Dynamic Predictors of Adverse Outcomes in Pregnant Women with Chronic Kidney Disease

Dynamic Predictors of Adverse Outcomes in Pregnant Women with Chronic Kidney Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dynamic Predictors of Adverse Outcomes in Pregnant Women with Chronic Kidney Disease Yiwen Ying, zhen zhang, jingran yu, jieying wang, hong cai, xiang li, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9408891/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Aim: To develop a risk prediction model including multiple dynamic predictors through means of machine learning Methods: We conducted a retrospective cohort study among pregnant women diagnosed with CKD either before or during pregnancy at China, Shanghai Renji Hospital from January 1, 2018, to December 31, 2021. The association between various risk factors and adverse outcomes (AOs) was assessed using the Cox proportional hazards regression model. We also quantified dynamic changes in blood pressure and laboratory results and examined their association with AOs. The performance of the predictive model was evaluated using time-dependent area under the receiver operating characteristic curves (AUCs) at gestational ages (GA) of 34 and 37 weeks. Results: The study included 240 participants, 79 (32.92%) encountered AOs. A history of immune diseases (hazard ratio (HR), 2.93), baseline serum creatinine level (elevated by 10 units) (1.24), the rate of change in serum albumin (0.29), and creatinine (2.38) during the first two trimesters were significantly associated with AOs. The predictive model achieved an AUC of 0.88 and 0.72 for predicting AOs at GA 34 and 37 weeks, respectively, in the test set. Conclusions: The rate of change in serum creatinine and albumin levels during the early stages of pregnancy could serve as predictors of adverse pregnancy outcomes in women with CKD. The predictive model we developed shows promise for predicting adverse pregnancy outcomes, particularly at GA 34 weeks. Chronic kidney disease pregnant women adverse pregnancy outcomes dynamic predictors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Chronic kidney disease (CKD) represents a significant global health concern, affecting millions of individuals worldwide. Its prevalence among pregnant women introduces a unique set of challenges, as the coexistence of CKD and pregnancy presents complex renal and obstetric dilemmas[ 1 ]. The prevalence of CKD in the general population has been steadily increasing over the past decade, with a notable rise observed in women of childbearing age. Studies have indicated that approximately 3–5% of pregnant women are impacted by CKD, either pre-existing or developed during pregnancy[ 2 ]. This prevalence varies across geographic regions and is influenced by socioeconomic factors, healthcare accessibility, and the prevalence of risk factors such as hypertension and diabetes mellitus[ 3 ]. Pregnancy in the presence of CKD poses substantial risks to both maternal and fetal health. Women with CKD are known to have an elevated risk of adverse outcomes (AOs) like preeclampsia, preterm birth, gestational hypertension, and fetal growth restriction[ 4 – 6 ]. Additionally, they are more susceptible to conditions like anemia and electrolyte imbalances, which can further complicate their clinical status. Overall, the perinatal morbidity and mortality rates in this population are higher than those in women without CKD[ 7 , 8 ]. There has been a growing body of research focusing on the association between clinical indices, such as serum creatinine, mean arterial pressure, and adverse pregnancy outcomes. Nevertheless, these indices refer to a static point of patient status, which cannot represent the whole gestation cycle well. A more comprehensive prediction model is needed to predict adverse pregnancy outcomes in women with comorbid CKD. The objective of this study was to provide an overview of pregnancy and renal outcomes, as well as the challenges faced by pregnant women with concurrent CKD. Furthermore, we aim to determine several dynamic predictors as well as to establish and evaluate a risk prediction model to assist CKD-afflicted women in making informed decisions regarding childbirth. Study Methods Study Participants and Design This retrospective cohort study enrolled 240 women who underwent singleton pregnancies with a comorbid diagnosis of chronic kidney disease (CKD) diagnosed either before or during early pregnancy, after obtaining full verbal or written consent, from January 1, 2018, to December 31, 2021, at Shanghai Jiao Tong University Renji Hospital. All pregnant women were assessed with study inclusion standards before being enrolled into the program and data were collected at inclusio and at specific gestational periods. The exclusion criteria included women with severe systemic diseases (e.g., congenital heart disease, cancer, severe hepatic diseases, etc.) and those with multiple pregnancies. Patients who received renal replacement therapy were also excluded from the study (Fig. 1 A). CKD was defined as the presence of kidney damage or an estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73 mt^2, persisting for 3 months or more, regardless of the underlying cause. In this study, kidney morphology was assessed to identify abnormalities and proteinuria was analyzed. CKD was further evaluated and categorized based on albuminuria and GFR levels.[ 9 ]. The study was designed with four primary objectives: (1) to estimate the incidence of adverse outcomes of interest; (2) to investigate the association between various risk factors and adverse outcomes; (3) to illustrate the dynamic changes in the magnitude and rate of laboratory indices throughout different pregnancy stages; and (4) to develop and validate a risk model for predicting adverse outcomes (Fig. 1 B). Definition of Outcomes The primary outcome was defined as a composite outcome, considering the following events, whichever occurred first: (1) Renal outcomes: A significant increase in serum creatinine levels by 50% compared to baseline or the requirement for renal replacement therapies during pregnancy. (2) Pregnancy outcomes: a) Preterm delivery: Gestational age less than 37 weeks[ 10 ]. b) Pregnancy loss: Spontaneous abortus or miscarriage occurring before 12 weeks or after 28 weeks' gestation, not attributed to fetal genetic abnormalities[ 11 , 12 ]. c) Small-for-gestational-age (SGA) neonate: Birthweight below the tenth percentile, without anatomical or genetic abnormalities, as per Chinese birth weight references[ 13 ]. Secondary outcomes included: (1) (superimposed) Preeclampsia (PE): De novo onset of hypertension after 20 weeks of gestation, coupled with either proteinuria or signs of systemic involvement, such as impaired liver function, pulmonary edema, and thrombocytopenia[ 14 ] for pregnant women without previous histories of hypertension or proteinuria. Alternatively, sudden increase in blood pressure accompanying a massive rise in proteinuria after 20 weeks of gestation was observed in patients with the above histories. (2) Gestational diabetes mellitus (GDM): Any degree of glucose intolerance identified for the first time during pregnancy[ 15 ]. Baseline Characteristics and Follow-up Baseline characteristics included women's age, body mass index (BMI), comorbidity profile (including a history of hypertension, diabetes mellitus, cardiovascular disease, and immune diseases), and CKD-related details (stage, duration, and pathology). Mean arterial pressure (MAP) and laboratory results, including serum creatinine, 24-hour proteinuria, and serum albumin, were taken from the beginning of enrollment and routinely assessed during pregnancy and categorized by trimesters as follows: first trimester (0–12 weeks of gestation), second trimester (13–28 weeks of gestation), and third trimester (28 weeks to the time of delivery). For each patient, only one major laboratory result from each trimester was selected by three specialist nephrologists with ten years of experience, following specific clinical experience and prevalence in pregnancy practice. The data of each and every patient has undergone random pairing and blinded evaluations. Subsequently, it was considered to be representative of the corresponding trimester. Statistical Analysis Continuous variables were summarized as means with standard deviations (SD) or medians with interquartile ranges (IQR) and compared between groups using Student's t-test or the appropriate nonparametric test when applicable. Categorical variables are presented as frequencies with percentages and compared between groups using the chi-square test or Fisher's exact test, as appropriate. To handle missing data, multiple imputation by chained equations (MICE) was employed under the assumption that the data were missing at random. No missing data were observed for the outcome variables. Given that the proportion of missing observations for all variables (except CKD pathology) was less than 30%, we performed imputation for 31 complete datasets over 70 iterations for subsequent analyses. Regression analyses were conducted for all datasets and then pooled using Rubin's rule[ 16 , 17 ]. We carefully evaluated the consequence of MICE after performing on the data we collected and adjusted parameters to meet with the optimal distribution. The merging and convergence of data also underwent close examination. Predictive mean matching and logistic regression were chosen as imputation methods for continuous and binary variables, respectively (see Method S1). The association between various risk factors and adverse outcomes (AOs) was assesessed using the Cox proportional hazards regression model. Two models were constructed, considering the potentially time-varying relationship between factors that were repeatedly measured during pregnancy (e.g., laboratory results) and AOs. Model 1 utilized the baseline characteristics to examine the association between these characteristics and AOs at any point during pregnancy. Model 2 incorporated variables from the second trimester, in addition to baseline characteristics, to assess the association with AOs after the 28th week of pregnancy. The use of immunosuppressive medications was excluded from adjustment because they were highly correlated with a history of immune disease (not shown in the data; all Chi-Square test P < 0.001). To prevent overfitting, a sensitivity analysis was conducted by selecting robust predictors when fitting Model 2. These predictors were chosen through forward and backward stepwise regression across the 31 imputed datasets (see Table S5). Furthermore, we calculated the maximum relative magnitude of change in these metrics by dividing the maximum change from baseline by the measurements at baseline and multiplying the result by 100%. To account for inter-patient heterogeneity in the time interval between two trimester-specific examinations, we computed the dynamic change in MAP and other laboratory indices as follows to profile the changing speed in the early stage (before the 3rd trimester): Changing rate (per three months) = (log((Observation at the 2nd trimester) / (Observation at baseline + 1))) / (Observation time 2 - Observation 1) All statistical analyses were conducted using R software (version 4.3.0; R Foundation, Vienna, Austria). Statistical significance was set at p < 0.05. Results Study Population and Pregnancy Outcomes Among the 240 participants, 79 women (32.92%) reached the study endpoints. The incidence rates for the primary endpoint was 32.9% [27.0–38.9] (N = 79). And the incidence of the secondary outcomes, preeclampsia (PE), and gestational diabetes mellitus (GDM), were 15.0% [10.5–19.5] (N = 36), and 18.8% [13.8–23.7] (N = 45), respectively. Notably, the incidence rates for preterm delivery (17.9% vs. 7.3%), small-for-gestational-age (SGA) neonates (14.2% vs. 6.5%), PE (15.0% vs. 2.3%), and GDM (18.8% vs. 3.7%) were higher in our study population than in the general population, except for pregnancy loss (2.9% vs. 13.9%) (Fig. 2 ). The baseline characteristics of patients with or without adverse outcomes are presented in Table 2 ; there were no significant differences in age, BMI, history of hypertension/diabetes/cardiovascular diseases, assisted reproduction, or duration of CKD between the two groups. However, there was considerable diversity in the pattern of CKD pathology between the groups: 41.5% (17/79) of the endpoint-positive group had lupus nephritis, while 16.9% (11/161) of patients in the endpoint-free group had LN. 46.3% (19/79) of women reaching the endpoints had IgA nephropathy,while 70.8% (46/161) without adverse outcomes had the same diagnosis. Regarding laboratory indices at the first check-up, serum albumin levels did not differ significantly between the two groups, but 24-hour proteinuria ( P = 0.049), serum creatinine ( P = 0.002), and mean arterial pressure (MAP) ( P = 0.048) were all significantly higher in the endpoint-positive group (Table 1 ). Table 1 Baseline information (before imputation) of 2 subgroups of overall study population divided according to whether adverse outcomes (AOs) occur. Characteristics No AO (N = 161) AO (N = 79) P value Age, mean (SD), years 32.0 (3.60) 31.4 (3.74) 0.27 BMI, mean (SD), kg/m2 (missing = 21, 8.8%) 21.9 (2.91) 21.8 (3.35) 0.89 History of hypertension 17 (10.6%) 14 (17.7%) 0.18 History of diabetes mellitus 0.551* 0 160 (99.4%) 78 (98.7%) 1 1 (0.62%) 1 (1.27%) CVD: > 0.99* 0 158 (98.1%) 78 (98.7%) 1 3 (1.86%) 1 (1.27%) History of immune disease 20 (12.4%) 21 (26.6%) 0.01 History of SLE 13 (8.07%) 19 (24.1%) 0.001 CKD pathology (missing = 134 [55.8%]) 0.02 IgA 46 (70.8%) 19 (46.3%) Lupus 11 (16.9%) 17 (41.5%) Others 8 (12.3%) 5 (12.2%) CKD duration (missing = 12 [5%]) 69.7 (57.0) 76.9 (69.7) 0.44 History of miscarriage 42 (26.1%) 25 (31.6%) 0.45 History of premature delivery 0.093* 0 159 (98.8%) 75 (94.9%) 1 2 (1.24%) 4 (5.06%) Assisted reproduction 21 (13.0%) 10 (12.7%) > 0.99 Use of antihypertension drug 30 (19.6%) 12 (15.4%) 0.54 Use of hydroxychloroquine 15 (9.68%) 17 (21.8%) 0.02 Use of other immune suppression drugs 9 (5.81%) 13 (16.7%) 0.02 Baseline MAP, mean (SD), mmHg (missing = 46 [19.2%]) 88.3 (11.2) 92.3 (13.4) 0.048 Baseline urine protein, mean (SD) (missing = 70 [29.2%]) 867 (1017) 1500 (2273) 0.049 = 1000 mg 29 (25.7%) 21 (36.8%) Baseline serum creatinine, mean (SD) 53.6 (15.0) 67.0 (36.2) 0.002 Baseline Albumin, mean (SD) (missing = 13 [5.4%]) 40.2 (3.24) 40.1 (5.18) 0.90 Baseline eGFR, mean (SD) 119 (17.3) 107 (29.6) 0.002 Baseline CKD stage 0.001# 1 148 (91.9%) 61 (77.2%) 2 12 (7.45%) 11 (13.9%) 3 1 (0.62%) 5 (6.33%) 4 0 (0.00%) 2 (2.53%) * Chi-square correction test was applied. # Fisher’s exact test was applied. *Abbreviations: CKD, Chronic Kidney Disease; SLE, Systemic Lupus Erythematosus; IVF, In Vitro Fertilization; MAP, Mean Arterial Pressure. Participants Follow-Up Table 2 Follow-up data of laboratory indexes among the population during 2nd and 3rd trimesters. Characteristics No AO (N = 161) AO (N = 79) P value 2nd trimester MAP, mean (SD), mmHg (missing = 16 [6.7%]) 87.3 (9.78) 92.2 (11.0) 0.002 2nd trimester urine protein, mean (SD) (missing = 65 [27.1%]) 756 (975) 1398 (1726) 0.009 = 1000 mg 23 (20.4%) 22 (36.1%) 2nd trimester serum creatinine, mean (SD) (missing = 45 [18.8%]) 52.6 (15.8) 70.2 (40.7) 0.001 2nd trimester Albumin, mean (SD) (missing = 29 [12.1%]) 35.8 (2.86) 35.7 (4.36) 0.75 2nd trimester eGFR, mean (SD) (missing = 45 [18.8%]) 120 (18.1) 104 (33.2) 0.001 2nd trimester CKD stage (missing = 45 [18.8%]) < 0.001# 1 119 (92.2%) 47 (71.2%) 2 8 (6.20%) 8 (12.1%) 3 2 (1.55%) 10 (15.2%) 4 0 (0.00%) 1 (1.52%) Median time of 2nd trimester MAP test (IQR), gestation week 23.4 (4.6) 22.7 (4.2) 0.08* Median time of 2nd trimester laboratory test (IQR), gestation week 24.8 (5.5) 24.6 (6.2) 0.79* Median duration from 2nd trimester MAP test to baseline MAP test (IQR), gestation week 11.9 (5.0) 11.0 (5.0) 0.83* Median duration from 2nd trimester laboratory test to baseline MAP test (IQR), gestation week 13.0 (5.6) 14.3 (5.4) 0.36* 3rd trimester MAP, mean (SD), mmHg (missing = 20 [8.3%]) 91.8 (8.70) 94.4 (12.0) 0.11 3rd trimester urine protein, mean (SD) (missing = 58 [24.2%]) 1095 (1289) 2403 (2941) 0.001 = 1000 mg 39 (32.5%) 33 (53.2%) 3rd trimester serum creatinine, mean (SD) (missing = [%]) 58.0 (17.6) 75.2 (46.4) 0.003 3rd trimester Alb, mean (SD) (missing = 24 [10.0%]) 33.8 (3.28) 32.9 (3.26) 0.07 3rd trimester eGFR, mean (SD) (missing = 11 [4.6%]) 114 (19.6) 101 (31.8) 0.002 3rd trimester CKD stage (missing = 11 [4.6%]) 0.001# 1 141 (89.2%) 50 (70.4%) 2 13 (8.23%) 12 (16.9%) 3 4 (2.53%) 6 (8.45%) 4 0 (0.00%) 3 (4.23%) * Wilcoxon test was applied. # Fisher’s exact test was applied. The follow-up data of laboratory indices among the study population during their 2nd and 3rd trimesters are presented in Table 2 . Notably, there were statistically significant differences in the patterns of indices during the 2nd trimester between women experiencing adverse outcomes (AOs) and those who remained endpoint-free, including MAP, urine protein and serum creatinine, with the exception of 2nd trimester serum albumin. However, in the 3rd trimester, all the indices exhibited significant differences between the two groups, including MAP, urine protein, serum creatinine and serum albumin. Association Between Risk Factors and Outcomes Table 3 Univariate analyses of predictors and adverse outcomes. Age Any Renal Pregnancy 0.99 (0.93 to 1.06) 0.98 (0.84 to 1.14) 0.98 (0.92 to 1.05) BMI 1.02 (0.94 to 1.10) 0.98 (0.82 to 1.19) 1.04 (0.96 to 1.13) Hypertension 1.90 (1.06 to 3.40) 2.46 (0.67 to 8.97) 2.01 (1.17 to 3.79) DM 2.86 (0.40 to 20.70) NA 3.04 (0.42 to 22.05) CVD 0.75 (0.10 to 5.37) NA 0.77 (0.11 to 5.55) Immune 2.09 (1.25 to 3.49) 0.51 (0.07 to 3.96) 2.34 (1.39 to 3.94) SLE 2.70 (1.58 to 4.62) 0.74 (0.10 to 5.77) 3.01 (1.74 to 5.18) CKD path* IgA Reference Reference Reference Lupus 2.67 (1.35 to 5.27) 0.46 (0.06 to 3.87) 3.02 (1.50 to 6.09) Others 1.18 (0.44 to 3.21) 0.66 (0.08 to 5.66) 1.37 (0.50 to 3.75) CKD duration 1.001 (0.998 to 1.005) 1.005 (1.000 to 1.011) 1.000 (0.997 to 1.004) Miscarriage history 1.35 (0.83 to 2.18) 1.65 (0.54 to 5.04) 1.46 (0.89 to 2.39) Premature history 3.15 (1.14 to 8.73) NA 3.37 (1.21 to 9.35) Assisted reproduction 1.09 (0.56 to 2.12) 1.28 (0.28 to 5.77) 1.08 (0.53 to 2.17) Antihypertension drug 0.98 (0.53 to 1.84) 2.47 (0.75 to 8.21) 1.07 (0.57 to 2.00) Prednisone use 2.07 (1.24 to 3.46) 0.45 (0.06 to 3.45) 2.35 (1.40 to 3.97) HCQ 1.98 (1.13 to 3.48) 1.35 (0.30 to 6.10) 2.23 (1.26 to 3.95) Other immune suppression 2.39 (1.25 to 4.56) 1.08 (0.14 to 8.48) 2.65 (1.39 to 5.08) Baseline MAP 1.04 (1.02 to 1.06) 1.04 (1.00 to 1.09) 1.04 (1.02 to 1.06) Baseline Alb 0.942 (0.888 to 0.999) 0.92 (0.81 to 1.05) 0.93 (0.87 to 0.99) Baseline SCr (per 10 unit) 1.28 (1.19 to 1.37) 1.26 (1.04 to 1.51) 1.30 (1.22 to 1.39) Baseline urine protein (per gram) 1.28 (1.16 to 1.42) 1.29 (1.06 to 1.57) 1.30 (1.18 to 1.44) 2nd MAP 1.06 (1.03 to 1.08) 1.07 (1.02 to 1.13) 1.06 (1.03 to 1.08) 2nd Alb 0.91 (0.84 to 0.99) 0.87 (0.74 to 1.03) 0.90 (0.83 to 0.98) 2nd SCr (per 10 unit) 1.28 (1.21 to 1.36) 1.41 (1.25 to 1.58) 1.26 (1.19 to 1.34) 2nd urine protein (per gram) 1.51 (1.32 to 1.72) 1.52 (1.14 to 2.03) 1.54 (1.35 to 1.75) MAP rate 0.99 (0.95 to 1.03) 1.00 (0.96 to 1.03) 0.98 (0.85 to 1.13) Alb rate 1.00 (0.99 to 1.01) 1.00 (0.99 to 1.01) 1.00 (0.99 to 1.01) SCr rate 0.999 (0.997 to 1.002) 1.000 (0.998 to 1.003) 1.000 (0.997 to 1.002) 24-hour proteinuria rate 1.00 (0.96 to 1.03) 1.00 (0.97 to 1.03) 0.99 (0.94 to 1.05) To assess the risk factors for adverse outcomes, multiple imputation by chained equations (MICE) were employed to handle missing data. The plausibility of MICE was assessed visually by examining the density of observed data and the imputed values (Figure S1 ), as well as by comparing the distribution of values in the original and imputed datasets (Figure S2). The association between risk factors and outcomes was assessed using univariate Cox regression models, adjustments for multiplicity have been implemented and the results are presented in Table 3 . Hypertension, combined with immune disease, SLE, lupus nephritis, premature delivery history, and immunosuppressive drug use(prednisone, HCQ, and other immune suppression), showed statistical significance in predicting adverse pregnancy outcomes. MAP, SCr-, and urine protein levels at both the baseline and second trimester also showed predictive value in univariate analysis. Additionally, we depicted the maximum relative change in mean arterial pressure (MAP), 24-hour proteinuria, serum creatinine, and albumin with respect to primary outcomes (Fig. 3 ). Notably, a significantly higher maximum relative change in serum creatinine level (i.e. Scr rate)was observed among women with adverse outcomes (22.48% vs. 5.19%, P -value = 0.0033).In predicting adverse renal outcomes, Scr- and urine protein levels at both baseline and the second trimester showed statistical significance(Table 3 ). In the univariate analysis, we selected several predictors of statistical and clinical significance to construct the Cox hazard proportional model. The associations between predictors and renal outcomes and pregnancy outcomes are plotted in Fig. 4 and listed in Table S1 -S3, respectively. Model 1 utilized baseline characteristics to examine the association between these characteristics and AOs at any point during pregnancy. In Model 1, history of hypertension, history of immune disease, history of premature birth, baseline MAP, baseline Alb, baseline Scr and baseline urine protein were included, while history of immune disease(HR 3.19, 96%CI 1.80 to 5.67, P < 0.001), baseline Scr (per 10 unit increase)(HR1.26, 95%CI 1.16 to 1.37, P < 0.001)and baseline urine protein(HR 1.24, 95%CI 1.08 to 1.41, P = 0.003) showed significant predictive value. In model 2, after incorporating dynamic change rates of certain variables, change rates in serum albumin,that is, alb rate,(HR 0.29, 95% CI 0.11 to 0.78, P < 0.001) and SCr rate(HR 2.38, 95%CI 1.59 to 3.56, P 0.05 for both models (Table S4). LASSO-Cox regression To conduct a LASSO-Cox regression anyalysis, a sensitivity analysis was conducted by employing forward and backward stepwise regression in the 31 imputed datasets with these 12 variables incorporated in multiple Cox regression analysis (Fig. 4 ). Among these variables, the SCr rate, Alb rate, baseline SCr, baseline MAP, baseline Alb, and history of hypertension were included in the most optimal model in 100% (31 out of 31), 100% (31 out of 31), 100% (31 out of 31), 83.9% (26 out of 31), 96.8% (30 out of 31), and 83.9% (26 out of 31), respectively (Table S5). Therefore, these six variables were included in the LASSO-Cox regression model, and the sensitivity analysis confirmed the robustness of our findings (Table S6). We employed a LASSO-Cox regression model to reduce the dimensionality of the variables (Fig. 5 A and B). One of the imputed datasets (No. 31) was randomly split into a training cohort (N = 192) and a validation cohort (N = 48), at a ratio of 8:2 [ 22 ] respectively for the training and internal validation of the model. The LASSO method selected five variables (history of hypertension, immune diseases, premature delivery, serum creatinine level, and early-stage changing speed of serum creatinine level), which were subsequently fitted by the Cox regression model. The performance of the risk model at gestational age (GA) of 34 weeks was better than that at 37 weeks (Fig. 5 C and D). Additionally, Kaplan-Meier survival curves demonstrated differences in adverse outcomes (AOs) between the high-risk and low-risk groups in both the development and validation cohorts (log-rank P-value = 0.005 and 0.02 in the training and validation sets, respectively) (Fig. 5 E & 5 F). Discussion In the present study, we enrolled 240 pregnant women with CKD to examine the association between pregnancy-related laboratory parameters and adverse pregnancy outcomes. The incidence for the primary endpoint was quite high, at 32.9% (N = 79), with preeclampsia (PE) and gestational diabetes mellitus (GDM), accounting for 15.0% and 18.8% of cases, respectively. Regarding the pattern of CKD pathology, the proportion of lupus nephritis diagnoses was significantly higher in the endpoint-positive group (41.5%, 17/79), compared to the endpoint free group(16.9%, 11/161), suggesting that specific CKD pathologies may be associated with an increased risk of adverse outcomes. The 24-hour proteinuria, serum creatinine, and mean arterial pressure (MAP) at baseline and 2nd trimester, and3rd trimester were all significantly higher in the endpoint-positive group, potentially indicating an increased risk of adverse outcomes. Furthermore, a higher maximum relative change in serum creatinine level was observed among women with adverse outcomes (22.48% vs. 5.19%, P -value = 0.0033). In the Cox regression model predicting adverse outcomes, history of immune disease, baseline Scr(per 10 unit increase), Alb rate and Scr rate showed significant predictive value(P < 0.05). Importantly, LASSO-Cox regression analysis showed that Cox regression model (including history of hypertension, immune diseases, premature delivery, serum creatinine level, and early-stage changing speed of serum creatinine) performed well in distinguishing high-risk and low-risk adverse outcomes (log-rank P-value = 0.005 and 0.02 in the training and validation sets, respectively). The safety of pregnancy in women with elevated serum creatinine levels or significant proteinuria has been a challenging question for both nephrologists and obstetricians because outcomes can vary widely among individuals[ 2 ]. However, there is a lack of a comprehensive predictive model for adverse outcomes in pregnant women with CKD, as well as a lack of demonstration of dynamic changes in significant laboratory indices. Therefore, our study aimed to analyze clinical data to determine the association between various baseline and laboratory factors and adverse pregnancy outcomes, followed by the construction of a predictive model to better guide clinicians in managing pregnant patients with comorbid CKD. Innovatively, we addressed missing data using an authoritative imputation method: multiple imputation by chained equations (MICE). We assessed the association between various risk factors and adverse outcomes using the Cox proportional hazards regression model. Additionally, we applied Lasso-Cox regression to reduce the dimensionality of variables and create a predictive model by splitting the imputed dataset into training and test sets. Specifically, we identified that the changing rate of serum creatinine, as well as albumin corrected by observation time intervals in the early stage of pregnancy, could serve as rational dynamic predictors of adverse pregnancy outcomes in women with CKD. The Lasso-Cox model we developed and validated were predictive of adverse pregnancy outcomes, particularly at a gestational age (GA) of 34 weeks. It is well known that serum creatinine levels or CKD stage and their changes are significant risk factors in pregnant women with CKD[ 12 ]. Wiles et al. reported that chronic hypertension, pre- or early pregnancy proteinuria, and a gestational decrease in serum creatinine of < 10% of pre-pregnancy values are more important predictors of adverse obstetric and renal outcomes than CKD Stages 3–5[ 23 ]. However, their study calculated the changing rate of serum creatinine levels without correcting for time bias. In contrast, our study identified two novel and dynamic predictors that underwent lab-time adjustment. Serum albumin is another laboratory index of great significance, as it is the most abundant circulating protein in plasma, representing half of the total protein content and closely related to 24-hour proteinuria in CKD patients. However, this pivotal factor has often been overlooked in pregnancies with CKD. Our research addressed this research gap by revealing the early-stage changing speed of serum albumin as an independent risk factor in our CKD pregnancy population. Nevertheless, the study has several limitations. As it is a retrospective cohort study, and patients were not recruited with stringent criteria, the study population included as well as the study type still needs refinement in the near future. Furthermore, this study was based on a single center (Renji Hospital in Shanghai), so the patients involved in the study may not fully represent all pregnant women with CKD in China due to differences in demographic characteristics and regional medical standards. The selection of control groups was subsequently limited to literature reports due to insufficiency of real-world pregnancy data, which we hope to obtain and preserve in future studies. Additionally, despite excluding patients with excessive information loss and those requiring renal replacement therapies, the medical records of patients included in the study were still not comprehensive enough, especially in the pathological diagnosis of CKD, where most patients are considered inappropriate to undergo kidney biopsies, which constitute a major limitation of this study, though MICE have been implemented to tackle with missing data, exclusion of pathology from MICE may bias our findings. The choice of renal endpoints by the standard of a 50% rise in serum creatinine levels, may not ideally comply with pregnant women because of physiological drops during the second trimester. However, this may contribute to illustrate that those who met with the endpoints had drastically deteriorated renal functions. Lastly, the study's timeframe was limited to four years, whereas research conducted in the UK and US often spans over ten years[ 4 , 24 , 25 ]. Therefore, our future work will focus on conducting a long-term and large-scale prospective study in several medical centers to validate and refine our predictive model for CKD pregnancy outcomes. In all, this model is appropriately preliminary and requires extensive validation in diverse populations and clinical observations before practical use. Conclusions In summary, our study is the first to propose two dynamic predictors in the early stage of pregnancy; the changing speed of serum albumin and serum creatinine corrected to the time interval between two laboratory tests. Along with other baseline information (history of immune diseases and baseline level of serum creatinine [per 10 unit]), these variables have been proven to be independent risk factors for adverse pregnancy outcomes in pregnancy women with CKD. Moreover, we successfully proposed a predictive model by employing LASSO-Cox regression analysis, distinguishing high- and low-risk groups at gestational ages of 34and 37 weeks. These two timepoints correspond to the classification of late-preterm as well as preterm delivery, giving clues to clinical decisions that patients with high-risk before GA of 34-week may require earlier discussions about potential delivery options as soon as possible while a lower risk may indicate further tocolysis and relatively safer transition to peri-partum stage. We look forward to conducting a long-term and multi-center prospective cohort study in the near future so as to further refine our research results. Abbreviations CKD Chronic Kidney Disease AOs Adverse Outcomes MICE MAP SCr eGFR SGA PE GDM SD IQR SLE IVF Multiple Imputation By Chained Equations Mean Arterial Pressure Serum Creatinine Estimated Glomerular Filtration Rate Small-For-Gestational-Age Preeclampsia Gestational Diabetes Mellitus Standard Deviations Interquartile Ranges Systemic Lupus Erythematosus In Vitro Fertilization Declarations Ethics approval and consent to participate This observational retrospective cohort study was approved by the Ethics Committee on Human Research of Renji Hospital, Shanghai Jiao Tong University, Shanghai, China (Approval number: LY2022-048-B). The study was an observational study without medical intervention, so informed consent was waived. Consent for publication Not applicable. Availability of data and materials Data will be made available on request. Competing interests The authors declare they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding: This study is supported by the following funding sources: National Natural Science Foundation of China (Grants: 81970574 & 82170685) Grant from Shanghai Municipal Health Commission (Grants: ZY(2021-2023)-0208 & NO.ZY(2021-2023)-0302) Grant from the Natural Science Foundation of the Science and Technology Commission of Shanghai Municipality (Grant No. 22ZR1438700) National Administration of Traditional Chinese Medicine High-level Key Disciplines Project: Translational integrated traditional Chinese and Western medicine. Authors’ contributions Y.Y, Z.Z, X.Y, S.M, Q.W, S.L and N.Z conceived and designed the study. Y.Y, Z.Z and X.L collected the data. Y.Y, J.Y and H.S analyzed the data. Y.Y and Z.Z drafted the manuscript while J.Y, S.L, S.M and N.Z revised the manuscript. Acknowledgments This work was supported by both Renal Division and Department of Obstetrics and Gynecology of Renji Hospital, Shanghai Jiao Tong University School of Medicine. References Zhang J-J, Ma X-X, Hao L, Liu L-J, Lv J-C, Zhang H. A Systematic Review and Meta-Analysis of Outcomes of Pregnancy in CKD and CKD Outcomes in Pregnancy. CJASN 2015, 10 , 1964–1978. 10.2215/CJN.09250914 Wiles KS, Nelson-Piercy C, Bramham K. Reproductive Health and Pregnancy in Women with Chronic Kidney Disease. Nat Rev Nephrol. 2018;14:165–84. 10.1038/nrneph.2017.187 . Kattah AG, Butler CR. The Nephrologist’s Role in Supporting People with CKD and Unplanned Pregnancy Post-Dobbs. JASN 2023, 34 , 530–532. 10.1681/ASN.0000000000000081 Al Khalaf SY, O’Reilly ÉJ, McCarthy FP, Kublickas M, Kublickiene K, Khashan AS. Pregnancy Outcomes in Women with Chronic Kidney Disease and Chronic Hypertension: A National Cohort Study. Am J Obstet Gynecol. 2021;225:298e1. 298.e20. 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Prevalence and Determinants of Gestational Diabetes Mellitus: A Cross-Sectional Study in China. IJERPH 2017, 14 , 1532. 10.3390/ijerph14121532 Eertmans W, Tran TMP, Genbrugge C, Peene L, Mesotten D, Dens J, Jans F, De Deyne C. A Prediction Model for Good Neurological Outcome in Successfully Resuscitated Out-of-Hospital Cardiac Arrest Patients. Scand J Trauma Resusc Emerg Med. 2018;26:93. 10.1186/s13049-018-0558-2 . Wiles K, Bramham K, Seed PT, Nelson-Piercy C, Lightstone L, Chappell LC. Serum Creatinine in Pregnancy: A Systematic Review. Kidney Int Rep. 2019;4:408–19. 10.1016/j.ekir.2018.10.015 . Park S, Lee SM, Park JS, Hong J-S, Chin HJ, Na KY, Kim DK, Oh K-H, Joo KW, Kim YS, et al. Midterm eGFR and Adverse Pregnancy Outcomes: The Clinical Significance of Gestational Hyperfiltration. CJASN. 2017;12:1048–56. 10.2215/CJN.12101116 . Wiles K, Webster P, Seed PT, Bennett-Richards K, Bramham K, Brunskill N, Carr S, Hall M, Khan R, Nelson-Piercy C, et al. The Impact of Chronic Kidney Disease Stages 3–5 on Pregnancy Outcomes. Nephrol Dialysis Transplantation. 2021;36:2008–17. 10.1093/ndt/gfaa247 . Additional Declarations No competing interests reported. Supplementary Files SubmissionSupplementarymaterials.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 15 May, 2026 Reviews received at journal 11 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 04 May, 2026 Editor invited by journal 18 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 13 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":23062,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Enrollment and exclusion of pregnant patients with chronic kidney disease in Renji Hospital from January 2018 to December 2021. Exclusion criteria is listed in the scheme with dotted line. (\u003cstrong\u003eB\u003c/strong\u003e) The detailed methodology workflow of the article, aiming for four objectives.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9408891/v1/fd397d4f5d6b76e5e5317f7a.png"},{"id":109214905,"identity":"1f951c90-79a6-4b7c-9e0b-5fb40954e0f1","added_by":"auto","created_at":"2026-05-13 17:49:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143395,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence of (\u003cstrong\u003eA\u003c/strong\u003e) primary outcomes and (\u003cstrong\u003eB\u003c/strong\u003e) secondary outcomes. The general population norm was obtained from published review.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9408891/v1/203cc22ddb2690d1630f46a4.png"},{"id":109249114,"identity":"11fd892d-3907-439c-b1cd-d2bd68803024","added_by":"auto","created_at":"2026-05-14 08:42:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":399644,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum change from baseline in (\u003cstrong\u003eA\u003c/strong\u003e) mean arterial pressure, (B) 24-hour proteinuria, (C) serum creatinine (D) serum albumin by primary outcome. Mean change and the standard error were estimated and pooled in Rubin’s rule, dataset 31 was used for demonstration purpose. Difference was justified by \u003cem\u003et\u003c/em\u003e test.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9408891/v1/62794f6a1abca3efd4dba0e5.png"},{"id":109405085,"identity":"02ba9fbd-928d-43a5-8889-d938116a69f5","added_by":"auto","created_at":"2026-05-17 12:54:46","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73566,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of two multivariate cox regression models, Model 1 utilizing baseline characteristics, model 2 incorporating variables from the second trimester, in addition to baseline characteristics, to assess the association with AOs after the 28th week of pregnancy. \u003cstrong\u003eMAP:\u003c/strong\u003eMean arterial pressure; \u003cstrong\u003eAlb:\u003c/strong\u003ealbumin; \u003cstrong\u003eSCr:\u003c/strong\u003eSerum Creatinine.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9408891/v1/ec3ff4b19857b336fecfc3a9.jpeg"},{"id":109214908,"identity":"bc2ad044-2c3b-40fb-81ed-685df4c1c773","added_by":"auto","created_at":"2026-05-13 17:49:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":150010,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA, B\u003c/strong\u003e) \u0026nbsp;The LASSO-penalized Cox analysis. The optimal penalty parameter, λ,was determined by C-index. (\u003cstrong\u003eC\u003c/strong\u003e) The performance of the training cohort. (\u003cstrong\u003eD\u003c/strong\u003e) The performance of the validation cohort. (\u003cstrong\u003eE\u003c/strong\u003e) \u0026nbsp;Kaplan-Meier survival curves showing the difference of AOs between high- (red) and low- (blue) risk groups in the training cohort. (\u003cstrong\u003eF\u003c/strong\u003e) \u0026nbsp;Kaplan-Meier survival curves showing the difference of AOs between high- (red) and low- (blue) risk groups in the validation cohort.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9408891/v1/f48271045fdcd4f297533557.png"},{"id":109406153,"identity":"e1a8b12b-3754-44b5-ace7-8cc0ae2c7792","added_by":"auto","created_at":"2026-05-17 13:25:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1192129,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9408891/v1/f6814ed1-e7e8-4b7c-95bf-53c4afeb3387.pdf"},{"id":109214904,"identity":"95eb6929-1459-4626-955c-ef417b48e2e9","added_by":"auto","created_at":"2026-05-13 17:49:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":7920442,"visible":true,"origin":"","legend":"","description":"","filename":"SubmissionSupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9408891/v1/d0dd84d117da3ee92de10286.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Predictors of Adverse Outcomes in Pregnant Women with Chronic Kidney Disease","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic kidney disease (CKD) represents a significant global health concern, affecting millions of individuals worldwide. Its prevalence among pregnant women introduces a unique set of challenges, as the coexistence of CKD and pregnancy presents complex renal and obstetric dilemmas[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The prevalence of CKD in the general population has been steadily increasing over the past decade, with a notable rise observed in women of childbearing age. Studies have indicated that approximately 3\u0026ndash;5% of pregnant women are impacted by CKD, either pre-existing or developed during pregnancy[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This prevalence varies across geographic regions and is influenced by socioeconomic factors, healthcare accessibility, and the prevalence of risk factors such as hypertension and diabetes mellitus[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePregnancy in the presence of CKD poses substantial risks to both maternal and fetal health. Women with CKD are known to have an elevated risk of adverse outcomes (AOs) like preeclampsia, preterm birth, gestational hypertension, and fetal growth restriction[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Additionally, they are more susceptible to conditions like anemia and electrolyte imbalances, which can further complicate their clinical status. Overall, the perinatal morbidity and mortality rates in this population are higher than those in women without CKD[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere has been a growing body of research focusing on the association between clinical indices, such as serum creatinine, mean arterial pressure, and adverse pregnancy outcomes. Nevertheless, these indices refer to a static point of patient status, which cannot represent the whole gestation cycle well. A more comprehensive prediction model is needed to predict adverse pregnancy outcomes in women with comorbid CKD. The objective of this study was to provide an overview of pregnancy and renal outcomes, as well as the challenges faced by pregnant women with concurrent CKD. Furthermore, we aim to determine several dynamic predictors as well as to establish and evaluate a risk prediction model to assist CKD-afflicted women in making informed decisions regarding childbirth.\u003c/p\u003e"},{"header":"Study Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Participants and Design\u003c/h2\u003e \u003cp\u003e This retrospective cohort study enrolled 240 women who underwent singleton pregnancies with a comorbid diagnosis of chronic kidney disease (CKD) diagnosed either before or during early pregnancy, after obtaining full verbal or written consent, from January 1, 2018, to December 31, 2021, at Shanghai Jiao Tong University Renji Hospital. All pregnant women were assessed with study inclusion standards before being enrolled into the program and data were collected at inclusio and at specific gestational periods. The exclusion criteria included women with severe systemic diseases (e.g., congenital heart disease, cancer, severe hepatic diseases, etc.) and those with multiple pregnancies. Patients who received renal replacement therapy were also excluded from the study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eCKD was defined as the presence of kidney damage or an estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73 mt^2, persisting for 3 months or more, regardless of the underlying cause. In this study, kidney morphology was assessed to identify abnormalities and proteinuria was analyzed. CKD was further evaluated and categorized based on albuminuria and GFR levels.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study was designed with four primary objectives: (1) to estimate the incidence of adverse outcomes of interest; (2) to investigate the association between various risk factors and adverse outcomes; (3) to illustrate the dynamic changes in the magnitude and rate of laboratory indices throughout different pregnancy stages; and (4) to develop and validate a risk model for predicting adverse outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of Outcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was defined as a composite outcome, considering the following events, whichever occurred first:\u003c/p\u003e \u003cp\u003e(1) Renal outcomes: A significant increase in serum creatinine levels by 50% compared to baseline or the requirement for renal replacement therapies during pregnancy.\u003c/p\u003e \u003cp\u003e(2) Pregnancy outcomes: a) Preterm delivery: Gestational age less than 37 weeks[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. b) Pregnancy loss: Spontaneous abortus or miscarriage occurring before 12 weeks or after 28 weeks' gestation, not attributed to fetal genetic abnormalities[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. c) Small-for-gestational-age (SGA) neonate: Birthweight below the tenth percentile, without anatomical or genetic abnormalities, as per Chinese birth weight references[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSecondary outcomes included:\u003c/p\u003e \u003cp\u003e(1) (superimposed) Preeclampsia (PE): De novo onset of hypertension after 20 weeks of gestation, coupled with either proteinuria or signs of systemic involvement, such as impaired liver function, pulmonary edema, and thrombocytopenia[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] for pregnant women without previous histories of hypertension or proteinuria. Alternatively, sudden increase in blood pressure accompanying a massive rise in proteinuria after 20 weeks of gestation was observed in patients with the above histories.\u003c/p\u003e \u003cp\u003e(2) Gestational diabetes mellitus (GDM): Any degree of glucose intolerance identified for the first time during pregnancy[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBaseline Characteristics and Follow-up\u003c/h3\u003e\n\u003cp\u003eBaseline characteristics included women's age, body mass index (BMI), comorbidity profile (including a history of hypertension, diabetes mellitus, cardiovascular disease, and immune diseases), and CKD-related details (stage, duration, and pathology). Mean arterial pressure (MAP) and laboratory results, including serum creatinine, 24-hour proteinuria, and serum albumin, were taken from the beginning of enrollment and routinely assessed during pregnancy and categorized by trimesters as follows: first trimester (0\u0026ndash;12 weeks of gestation), second trimester (13\u0026ndash;28 weeks of gestation), and third trimester (28 weeks to the time of delivery). For each patient, only one major laboratory result from each trimester was selected by three specialist nephrologists with ten years of experience, following specific clinical experience and prevalence in pregnancy practice. The data of each and every patient has undergone random pairing and blinded evaluations. Subsequently, it was considered to be representative of the corresponding trimester.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized as means with standard deviations (SD) or medians with interquartile ranges (IQR) and compared between groups using Student's t-test or the appropriate nonparametric test when applicable. Categorical variables are presented as frequencies with percentages and compared between groups using the chi-square test or Fisher's exact test, as appropriate.\u003c/p\u003e \u003cp\u003eTo handle missing data, multiple imputation by chained equations (MICE) was employed under the assumption that the data were missing at random. No missing data were observed for the outcome variables. Given that the proportion of missing observations for all variables (except CKD pathology) was less than 30%, we performed imputation for 31 complete datasets over 70 iterations for subsequent analyses. Regression analyses were conducted for all datasets and then pooled using Rubin's rule[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We carefully evaluated the consequence of MICE after performing on the data we collected and adjusted parameters to meet with the optimal distribution. The merging and convergence of data also underwent close examination. Predictive mean matching and logistic regression were chosen as imputation methods for continuous and binary variables, respectively (see Method S1).\u003c/p\u003e \u003cp\u003eThe association between various risk factors and adverse outcomes (AOs) was assesessed using the Cox proportional hazards regression model. Two models were constructed, considering the potentially time-varying relationship between factors that were repeatedly measured during pregnancy (e.g., laboratory results) and AOs. Model 1 utilized the baseline characteristics to examine the association between these characteristics and AOs at any point during pregnancy. Model 2 incorporated variables from the second trimester, in addition to baseline characteristics, to assess the association with AOs after the 28th week of pregnancy. The use of immunosuppressive medications was excluded from adjustment because they were highly correlated with a history of immune disease (not shown in the data; all Chi-Square test P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). To prevent overfitting, a sensitivity analysis was conducted by selecting robust predictors when fitting Model 2. These predictors were chosen through forward and backward stepwise regression across the 31 imputed datasets (see Table S5).\u003c/p\u003e \u003cp\u003eFurthermore, we calculated the maximum relative magnitude of change in these metrics by dividing the maximum change from baseline by the measurements at baseline and multiplying the result by 100%. To account for inter-patient heterogeneity in the time interval between two trimester-specific examinations, we computed the dynamic change in MAP and other laboratory indices as follows to profile the changing speed in the early stage (before the 3rd trimester): Changing rate (per three months) = (log((Observation at the 2nd trimester) / (Observation at baseline\u0026thinsp;+\u0026thinsp;1))) / (Observation time 2 - Observation 1)\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using R software (version 4.3.0; R Foundation, Vienna, Austria). Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStudy Population and Pregnancy Outcomes\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the 240 participants, 79 women (32.92%) reached the study endpoints. The incidence rates for the primary endpoint was 32.9% [27.0\u0026ndash;38.9] (N\u0026thinsp;=\u0026thinsp;79). And the incidence of the secondary outcomes, preeclampsia (PE), and gestational diabetes mellitus (GDM), were 15.0% [10.5\u0026ndash;19.5] (N\u0026thinsp;=\u0026thinsp;36), and 18.8% [13.8\u0026ndash;23.7] (N\u0026thinsp;=\u0026thinsp;45), respectively. Notably, the incidence rates for preterm delivery (17.9% vs. 7.3%), small-for-gestational-age (SGA) neonates (14.2% vs. 6.5%), PE (15.0% vs. 2.3%), and GDM (18.8% vs. 3.7%) were higher in our study population than in the general population, except for pregnancy loss (2.9% vs. 13.9%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ).\u003c/p\u003e \u003cp\u003eThe baseline characteristics of patients with or without adverse outcomes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; there were no significant differences in age, BMI, history of hypertension/diabetes/cardiovascular diseases, assisted reproduction, or duration of CKD between the two groups. However, there was considerable diversity in the pattern of CKD pathology between the groups: 41.5% (17/79) of the endpoint-positive group had lupus nephritis, while 16.9% (11/161) of patients in the endpoint-free group had LN. 46.3% (19/79) of women reaching the endpoints had IgA nephropathy,while 70.8% (46/161) without adverse outcomes had the same diagnosis. Regarding laboratory indices at the first check-up, serum albumin levels did not differ significantly between the two groups, but 24-hour proteinuria (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), serum creatinine (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), and mean arterial pressure (MAP) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048) were all significantly higher in the endpoint-positive group (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\u003eBaseline information (before imputation) of 2 subgroups of overall study population divided according to whether adverse outcomes (AOs) occur.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo AO (N\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAO (N\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.0 (3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.4 (3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, mean (SD), kg/m2 (missing\u0026thinsp;=\u0026thinsp;21, 8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.9 (2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8 (3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of diabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.551*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (99.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (98.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (98.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (98.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of immune disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of SLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (8.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD pathology (missing\u0026thinsp;=\u0026thinsp;134 [55.8%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (70.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (46.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLupus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (12.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD duration (missing\u0026thinsp;=\u0026thinsp;12 [5%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.7 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.9 (69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of miscarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (26.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of premature delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (98.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (94.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (5.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssisted reproduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of antihypertension drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of hydroxychloroquine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (9.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUse of other immune suppression drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (5.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline MAP, mean (SD), mmHg\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;46 [19.2%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88.3 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.3 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline urine protein, mean (SD)\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;70 [29.2%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e867 (1017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1500 (2273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1000mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 1000 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline serum creatinine, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.6 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.0 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Albumin, mean (SD)\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;13 [5.4%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.2 (3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.1 (5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline eGFR, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline CKD stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (91.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (77.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (7.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (6.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Chi-square correction test was applied. # Fisher\u0026rsquo;s exact test was applied.\u003c/p\u003e \u003cp\u003e*Abbreviations: CKD, Chronic Kidney Disease; SLE, Systemic Lupus Erythematosus; IVF, In Vitro Fertilization; MAP, Mean Arterial Pressure.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eParticipants Follow-Up\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFollow-up data of laboratory indexes among the population during 2nd and 3rd trimesters.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo AO (N\u0026thinsp;=\u0026thinsp;161)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAO (N\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd trimester MAP, mean (SD), mmHg\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;16 [6.7%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.3 (9.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.2 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd trimester urine protein, mean (SD)\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;65 [27.1%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e756 (975)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1398 (1726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1000mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (79.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (63.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 1000 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (36.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd trimester serum creatinine, mean (SD)\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;45 [18.8%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.6 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.2 (40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd trimester Albumin, mean (SD)\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;29 [12.1%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.8 (2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.7 (4.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd trimester eGFR, mean (SD)\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;45 [18.8%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd trimester CKD stage\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;45 [18.8%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (92.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (71.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (6.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian time of 2nd trimester MAP test (IQR), gestation week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.4 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.7 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian time of 2nd trimester laboratory test (IQR), gestation week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.8 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.6 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian duration from 2nd trimester MAP test to baseline MAP test (IQR), gestation week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.9 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian duration from 2nd trimester laboratory test to baseline MAP test (IQR), gestation week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd trimester MAP, mean (SD), mmHg\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;20 [8.3%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.8 (8.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.4 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd trimester urine protein, mean (SD)\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;58 [24.2%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1095 (1289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2403 (2941)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1000mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (67.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;= 1000 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (53.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd trimester serum creatinine, mean (SD)\u003c/p\u003e \u003cp\u003e(missing = [%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.0 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.2 (46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd trimester Alb, mean (SD)\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;24 [10.0%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.8 (3.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.9 (3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd trimester eGFR, mean (SD)\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;11 [4.6%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd trimester CKD stage\u003c/p\u003e \u003cp\u003e(missing\u0026thinsp;=\u0026thinsp;11 [4.6%])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001#\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (89.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (8.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Wilcoxon test was applied. # Fisher\u0026rsquo;s exact test was applied.\u003c/p\u003e \u003cp\u003eThe follow-up data of laboratory indices among the study population during their 2nd and 3rd trimesters are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Notably, there were statistically significant differences in the patterns of indices during the 2nd trimester between women experiencing adverse outcomes (AOs) and those who remained endpoint-free, including MAP, urine protein and serum creatinine, with the exception of 2nd trimester serum albumin. However, in the 3rd trimester, all the indices exhibited significant differences between the two groups, including MAP, urine protein, serum creatinine and serum albumin.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAssociation Between Risk Factors and Outcomes\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\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 analyses of predictors and adverse outcomes.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRenal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePregnancy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.93 to 1.06)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.84 to 1.14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.92 to 1.05)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.94 to 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.82 to 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (0.96 to 1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.90 (1.06 to 3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46 (0.67 to 8.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.01 (1.17 to 3.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.86 (0.40 to 20.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04 (0.42 to 22.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75 (0.10 to 5.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.11 to 5.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.09 (1.25 to 3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51 (0.07 to 3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34 (1.39 to 3.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.70 (1.58 to 4.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.10 to 5.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.01 (1.74 to 5.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD path*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLupus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.67 (1.35 to 5.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 (0.06 to 3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.02 (1.50 to 6.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (0.44 to 3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.08 to 5.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37 (0.50 to 3.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD duration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.001 (0.998 to 1.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.005 (1.000 to 1.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (0.997 to 1.004)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiscarriage history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (0.83 to 2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.65 (0.54 to 5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46 (0.89 to 2.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremature history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.15 (1.14 to 8.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37 (1.21 to 9.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssisted reproduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.56 to 2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (0.28 to 5.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.53 to 2.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntihypertension drug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.53 to 1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47 (0.75 to 8.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07 (0.57 to 2.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrednisone use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.07 (1.24 to 3.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45 (0.06 to 3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35 (1.40 to 3.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.13 to 3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35 (0.30 to 6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.23 (1.26 to 3.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther immune suppression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.39 (1.25 to 4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (0.14 to 8.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.65 (1.39 to 5.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline MAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.02 to 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (1.00 to 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (1.02 to 1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline Alb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.942 (0.888 to 0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.81 to 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.87 to 0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline SCr (per 10 unit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (1.19 to 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (1.04 to 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.22 to 1.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline urine protein (per gram)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (1.16 to 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (1.06 to 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (1.18 to 1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd MAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.03 to 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.02 to 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.03 to 1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd Alb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.84 to 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87 (0.74 to 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.83 to 0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd SCr (per 10 unit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28 (1.21 to 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41 (1.25 to 1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 (1.19 to 1.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd urine protein (per gram)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51 (1.32 to 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52 (1.14 to 2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54 (1.35 to 1.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.95 to 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.96 to 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.85 to 1.13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlb rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99 to 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.99 to 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.99 to 1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCr rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999 (0.997 to 1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000 (0.998 to 1.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000 (0.997 to 1.002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24-hour proteinuria rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.96 to 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (0.97 to 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.94 to 1.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo assess the risk factors for adverse outcomes, multiple imputation by chained equations (MICE) were employed to handle missing data. The plausibility of MICE was assessed visually by examining the density of observed data and the imputed values (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), as well as by comparing the distribution of values in the original and imputed datasets (Figure S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe association between risk factors and outcomes was assessed using univariate Cox regression models, adjustments for multiplicity have been implemented and the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Hypertension, combined with immune disease, SLE, lupus nephritis, premature delivery history, and immunosuppressive drug use(prednisone, HCQ, and other immune suppression), showed statistical significance in predicting adverse pregnancy outcomes. MAP, SCr-, and urine protein levels at both the baseline and second trimester also showed predictive value in univariate analysis. Additionally, we depicted the maximum relative change in mean arterial pressure (MAP), 24-hour proteinuria, serum creatinine, and albumin with respect to primary outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, a significantly higher maximum relative change in serum creatinine level (i.e. Scr rate)was observed among women with adverse outcomes (22.48% vs. 5.19%, \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.0033).In predicting adverse renal outcomes, Scr- and urine protein levels at both baseline and the second trimester showed statistical significance(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the univariate analysis, we selected several predictors of statistical and clinical significance to construct the Cox hazard proportional model. The associations between predictors and renal outcomes and pregnancy outcomes are plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e and listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S3, respectively. Model 1 utilized baseline characteristics to examine the association between these characteristics and AOs at any point during pregnancy. In Model 1, history of hypertension, history of immune disease, history of premature birth, baseline MAP, baseline Alb, baseline Scr and baseline urine protein were included, while history of immune disease(HR 3.19, 96%CI 1.80 to 5.67, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), baseline Scr (per 10 unit increase)(HR1.26, 95%CI 1.16 to 1.37, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)and baseline urine protein(HR 1.24, 95%CI 1.08 to 1.41, P\u0026thinsp;=\u0026thinsp;0.003) showed significant predictive value. In model 2, after incorporating dynamic change rates of certain variables, change rates in serum albumin,that is, alb rate,(HR 0.29, 95% CI 0.11 to 0.78, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SCr rate(HR 2.38, 95%CI 1.59 to 3.56, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly associated with AO outcomes, while the history of premature delivery were insignificant. It is worth noting that the proportional hazard assumption test revealed P-values\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for both models (Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLASSO-Cox regression\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo conduct a LASSO-Cox regression anyalysis, a sensitivity analysis was conducted by employing forward and backward stepwise regression in the 31 imputed datasets with these 12 variables incorporated in multiple Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among these variables, the SCr rate, Alb rate, baseline SCr, baseline MAP, baseline Alb, and history of hypertension were included in the most optimal model in 100% (31 out of 31), 100% (31 out of 31), 100% (31 out of 31), 83.9% (26 out of 31), 96.8% (30 out of 31), and 83.9% (26 out of 31), respectively (Table S5). Therefore, these six variables were included in the LASSO-Cox regression model, and the sensitivity analysis confirmed the robustness of our findings (Table S6).\u003c/p\u003e \u003cp\u003eWe employed a LASSO-Cox regression model to reduce the dimensionality of the variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B). One of the imputed datasets (No. 31) was randomly split into a training cohort (N\u0026thinsp;=\u0026thinsp;192) and a validation cohort (N\u0026thinsp;=\u0026thinsp;48), at a ratio of 8:2 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] respectively for the training and internal validation of the model. The LASSO method selected five variables (history of hypertension, immune diseases, premature delivery, serum creatinine level, and early-stage changing speed of serum creatinine level), which were subsequently fitted by the Cox regression model. The performance of the risk model at gestational age (GA) of 34 weeks was better than that at 37 weeks (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and D). Additionally, Kaplan-Meier survival curves demonstrated differences in adverse outcomes (AOs) between the high-risk and low-risk groups in both the development and validation cohorts (log-rank P-value\u0026thinsp;=\u0026thinsp;0.005 and 0.02 in the training and validation sets, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eE \u0026amp; \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we enrolled 240 pregnant women with CKD to examine the association between pregnancy-related laboratory parameters and adverse pregnancy outcomes. The incidence for the primary endpoint was quite high, at 32.9% (N\u0026thinsp;=\u0026thinsp;79), with preeclampsia (PE) and gestational diabetes mellitus (GDM), accounting for 15.0% and 18.8% of cases, respectively. Regarding the pattern of CKD pathology, the proportion of lupus nephritis diagnoses was significantly higher in the endpoint-positive group (41.5%, 17/79), compared to the endpoint free group(16.9%, 11/161), suggesting that specific CKD pathologies may be associated with an increased risk of adverse outcomes. The 24-hour proteinuria, serum creatinine, and mean arterial pressure (MAP) at baseline and 2nd trimester, and3rd trimester were all significantly higher in the endpoint-positive group, potentially indicating an increased risk of adverse outcomes. Furthermore, a higher maximum relative change in serum creatinine level was observed among women with adverse outcomes (22.48% vs. 5.19%, \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.0033). In the Cox regression model predicting adverse outcomes, history of immune disease, baseline Scr(per 10 unit increase), Alb rate and Scr rate showed significant predictive value(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Importantly, LASSO-Cox regression analysis showed that Cox regression model (including history of hypertension, immune diseases, premature delivery, serum creatinine level, and early-stage changing speed of serum creatinine) performed well in distinguishing high-risk and low-risk adverse outcomes (log-rank P-value\u0026thinsp;=\u0026thinsp;0.005 and 0.02 in the training and validation sets, respectively).\u003c/p\u003e \u003cp\u003eThe safety of pregnancy in women with elevated serum creatinine levels or significant proteinuria has been a challenging question for both nephrologists and obstetricians because outcomes can vary widely among individuals[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, there is a lack of a comprehensive predictive model for adverse outcomes in pregnant women with CKD, as well as a lack of demonstration of dynamic changes in significant laboratory indices. Therefore, our study aimed to analyze clinical data to determine the association between various baseline and laboratory factors and adverse pregnancy outcomes, followed by the construction of a predictive model to better guide clinicians in managing pregnant patients with comorbid CKD.\u003c/p\u003e \u003cp\u003eInnovatively, we addressed missing data using an authoritative imputation method: multiple imputation by chained equations (MICE). We assessed the association between various risk factors and adverse outcomes using the Cox proportional hazards regression model. Additionally, we applied Lasso-Cox regression to reduce the dimensionality of variables and create a predictive model by splitting the imputed dataset into training and test sets. Specifically, we identified that the changing rate of serum creatinine, as well as albumin corrected by observation time intervals in the early stage of pregnancy, could serve as rational dynamic predictors of adverse pregnancy outcomes in women with CKD. The Lasso-Cox model we developed and validated were predictive of adverse pregnancy outcomes, particularly at a gestational age (GA) of 34 weeks.\u003c/p\u003e \u003cp\u003eIt is well known that serum creatinine levels or CKD stage and their changes are significant risk factors in pregnant women with CKD[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Wiles et al. reported that chronic hypertension, pre- or early pregnancy proteinuria, and a gestational decrease in serum creatinine of \u0026lt;\u0026thinsp;10% of pre-pregnancy values are more important predictors of adverse obstetric and renal outcomes than CKD Stages 3\u0026ndash;5[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, their study calculated the changing rate of serum creatinine levels without correcting for time bias. In contrast, our study identified two novel and dynamic predictors that underwent lab-time adjustment.\u003c/p\u003e \u003cp\u003eSerum albumin is another laboratory index of great significance, as it is the most abundant circulating protein in plasma, representing half of the total protein content and closely related to 24-hour proteinuria in CKD patients. However, this pivotal factor has often been overlooked in pregnancies with CKD. Our research addressed this research gap by revealing the early-stage changing speed of serum albumin as an independent risk factor in our CKD pregnancy population.\u003c/p\u003e \u003cp\u003eNevertheless, the study has several limitations. As it is a retrospective cohort study, and patients were not recruited with stringent criteria, the study population included as well as the study type still needs refinement in the near future. Furthermore, this study was based on a single center (Renji Hospital in Shanghai), so the patients involved in the study may not fully represent all pregnant women with CKD in China due to differences in demographic characteristics and regional medical standards. The selection of control groups was subsequently limited to literature reports due to insufficiency of real-world pregnancy data, which we hope to obtain and preserve in future studies. Additionally, despite excluding patients with excessive information loss and those requiring renal replacement therapies, the medical records of patients included in the study were still not comprehensive enough, especially in the pathological diagnosis of CKD, where most patients are considered inappropriate to undergo kidney biopsies, which constitute a major limitation of this study, though MICE have been implemented to tackle with missing data, exclusion of pathology from MICE may bias our findings. The choice of renal endpoints by the standard of a 50% rise in serum creatinine levels, may not ideally comply with pregnant women because of physiological drops during the second trimester. However, this may contribute to illustrate that those who met with the endpoints had drastically deteriorated renal functions. Lastly, the study's timeframe was limited to four years, whereas research conducted in the UK and US often spans over ten years[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, our future work will focus on conducting a long-term and large-scale prospective study in several medical centers to validate and refine our predictive model for CKD pregnancy outcomes. In all, this model is appropriately preliminary and requires extensive validation in diverse populations and clinical observations before practical use.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study is the first to propose two dynamic predictors in the early stage of pregnancy; the changing speed of serum albumin and serum creatinine corrected to the time interval between two laboratory tests. Along with other baseline information (history of immune diseases and baseline level of serum creatinine [per 10 unit]), these variables have been proven to be independent risk factors for adverse pregnancy outcomes in pregnancy women with CKD. Moreover, we successfully proposed a predictive model by employing LASSO-Cox regression analysis, distinguishing high- and low-risk groups at gestational ages of 34and 37 weeks. These two timepoints correspond to the classification of late-preterm as well as preterm delivery, giving clues to clinical decisions that patients with high-risk before GA of 34-week may require earlier discussions about potential delivery options as soon as possible while a lower risk may indicate further tocolysis and relatively safer transition to peri-partum stage. We look forward to conducting a long-term and multi-center prospective cohort study in the near future so as to further refine our research results.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAOs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdverse Outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMICE\u003c/p\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003cp\u003eSCr\u003c/p\u003e\n \u003cp\u003eeGFR\u003c/p\u003e\n \u003cp\u003eSGA\u003c/p\u003e\n \u003cp\u003ePE\u003c/p\u003e\n \u003cp\u003eGDM\u003c/p\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003cp\u003eSLE\u003c/p\u003e\n \u003cp\u003eIVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMultiple Imputation By Chained Equations\u003c/p\u003e\n \u003cp\u003eMean Arterial Pressure\u003c/p\u003e\n \u003cp\u003eSerum Creatinine\u003c/p\u003e\n \u003cp\u003eEstimated Glomerular Filtration Rate\u003c/p\u003e\n \u003cp\u003eSmall-For-Gestational-Age\u003c/p\u003e\n \u003cp\u003ePreeclampsia\u003c/p\u003e\n \u003cp\u003eGestational Diabetes Mellitus\u003c/p\u003e\n \u003cp\u003eStandard Deviations\u003c/p\u003e\n \u003cp\u003eInterquartile Ranges\u003c/p\u003e\n \u003cp\u003eSystemic Lupus Erythematosus\u003c/p\u003e\n \u003cp\u003eIn Vitro Fertilization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis observational retrospective cohort study was approved by the Ethics Committee on Human Research of Renji Hospital, Shanghai Jiao Tong University, Shanghai, China (Approval number: LY2022-048-B). The study was an observational study without medical intervention, so informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by the following funding sources:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eNational Natural Science Foundation of China (Grants: 81970574 \u0026amp; 82170685)\u003c/li\u003e\n \u003cli\u003eGrant from Shanghai Municipal Health Commission (Grants: ZY(2021-2023)-0208 \u0026amp; NO.ZY(2021-2023)-0302)\u003c/li\u003e\n \u003cli\u003eGrant from the Natural Science Foundation of the Science and Technology Commission of Shanghai Municipality (Grant No. 22ZR1438700)\u003c/li\u003e\n \u003cli\u003eNational Administration of Traditional Chinese Medicine High-level Key Disciplines Project: Translational integrated traditional Chinese and Western medicine.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.Y, Z.Z, X.Y, S.M, Q.W, S.L and N.Z conceived and designed the study. Y.Y, Z.Z and X.L collected the data. Y.Y, J.Y and H.S analyzed the data. Y.Y and Z.Z drafted the manuscript while J.Y, S.L, S.M and N.Z revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by both Renal Division and Department of Obstetrics and Gynecology of Renji Hospital, Shanghai Jiao Tong University School of Medicine.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang J-J, Ma X-X, Hao L, Liu L-J, Lv J-C, Zhang H. 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Nephrol Dialysis Transplantation. 2021;36:2008\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ndt/gfaa247\u003c/span\u003e\u003cspan address=\"10.1093/ndt/gfaa247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic kidney disease, pregnant women, adverse pregnancy outcomes, dynamic predictors","lastPublishedDoi":"10.21203/rs.3.rs-9408891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9408891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim: \u003c/strong\u003eTo develop a risk prediction model including multiple dynamic predictors\u003c/p\u003e\n\u003cp\u003ethrough means of machine learning\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a retrospective cohort study among pregnant women diagnosed with CKD either before or during pregnancy at China, Shanghai Renji Hospital from January 1, 2018, to December 31, 2021. The association between various risk factors and adverse outcomes (AOs) was assessed using the Cox proportional hazards regression model. We also quantified dynamic changes in blood pressure and laboratory results and examined their association with AOs. The performance of the predictive model was evaluated using time-dependent area under the receiver operating characteristic curves (AUCs) at gestational ages (GA) of 34 and 37 weeks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study included 240 participants, 79 (32.92%) encountered AOs. A history of immune diseases (hazard ratio (HR), 2.93), baseline serum creatinine level (elevated by 10 units) (1.24), the rate of change in serum albumin (0.29), and creatinine (2.38) during the first two trimesters were significantly associated with AOs. The predictive model achieved an AUC of 0.88 and 0.72 for predicting AOs at GA 34 and 37 weeks, respectively, in the test set.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe rate of change in serum creatinine and albumin levels during the early stages of pregnancy could serve as predictors of adverse pregnancy outcomes in women with CKD. The predictive model we developed shows promise for predicting adverse pregnancy outcomes, particularly at GA 34 weeks.\u003c/p\u003e","manuscriptTitle":"Dynamic Predictors of Adverse Outcomes in Pregnant Women with Chronic Kidney Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 17:49:15","doi":"10.21203/rs.3.rs-9408891/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T06:07:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T09:09:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T11:47:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306653081049055487780385520871710924281","date":"2026-05-06T08:24:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214785060199429383824212836286406943993","date":"2026-05-05T05:08:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T01:49:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-18T14:27:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T12:46:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-17T12:46:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-04-14T00:05:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3318512-b25b-4c17-8682-446170277011","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T06:07:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T09:09:27+00:00","index":144,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-11T11:47:44+00:00","index":143,"fulltext":""},{"type":"reviewerAgreed","content":"306653081049055487780385520871710924281","date":"2026-05-06T08:24:42+00:00","index":133,"fulltext":""},{"type":"reviewerAgreed","content":"214785060199429383824212836286406943993","date":"2026-05-05T05:08:12+00:00","index":126,"fulltext":""},{"type":"reviewersInvited","content":"99","date":"2026-05-05T01:49:45+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T06:24:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 17:49:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9408891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9408891","identity":"rs-9408891","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}