during pregnancy. This study provides a novel way to evaluate and identify maternal eye conditions and to implement early interventions. 2. Results 2.1 Descriptive statistics The patients’ demographic characteristics are shown in Table 1 . Overall, 760 women were enrolled. The following complications of the pregnant women and their fetuses were considered: pregnancy-induced hypertension (PIH), eclampsia and preeclampsia, gestational diabetes mellitus (GDM), multiple pregnancy (MP), in vitro fertilization and embryo transfer (IVF-ET), stillbirth, and intrauterine growth retardation (IUGR), amongst others (Table 1 ). Table 1 Maternal baseline characteristics. Parameter Number (%) Age, years 33.4 ± 4.99 PIH 85 (11.18%) Eclampsia and pre-eclampsia 213 (28.03%) GDM 452 (59.47%) Multiple pregnancy 97 (12.76%) Myoma of uterus 77 (10.13%) IVF-ET 171 (22.5%) Stillbirth 18 (2.37%) Repeated pregnancy 520 (68.42%) History of chronic hypertension 103 (13.55%) Pregnancy with thyroid disease 82 (10.79%) Scarred uterus 201 (26.45% Anemia 171 (22.5%) Hypoproteinemia 44 (5.79%) Pregnancy with infectious disease 68 (8.95%) Cervical incompetence 47 (6.18%) Pregnancy with antiphospholipid syndrome 35 (4.61%) Hyperlipidemia 2 (0.26%) IUGR 53 (6.97%) Obesity 48 (6.31%) Endometriosis 2 (0.26%) Pregnancy with cardiac disease 24 (3.16%) Infectious reproductive disease 31 (4.08%) Pregnancy with systemic lupus erythematosus 14 (1.84%) Pregnancy with rheumatoid arthritis 2 (0.26%) Pregnancy with IgA nephropathy 7 (0.92%) Intrahepatic cholestasis of pregnancy. 19 (2.5%) HELLP syndrome 8 (1.05%) GDM, gestational diabetes mellitus; HELLP, hemolysis, elevated liver enzymes, and low platelets; IgA, immunoglobulin A; IUGR, intrauterine growth retardation; IVF-ET, in vitro fertilization and embryo transfer; PIH, pregnancy-induced hypertension. The study included 760 pregnant women who attended ophthalmic consultation and delivered at our hospital (Fig. 1 ). The demographic characteristics of the pregnant women and their fetuses are summarized in Table 1 . The maternal age ranged from 19 to 50 years, with an average age of 33.4 ± 4.99 years. PIH was observed in 86 patients (11.18%); eclampsia and pre-eclampsia in 213 (28.03%); history of chronic hypertension in 103 (13.55%); and hemolysis, elevated liver enzymes, and low platelet (HELLP) syndrome in 8 (1.05%). Totally, 452 pregnant women (59.47%) developed GDM and 48 (6.31%) had obesity. There were 97 patients (12.76%) with MP and 520 patients (68.42%) with repeated pregnancy. With regard to uterine disease, uterine myoma was observed in 77 patients (10.13%), uterine scarring in 201 (26.45%), cervical incompetence in 47 (6.18%), and endometriosis in 2 (0.26%). Of the 71 pregnant women with fetal abnormalities, 18 (2.37%) had stillbirth and 53 (6.97%) had IUGR. Among the patients, 171 pregnant women (22.5%) had a history of IVF-ET. Anemia, hypoproteinemia, hyperlipidemia, and infectious reproductive disease were observed in 171 (22.5%), 44 (5.79%), 53 (6.97%), and 31 (4.08%) patients, respectively. In terms of major comorbidities during pregnancy, 82 patients (10.79%) had thyroid disease, 68 (8.95%) had infectious disease, 35 (4.61%) had antiphospholipid syndrome, 24 (3.16%) had cardiac disease, 14 (1.84%) had systemic lupus erythematosus, 2 (0.26%) had rheumatoid arthritis, 7 (0.92%) had immunoglobulin A (IgA) nephropathy, and 19 (2.5%) had intrahepatic cholestasis of pregnancy. 2.2 Comparison with the incidence of adverse ocular outcomes and the occurrence of risk factors in pregnant women The results of the univariate logistic regression analysis of the grade of adverse ocular outcomes and risk factors in pregnant women are shown in Tables 2 and 3 , respectively. Maternal age (95% CI 1.00–1.08), occurrence of PIH (95% CI 1.07–2.88), eclampsia and pre-eclampsia (95% CI 1.99–4.09), GDM (95% CI 0.25–0.49), history of chronic hypertension (95% CI 0.25–0.49), hypoproteinemia (95% CI 1.42–5.24), IUGR (95% CI 1.02–3.45), obesity (95% CI 1.01–3.54), and pregnancy with IgA nephropathy (95% CI 1.77–131.4) differed significantly with the incidence of grade 1 adverse ocular outcomes (all P < 0.05). As shown in Table 3 , there were significant differences in the incidence of grade 2 adverse ocular outcomes with the occurrence of eclampsia and pre-eclampsia (95% CI 2.39–8.88), GDM (95% CI 0.27–0.96), history of chronic hypertension (95% CI 1.53–7.69), hypoproteinemia (95% CI 1.25–9.91), and HELLP syndrome (95% CI 3.53–134.3) (all P < 0.05). However, we did not observe significant differences for IVF-ET, MP, repeated pregnancy, uterine scarring, uterine myoma, cervical incompetence and endometriosis, fetal abnormalities, and some comorbidities in the univariate logistic regression analysis, either in the grade 1 adverse ocular outcomes group or the grade 2 adverse ocular outcomes group (all P > 0.05). Table 2 Univariate logistic regression analysis for grade 1 adverse ocular outcomes among risk factors in pregnant women Parameter Incidence of grade 1 adverse ocular outcome, % Estimate P value OR (95% CI) Age 0.0386 0.0276 1.04 (1.00–1.08) PIH 23.87 vs. 35.44 0.5607 0.0266 1.75 (1.07–2.88) Eclampsia and pre-eclampsia 19.47 vs. 40.84 1.049 < .0001 2.85 (1.99–4.09) GDM 37.15 vs. 17.13 −1.0508 < .0001 0.35 (0.25–0.49) Multiple pregnancy 26.31 vs. 16.85 −0.566 0.0569 0.57 (0.32–1.02) Myoma of uterus 25.27 vs. 23.94 −0.0715 0.8069 0.93 (0.52–1.65) IVF-ET 25.36 vs. 24.38 −0.0526 0.8006 0.95 (0.63–1.43) Stillbirth 24.72 vs. 43.75 0.8633 0.0914 2.37 (0.87–6.46) Repeated pregnancy 21.43 vs. 26.81 0.2952 0.1238 1.34 (0.92–1.96) History of chronic hypertension 20.61 vs. 55.32 1.5623 < .0001 4.77 (3.04–7.48) Pregnancy with thyroid disease 25.31 vs. 23.68 −0.088 0.7573 0.92 (0.52–1.60) Scarred uterus 23.53 vs. 29.53 0.3091 0.1006 1.36 (0.94–1.97) Anemia 24.51 vs. 27.33 0.147 0.4674 1.16 (0.78–1.72) Hypoproteinemia 23.94 vs. 46.15 1.0024 0.0027 2.72 (1.42–5.24) Pregnancy with infectious disease 25.27 vs. 23.81 −0.0787 0.7991 0.92 (0.50–1.69) Cervical incompetence 25.71 vs. 17.02 −0.5228 0.189 0.59 (0.27–1.29) Pregnancy with antiphospholipid syndrome 24.93 vs. 29.41 0.2271 0.557 1.25 (0.59–2.68) Hyperlipidaemia 25.10 vs. 0.00 −12.472 0.9841 0.00 (0.00, > 999.99) IUGR 24.26 vs. 37.50 0.6279 0.0438 1.87 (1.02–3.45) Obesity 24.30 vs. 37.78 0.6375 0.0466 1.89 (1.01–3.54) Endometriosis 25.21 vs. 0.00 −12.474 0.984 0.00 (0.00, > 999.99) Pregnancy with cardiac disease 24.79 vs. 36.36 0.551 0.2226 1.74 (0.72–4.21) Infectious reproductive disease 25.07 vs. 26.67 0.0832 0.8438 1.09 (0.48–2.49) Pregnancy with systemic lupus erythematosus 25.07 vs. 28.57 0.1786 0.7652 1.20 (0.37–3.86) Pregnancy with rheumatoid arthritis 25.21 vs. 0.00 −12.474 0.984 0.00 (0.00, > 999.99) Pregnancy with IgA nephropathy 24.65 vs. 83.33 2.7256 0.0131 15.27 (1.77–131.4) Intrahepatic cholestasis of pregnancy 25.32 vs. 17.65 −0.458 0.4755 0.63 (0.18–2.23) HELLP syndrome 24.79 vs. 60.00 1.5153 0.0984 4.55 (0.75–27.45) Incidence data are presented as yes vs. no. CI, confidence interval; GDM, gestational diabetes mellitus; HELLP, hemolysis, elevated liver enzymes, and low platelets; IgA, immunoglobulin A; IUGR, intrauterine growth retardation; IVF-ET, in vitro fertilization and embryo transfer; OR, odds ratio; PIH, pregnancy-induced hypertension. Table 3 Univariate logistic regression analysis for grade 2 adverse ocular outcomes among the risk factors in pregnant women Parameter Incidence of grade 2 adverse ocular outcome, % Estimate P value OR (95% CI) Age 0.0117 0.7291 1.01 (0.95–1.08) PIH 6.69 vs. 8.93 0.3131 0.5313 1.37 (0.51–3.64) Eclampsia and pre-eclampsia 4.05 vs. 16.30 1.5277 < .0001 4.61 (2.39–8.88) GDM 9.95 vs. 5.29 −0.682 0.0382 0.51 (0.27–0.96) Multiple pregnancy 6.63 vs. 8.64 0.2877 0.5079 1.33 (0.57–3.12) Myoma of uterus 6.55 vs. 10.00 0.4606 0.3224 1.58 (0.64–3.95) IVF-ET 6.49 vs. 8.33 0.2704 0.4634 1.31 (0.64–2.70) Stillbirth 6.69 vs. 18.18 1.1312 0.1571 3.10 (0.65–14.86) Repeated pregnancy 8.33 vs. 6.20 −0.3186 0.3424 0.73 (0.38–1.40) History of chronic hypertension 5.87 vs. 17.65 1.2342 0.0027 3.44 (1.53–7.69) Pregnancy with thyroid disease 6.60 vs. 9.38 0.3808 0.4119 1.46 (0.59–3.63) Scarred uterus 7.36 vs. 5.56 −0.2998 0.4619 0.74 (0.33–1.65) Anemia 6.64 vs. 7.87 0.1843 0.6276 1.20 (0.57–2.53) Hypoproteinemia 6.33 vs. 19.23 1.2595 0.0169 3.52 (1.25–9.91) Pregnancy with infectious disease 6.65 vs. 9.43 0.3793 0.4494 1.46 (0.55–3.90) Cervical incompetence 7.41 vs. 0.00 −13.189 0.9746 0.00 (0.00, > 999.99) Pregnancy with antiphospholipid syndrome 7.04 vs. 4.00 −0.5974 0.5634 0.55 (0.07–4.18) Hyperlipidemia 6.93 vs. 0.00 −12.118 0.9913 0.00 (0.00, > 999.99) IUGR 6.43 vs. 14.29 0.8858 0.0846 2.42 (0.89–6.64) Obesity 6.75 vs. 9.68 0.3919 0.5345 1.48 (0.43–5.10) Endometriosis 6.93 vs. 0.00 −12.118 0.9913 0.00 (0.00, > 999.99) Pregnancy with cardiac disease 6.75 vs. 12.50 0.68 0.3799 1.97 (0.43–9.00) Infectious reproductive disease 7.01 vs. 4.35 −0.5066 0.6248 0.60 (0.08–4.59) Pregnancy with systemic lupus erythematosus 7.03 vs. 0.00 −13.133 0.9872 0.00 (0.00, > 999.99) Pregnancy with rheumatoid arthritis 6.93 vs. 0.00 −12.118 0.9913 0.00 (0.00, > 999.99) Pregnancy with IgA nephropathy 6.92 vs. 0.00 −11.116 0.9907 0.00 (0.00, > 999.99) Intrahepatic cholestasis of pregnancy 6.75 vs. 12.50 0.68 0.3799 1.97 (0.43–9.00) HELLP syndrome 6.45 vs. 60.00 3.0801 0.0009 21.76 (3.53–134.3) Incidence data are presented as yes vs. no. CI, confidence interval; GDM, gestational diabetes mellitus; HELLP, hemolysis, elevated liver enzymes, and low platelets; IgA, immunoglobulin A; IUGR, intrauterine growth retardation; IVF-ET, in vitro fertilization with embryo transfer; OR, odds ratio; PIH, pregnancy-induced hypertension. 2.3 Multivariate logistic regression of the prediction model Multivariate logistic regression was used to analyze the correlations of the risk factors mentioned above with the adverse ocular outcomes of the pregnant women (Tables 4 and 5 ). The following risk factors were identified as predictors of grade 1 adverse ocular outcomes during pregnancy: maternal age (95% CI 0.916–0.991, P = 0.0154); PIH (95% CI 1.03–3.071, P = 0.0387); eclampsia and pre-eclampsia (95% CI 1.392–3.182, P = 0.0004); GDM (95% CI 0.29–0.657, P < 0.0001); history of chronic hypertension (95% CI 2.5–6.67, P < 0.0001); hypoproteinemia (95% CI 0.708–3.089, P = 0.2972), IUGR (95% CI 0.673–2.595, P = 0.4182), obesity (95% CI 0.779–3.189, P = 0.206), and pregnancy with IgA nephropathy (95% CI 1.078–101.115, P = 0.0429). After calculation, the final model for grade 1 adverse ocular outcome prediction among pregnant women was as follows: Ln(P ÷ (1 − P)) = 0.2279 − 0.0483 × maternal age + 0.288 × 1(PIH) + 0.3721 × 1(eclampsia and pre-eclampsia) − 0.4146 × 1(GDM) + 0.7035 × 1(history of chronic hypertension) + 0.1958 × 1(hypoproteinemia) + 0.1394 × 1(IUGR) + 0.2274 × 1(obesity) + 1.1729 × 1(pregnancy with IgA nephropathy). Table 4 Multivariate logistic regression analysis for grade 1 adverse ocular outcomes among the risk factors in pregnant women Parameter Estimate P value OR (95% CI) Age −0.0483 0.0154 0.953 (0.916–0.991) PIH 0.288 0.0387 1.779 (1.03–3.071) Eclampsia and pre-eclampsia 0.3721 0.0004 2.105 (1.392–3.182) GDM −0.4146 < .0001 0.436 (0.29–0.657) History of chronic hypertension 0.7035 < .0001 4.083 (2.5–6.669) Hypoproteinemia 0.1958 0.2972 1.479 (0.708–3.089) IUGR 0.1394 0.4182 1.321 (0.673–2.595) Obesity 0.2274 0.206 1.576 (0.779–3.189) Pregnancy with IgA nephropathy 1.1729 0.0429 10.442 (1.078–101.115) CI, confidence interval; GDM, gestational diabetes mellitus; IgA, immunoglobulin A; IUGR, intrauterine growth retardation; OR, odds ratio; PIH, pregnancy-induced hypertension. Table 5 Multivariate logistic regression analysis for grade 2 adverse ocular outcomes among the risk factors in pregnant women Parameter Estimate P value OR (95% CI) Age −0.0224 0.5341 0.978 (0.911–1.049) Eclampsia and pre-eclampsia 0.6712 0.0003 3.828 (1.85–7.924) GDM −0.039 0.8366 0.925 (0.44–1.942) History of chronic hypertension 0.4833 0.0306 2.629 (1.094–6.315) Hypoproteinemia 0.3272 0.2595 1.924 (0.617–6.002) HELLP syndrome 1.2646 0.0155 12.543 (1.617–97.296) CI, confidence interval; GDM, gestational diabetes mellitus; HELLP, hemolysis, elevated liver enzymes, and low platelets; OR, odds ratio. For the grade 2 adverse ocular outcome group, maternal age (95% CI 0.911–1.049, P = 0.5341), eclampsia and pre-eclampsia (95% CI 1.85–7.924, P = 0.0003), GDM (95% CI 0.44–1.942, P = 0.8366), history of chronic hypertension (95% CI 1.094–6.315, P = 0.0306), hypoproteinemia (95% CI 0.617–6.002, P = 0.2595), and HELLP syndrome (95% CI 1.617–97.296, P = 0.0155) were predictors of the incidence of grade 2 adverse ocular outcomes during pregnancy. The final model for grade 2 adverse ocular outcome prediction among pregnant women was as follows: Ln(P ÷ (1 − P)) = 1.3273 − 0.0224 × maternal age + 0.6712 × 1(PIH) − 0.039 × 1(GDM) + 0.4833 × 1(history of chronic hypertension) + 0.3272 × 1(hypoproteinemia) + 1.2646 × 1(HELLP syndrome). 2.4 ROC curve analysis The ROC curve analysis was based on multivariate logistic regression among different adverse ocular outcomes in pregnant women to carry out with the available study variables. The area under the ROC curve was 0.75 in the grade 1 adverse ocular outcomes group and 0.74 in the grade 2 adverse ocular outcomes group (Fig. 2 , Fig. 3 ). 3. Methods 3.1 Participants This retrospective study analyzed the data of pregnant women who attended The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, between September 2020 and September 2022 for ophthalmic consultation. All patients were examined by an experienced doctor and underwent a detailed ophthalmologic examination involving slit-lamp biomicroscopy and non-mydriatic fundus photography. This study was approved by the institutional ethics committee of The Third Affiliated Hospital of Guangzhou Medical University (approval number [2023] No. 152) and was conducted in accordance with the tenets of the Declaration of Helsinki. Written informed consent was obtained from all of the subjects. 3.2 Inclusion and exclusion criteria The data of patients who delivered at our hospital, attended ophthalmic consultation, and understood and were willing to participate in the study were included. Patients with keratitis, cataracts, glaucoma, ocular surgery, ocular trauma, or preexisting retinopathy were excluded. 3.3 Parameter selection for adverse ocular outcomes The grading used to describe adverse ocular changes was based on the modified and simplified classification system for hypertensive retinopathy, as follows[9]: grade 0: no detectable signs; grade 1: mild, moderate, and severe retinal arteriole sclerosis, arteriolar narrowing, arteriovenous nicking, and arteriovenous ratio (AVR) of < 0.67; grade 2: hemorrhage (blot-shaped, dot-shaped, or flame-shaped), microaneurysm, cotton wool spot, hard exudate, or a combination of these signs, or retinal detachment and any other disease that may affect the vision of the patient. 3.4 Retinal vessel diameter measurements All pregnant women were assessed with a fundus camera (Kowa Fundus Camera VX-10α; Aichi, Japan). Two pairs of fundus photographs were obtained at the center of the optic disc and on the macula. The retinal vessel diameters of the six largest retinal arteries and veins within a specified zone (0.5–1 disc diameter) from the optic disc margin were measured using a semiautomated system (IVAN, Department of Ophthalmology Visual Science, University of Wisconsin, Madison, WI, US). The Atherosclerosis Risk in Communities study protocol was performed for retinal vessel grading. The calculation formula of the revised Parr–Hubbard–Knudtson formula was used to standardize and summarize the retinal arteriolar and venular calibres as the central retinal artery equivalent and the central retinal vein equivalent. The AVR screenshot of IVAN software is shown in Fig. 1 . Two masked graders completed the measurement of the images. If the difference between the two graders was > 10%, a third grader assessed the images, and the average of these three values was used in the analysis. 3.5 Statistical analysis Statistical data analyses were performed using SPSS software (version 23.0; IBM Corporation, Chicago, IL, US). Categorical variables are described as frequency and percentage. The univariate logistic regression analysis was performed to investigate the effect of a single risk factor on different adverse ocular outcomes. The variables that were significant at P < 0.05 in the univariate analysis were included in the multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve analysis was performed to decide the cut-off probability with optimum sensitivity and specificity. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. P < 0.05 was considered statistically significant. 4. Discussion To our knowledge, this is the first study to identify the risk predictors of adverse ocular outcomes during pregnancy. We found that eclampsia and pre-eclampsia, GDM, history of chronic hypertension, and hypoproteinemia were independent predictors of both grade 1 and grade 2 adverse ocular outcomes during pregnancy (P < 0.05). Moreover, we found that maternal age, PIH, IUGR, obesity, and pregnancy with IgA nephropathy were predictors of moderate and severe retinal arteriole sclerosis during pregnancy (P < 0.05). Moreover, HELLP syndrome was a risk predictor of retinal hemorrhage and exudate and other diseases that may disrupt vision during pregnancy. Two predictive models were proposed in this study, for which the area under the ROC curve values were 0.75 and 0.74, illustrating that the models had reasonable accuracy and sensitivity. Pregnancy complications that may result in pregnancy-specific ocular diseases include eclampsia/pre-eclampsia and cortical blindness[28]. Vision is affected in approximately 25% of pregnancies with pre-eclampsia and in 50% of pregnancies with eclampsia[25]. In patients with eclampsia and pre-eclampsia, the most common ocular findings are retinal arteriole constriction,[29] which is in accordance with the correlation of this risk factor with grade 1 adverse ocular outcomes in the present study. Furthermore, with the exacerbation of the eclampsia and pre-eclampsia, retinal edema, hemorrhage, exudate, and cotton wool spots can occur, which is consistent with our results pertaining to grade 2 adverse ocular outcomes. A previous study showed that diabetic retinopathy can be exacerbated in pregnancy[7]. Horvat et al.[12] showed that GDM does not show a similar association with the development of diabetic retinopathy, suggesting that ophthalmic examination is not necessary. However, in the present study, we found that the presence of GDM influences both grade 1 and 2 adverse ocular outcomes (P < 0.05). There are two possible explanations for the differences in these results. On the one hand, hyperglycemia during pregnancy may lead to increased retinal capillary basement membrane thickness and gliosis by oxidative stress, the polyol pathway, and advanced glycation end-products[39; 5]. On the other hand, GDM was not significantly correlated with grade 2 ocular adverse outcomes in the multivariate logistic regression analysis, suggesting that GDM was not the primary risk indicator of severe adverse ocular outcomes among the multiple confounding factors. Many studies have concluded that women of advanced maternal age may have an increased risk of maternal and fetal complications than younger women, including ectopic pregnancy, spontaneous abortion, GDM, pre-eclampsia, and cesarean delivery[22; 24]. However, no studies have evaluated the ocular changes associated with age. In the present study, age was a significant predictor of grade 1 ocular adverse outcomes (P < 0.05). Previous studies have indicated that aging is associated with uterine and systemic artery impairments in pregnancy, which is likely related to increased vascular oxidative stress and nitric oxide synthesis[2]. Excessive nitric oxide and dysregulation of oxidative stress may lead to retinal arteriole narrowing[35]. Obesity during pregnancy is the most common comorbidity that is associated with many complications in both pregnant women and fetuses[3]. Recent studies have shown that obesity can increase the risk of GDM, PIH, pre-eclampsia, and venous embolism, which are adverse maternal outcomes[27; 37; 17]. In the present study, obesity was a risk predictor of adverse ocular outcomes during pregnancy (P < 0.05). Köchli et al.[14] indicated that obesity in young children may induce retinal arteriole narrowing and retinal venular widening. Oxidative stress and complement activation in the retinal environment of patients with obesity are associated with changes in the retinal vasculature[21]. Furthermore, we demonstrated that IUGR differed significantly with moderate and severe retinal arteriole sclerosis during pregnancy (P < 0.05). Previous studies evaluating IUGR have often focused on the adverse outcomes of the fetus. For instance, Hellstrom et al.[10] showed that IUGR with abnormal fetal blood flow may lead to abnormal retinal vascular morphology in young adult life. However, no studies have evaluated the effects of IUGR on changes in the maternal microvasculature; therefore, more studies on this topic need to be conducted in the future. Pregnancy with IgA nephropathy has attracted much attention due to the high risk of adverse pregnancy outcomes[19]. Proteinuria during pregnancy in patients with IgA nephropathy has been proposed as a significant risk factor for pre-eclampsia, while severe proteinuria is known to cause hypoproteinemia[19; 6; 18; 30]. The aggravation of IgA nephropathy and hypoproteinemia can decrease intravascular volume and damage vascular endothelial function, which may lead to hypoxia and increased oxidative stress[19]. It can be inferred that oxidative stress and hypoxia in the retinal environment cause changes in the retinal vasculature, leading to the occurrence of adverse ocular outcomes during pregnancy (P < 0.05). HELLP syndrome is a severe form of pre-eclampsia/eclampsia, which may induce retinal vascular occlusion, serous retinal detachment, and even acute visual loss[33]. Grade 2 adverse ocular outcomes during pregnancy are more likely to disturb vision in pregnant women than grade 1 adverse ocular outcomes, and we proved that HELLP syndrome is an independent risk factor for grade 2 adverse ocular outcomes during pregnancy. This study has several limitations that should be considered. First, the study was limited to patients who attended ophthalmic consultations, so the number of included patients was relatively small. Further studies with larger sample sizes are needed to evaluate the proposed risk model. Second, prospective studies are needed to clarify the correlations of GDM, obesity, and IUGR with adverse ocular outcomes. Finally, due to the lack of relevant study types, our prediction model data lacked validation. 5. Conclusion In summary, our model was able to effectively predict the occurrence of adverse ocular outcomes during pregnancy with high sensitivity and specificity, with the risk factors including maternal age, eclampsia and pre-eclampsia, GDM, obesity, history of chronic hypertension, hypoproteinemia, IUGR, pregnancy with IgA nephropathy, and HELLP syndrome. We identified several new risk factors for adverse ocular outcomes during pregnancy. Further prospective studies should clarify the correlations of these identified risk factors with adverse ocular outcomes. This study provides a novel way to identify and evaluate maternal ocular conditions and facilitate early interventions. Declarations Competing interests The authors declare no competing interests Informed consent statement Written informed consent was obtained from all of the subjects. Author Contribution Contribution statementX.T. Liu, S. Y. Wang designed the study. X.T. Liu, Y.Y. Wen, H.Q. Zou, S. Y. Wang performed the literature research, data acquisition, data analysis, and manuscript editing. X.T. Liu, S. Y. Wang conducted the clinical studies. X.T. Liu and S. Y. Wang reviewed the manuscript. All authors read and approved the final version of the manuscript. Acknowledgement The authors thank the use of the IVAN software and Dr. Nicola Ferrier of the University of Wisconsin - Madison School of Engineering and the Department of Ophthalmology and Visual Sciences, University of Wisconsin - Madison. Data Availability Data available on request from the authors. References Global, Regional, and National Levels of Maternal Mortality, 1990–2015: A Systematic Analysis for the Global Burden of Disease Study 2015. LANCET. 388, 1775–1812 (2016). Care, A. S., Bourque, S. L., Morton, J. S., Hjartarson, E. P. & Davidge, S. T. Effect of Advanced Maternal Age On Pregnancy Outcomes and Vascular Function in the Rat. HYPERTENSION. 65, 1324–1330 (2015). Catalano, P. M. & Shankar, K. Obesity and Pregnancy: Mechanisms of Short Term and Long Term Adverse Consequences for Mother and Child. BMJ. 356, j1 (2017). Chan, W. C. et al. Management and Outcome of Sight-Threatening Diabetic Retinopathy in Pregnancy. Eye (Lond). 18, 826–832 (2004). Chandrasekaran, P. R., Madanagopalan, V. G. & Narayanan, R. Diabetic Retinopathy in Pregnancy - a Review. INDIAN J OPHTHALMOL. 69, 3015–3025 (2021). Cheung, C. K. & Barratt, J. Pregnancy in IgA Nephropathy: An Effect On Renal Outcome? AM J NEPHROL. 49, 212–213 (2019). Chew, E. Y. et al. Metabolic Control and Progression of Retinopathy. The Diabetes in Early Pregnancy Study. National Institute of Child Health and Human Development Diabetes in Early Pregnancy Study. DIABETES CARE. 18, 631–637 (1995). Daruich, A. et al. Central Serous Chorioretinopathy: Recent Findings and New Physiopathology Hypothesis. PROG RETIN EYE RES. 48, 82–118 (2015). Downie, L. E. et al. Hypertensive Retinopathy: Comparing the Keith-Wagener-Barker to a Simplified Classification. J HYPERTENS. 31, 960–965 (2013). Hellstrom, A., Dahlgren, J., Marsal, K. & Ley, D. Abnormal Retinal Vascular Morphology in Young Adults Following Intrauterine Growth Restriction. PEDIATRICS . 113, e77-e80 (2004). Higginbotham, E. J. Managing Glaucoma During Pregnancy. JAMA. 296, 1284–1285 (2006). Horvat, M., Maclean, H., Goldberg, L. & Crock, G. W. Diabetic Retinopathy in Pregnancy: A 12-Year Prospective Survey. Br J Ophthalmol. 64, 398–403 (1980). Jing, G. et al. A Predictive Model of Macrosomic Birth Based upon Real-World Clinical Data From Pregnant Women. BMC Pregnancy Childbirth. 22, 651 (2022). Kochli, S. et al. Obesity, Blood Pressure and Retinal Microvascular Phenotype in a Bi-Ethnic Cohort of Young Children. ATHEROSCLEROSIS. 350, 51–57 (2022). Kumar, A., Vanamail, P., Gupta, R. K. & Husain, S. A. Prediction of Pre-Eclampsia in Diabetic Pregnant Women. INDIAN J MED RES. 157, 330–344 (2023). Li, X. et al. Risk Factors for Adverse Maternal and Perinatal Outcomes in Women with Preeclampsia: Analysis of 1396 Cases. J Clin Hypertens (Greenwich). 20, 1049–1057 (2018). Lin, J., Gu, W. & Huang, H. Effects of Paternal Obesity on Fetal Development and Pregnancy Complications: A Prospective Clinical Cohort Study. Front Endocrinol (Lausanne). 13, 826665 (2022). Liu, Y. et al. Risk Factors for Pregnancy Outcomes in Patients with IgA Nephropathy: A Matched Cohort Study. AM J KIDNEY DIS. 64, 730–736 (2014). Liu, Y., Ma, X., Zheng, J., Liu, X. & Yan, T. A Systematic Review and Meta-Analysis of Kidney and Pregnancy Outcomes in IgA Nephropathy. AM J NEPHROL. 44, 187–193 (2016). Marshall, H. et al. Cardiovascular Disease Predicts Structural and Functional Progression in Early Glaucoma. OPHTHALMOLOGY. 128, 58–69 (2021). Natoli, R. et al. Obesity-Induced Metabolic Disturbance Drives Oxidative Stress and Complement Activation in the Retinal Environment. MOL VIS. 24, 201–217 (2018). Nybo, A. A., Wohlfahrt, J., Christens, P., Olsen, J. & Melbye, M. Maternal Age and Fetal Loss: Population Based Register Linkage Study. BMJ. 320, 1708–1712 (2000). Oskarsdottir, S. E., Heijl, A. & Bengtsson, B. Predicting Undetected Glaucoma According to Age and IOP: A Prediction Model Developed From a Primarily European-derived Population. ACTA OPHTHALMOL. 97, 422–426 (2019). Pinheiro, R. L., Areia, A. L., Mota, P. A. & Donato, H. Advanced Maternal Age: Adverse Outcomes of Pregnancy, a Meta-Analysis. Acta Med Port. 32, 219–226 (2019). Qin, Q., Chen, C. & Cugati, S. Ophthalmic Associations in Pregnancy. Aust J Gen Pract. 49, 673–680 (2020). Rabiah, P. K. & Vitale, A. T. Noninfectious Uveitis and Pregnancy. AM J OPHTHALMOL. 136, 91–98 (2003). Satpathy, H. K. et al. Maternal Obesity and Pregnancy. POSTGRAD MED. 120, E1-E9 (2008). Schultz, K. L., Birnbaum, A. D. & Goldstein, D. A. Ocular Disease in Pregnancy. CURR OPIN OPHTHALMOL. 16, 308–314 (2005). Soullane, S., Rheaume, M. A. & Auger, N. Preeclampsia and the Retina. CURR HYPERTENS REP. 26, 169–174 (2024). Suetsugu, Y. et al. [Study On the Predictors for Superimposed Preeclampsia in Patients with IgA Nephropathy]. Nihon Jinzo Gakkai Shi. 53, 1139–1149 (2011). Taradaj, K. et al. Pregnancy and the Eye. Changes in Morphology of the Cornea and the Anterior Chamber of the Eye in Pregnant Woman. GINEKOL POL. 89, 695–699 (2018). Trivedi, R. H., Barnwell, E., Wolf, B. & Wilson, M. E. A Model to Predict Postoperative Axial Length in Children Undergoing Bilateral Cataract Surgery with Primary Intraocular Lens Implantation. AM J OPHTHALMOL. 206, 228–234 (2019). Vigil-De, G. P. & Ortega-Paz, L. Retinal Detachment in Association with Pre-Eclampsia, Eclampsia, and HELLP Syndrome. Int J Gynaecol Obstet. 114, 223–225 (2011). von Dadelszen, P. et al. Prediction of Adverse Maternal Outcomes in Pre-Eclampsia: Development and Validation of the fullPIERS Model. LANCET. 377, 219–227 (2011). Wong, T. Y. et al. Retinal Vascular Caliber, Cardiovascular Risk Factors, and Inflammation: The Multi-Ethnic Study of Atherosclerosis (MESA). Invest Ophthalmol Vis Sci. 47, 2341–2350 (2006). Wu, Y. et al. A Risk Prediction Model of Gestational Diabetes Mellitus Before 16 Gestational Weeks in Chinese Pregnant Women. Diabetes Res Clin Pract. 179, 109001 (2021). Zehravi, M., Maqbool, M. & Ara, I. Correlation Between Obesity, Gestational Diabetes Mellitus, and Pregnancy Outcomes: An Overview. Int J Adolesc Med Health. 33, 339–345 (2021). Zhou, Q., Xu, J., Xiong, Y. & Li, X. Preeclampsia Risk Prediction Model for Chinese Pregnant Women (ChiPERM): Research Protocol for a Randomized Stepped-Wedge Cluster Trial. BMC Pregnancy Childbirth. 22, 532 (2022). Zhu, C. et al. Association of Oxidative Stress Biomarkers with Gestational Diabetes Mellitus in Pregnant Women: A Case-Control Study. PLOS ONE. 10, e126490 (2015). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-4454924","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310045462,"identity":"9879e9f7-a9d6-47f0-9531-afa600140937","order_by":0,"name":"Xintian Liu","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xintian","middleName":"","lastName":"Liu","suffix":""},{"id":310045463,"identity":"e22345ef-8583-47b1-ae34-415eb484b942","order_by":1,"name":"Yiyi Wen","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yiyi","middleName":"","lastName":"Wen","suffix":""},{"id":310045464,"identity":"d2feec7e-2c2a-4886-acc1-307bc7709e94","order_by":2,"name":"Haiqing Zou","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haiqing","middleName":"","lastName":"Zou","suffix":""},{"id":310045465,"identity":"7a927197-907c-4fed-8126-f647d2df9ec8","order_by":3,"name":"Shuangyong Y. Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACefaGxMc/fkjIARlEajHsOfDYmLHHxhjIINaaG47PpBnY0hIZbiQQqYNxBnOycQHP4QTGmY833mCosYkmqIVdui3x8QyLw3ns0mnFFgzH0nIbCNoy50yyAQ/P4WLG2TlmEowNhwlrYbiR/02Ch+1wYsPNM0RrSUiT5gF6v+EGD5FagGGbbDgTHMhAvyQQ4xdQVD74AI7KwxtvfKixIcJhSMBAIoEU5RAtpOoYBaNgFIyCkQEAHM5DYmXh/McAAAAASUVORK5CYII=","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shuangyong","middleName":"Y.","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-21 12:48:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4454924/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4454924/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57919167,"identity":"a28b0079-d3d9-4ff0-a52a-b72a11c492be","added_by":"auto","created_at":"2024-06-07 12:50:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25188,"visible":true,"origin":"","legend":"\u003cp\u003ePatient flowchart.\u003c/p\u003e","description":"","filename":"Onlinefig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4454924/v1/b5c0de75fe2c1940540283d5.png"},{"id":57919168,"identity":"7f358f99-55e3-406d-82c5-24138c26b6d8","added_by":"auto","created_at":"2024-06-07 12:50:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45152,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for predicting grade 1 adverse ocular outcomes by risk factors during pregnancy. ROC, receiver operating characteristic.\u003c/p\u003e","description":"","filename":"Onlinefig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4454924/v1/fe55a7f802540a5416e3681f.png"},{"id":57919169,"identity":"9c4fdacb-8e93-4869-950a-8ca2f8fa6332","added_by":"auto","created_at":"2024-06-07 12:50:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42735,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for predicting grade 2 adverse ocular outcomes by risk factor during pregnancy. ROC, receiver operating characteristic.\u003c/p\u003e","description":"","filename":"Onlinefig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4454924/v1/946a7266f3cb7effd756bfe5.png"},{"id":58493123,"identity":"ba39e425-8ed4-4d08-9175-71104aa4334b","added_by":"auto","created_at":"2024-06-17 11:07:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":907063,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4454924/v1/03c9cc1c-4403-4f0c-a3f9-d85c2f86d56a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A model to predict the risk of adverse ocular outcomes in pregnant women","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the development of assisted reproductive technology, the incidence of complications in pregnant women is increasing[1]. Pregnancy can influence multiple bodily functions, including metabolism, cardiovascular function, and eyesight[20]. As an end organ, the eyes may directly demonstrate changes in the terminal microvasculature. Pregnancy affects the eyes in several ways, both physiologically and pathologically[25; 28]. Alterations in corneal shape and sensation, as well as intraocular pressure, have been documented[31]. Moreover, changes related to the exacerbation of preexisting ocular disease may occur. Glaucoma[11], diabetic chorioretinopathy[4], uveitis[26], and retinal changes[8] in patients with pregnancy-induced hypertension (PIH) are some of the pregnancy features that have health implications.\u003c/p\u003e \u003cp\u003eMany predictive models have been utilized to evaluate the associations between risk factors and adverse pregnancy outcomes[16; 36; 13]. The most common independent predictive factors are pre-eclampsia, GDM, obesity, cardiac disease, and method of delivery, amongst others[15; 38; 36]. Adverse maternal and fetal outcomes can be assessed by evaluating the maternal disease history, lifestyle, disease onset, and biochemical markers, amongst other factors. For instance, the fullPIERS model can predict patients with pre-eclampsia within 48 hours and shows that 59% of high-risk patients have an adverse outcome up to 7 days[34]. The predictors in the fullPIERS model include gestational age, chest pain or dyspnea, oxygen saturation, platelet count, and creatinine and aspartate transaminase concentrations. Utilizing this model, clinicians can improve maternal and fetal outcomes by implementing timely clinical intervention in high-risk patients with these predictors. Predictive models in ophthalmology have often focused on glaucoma, age-related macular regeneration, and cataract surgery[32; 23]. However, few studies have evaluated ocular changes in system disease.\u003c/p\u003e \u003cp\u003eIn this study, we aim to identify the independent predictors of adverse ocular outcomes during pregnancy and develop a risk prediction model, which may enable earlier intervention in pregnant women who are at risk of adverse ocular outcomes. We performed this retrospective study of common clinical data, disease history, and maternal characteristics to determine ocular parameters during pregnancy. This study provides a novel way to evaluate and identify maternal eye conditions and to implement early interventions.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eThe patients\u0026rsquo; demographic characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Overall, 760 women were enrolled. The following complications of the pregnant women and their fetuses were considered: pregnancy-induced hypertension (PIH), eclampsia and preeclampsia, gestational diabetes mellitus (GDM), multiple pregnancy (MP), in vitro fertilization and embryo transfer (IVF-ET), stillbirth, and intrauterine growth retardation (IUGR), amongst others (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eMaternal baseline characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (11.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEclampsia and pre-eclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213 (28.03%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e452 (59.47%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (12.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyoma of uterus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (10.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF-ET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (2.37%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeated pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e520 (68.42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of chronic hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103 (13.55%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with thyroid disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (10.79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScarred uterus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201 (26.45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoproteinemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (5.79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with infectious disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (8.95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical incompetence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (6.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with antiphospholipid syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (4.61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIUGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (6.97%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (6.31%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with cardiac disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (3.16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious reproductive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (4.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with systemic lupus erythematosus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (1.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with rheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with IgA nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (0.92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrahepatic cholestasis of pregnancy.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHELLP syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (1.05%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eGDM, gestational diabetes mellitus; HELLP, hemolysis, elevated liver enzymes, and low platelets; IgA, immunoglobulin A; IUGR, intrauterine growth retardation; IVF-ET, in vitro fertilization and embryo transfer; PIH, pregnancy-induced hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study included 760 pregnant women who attended ophthalmic consultation and delivered at our hospital (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The demographic characteristics of the pregnant women and their fetuses are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The maternal age ranged from 19 to 50 years, with an average age of 33.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.99 years. PIH was observed in 86 patients (11.18%); eclampsia and pre-eclampsia in 213 (28.03%); history of chronic hypertension in 103 (13.55%); and hemolysis, elevated liver enzymes, and low platelet (HELLP) syndrome in 8 (1.05%). Totally, 452 pregnant women (59.47%) developed GDM and 48 (6.31%) had obesity. There were 97 patients (12.76%) with MP and 520 patients (68.42%) with repeated pregnancy. With regard to uterine disease, uterine myoma was observed in 77 patients (10.13%), uterine scarring in 201 (26.45%), cervical incompetence in 47 (6.18%), and endometriosis in 2 (0.26%). Of the 71 pregnant women with fetal abnormalities, 18 (2.37%) had stillbirth and 53 (6.97%) had IUGR.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the patients, 171 pregnant women (22.5%) had a history of IVF-ET. Anemia, hypoproteinemia, hyperlipidemia, and infectious reproductive disease were observed in 171 (22.5%), 44 (5.79%), 53 (6.97%), and 31 (4.08%) patients, respectively. In terms of major comorbidities during pregnancy, 82 patients (10.79%) had thyroid disease, 68 (8.95%) had infectious disease, 35 (4.61%) had antiphospholipid syndrome, 24 (3.16%) had cardiac disease, 14 (1.84%) had systemic lupus erythematosus, 2 (0.26%) had rheumatoid arthritis, 7 (0.92%) had immunoglobulin A (IgA) nephropathy, and 19 (2.5%) had intrahepatic cholestasis of pregnancy.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 Comparison with the incidence of adverse ocular outcomes and the occurrence of risk factors in pregnant women\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results of the univariate logistic regression analysis of the grade of adverse ocular outcomes and risk factors in pregnant women are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively. Maternal age (95% CI 1.00\u0026ndash;1.08), occurrence of PIH (95% CI 1.07\u0026ndash;2.88), eclampsia and pre-eclampsia (95% CI 1.99\u0026ndash;4.09), GDM (95% CI 0.25\u0026ndash;0.49), history of chronic hypertension (95% CI 0.25\u0026ndash;0.49), hypoproteinemia (95% CI 1.42\u0026ndash;5.24), IUGR (95% CI 1.02\u0026ndash;3.45), obesity (95% CI 1.01\u0026ndash;3.54), and pregnancy with IgA nephropathy (95% CI 1.77\u0026ndash;131.4) differed significantly with the incidence of grade 1 adverse ocular outcomes (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, there were significant differences in the incidence of grade 2 adverse ocular outcomes with the occurrence of eclampsia and pre-eclampsia (95% CI 2.39\u0026ndash;8.88), GDM (95% CI 0.27\u0026ndash;0.96), history of chronic hypertension (95% CI 1.53\u0026ndash;7.69), hypoproteinemia (95% CI 1.25\u0026ndash;9.91), and HELLP syndrome (95% CI 3.53\u0026ndash;134.3) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, we did not observe significant differences for IVF-ET, MP, repeated pregnancy, uterine scarring, uterine myoma, cervical incompetence and endometriosis, fetal abnormalities, and some comorbidities in the univariate logistic regression analysis, either in the grade 1 adverse ocular outcomes group or the grade 2 adverse ocular outcomes group (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eUnivariate logistic regression analysis for grade 1 adverse ocular outcomes among risk factors in pregnant women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncidence of grade 1 adverse ocular outcome, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04 (1.00\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.87 vs. 35.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.75 (1.07\u0026ndash;2.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEclampsia and pre-eclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.47 vs. 40.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.85 (1.99\u0026ndash;4.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.15 vs. 17.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.0508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35 (0.25\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.31 vs. 16.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.57 (0.32\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyoma of uterus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.27 vs. 23.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.0715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.93 (0.52\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF-ET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.36 vs. 24.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.0526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.63\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.72 vs. 43.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.37 (0.87\u0026ndash;6.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeated pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.43 vs. 26.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34 (0.92\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of chronic hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.61 vs. 55.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.77 (3.04\u0026ndash;7.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with thyroid disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.31 vs. 23.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92 (0.52\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScarred uterus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.53 vs. 29.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.36 (0.94\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.51 vs. 27.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.16 (0.78\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoproteinemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.94 vs. 46.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.72 (1.42\u0026ndash;5.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with infectious disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.27 vs. 23.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.0787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92 (0.50\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical incompetence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.71 vs. 17.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.5228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59 (0.27\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with antiphospholipid syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.93 vs. 29.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25 (0.59\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidaemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.10 vs. 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, \u0026gt;\u0026thinsp;999.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIUGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.26 vs. 37.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.87 (1.02\u0026ndash;3.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.30 vs. 37.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.89 (1.01\u0026ndash;3.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.21 vs. 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, \u0026gt;\u0026thinsp;999.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with cardiac disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.79 vs. 36.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.74 (0.72\u0026ndash;4.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious reproductive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.07 vs. 26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09 (0.48\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with systemic lupus erythematosus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.07 vs. 28.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20 (0.37\u0026ndash;3.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with rheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.21 vs. 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, \u0026gt;\u0026thinsp;999.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with IgA nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.65 vs. 83.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.27 (1.77\u0026ndash;131.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrahepatic cholestasis of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.32 vs. 17.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63 (0.18\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHELLP syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.79 vs. 60.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.55 (0.75\u0026ndash;27.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eIncidence data are presented as yes vs. no. CI, confidence interval; GDM, gestational diabetes mellitus; HELLP, hemolysis, elevated liver enzymes, and low platelets; IgA, immunoglobulin A; IUGR, intrauterine growth retardation; IVF-ET, in vitro fertilization and embryo transfer; OR, odds ratio; PIH, pregnancy-induced hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate logistic regression analysis for grade 2 adverse ocular outcomes among the risk factors in pregnant women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncidence of grade 2 adverse ocular outcome, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.95\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.69 vs. 8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.37 (0.51\u0026ndash;3.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEclampsia and pre-eclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.05 vs. 16.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.5277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.61 (2.39\u0026ndash;8.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.95 vs. 5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51 (0.27\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.63 vs. 8.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33 (0.57\u0026ndash;3.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyoma of uterus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.55 vs. 10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.58 (0.64\u0026ndash;3.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF-ET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.49 vs. 8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.31 (0.64\u0026ndash;2.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStillbirth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.69 vs. 18.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.10 (0.65\u0026ndash;14.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeated pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.33 vs. 6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.3186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.73 (0.38\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of chronic hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.87 vs. 17.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.44 (1.53\u0026ndash;7.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with thyroid disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.60 vs. 9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46 (0.59\u0026ndash;3.63)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScarred uterus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.36 vs. 5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.2998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74 (0.33\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.64 vs. 7.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20 (0.57\u0026ndash;2.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoproteinemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.33 vs. 19.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.52 (1.25\u0026ndash;9.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with infectious disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.65 vs. 9.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46 (0.55\u0026ndash;3.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCervical incompetence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.41 vs. 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;13.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, \u0026gt;\u0026thinsp;999.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with antiphospholipid syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.04 vs. 4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.5974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55 (0.07\u0026ndash;4.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.93 vs. 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, \u0026gt;\u0026thinsp;999.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIUGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.43 vs. 14.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.42 (0.89\u0026ndash;6.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.75 vs. 9.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.48 (0.43\u0026ndash;5.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.93 vs. 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, \u0026gt;\u0026thinsp;999.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with cardiac disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.75 vs. 12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.97 (0.43\u0026ndash;9.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious reproductive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.01 vs. 4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.5066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60 (0.08\u0026ndash;4.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with systemic lupus erythematosus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.03 vs. 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;13.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, \u0026gt;\u0026thinsp;999.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with rheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.93 vs. 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, \u0026gt;\u0026thinsp;999.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with IgA nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.92 vs. 0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;11.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, \u0026gt;\u0026thinsp;999.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntrahepatic cholestasis of pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.75 vs. 12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.97 (0.43\u0026ndash;9.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHELLP syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.45 vs. 60.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.76 (3.53\u0026ndash;134.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eIncidence data are presented as yes vs. no. CI, confidence interval; GDM, gestational diabetes mellitus; HELLP, hemolysis, elevated liver enzymes, and low platelets; IgA, immunoglobulin A; IUGR, intrauterine growth retardation; IVF-ET, in vitro fertilization with embryo transfer; OR, odds ratio; PIH, pregnancy-induced hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Multivariate logistic regression of the prediction model\u003c/h2\u003e \u003cp\u003eMultivariate logistic regression was used to analyze the correlations of the risk factors mentioned above with the adverse ocular outcomes of the pregnant women (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The following risk factors were identified as predictors of grade 1 adverse ocular outcomes during pregnancy: maternal age (95% CI 0.916\u0026ndash;0.991, P\u0026thinsp;=\u0026thinsp;0.0154); PIH (95% CI 1.03\u0026ndash;3.071, P\u0026thinsp;=\u0026thinsp;0.0387); eclampsia and pre-eclampsia (95% CI 1.392\u0026ndash;3.182, P\u0026thinsp;=\u0026thinsp;0.0004); GDM (95% CI 0.29\u0026ndash;0.657, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); history of chronic hypertension (95% CI 2.5\u0026ndash;6.67, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); hypoproteinemia (95% CI 0.708\u0026ndash;3.089, P\u0026thinsp;=\u0026thinsp;0.2972), IUGR (95% CI 0.673\u0026ndash;2.595, P\u0026thinsp;=\u0026thinsp;0.4182), obesity (95% CI 0.779\u0026ndash;3.189, P\u0026thinsp;=\u0026thinsp;0.206), and pregnancy with IgA nephropathy (95% CI 1.078\u0026ndash;101.115, P\u0026thinsp;=\u0026thinsp;0.0429). After calculation, the final model for grade 1 adverse ocular outcome prediction among pregnant women was as follows: Ln(P \u0026divide; (1\u0026thinsp;\u0026minus;\u0026thinsp;P))\u0026thinsp;=\u0026thinsp;0.2279\u0026thinsp;\u0026minus;\u0026thinsp;0.0483 \u0026times; maternal age\u0026thinsp;+\u0026thinsp;0.288 \u0026times; 1(PIH)\u0026thinsp;+\u0026thinsp;0.3721 \u0026times; 1(eclampsia and pre-eclampsia)\u0026thinsp;\u0026minus;\u0026thinsp;0.4146 \u0026times; 1(GDM)\u0026thinsp;+\u0026thinsp;0.7035 \u0026times; 1(history of chronic hypertension)\u0026thinsp;+\u0026thinsp;0.1958 \u0026times; 1(hypoproteinemia)\u0026thinsp;+\u0026thinsp;0.1394 \u0026times; 1(IUGR)\u0026thinsp;+\u0026thinsp;0.2274 \u0026times; 1(obesity)\u0026thinsp;+\u0026thinsp;1.1729 \u0026times; 1(pregnancy with IgA nephropathy).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis for grade 1 adverse ocular outcomes among the risk factors in pregnant women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.0483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.953 (0.916\u0026ndash;0.991)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.779 (1.03\u0026ndash;3.071)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEclampsia and pre-eclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.105 (1.392\u0026ndash;3.182)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.4146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.436 (0.29\u0026ndash;0.657)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of chronic hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.083 (2.5\u0026ndash;6.669)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoproteinemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.479 (0.708\u0026ndash;3.089)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIUGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.321 (0.673\u0026ndash;2.595)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.576 (0.779\u0026ndash;3.189)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregnancy with IgA nephropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.1729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.442 (1.078\u0026ndash;101.115)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCI, confidence interval; GDM, gestational diabetes mellitus; IgA, immunoglobulin A; IUGR, intrauterine growth retardation; OR, odds ratio; PIH, pregnancy-induced hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis for grade 2 adverse ocular outcomes among the risk factors in pregnant women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.0224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978 (0.911\u0026ndash;1.049)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEclampsia and pre-eclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.828 (1.85\u0026ndash;7.924)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.925 (0.44\u0026ndash;1.942)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of chronic hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.629 (1.094\u0026ndash;6.315)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoproteinemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.924 (0.617\u0026ndash;6.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHELLP syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.2646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.543 (1.617\u0026ndash;97.296)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCI, confidence interval; GDM, gestational diabetes mellitus; HELLP, hemolysis, elevated liver enzymes, and low platelets; OR, odds ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the grade 2 adverse ocular outcome group, maternal age (95% CI 0.911\u0026ndash;1.049, P\u0026thinsp;=\u0026thinsp;0.5341), eclampsia and pre-eclampsia (95% CI 1.85\u0026ndash;7.924, P\u0026thinsp;=\u0026thinsp;0.0003), GDM (95% CI 0.44\u0026ndash;1.942, P\u0026thinsp;=\u0026thinsp;0.8366), history of chronic hypertension (95% CI 1.094\u0026ndash;6.315, P\u0026thinsp;=\u0026thinsp;0.0306), hypoproteinemia (95% CI 0.617\u0026ndash;6.002, P\u0026thinsp;=\u0026thinsp;0.2595), and HELLP syndrome (95% CI 1.617\u0026ndash;97.296, P\u0026thinsp;=\u0026thinsp;0.0155) were predictors of the incidence of grade 2 adverse ocular outcomes during pregnancy. The final model for grade 2 adverse ocular outcome prediction among pregnant women was as follows: Ln(P \u0026divide; (1\u0026thinsp;\u0026minus;\u0026thinsp;P))\u0026thinsp;=\u0026thinsp;1.3273\u0026thinsp;\u0026minus;\u0026thinsp;0.0224 \u0026times; maternal age\u0026thinsp;+\u0026thinsp;0.6712 \u0026times; 1(PIH)\u0026thinsp;\u0026minus;\u0026thinsp;0.039 \u0026times; 1(GDM)\u0026thinsp;+\u0026thinsp;0.4833 \u0026times; 1(history of chronic hypertension)\u0026thinsp;+\u0026thinsp;0.3272 \u0026times; 1(hypoproteinemia)\u0026thinsp;+\u0026thinsp;1.2646 \u0026times; 1(HELLP syndrome).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.4 ROC curve analysis\u003c/h2\u003e \u003cp\u003eThe ROC curve analysis was based on multivariate logistic regression among different adverse ocular outcomes in pregnant women to carry out with the available study variables. The area under the ROC curve was 0.75 in the grade 1 adverse ocular outcomes group and 0.74 in the grade 2 adverse ocular outcomes group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participants\u003c/h2\u003e \u003cp\u003eThis retrospective study analyzed the data of pregnant women who attended The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China, between September 2020 and September 2022 for ophthalmic consultation. All patients were examined by an experienced doctor and underwent a detailed ophthalmologic examination involving slit-lamp biomicroscopy and non-mydriatic fundus photography. This study was approved by the institutional ethics committee of The Third Affiliated Hospital of Guangzhou Medical University (approval number [2023] No. 152) and was conducted in accordance with the tenets of the Declaration of Helsinki. Written informed consent was obtained from all of the subjects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe data of patients who delivered at our hospital, attended ophthalmic consultation, and understood and were willing to participate in the study were included. Patients with keratitis, cataracts, glaucoma, ocular surgery, ocular trauma, or preexisting retinopathy were excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Parameter selection for adverse ocular outcomes\u003c/h2\u003e \u003cp\u003eThe grading used to describe adverse ocular changes was based on the modified and simplified classification system for hypertensive retinopathy, as follows[9]: grade 0: no detectable signs; grade 1: mild, moderate, and severe retinal arteriole sclerosis, arteriolar narrowing, arteriovenous nicking, and arteriovenous ratio (AVR) of \u0026lt;\u0026thinsp;0.67; grade 2: hemorrhage (blot-shaped, dot-shaped, or flame-shaped), microaneurysm, cotton wool spot, hard exudate, or a combination of these signs, or retinal detachment and any other disease that may affect the vision of the patient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Retinal vessel diameter measurements\u003c/h2\u003e \u003cp\u003eAll pregnant women were assessed with a fundus camera (Kowa Fundus Camera VX-10α; Aichi, Japan). Two pairs of fundus photographs were obtained at the center of the optic disc and on the macula. The retinal vessel diameters of the six largest retinal arteries and veins within a specified zone (0.5\u0026ndash;1 disc diameter) from the optic disc margin were measured using a semiautomated system (IVAN, Department of Ophthalmology Visual Science, University of Wisconsin, Madison, WI, US). The Atherosclerosis Risk in Communities study protocol was performed for retinal vessel grading. The calculation formula of the revised Parr\u0026ndash;Hubbard\u0026ndash;Knudtson formula was used to standardize and summarize the retinal arteriolar and venular calibres as the central retinal artery equivalent and the central retinal vein equivalent. The AVR screenshot of IVAN software is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Two masked graders completed the measurement of the images. If the difference between the two graders was \u0026gt;\u0026thinsp;10%, a third grader assessed the images, and the average of these three values was used in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical data analyses were performed using SPSS software (version 23.0; IBM Corporation, Chicago, IL, US). Categorical variables are described as frequency and percentage. The univariate logistic regression analysis was performed to investigate the effect of a single risk factor on different adverse ocular outcomes. The variables that were significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were included in the multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve analysis was performed to decide the cut-off probability with optimum sensitivity and specificity. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study to identify the risk predictors of adverse ocular outcomes during pregnancy. We found that eclampsia and pre-eclampsia, GDM, history of chronic hypertension, and hypoproteinemia were independent predictors of both grade 1 and grade 2 adverse ocular outcomes during pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, we found that maternal age, PIH, IUGR, obesity, and pregnancy with IgA nephropathy were predictors of moderate and severe retinal arteriole sclerosis during pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, HELLP syndrome was a risk predictor of retinal hemorrhage and exudate and other diseases that may disrupt vision during pregnancy. Two predictive models were proposed in this study, for which the area under the ROC curve values were 0.75 and 0.74, illustrating that the models had reasonable accuracy and sensitivity.\u003c/p\u003e \u003cp\u003ePregnancy complications that may result in pregnancy-specific ocular diseases include eclampsia/pre-eclampsia and cortical blindness[28]. Vision is affected in approximately 25% of pregnancies with pre-eclampsia and in 50% of pregnancies with eclampsia[25]. In patients with eclampsia and pre-eclampsia, the most common ocular findings are retinal arteriole constriction,[29] which is in accordance with the correlation of this risk factor with grade 1 adverse ocular outcomes in the present study. Furthermore, with the exacerbation of the eclampsia and pre-eclampsia, retinal edema, hemorrhage, exudate, and cotton wool spots can occur, which is consistent with our results pertaining to grade 2 adverse ocular outcomes.\u003c/p\u003e \u003cp\u003eA previous study showed that diabetic retinopathy can be exacerbated in pregnancy[7]. Horvat et al.[12] showed that GDM does not show a similar association with the development of diabetic retinopathy, suggesting that ophthalmic examination is not necessary. However, in the present study, we found that the presence of GDM influences both grade 1 and 2 adverse ocular outcomes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There are two possible explanations for the differences in these results. On the one hand, hyperglycemia during pregnancy may lead to increased retinal capillary basement membrane thickness and gliosis by oxidative stress, the polyol pathway, and advanced glycation end-products[39; 5]. On the other hand, GDM was not significantly correlated with grade 2 ocular adverse outcomes in the multivariate logistic regression analysis, suggesting that GDM was not the primary risk indicator of severe adverse ocular outcomes among the multiple confounding factors.\u003c/p\u003e \u003cp\u003eMany studies have concluded that women of advanced maternal age may have an increased risk of maternal and fetal complications than younger women, including ectopic pregnancy, spontaneous abortion, GDM, pre-eclampsia, and cesarean delivery[22; 24]. However, no studies have evaluated the ocular changes associated with age. In the present study, age was a significant predictor of grade 1 ocular adverse outcomes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Previous studies have indicated that aging is associated with uterine and systemic artery impairments in pregnancy, which is likely related to increased vascular oxidative stress and nitric oxide synthesis[2]. Excessive nitric oxide and dysregulation of oxidative stress may lead to retinal arteriole narrowing[35].\u003c/p\u003e \u003cp\u003eObesity during pregnancy is the most common comorbidity that is associated with many complications in both pregnant women and fetuses[3]. Recent studies have shown that obesity can increase the risk of GDM, PIH, pre-eclampsia, and venous embolism, which are adverse maternal outcomes[27; 37; 17]. In the present study, obesity was a risk predictor of adverse ocular outcomes during pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). K\u0026ouml;chli et al.[14] indicated that obesity in young children may induce retinal arteriole narrowing and retinal venular widening. Oxidative stress and complement activation in the retinal environment of patients with obesity are associated with changes in the retinal vasculature[21]. Furthermore, we demonstrated that IUGR differed significantly with moderate and severe retinal arteriole sclerosis during pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Previous studies evaluating IUGR have often focused on the adverse outcomes of the fetus. For instance, Hellstrom et al.[10] showed that IUGR with abnormal fetal blood flow may lead to abnormal retinal vascular morphology in young adult life. However, no studies have evaluated the effects of IUGR on changes in the maternal microvasculature; therefore, more studies on this topic need to be conducted in the future.\u003c/p\u003e \u003cp\u003ePregnancy with IgA nephropathy has attracted much attention due to the high risk of adverse pregnancy outcomes[19]. Proteinuria during pregnancy in patients with IgA nephropathy has been proposed as a significant risk factor for pre-eclampsia, while severe proteinuria is known to cause hypoproteinemia[19; 6; 18; 30]. The aggravation of IgA nephropathy and hypoproteinemia can decrease intravascular volume and damage vascular endothelial function, which may lead to hypoxia and increased oxidative stress[19]. It can be inferred that oxidative stress and hypoxia in the retinal environment cause changes in the retinal vasculature, leading to the occurrence of adverse ocular outcomes during pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). HELLP syndrome is a severe form of pre-eclampsia/eclampsia, which may induce retinal vascular occlusion, serous retinal detachment, and even acute visual loss[33]. Grade 2 adverse ocular outcomes during pregnancy are more likely to disturb vision in pregnant women than grade 1 adverse ocular outcomes, and we proved that HELLP syndrome is an independent risk factor for grade 2 adverse ocular outcomes during pregnancy.\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered. First, the study was limited to patients who attended ophthalmic consultations, so the number of included patients was relatively small. Further studies with larger sample sizes are needed to evaluate the proposed risk model. Second, prospective studies are needed to clarify the correlations of GDM, obesity, and IUGR with adverse ocular outcomes. Finally, due to the lack of relevant study types, our prediction model data lacked validation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, our model was able to effectively predict the occurrence of adverse ocular outcomes during pregnancy with high sensitivity and specificity, with the risk factors including maternal age, eclampsia and pre-eclampsia, GDM, obesity, history of chronic hypertension, hypoproteinemia, IUGR, pregnancy with IgA nephropathy, and HELLP syndrome. We identified several new risk factors for adverse ocular outcomes during pregnancy. Further prospective studies should clarify the correlations of these identified risk factors with adverse ocular outcomes. This study provides a novel way to identify and evaluate maternal ocular conditions and facilitate early interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003ch2\u003eInformed consent statement\u003c/h2\u003e \u003cp\u003e\nWritten informed consent was obtained from all of the subjects.\n \u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eContribution statementX.T. Liu, S. Y. Wang designed the study. X.T. Liu, Y.Y. Wen, H.Q. Zou, S. Y. Wang performed the literature research, data acquisition, data analysis, and manuscript editing. X.T. Liu, S. Y. Wang conducted the clinical studies. X.T. Liu and S. Y. Wang reviewed the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e \u003cp\u003eThe authors thank the use of the IVAN software and Dr. Nicola Ferrier of the University of Wisconsin - Madison School of Engineering and the Department of Ophthalmology and Visual Sciences, University of Wisconsin - Madison.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData available on request from the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal, Regional, and National Levels of Maternal Mortality, 1990\u0026ndash;2015: A Systematic Analysis for the Global Burden of Disease Study 2015. LANCET. 388, 1775\u0026ndash;1812 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCare, A. S., Bourque, S. L., Morton, J. S., Hjartarson, E. P. \u0026amp; Davidge, S. T. Effect of Advanced Maternal Age On Pregnancy Outcomes and Vascular Function in the Rat. HYPERTENSION. 65, 1324\u0026ndash;1330 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatalano, P. M. \u0026amp; Shankar, K. Obesity and Pregnancy: Mechanisms of Short Term and Long Term Adverse Consequences for Mother and Child. BMJ. 356, j1 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan, W. C. et al. Management and Outcome of Sight-Threatening Diabetic Retinopathy in Pregnancy. Eye (Lond). 18, 826\u0026ndash;832 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandrasekaran, P. R., Madanagopalan, V. G. \u0026amp; Narayanan, R. Diabetic Retinopathy in Pregnancy - a Review. INDIAN J OPHTHALMOL. 69, 3015\u0026ndash;3025 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung, C. K. \u0026amp; Barratt, J. Pregnancy in IgA Nephropathy: An Effect On Renal Outcome? 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BMJ. 320, 1708\u0026ndash;1712 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOskarsdottir, S. E., Heijl, A. \u0026amp; Bengtsson, B. Predicting Undetected Glaucoma According to Age and IOP: A Prediction Model Developed From a Primarily European-derived Population. ACTA OPHTHALMOL. 97, 422\u0026ndash;426 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinheiro, R. L., Areia, A. L., Mota, P. A. \u0026amp; Donato, H. Advanced Maternal Age: Adverse Outcomes of Pregnancy, a Meta-Analysis. Acta Med Port. 32, 219\u0026ndash;226 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin, Q., Chen, C. \u0026amp; Cugati, S. Ophthalmic Associations in Pregnancy. Aust J Gen Pract. 49, 673\u0026ndash;680 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabiah, P. K. \u0026amp; Vitale, A. T. Noninfectious Uveitis and Pregnancy. AM J OPHTHALMOL. 136, 91\u0026ndash;98 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatpathy, H. K. et al. Maternal Obesity and Pregnancy. POSTGRAD MED. 120, E1-E9 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchultz, K. L., Birnbaum, A. D. \u0026amp; Goldstein, D. A. Ocular Disease in Pregnancy. CURR OPIN OPHTHALMOL. 16, 308\u0026ndash;314 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoullane, S., Rheaume, M. A. \u0026amp; Auger, N. Preeclampsia and the Retina. CURR HYPERTENS REP. 26, 169\u0026ndash;174 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuetsugu, Y. et al. [Study On the Predictors for Superimposed Preeclampsia in Patients with IgA Nephropathy]. Nihon Jinzo Gakkai Shi. 53, 1139\u0026ndash;1149 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaradaj, K. et al. Pregnancy and the Eye. 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PLOS ONE. 10, e126490 (2015).\u003c/span\u003e\u003c/li\u003e\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":"risk prediction model, adverse ocular outcome, pregnancy, maternal health","lastPublishedDoi":"10.21203/rs.3.rs-4454924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4454924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study aims to analyze common clinical data obtained during pregnancy, disease history, and maternal characteristics to determine ocular parameters and develop a risk prediction model for adverse ocular outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed the medical records of 760 pregnant women (1,520 eyes) from September 2020 to September 2022 at the Third Affiliated Hospital of Guangzhou Medical University. The maternal variables that could influence adverse ocular outcomes were identified, including maternal age, pregnancy-induced hypertension (PIH), gestational diabetes mellitus (GDM), eclampsia and pre-eclampsia, uterine disease, fetal abnormalities, in vitro fertilization with embryo transfer, hypoproteinemia, and major comorbidities during pregnancy. Univariate and multivariate logistic regression analyses were performed to evaluate the effects of the independent predictors on adverse ocular outcomes. The receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off probability with optimum sensitivity and specificity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEclampsia and pre-eclampsia, GDM, history of chronic hypertension, and hypoproteinemia were independent predictors of adverse ocular outcomes during pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Maternal age, PIH, intrauterine growth retardation (IUGR), obesity, and pregnancy with immunoglobulin A nephropathy were predictors of moderate and severe retinal arteriole sclerosis during pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, hemolysis, elevated liver enzymes, and low platelet (HELLP) syndrome was a predictor of retinal hemorrhage and exudate during pregnancy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Adverse ocular outcomes showed area under the ROC curve values of 0.75 and 0.74.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur predictive model could effectively predict adverse ocular outcomes during pregnancy, with the risk factors including maternal age, eclampsia and pre-eclampsia, GDM, obesity, history of chronic hypertension, hypoproteinemia, IUGR, pregnancy with immunoglobulin A nephropathy, and HELLP syndrome.\u003c/p\u003e","manuscriptTitle":"A model to predict the risk of adverse ocular outcomes in pregnant women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 12:50:33","doi":"10.21203/rs.3.rs-4454924/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":"9006429c-b2c9-408c-b436-acc488ebe19f","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32761969,"name":"Health sciences/Risk factors"},{"id":32761970,"name":"Health sciences/Health care/Public health"},{"id":32761971,"name":"Health sciences/Medical research/Study design"},{"id":32761972,"name":"Health sciences/Diseases/Eye diseases"}],"tags":[{"value":"featured","date":"2024-06-07 16:29:46"}],"updatedAt":"2024-06-17T10:59:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 12:50:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4454924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4454924","identity":"rs-4454924","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}