Modified Multiple Marker Aneuploidy Screening as a Primary Screening Test for Preeclampsia

Modified Multiple Marker Aneuploidy Screening as a Primary Screening Test for Preeclampsia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modified Multiple Marker Aneuploidy Screening as a Primary Screening Test for Preeclampsia Tianhua Huang, H. Melanie Bedford, Shamim Rashid, Evasha Rasasakaram, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-492997/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Mar, 2022 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Maternal biochemical markers used in multiple marker aneuploidy screening have been associated with adverse pregnancy outcomes. This study aims to assess if a combination of maternal characteristics and biochemical markers in the first and second trimesters can be used to screen for preeclampsia (PE), gestational hypertension and preterm birth. Methods: This case-control study used information on maternal characteristics and residual blood samples from pregnant women who have undergone multiple marker aneuploidy screening. The median multiple of the median (MoM) of first and second trimester biochemical markers in cases (women with PE, gestational hypertension and preterm birth) and controls were compared. Biochemical markers included pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PlGF), human chorionic gonadotropin (hCG), alpha feto-protein (AFP), unconjugated estriol (uE3) and Inhibin A. Logistic regression analysis was used to estimate screening performance using different marker combinations. Screening performance was defined as detection rate (DR) and false positive rate (FPR). Preterm and early-onset preeclampsia PE were defined as women with PE delivered < 37 and < 34 weeks of gestation. Results: There were 147 pregnancies with PE (81 term, 49 preterm and 17 early-onset), 295 with gestational hypertension, and 166 preterm birth. Compared to controls, PE cases had significantly lower median MoM of PAPP-A (0.77 vs 1.10, p<0.0001), PlGF (0.76 vs 1.01, p<0.0001) and free-β hCG (0.81 vs. 0.98, p<0.001) in the first trimester along with PAPP-A (0.82 vs 0.99, p<0.01) and PlGF (0.75 vs 1.02, p<0.0001) in the second trimester. The lowest first trimester PAPP-A, PlGF and free β-hCG were seen in those with preterm and early-onset PE. At a 20% FPR, 67% of preterm and 76% of early-onset PE cases can be predicted using a combination of maternal characteristics with PAPP-A and PlGF in the first trimester. Conclusions: Maternal characteristics with first trimester PAPP-A and PlGF measured for aneuploidy screening provided reasonable accuracy in identifying women at risk of developing early onset PE, allowing triage of high-risk women for further investigation and risk-reducing therapy. Maternal & Fetal Medicine Sexual & Reproductive Medicine Multiple marker screening preeclampsia gestational hypertension preterm birth pregnancy-associated plasma protein A placental growth factor human chorionic gonadotropin alpha feto-protein unconjugated estriol and Inhibin A Figures Figure 1 Figure 2 Background Maternal multiple marker screening for fetal aneuploidy is part of routine prenatal care. Over the past decades, multiple marker screening tests, comprising of biochemical markers and the ultrasound marker nuchal translucency (NT), have evolved from second to first trimester screening. In recent years, multiple marker screening has been used as a first-tier screen to identify pregnancies at increased risk of fetal aneuploidy followed by cell-free fetal DNA (cffDNA) or diagnostic testing. 1 , 2 Maternal biochemical markers used in multiple marker screening have long been associated with adverse pregnancy outcomes such as preeclampsia (PE), preterm birth and intrauterine growth restriction (IUGR). 3 , 4 Results from recent studies have shown that about 65% of early-onset PE can be predicted using first trimester maternal serum placental growth factor (PlGF), pregnancy-associated plasma protein-A (PAPP-A) and maternal demographics and history at a false positive rate (FPR) of 5%. 5 The accuracy increased to 90% for early-onset PE and 80% for preterm PE with the addition of mean arterial blood pressure (MAP) and uterine artery pulsatility index (UTPI) at a 5% FPR. 5 Moreover, studies have shown that the administration of nightly low-dose aspirin before 16 weeks of gestation, to women who are identified as high-risk for PE, can prevent about 90% of early-onset PE cases. 6 Second trimester biochemical markers were also found to be helpful in identifying women at risk of developing PE to allow for close surveillance and a timely intervention. 7 – 9 The ideal screening test for PE should include maternal characteristics, medical and family history, biochemical and biophysical markers. 10 In reality, biophysical markers such as MAP and especially UTPI might not be readily accessible, especially for women in remote areas. Since PlGF has recently been added to some screening programs to improve the accuracy of aneuploidy screening, maternal characteristics (questionnaire) combined with biochemical markers (blood test) could be used as a first-tier test to identify women for secondary MAP (physical test) and UTPI (ultrasound scan), allowing for the expansion of current aneuploidy screening programs to include PE screening. A contingent PE screening approach would not only be cost effective but also easy to implement, as it will require minimal change to current aneuploidy screening. Women who are identified as high-risk can be triaged for MAP and UTPI and be considered for prophylactic therapy (e.g. Aspirin) to reduce the risk for developing PE. 6 ,10 Our study assessed the first stage of this contingent approach by using maternal characteristics with first and/or second trimester biochemical markers to predict PE, in addition to other adverse pregnancy outcomes including gestational hypertension and preterm birth. Methods Study Population A retrospective case-control study was carried out using frozen residual serum samples from women who had undergone multiple marker screening and delivered at a tertiary center in Toronto, Ontario, Canada between January 1, 2014 and October 31, 2017. A combined dataset was created by linking multiple marker screening records with maternal and newborn records using patient identifiers and expected date of delivery (EDD). Women who had a first and/or second trimester serum sample available were included in this study. The exclusion criteria included multiple pregnancies, pregnancies with a fetal anomaly, or those with unavailable or immeasurable residual samples. Cases were identified from maternal records of women who had PE, gestational hypertension, or preterm birth. PE cases were further classified as preterm (women who developed PE and gave birth at < 37 weeks of gestation), early-onset (women who developed PE and gave birth at < 34 weeks of gestation) or all PE. Women with early-onset PE were included in the subgroup of preterm PE cases. Early-onset, preterm PE and term PE cases were included in the group of all PE cases. Each affected pregnancy was matched with three controls by date of blood sample draw (within 30 days), gestational age at first blood sample draw (within 7 days), maternal age (within 5 years), maternal ethnicity and amount of residual sample. During the study period, most of the women were screened using Integrated prenatal screening. Others were screened using first trimester screening, enhanced first trimester screening or second trimester serum screening (QUAD). Integrated prenatal screening consisted of NT and PAPP-A measured between 11 + 0 and 13 + 6 weeks, and alpha fetoprotein (AFP), unconjugated estriol (uE3) and intact human chorionic gonadotrophin (hCG) measured between 15 + 0 and 18 + 6 weeks. Integrated prenatal screening results were reported after the second blood test was completed. First trimester screening consisted of NT, PAPP-A and free β-hCG measured between 11 + 0 and 13 + 6 weeks. Enhanced first trimester screening was introduced in April 2016 which includes all markers used for first trimester screening and at the same gestational window with the addition of PlGF and AFP. Second trimester QUAD consisted of AFP, uE3, total hCG and inhibin A measured between 15 + 0 to 20 + 6 weeks. As all first trimester samples had PAPP-A measured and all second trimester samples had AFP, uE3 and total hCG measured, the concentrations of these markers from routine aneuploidy screening records were used in this study. Other markers were measured using frozen residual samples. Biochemical Assays All samples were processed and assayed in a local genetic laboratory. Frozen serum samples had been stored at -20°C immediately after an initial assay for routine screening and at -70°C four weeks later for long term storage. All except 33 samples had one freeze-thaw cycle, and none had been used in other studies. Samples having two freeze-thaw cycles were from women who had originally enrolled for Integrated prenatal screening but did not provide a second sample in time to complete the test. Therefore, the first sample was converted to first trimester screening and was measured for free β-hCG -A. All assays were performed on the AutoDELFIA analyzer (PerkinElmer). The AutoDELFIA routine dictates a solid phase, two-site fluorometric assay using two monoclonal antibodies directed against two separate antigenic sites on the molecule. One antibody is labelled with a fluorescent marker, and after incubation, the europium or samarium forms fluorescent chelates. The fluorescence is proportional to the concentration of analyte in the sample. Statistical Analysis The differences in the median of continuous variables including maternal age at (EDD, maternal weight and body mass index (BMI), gestational age at birth, gestational age at first and second sample among cases and controls were tested using Mann-Whitney U-tests. Categorical variables such as ethnicity, pregnancy outcome, parity, gravidity, chronic hypertension, Type 1 diabetes, Type 2 diabetes, smoking and in vitro fertilization (IVF) were compared using chi-square or Fisher’s exact tests. The concentrations of biochemical markers in this study were expressed as Multiple of the Median (MoM) calculated by dividing marker concentrations by the expected median values at a particular gestational age. The expected median was generated by weighted regression analysis on marker concentrations of all the controls. The MoM values were then adjusted for maternal weight and were further corrected for ethnicity, parity and smoking status where applicable. The adjustment factors for ethnicity, parity and smoking status were generated based on MoM values calculated from the current study. Multiple of the median values of markers that were taken from the routine screening records were already adjusted for ethnicity, parity and smoking status using factors that were implemented in the screening software Alpha 8.0 (Logical Medical Systems Ltd, London). All analyses going forward were based on adjusted MoM values, unless otherwise specified. To assess if there were significant variations in biochemical markers in pregnancies with adverse outcomes, median MoM values of biochemical markers from cases and controls were compared using Mann-Whitney U-tests. Of women who had both the first and second trimester serum samples available, a subsequent data set was created to assess the change in biochemical marker concentrations and MoM values between the second and first trimester samples in the case and control groups. The changes were calculated by subtracting the values of the first trimester sample from the second trimester sample. The changes in cases and controls were compared using Mann-Whitney U-tests. Logistic regression was used to assess if the risk of developing PE, gestational hypertension, and preterm birth can be predicted using a combination of maternal biochemical markers and maternal characteristics. Logistic regression analysis with backward selection were run with all first trimester biochemical markers to determine the best predictive models for each case group using only first trimester markers. This was repeated using second trimester biochemical markers. For models with maternal characteristics, an initial logistic regression with backward selection was run to identify significant maternal characteristics for each case group. Varying biochemical marker combinations were then added to significant maternal characteristics to develop predictive models with a combination of maternal characteristics and biochemical markers in each case group. Receiver operating characteristic (ROC) curves were created to determine detection rates (DR) at a 5%, 10%, and 20% FPR, respectively. All statistical analysis was carried out using SAS 9.4. The study was approved by the Research Ethics Board of North York General Hospital on May 17, 2017. All methods were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki). Results In total, 608 cases and 1,815 controls were identified from 15,640 singleton pregnancies. The case group included 147 pregnancies with PE (81 term, 49 preterm and 17 early-onset), 295 with gestational hypertension, and 166 preterm birth. All but eight cases were matched to three controls. Eight cases had 1–2 controls because the control samples were either unusable or unavailable. 459 cases and 1358 controls had samples available from both the first and second trimesters. Table 1 compares the maternal characteristics, medical history, and pregnancy outcomes among case and control groups. Maternal age and ethnicity were similar among case and control groups. Women with PE delivered two weeks earlier, and those with gestational hypertension delivered one week earlier than the control group. The PE group included a greater proportion of women with a history of chronic hypertension, higher BMI and nulliparous women compared to controls. Live birth pregnancies were lowest among preterm birth cases (72%), followed by early-onset PE (94%), preterm PE (98%), and all PE cases (99%). Median gestational age was 88 days for first trimester samples and 114 days for second trimester samples in both case and control groups. The smoking and IVF status were similar in cases and controls (not shown in the table). Table 1 Maternal characteristics, medical history, and pregnancy outcomes among case and control groups Maternal Characteristics Preeclampsia Gestational Hypertension (n = 295) Preterm birth (n = 166) Controls (n = 1815) All (n = 147) Preterm (n = 49) Early-onset ( n = 17) n Median (5th,95th percentile) /Proportion(%) n Median (5th,95th percentile) / Proportion (%) n Median (5th,95th percentile) / Proportion(%) n Median (5th,95th percentile) / Proportion(%) n Median (5th,95th percentile) / Proportion(%) n Median (5th,95th percentile) / Proportion(%) Maternal age, years 147 34.1 NS (26.5–40.9) 49 34.9 NS (26.0-42.8) 17 34.4 NS (28.1–41.6) 295 33.5 NS (26.4–41.4) 166 33.9 NS (24.9–41.7) 1815 33.5 (26.3–39.9) Maternal weight, kg 146 66.0 * (48.6-113.2) 49 65.4 % (47.7–98.6) 17 65.5 NS (42.3–97.7) 282 71.5 * (50.8–110.0) 161 60.5 NS (45.5–92.7) 1772 60.5 (46.8–88.5) Maternal BMI, kg/M2 124 25.7 * (18.7–43.1) 37 25.2 % (18.6–36.0) 13 24.8 NS (17.9–32.4) 264 26.3 * (19.5–38.5) 134 23.3 NS (17.6–35.3) 1560 22.4 (17.9–32.9) Gestational age at birth, weeks 147 37.0 * (32.0–40.0) 49 35.0 * (28.0–36.0) 17 33.0 * (23.0–33.0) 295 38.0 * (36.0–40.0) 166 31.0 * (20.0–33.0) 1815 39.0 (37.0–41.0) Ethnicity Caucasian 36 24.5 NS 8 16.3 NS < 6 --- NS 111 37.6 NS 50 30.1 NS 589 32.5 Asian 85 57.8 35 71.4 12 70.6 154 52.2 90 54.2 983 54.2 Black 16 10.9 < 6 - 0 0 19 6.4 11 6.6 137 7.5 Other/ Unknown 10 6.8 < 6 - < 6 ---- 11 3.7 15 9.0 106 5.8 Chronic hypertension 40 27.2 * 11 22.4 * < 6 --- * 20 6.8 * 0 0 < 6 --- Parity a, c Nulliparous 90 62.5 * 27 57.4 % 9 52.9 NS 164 56.6 * 80 51.6 ¶ 729 41.0 Pregnancy Outcome d Live Birth 146 99.3 NS 48 98.0 % 16 94.1 ¶ 295 100 120 72.3 * 1815 100 * p < 0.0001; ^ p < 0.001; ¶ p < 0.01; % p < 0.05; NS Not significant Comparison between case and control groups: Chi-Square or Fisher’s exact test for categorical variables; Mann-Whithey U test for continuous variables BMI, Body Mass Index a Parity is a binary variable categorized as nulliparous or parous c Parity and Gravidity were missing among cases and controls: 3 missing among all preeclampsia cases(n = 144), 2 missing among preterm preeclampsia cases (n = 47), 5 missing among gestational hypertension cases(n = 290), 11 missing among preterm birth cases(n = 155), and 35 missing among controls(n = 1780). d Pregnancy Outcome is a binary variable categorized as live birth or stillbirth/pregnancy loss Table 2 gives the median MoM of first trimester and second trimester biochemical markers seen in case and control groups. The median MoM of first trimester PAPP-A, PlGF, and free-β hCG were significantly reduced among cases compared to controls. Of women who developed PE, the lowest median MoM of PAPP-A and PlGF were seen in those with preterm PE (0.63 and 0.73), and early-onset PE (0.54 and 0.72), respectively. First trimester median MoM values of AFP increased in the overall PE, gestational hypertension and preterm birth groups. Free-β hCG decreased in PE, gestational hypertension and preterm birth groups. No change in the first trimester inhibin A median MoM was observed in case groups. Table 2 Median multiple of the medians (MoMs) of biochemical markers among case and control groups Marker Tested Preeclampsia Gestational Hypertension Preterm birth Controls All Preterm Early-onset n Median (MoM) n Median (MoM) n Median (MoM) n Median (MoM) n Median (MoM) n Median (MoM) First trimester (T1) PAPP-A Routine a 141 0.77 * 48 0.63 * 17 0.54 ¶ 283 0.93 * 157 0.86 ^ 1746 1.10 free-β hCG Study b 141 0.81 ^ 48 0.82 % 17 0.81 NS 284 0.88 % 157 0.88 NS 1751 0.98 PlGF Study 141 0.76 * 48 0.73 * 17 0.72 ^ 284 0.92 * 157 0.91 ^ 1751 1.01 AFP Study 141 1.16 ¶ 48 1.17 NS 17 0.92 NS 284 1.11 ^ 157 1.27 * 1751 1.00 Inhibin A Study 141 1.03 NS 48 1.07 NS 17 0.98 NS 284 1.08 NS 157 0.95 NS 1751 1.00 Second trimester (T2) AFP Routine 126 1.03 NS 43 1.01 NS 15 1.01 NS 223 1.04 NS 130 1.18 * 1402 1.01 ^ uE 3 Routine 91 1.00 NS 31 0.92 % 10 0.74 NS 163 1.01 NS 101 0.98 NS 1056 1.01 hCG Routine 91 1.17 % 31 1.30 % 10 1.62 ¶ 163 1.14 NS 101 1.09 NS 1056 1.06 PAPP-A Study 110 0.82 ¶ 36 0.80 % 13 0.87 NS 189 0.90 NS 97 0.98 NS 1140 0.99 PlGF Study 113 0.75 * 37 0.74 * 13 0.42 * 197 0.89 ¶ 103 0.79 ^ 1187 1.02 Inhibin A Routine 6 1.45 % < 6 1.51 NS 0 - 12 0.95 NS 9 1.36 NS 75 0.98 * p < 0.0001; ^ p < 0.001; ¶ p < 0.01; % p < 0.05; NS Not significant a Routine- marker measurement from routine screening tests b Study- marker (re)/measured specifically for this study In the second trimester, median MoM values of PlGF were lower in overall PE, gestational hypertension and preterm birth groups compared to controls. MoM values of PAPP-A were lower in PE group but not gestational hypertension and preterm birth groups. Median MoM of hCG was higher in PE groups. The lowest median MoM of PlGF (0.42) and PAPP-A (0.80) was seen in women with early-onset PE and preterm PE, respectively. Median MoM of PlGF was lower and AFP was higher in women with preterm birth, compared to controls. Figure 1 illustrates the increase in PlGF concentrations between first and second trimester samples; revealing smaller increases among all PE (43.70 pg/ml), preterm PE (32.85 pg/ml), early-onset PE (21.40 pg/ml), and preterm birth cases (51.50 pg/ml) compared to controls (64.40 pg/ml, p < 0.01) (Fig. 1 ). Trends among other biomarkers were not statistically or clinically significant. Table 3 gives the coefficients of logistic regressions which indicate the degree of the associations between biochemical markers, maternal characteristics and the risk of developing PE, gestational hypertension, and preterm birth. When maternal characteristics were not included in the model, first and second trimester PlGF was associated with all PE, preterm PE and early-onset PE. First trimester PAPP-A, free-β hCG and inhibin A were associated with all PE and preterm PE. When maternal characteristics were included in the model, the associations between PlGF and all PE, preterm PE and early-onset PE remained statistically significant. Table 3 Logistic regression results for the prediction of adverse pregnancy outcomes using maternal characteristics and biochemical markers Marker Preeclampsia Gestational hypertension Preterm birth All Preterm Early-onset T1 Biochemical Markers a Intercept -1.1692 ¶ -1.1523 NS -1.6729 % -1.1012 * -1.9447 * PAPP-A -0.7193 ^ -1.5537 * - -0.4694 * -0.3024 % free-β hCG -0.4488 ¶ -0.6470 % - -0.2657 ¶ - PlGF -0.9420 ^ -1.3867 ¶ -3.3491 ^ -0.3503 % -0.5498 % AFP 0.1868 % - - - 0.3657 ^ Inhibin A 0.5241 ^ 0.8701 ^ - 0.4195 ¶ - T2 Biochemical Markers b Intercept -1.5231 * -2.5203 * -2.8837 ¶ -2.2967* -3.9344 * AFP - - - 0.7503 ^ 1.2020 * uE 3 - - - - - hCG 0.5895 * 0.5332 ¶ 0.6212 % - - PAPP-A -0.6405 % - - -0.3987 % - PlGF -1.0464 ^ -1.9338 ¶ -3.1643 % - - Maternal Characteristics c + T1 PlGF + T1 PAPP-A Intercept -6.9419 * -5.2600 ¶ -5.7611 % -6.2080 * -1.7145 * PlGF -0.9925 ^ -1.7875 ¶ -2.8433 ¶ -0.3273 NS -0.4626 NS PAPP-A -0.9265 * -1.5843 * -1.1291 NS -0.6066 * -0.4302 ¶ Maternal Characteristics c + All T1 Biochemical Markers a Intercept -7.4496 * -5.9671 ^ -6.2326 % -6.5085 * -2.0900 * PAPP-A -0.9335 * -1.6698 * -1.1970 % -0.6177 * -0.3836 % free-β hCG -0.3955 % -0.5637 % -0.2477 NS -0.1426 NS -0.0709 NS PlGF -0.7713 ¶ -1.2278 % -2.4894 % -0.2676 NS -0.4342 NS AFP 0.2180 % 0.1505 NS -0.0942 NS 0.1697 % 0.3204 ¶ Inhibin A 0.4649 ¶ 0.8114 ^ 0.5278 NS 0.2345 NS 0.00160 NS Maternal Characteristics c + All T2 Biochemical Markers b Intercept -5.5285 * -1.1855 NS -3.1794 NS -7.2465 * -2.9484 * AFP -0.4117 NS -0.7740 NS -1.1304 NS 0.4014 % 0.9651 ¶ uE 3 -0.4744 NS -0.9717 NS -1.9243 NS -0.0260 NS -0.5001 NS hCG 0.6030 ^ 0.9158 ^ 0.7432 NS 0.2084 NS 0.1450 NS PAPP-A -0.8083 ¶ -0.8946 % 0.3250 NS -0.6117 ¶ -0.5368 NS PlGF -1.0086 ¶ -1.4560 % -3.0874 % -0.3119 NS -0.3736 NS * p < 0.0001; ^ p < 0.001; ¶ p < 0.01; % p < 0.05; NS Not significant PAPP-A, pregnancy-associated plasma protein A; hCG, human chorionic gonadotropin; PlGF, placental growth factor; AFP, alpha feto-protein a First Trimester (T1) Biochemical Markers: PAPP-A, free-β hCG ,PlGF, AFP, Inhibin A b Second Trimester (T2) Biochemical Markers: AFP, uE3, hCG, PAPP-A, PlGF c Through backward selection significant maternal characteristics for each case group were identified. Significant maternal characteristics for each case group include - All PE: age, weight, ethnicity, nulliparous; Preterm PE: age, weight, ethnicity, gravidity; Early-onset PE: age, gravidity; Gestational Hypertension: age, weight, ethnicity, nulliparous; Preterm Birth: nulliparous Table 4 shows the estimated DR for PE, gestational hypertension and preterm birth at a FPR of 5%, 10%, and 20% using different biochemical marker combinations with and without maternal characteristics. Detection rates for preterm PE and early-onset PE were higher than for all PE cases. Adding maternal characteristics to any combinations yielded better DR values than maternal characteristics or biochemical marker combination alone. Detection rates for both gestational hypertension and preterm birth, using different biochemical marker combinations with and without maternal characteristics, were low. Table 4 Model-predicted screening performance using maternal characteristics and different serum marker combinations Marker Preeclampsia Gestational hypertension Preterm birth All Preterm Early-onset T1 Biochemical Markers a AUC 0.70 0.80 0.76 0.63 0.64 DR for 5% FPR 23% 25% 35% 13% 17% DR for 10% FPR 33% 42% 35% 19% 25% DR for 20% FPR 55% 73% 65% 35% 40% T2 Biochemical Markers b AUC 0.69 0.76 0.83 0.58 0.65 DR for 5% FPR 23% 29% 50% 12% 19% DR for 10% FPR 31% 42% 63% 20% 29% DR for 20% FPR 44% 58% 88% 28% 38% Maternal Characteristics Only c AUC 0.70 0.71 0.68 0.74 0.55 DR for 5% FPR 18% 15% 18% 22% 7% DR for 10% FPR 31% 30% 18% 34% 15% DR for 20% FPR 45% 47% 41% 54% 25% Maternal Characteristics c + T1 PlGF + T1 PAPP-A AUC 0.78 0.83 0.82 0.75 0.61 DR for 5% FPR 30% 35% 41% 23% 15% DR for 10% FPR 42% 52% 65% 39% 21% DR for 20% FPR 60% 67% 76% 58% 36% Maternal Characteristics c + All T1 Biochemical Markers a AUC 0.79 0.84 0.84 0.76 0.64 DR for 5% FPR 30% 37% 47% 24% 19% DR for 10% FPR 45% 59% 71% 40% 29% DR for 20% FPR 59% 72% 82% 59% 40% Maternal Characteristics c + All T2 Biochemical Markers b AUC 0.78 0.80 0.83 0.76 0.70 DR for 5% FPR 27% 39% 75% 29% 18% DR for 10% FPR 38% 48% 75% 40% 37% DR for 20% FPR 63% 65% 75% 56% 58% DR, Detection Rate; FPR, False Positive Rate; AUC, Area under the Curve a First Trimester (T1) Biochemical Markers: PAPP-A, free-β hCG ,PlGF, AFP, Inhibin A. Refer to Table 3 for specific markers used in each case group b Second Trimester (T2) Biochemical Markers: AFP, uE3, hCG, PAPP-A, PlGF. Refer to Table 3 for specific markers used in each case group c Significant maternal characteristics for each case group include- All PE: age, weight, ethnicity, nulliparous; Preterm PE: age, weight, ethnicity, gravidity; Early-onset PE: age, gravidity; Gestational Hypertension: age, weight, ethnicity, nulliparous; Preterm Birth: nulliparous Figure 2 demonstrates the ROC curves for PE, gestational hypertension and preterm birth using maternal characteristics and PlGF and PAPP-A in the first trimester. The area under the ROC curve (AUC) was largest among preterm PE (0.83) and early-onset PE (0.82) cases followed by all PE (0.78), gestational hypertension (0.75) and preterm birth cases (0.61). The best screening performance for a FPR < 25% was seen for early-onset PE followed by preterm PE and all PE. Discussion Our study assessed whether first and second trimester biochemical markers used in prenatal aneuploidy screening could accurately identify pregnancies at risk of developing adverse pregnancy outcomes, mainly PE. We found that the combination of maternal characteristics with biochemical markers in both trimesters can provide reasonable accuracy in identifying women at risk of developing preterm and early-onset PE but not women at risk for gestational hypertension or preterm birth. PAPP-A and PlGF were the most reliable biochemical markers to be associated with and to predict adverse pregnancy outcomes, particularly early-onset PE. Our results have confirmed previously reported associations between individual biochemical markers and PE and preterm birth. 7 – 9 Similar to previous studies, we found the performance of PE prediction to be improved by using multiple biochemical markers in combination with maternal characteristics . 5 , 11 ,12 The most commonly used marker combination in the first trimester PAPP-A and PlGF along with maternal characteristics can predict 65% and 76% of early-onset PE with a FPR of 10% and 20% respectively. Second trimester biochemical markers can identify 88% of the pregnancies at risk of developing early-onset PE with a FPR of 20%. However, this will require the addition of PAPP-A and PlGF which are not currently used in second trimester aneuploidy screening. In addition, we found that the increase of PlGF with gestational age was smaller in women affected by PE compared to the control group. Low first trimester PAPP-A and PlGF have been associated with adverse pregnancy outcomes, especially PE in multiple studies. 5,11−13 The case-control study by Keikkala et al. (2016) investigating maternal serum samples from 8–13 weeks of gestation found lower median MoM values for PlGF and PAPP-A in women with PE compared to control. 13 Consistent with our study, the lowest median MoM values were seen in cases with preterm and early-onset PE. As with previous studies, our study found median first trimester AFP MoM values to be higher and median free β-hCG MoM values to be lower in women with PE, although the significance of these changes was smaller in comparison to those of PAPP-A and PlGF. 8 , 9 The previously described association between first trimester inhibin A and PE was not confirmed in our study. 14 , 15 Second trimester PlGF and PAPP-A median MoM values, were lower in our study than those reported by other studies. 9 , 16 However, this was mainly true for PlGF and not significant for PAPP-A in women with preterm or early-onset PE, likely due to the small number of pregnancies in this group. We found an increase in total hCG in PE pregnancies, as reported in previous studies. 17 In contrast, no change in Inhibin A was seen in our study although reported as an optimal second trimester marker by others. 18 We suspect this is due to the small number of early PE pregnancies involved in our study. Lastly, the change in the first and second trimester markers in gestational hypertension and preterm birth cases was consistent with other studies in general although the magnitude of the change was variable. 19 , 20 In recent years, the results of several large clinical trials have suggested that the performance of PE screening can be improved by using multiple biochemical markers together with maternal characteristics and biophysical markers. 5 , 12 , 21 A prospective study by Akolekar et al. (2013) on 58,884 singleton pregnancies at 11–13 weeks reported a 50.5% DR at a 10% FPR with maternal characteristics alone for early-onset PE, 74.3% with the addition of PlGF and PAPP-A and 89.7% with the addition of biophysical markers, MAP and UTPI. The DR improved to 96.3% when maternal characteristics and biochemical and biophysical markers were combined. 12 Similarly, in a prospective study of 35,948 singleton pregnancies at 11–13 weeks’ gestation, O’Gorman et al. (2016) found that combined screening obtained a DR of 75% and 47% at a 10% FPR for preterm PE and term PE pregnancies, respectively. 12 When such modelling combinations were applied to the ASPRE trial, similar DR values were observed. First-trimester screening for preterm PE with a risk cut-off of 1 in 100 detected 76.7% of preterm PE and 43.1% of term PE pregnancies, at a screen-positive rate of 10.5% and a FPR of 9.2%. 21 While the best performance can be achieved by combining multiple variables (e.g., maternal characteristics, medical and family history, biochemical and biophysical markers), MAP and especially UTPI might not be readily accessible, especially for women in remote areas. If the combination of maternal characteristics and biochemical markers can predict the risk of PE with reasonable accuracy, the current aneuploidy screening could be expanded to include PE screening to identify women at increased risk of developing PE, with biophysical markers to be followed as a second line or contingent screen. In our study, the PE screening performance was comparable to previous studies when using maternal characteristics in combination with first trimester PAPP-A and PlGF. At a FPR of 20%, the DR was 76%, 67% and 60% for early-onset PE, preterm PE and all PE, respectively. This suggests that for 20% of women who screen positive for early-onset PE, a contingent PE screening strategy using MAP and UTPI can predict up to 76% of early-onset PE. As with the one-time screening approach, our contingent screen can yield results before 16 weeks of gestation in order to initiate prophylactic therapy with Aspirin. If first trimester screening is aiming to initiate Aspirin treatment, second trimester screening is useful for patient triage by identifying pregnancies needing close surveillance and more urgent medical attention. For example, numerous studies have focused on second trimester PlGF or soluble fms-like tyrosine kinase-1 (sFlt-1)/PlGF ratio. 22 In our study, in addition to the PE screening performance using second trimester markers, we compared the changes in biochemical markers between the first and second trimester samples. For women who developed PE, the increase in PlGF concentrations was significantly smaller compared to controls. These results have been confirmed by the findings from other studies. 23 – 25 Theoretically, for women who are identified to be at high risk for PE in the first trimester, a repeat test for PlGF and PAPP-A in the second trimester might provide useful information which can be used for risk modification. Nevertheless, a calculation of biomarker changes between trimesters did not yield a better prediction for PE than using second trimester markers alone in our study. Further investigation of this trend may provide clinicians with valuable information for monitoring and early detection of high-risk women. In our study, the preterm birth screening performance was less optimal than previously reported. 20 A possible reason for the lower DR is that unlike some previous studies, our preterm birth group excluded all women with PE or gestational hypertension. Incomplete maternal characteristics data may have also contributed to the lower-than-expected DR values for preterm birth. The strengths of our study include the identification of cases and controls from a routine unselected screening population, representing a true sample of women undergoing prenatal screening in Ontario. Also unique to our study is the availability of both first and second trimester serum samples for most cases and controls. Having samples from both trimesters enabled us to investigate the change between the first and second trimesters, which might provide additional information for PE screening and monitoring. The limitations of this study include, the transfer of some women to other obstetrical centres, potentially lowering the incidence of PE in our population. Additionally, our local population includes a greater proportion of women of Asian ancestry compared with other studies. Varying ethnicity between studies may impact PE prevalence; in particular, a lower prevalence of PE among Asian women has been noted previously. 26 Our study lacked complete information on all maternal characteristics and as such, the accuracy of PE screening using maternal characteristics cannot be directly compared to studies where maternal characteristics were explicitly collected for PE screening. However, the contribution of biochemical markers and overall PE screening performance using the combination of maternal characteristics and biochemical markers were consistent with previous studies. In a real-life clinical setting, it will be practical to collect all maternal characteristics missed in this study. Since information on MAP and UTPI was not available to our study, we were not able to assess the final performance of a contingent PE screening strategy. Nevertheless, we achieved our goal of assessing the first tier of a contingent screening strategy to provide reasonable performance and warrant the expansion of current aneuploidy screening to include preeclampsia. Conclusions Our study, based on a routine aneuploidy screening, showed that the combination of maternal characteristics and first trimester serum PAPP-A and PlGF can provide reasonable performance for PE screening. If our study results can be validated by prospective studies, the current aneuploidy screening program can be expanded to include identification of women at risk of developing PE, particularly early-onset PE. As a contingent strategy, it can provide first-tier PE screening with minimal associated costs and minimal change to the current workflow. Abbreviations AFP: α-fetoprotein AUC: Area Under the Curve BMI: body mass index cfDNA: cell-free fetal DNA DR: detection rate EDD: expected date of delivery FPR: false positive rate hCG: human chorionic gonadotrophin IUGR: intrauterine growth restriction IVF: in vitro fertilization MAP: mean arterial blood pressure MoM: multiple of the median NT: nuchal translucency PAPP-A: pregnancy-associated plasma protein A PE: preeclampsia PlGF: placental growth factor QUAD: second trimester serum screening ROC: Receiver operating characteristic sFlt-1: soluble fms-like tyrosine kinase-1 T1: first trimester T2: second trimester uE3: unconjugated estriol UTPI: uterine artery pulsatility index Declarations Ethics approval and consent to participate The Research Ethics Board of North York General Hospital has approved the study on May 17, 2017. The Informed consent was waived by NYGH’s REB. The informed consent was waived because our study meets the requirements specified in Canada’s Tri-council policy statements C, Article 12.3 (https://ethics.gc.ca/eng/tcps2-eptc2_chapter12-chapitre12.html) for following reasons: 1) The study is retrospective in nature. It is impracticable to seek consent from each individual patient. At the time of the sample collection, we did not know if a pregnancy will have an adverse pregnancy outcome (therefore will be include in the study). As screening tests were ordered by thousands of physicians across the province of Ontario, Canada, we do not have direct contact with patients. 2) This study cannot be carried out without using residual samples. 3) Using residual serum samples without patient consent will not have an adverse effect on patients because all subjects in the study have been managed using current standard of care guidelines for detection, follow-up, confirmation and treatment. 4). We have de-identified the study dataset and study samples to protect the privacy of individuals and safeguard residual blood samples. No patients will be contacted. Only aggregate data will be published. All methods were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki). Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article. Competing interests The reagent kits used in the study were provided by PerkinElmer free of charge. Professor Howard Cuckle is a consultant of PerkinElmer. Dr. Elad Mei-Dan is an Editorial Board Member of BMC Pregnancy & Childbirth. The rest of the authors declare to have no competing interest. Funding Funding: The study was supported by Imperial Order Daughters of the Empire (IODE) Genetics Program Research Fund of North York General Hospital. Authors Contributions TH contributed to conception and design, performed data analysis, interpreted the data, drafted the manuscript and agreed to act as guarantors of the work presented in this article. SR performed laboratory assay and revised the manuscript critically. HC performed data analysis, interpreted the data, and revised the manuscript critically. ER performed data analysis and drafted the manuscript. EM, HMB, MP, EMT, CG and WSM and contributed substantially to the interpretation of data, and revised the manuscript critically. All authors have given the final approval of version to be published. Acknowledgements The authors thank PerkinElmer for providing assay kits used in this study free of charge. We wish to acknowledge staff of the Maternal Multiple Marker Screening Laboratory at North York General Hospital for preparing study samples and performing laboratory assays. References Committee Opinion No. 640: Cell-Free DNA Screening For Fetal Aneuploidy. Obstet Gynecol . 2015;126(3):e31-e37 Sánchez-Durán MÁ, Bernabeu García A, Calero I,Ramis Fossas J, Illescac T, Aviles MT et al. Clinical application of a contingent screening strategy for trisomies with cell-free DNA: A pilot study. BMC Pregnancy Childbirth . 2019;19:274 Litwińska E, Litwińska M, Oszukowski P, Szaflik K, Litwinski W, Korcz M, et al. Biochemical markers in screening for preeclampsia and intrauterine growth restriction. Ginekol Pol . 2015;86(8):611-5 Cohen JL, Smilen KE, Bianco AT, Moshier EL, Ferrara LA, L. Stone J. Predictive value of combined serum biomarkers for adverse pregnancy outcomes. Eur J Obstet Gynecol Reprod Biol . 2014;181:89-94 Akolekar R, Syngelaki A, Poon L, Wright D, Nicolaides KH. Competing risks model in early screening for preeclampsia by biophysical and biochemical markers.[Erratum appears in Fetal Diagn Ther. 2013;34(1):43]. Park F, Russo K, Williams P, Pelosi M, Puddephatt R, Walter M, et al. Prediction and prevention of early-onset pre-eclampsia: Impact of aspirin after first-trimester screening. Ultrasound Obstet Gynecol . 2015;46:419-23. Tsiakkas A, Duvdevani N, Wright A, Wright D, Nicolaides KH. Serum placental growth factor in the three trimesters of pregnancy: effects of maternal characteristics and medical history. Ultrasound Obstet Gynecol. 2015;45:591–8 Bredaki FE, Mataliotakis M, Wright A, Wright D, Nicolaides KH. Maternal serum alpha-fetoprotein at 12, 22 and 32 weeks’ gestation in screening for pre-eclampsia. Ultrasound Obstet Gynecol . 2016;50:807. Wright A, Guerra L, Pellegrino M, Wright D, Nicolaides KH. Maternal serum PAPP-A and free β-hCG at 12, 22 and 32 weeks’ gestation in screening for pre-eclampsia. Ultrasound Obstet Gynecol . 2016;47:762-7 Poon LC, Shennan A, Hyett JA, Kapur A, Hadar E, Divakar H et al. The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention. Int J Gynaecol Obstet. 2019;145 Suppl 1:1-33. Erratum in: Int J Gynaecol Obstet. 2019;146:390-391 Poon LC, Nicolaides KH. First-trimester maternal factors and biomarker screening for preeclampsia. Prenat Diagn . 2014;34:618-27 O’Gorman N, Wright D, Syngelaki A, Akolekar R, Wright A, Poon LC, et al. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks gestation. Am J Obstet Gynecol . 2016;214:103.e1-103.e12 Keikkala E, Koskinen S, Vuorela P, Laivuori H, Romppanen J, Heinonen S, et al First trimester serum placental growth factor and hyperglycosylated human chorionic gonadotropin are associated with pre-eclampsia: a case control study. BMC Pregnancy Childbirth. 2016;16:378. Cuckle H, Sehmi I, Jones R. Maternal serum inhibin A can predict pre-eclampsia. BJOG An Int J Obstet Gynaecol . 1998;105:1101-3 Spencer K, Cowans NJ, Nicolaides KH. Maternal serum inhibin-A and activin-A levels in the first trimester of pregnancies developing pre-eclampsia. Ultrasound Obstet Gynecol . 2008;32:622-6 Wright D, Silva M, Papadopoulos S, Wright A, Nicolaides KH. Serum pregnancy-associated plasma protein-A in the three trimesters of pregnancy: Effects of maternal characteristics and medical history. Ultrasound Obstet Gynecol . 2015;46:42-50 Barjaktarovic M, Korevaar TIM, Jaddoe VWV, de Rijke YB, Peeters RP, Steegers EAP. Human chorionic gonadotropin and risk of pre-eclampsia: prospective population-based cohort study. Ultrasound Obstet Gynecol . 2019;54:477-483 Broumand F, Salari Lak S, Nemati F, Mazidi A. A study of the diagnostic value of Inhibin A Tests for occurrence of preeclampsia in pregnant women. Electron Physician . 2018;10:6186-6192 Lakhi N, Govind A, Moretti M, Jones J. Maternal serum analytes as markers of adverse obstetric outcome. Obstet Gynaecol . 2012;14(4):267-273 Jelliffe-Pawlowski LL, Baer RJ, Blumenfeld YJ, et al. Maternal characteristics and mid-pregnancy serum biomarkers as risk factors for subtypes of preterm birth. BJOG An Int J Obstet Gynaecol . 2015;122:1484-93 Rolnik DL, Wright D, Poon LCY, et al. ASPRE trial: performance of screening for preterm pre-clampsia. Ultrasound Obstet Gynecol . 2017;50:492-495 Caillon H, Tardif C, Dumontet E, Winer N, Masson D. Evaluation of sFlt-1/PlGF ratio for predicting and improving clinical management of pre-eclampsia: Experience in a specialized perinatal care center. Ann Lab Med . 2018;38:95-101 Myatt L, Clifton RG, Roberts JM, Spong CY, Wapner RJ, Thorp Jr JM, et al. Can changes in angiogenic biomarkers between the first and second trimesters of pregnancy predict development of pre-eclampsia in a low-risk nulliparous patient population? BJOG An Int J Obstet Gynaecol. 2013;120:1183–91 Vatten LJ, Eskild A, Nilsen TIL, Jeansson S, Jenum PA, Staff AC. Changes in circulating level of angiogenic factors from the first to second trimester as predictors of preeclampsia. Am J Obstet Gynecol. 2007;196:239.e1-6 Erez O, Romero R, Espinoza J, Fu W, Todem D, Kusanovic JP, et al. The change in concentrations of angiogenic and anti-angiogenic factors in maternal plasma between the first and second trimesters in risk assessment for the subsequent development of preeclampsia and small-for-gestational age. J Matern Fetal Neonatal Med. 2008;21:279–87 Xiao J, Shen F, Xue Q, et al. Is ethnicity a risk factor for developing preeclampsia? An analysis of the prevalence of preeclampsia in China. J Hum Hypertens . 2014;28:694-8 Additional Declarations Competing interest reported. The reagent kits used in the study were provided by PerkinElmer free of charge. Professor Howard Cuckle is a consultant of PerkinElmer. Dr. Elad Mei-Dan is an Editorial Board Member of BMC Pregnancy & Childbirth. The rest of the authors declare to have no competing interest. Cite Share Download PDF Status: Published Journal Publication published 08 Mar, 2022 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Major revision 08 Nov, 2021 Reviews received at journal 11 Oct, 2021 Reviews received at journal 05 Jul, 2021 Reviewers agreed at journal 15 Jun, 2021 Reviewers agreed at journal 12 Jun, 2021 Reviewers invited by journal 08 Jun, 2021 Editor assigned by journal 31 May, 2021 Editor invited by journal 31 May, 2021 Submission checks completed at journal 31 May, 2021 First submitted to journal 04 May, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-492997","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":30376998,"identity":"c49e399a-3539-4850-b695-71e2d4ef2654","order_by":0,"name":"Tianhua Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYDACZjBiYDAA8yqAWIKADh5ULWeI0cKArIWxjQgt9uzMzx4XttkxmEskP3v4dd6dxPnRDYyPCxjs5HE7jM3ceGZbMoPljDRzY9ltzxI33jnAbDyDIdmwAbdfzKR525gZDG4kmElLbjucuHFGAps0D8MBRtxa2L8BtdQDtaR/k5acg9Bij1sLD8iWw0AtOWaSHxsOJ86XgGhJxKnlME+ZNM+54zwGZ96USTMcO2y8QSKx2ZjHIDkZlxb2/uPbpHnKquUMjqdvk/xRc1h2/ozkg495KuxscWmB2wYimIGk4wawxw0IqIcBxh/AiJInZPooGAWjYBSMOAAAbedOmtE6WywAAAAASUVORK5CYII=","orcid":"","institution":"North York General Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Tianhua","middleName":"","lastName":"Huang","suffix":""},{"id":30377000,"identity":"18fd32d3-4cf2-41e7-bc79-6606f4b5edd4","order_by":1,"name":"H. Melanie Bedford","email":"","orcid":"","institution":"North York General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"H.","middleName":"Melanie","lastName":"Bedford","suffix":""},{"id":30377001,"identity":"bb5f2a4c-0a43-414c-8104-e2ee22f18763","order_by":2,"name":"Shamim Rashid","email":"","orcid":"","institution":"North York General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shamim","middleName":"","lastName":"Rashid","suffix":""},{"id":30377002,"identity":"ba28b679-272e-4c68-b6ca-2cfd4c104b03","order_by":3,"name":"Evasha Rasasakaram","email":"","orcid":"","institution":"North York General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Evasha","middleName":"","lastName":"Rasasakaram","suffix":""},{"id":30377003,"identity":"78bf8f76-ef76-471b-952e-c170abb5e8d5","order_by":4,"name":"Megan Priston","email":"","orcid":"","institution":"North York General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Megan","middleName":"","lastName":"Priston","suffix":""},{"id":30377004,"identity":"623e447f-259f-454d-9ccc-4dc37f50dbbe","order_by":5,"name":"Ellen Mak-Tam","email":"","orcid":"","institution":"North York General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ellen","middleName":"","lastName":"Mak-Tam","suffix":""},{"id":30377005,"identity":"d3435f7a-c0b1-44b9-86f9-6bf6afce4370","order_by":6,"name":"Clare Gibbons","email":"","orcid":"","institution":"North York General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Clare","middleName":"","lastName":"Gibbons","suffix":""},{"id":30377006,"identity":"33f1f834-7629-49e4-b238-96eaf9fda38a","order_by":7,"name":"Wendy S. Meschino","email":"","orcid":"","institution":"North York General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wendy","middleName":"S.","lastName":"Meschino","suffix":""},{"id":30377007,"identity":"d4ed401d-30da-4069-9b09-e6329504a0d8","order_by":8,"name":"Howard Cuckle","email":"","orcid":"","institution":"Tel Aviv University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Howard","middleName":"","lastName":"Cuckle","suffix":""},{"id":30377008,"identity":"e8699602-1e1f-4b25-9994-d033232bb381","order_by":9,"name":"Elad Mei-Dan","email":"","orcid":"","institution":"North York General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elad","middleName":"","lastName":"Mei-Dan","suffix":""}],"badges":[],"createdAt":"2021-05-04 13:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-492997/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-492997/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-022-04514-4","type":"published","date":"2022-03-08T12:50:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9894436,"identity":"ef6c0dc1-7c33-46fa-b63c-673695c57517","added_by":"auto","created_at":"2021-06-02 15:35:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25028,"visible":true,"origin":"","legend":"Changes in PlGF concentration between the first and second trimester samples among preeclampsia (PE), gestational hypertension, preterm birth cases and controls. ","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-492997/v1/d6ec523699ad8dbd38954c2b.png"},{"id":9894437,"identity":"6bfe637f-fd57-4b1a-b324-0776b2bed945","added_by":"auto","created_at":"2021-06-02 15:35:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20821,"visible":true,"origin":"","legend":"ROC curves for models including maternal characteristics and first trimester PAPP-A + PlGF for preterm delivery (AUC 0.61), gestational hypertension (AUC 0.75), all preeclampsia (PE) (AUC 0.78), preterm preeclampsia (AUC 0.83), and early-onset preeclampsia (AUC 0.82) cases.","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-492997/v1/b5d2755f61359aff9c7ea615.png"},{"id":18976713,"identity":"8a3c67b8-228d-464f-acd9-06d57afbadad","added_by":"auto","created_at":"2022-03-08 12:50:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":581353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-492997/v1/5476ecd9-cd9b-4871-8a16-b9e6ee50017e.pdf"}],"financialInterests":"Competing interest reported. The reagent kits used in the study were provided by PerkinElmer free of charge. Professor Howard Cuckle is a consultant of PerkinElmer. Dr. Elad Mei-Dan is an Editorial Board Member of BMC Pregnancy \u0026 Childbirth. The rest of the authors declare to have no competing interest.","formattedTitle":"\u003cp\u003eModified Multiple Marker Aneuploidy Screening as a Primary Screening Test for Preeclampsia\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eMaternal multiple marker screening for fetal aneuploidy is part of routine prenatal care. Over the past decades, multiple marker screening tests, comprising of biochemical markers and the ultrasound marker nuchal translucency (NT), have evolved from second to first trimester screening. In recent years, multiple marker screening has been used as a first-tier screen to identify pregnancies at increased risk of fetal aneuploidy followed by cell-free fetal DNA (cffDNA) or diagnostic testing.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Maternal biochemical markers used in multiple marker screening have long been associated with adverse pregnancy outcomes such as preeclampsia (PE), preterm birth and intrauterine growth restriction (IUGR).\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Results from recent studies have shown that about 65% of early-onset PE can be predicted using first trimester maternal serum placental growth factor (PlGF), pregnancy-associated plasma protein-A (PAPP-A) and maternal demographics and history at a false positive rate (FPR) of 5%.\u003csup\u003e5\u003c/sup\u003e The accuracy increased to 90% for early-onset PE and 80% for preterm PE with the addition of mean arterial blood pressure (MAP) and uterine artery pulsatility index (UTPI) at a 5% FPR.\u003csup\u003e5\u003c/sup\u003e Moreover, studies have shown that the administration of nightly low-dose aspirin before 16 weeks of gestation, to women who are identified as high-risk for PE, can prevent about 90% of early-onset PE cases.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Second trimester biochemical markers were also found to be helpful in identifying women at risk of developing PE to allow for close surveillance and a timely intervention.\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe ideal screening test for PE should include maternal characteristics, medical and family history, biochemical and biophysical markers.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In reality, biophysical markers such as MAP and especially UTPI might not be readily accessible, especially for women in remote areas. Since PlGF has recently been added to some screening programs to improve the accuracy of aneuploidy screening, maternal characteristics (questionnaire) combined with biochemical markers (blood test) could be used as a first-tier test to identify women for secondary MAP (physical test) and UTPI (ultrasound scan), allowing for the expansion of current aneuploidy screening programs to include PE screening. A contingent PE screening approach would not only be cost effective but also easy to implement, as it will require minimal change to current aneuploidy screening. Women who are identified as high-risk can be triaged for MAP and UTPI and be considered for prophylactic therapy (e.g. Aspirin) to reduce the risk for developing PE. \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,10\u003c/sup\u003eOur study assessed the first stage of this contingent approach by using maternal characteristics with first and/or second trimester biochemical markers to predict PE, in addition to other adverse pregnancy outcomes including gestational hypertension and preterm birth.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eA retrospective case-control study was carried out using frozen residual serum samples from women who had undergone multiple marker screening and delivered at a tertiary center in Toronto, Ontario, Canada between January 1, 2014 and October 31, 2017.\u003c/p\u003e \u003cp\u003eA combined dataset was created by linking multiple marker screening records with maternal and newborn records using patient identifiers and expected date of delivery (EDD). Women who had a first and/or second trimester serum sample available were included in this study. The exclusion criteria included multiple pregnancies, pregnancies with a fetal anomaly, or those with unavailable or immeasurable residual samples. Cases were identified from maternal records of women who had PE, gestational hypertension, or preterm birth. PE cases were further classified as preterm (women who developed PE and gave birth at \u0026lt;\u0026thinsp;37 weeks of gestation), early-onset (women who developed PE and gave birth at \u0026lt;\u0026thinsp;34 weeks of gestation) or all PE. Women with early-onset PE were included in the subgroup of preterm PE cases. Early-onset, preterm PE and term PE cases were included in the group of all PE cases. Each affected pregnancy was matched with three controls by date of blood sample draw (within 30 days), gestational age at first blood sample draw (within 7 days), maternal age (within 5 years), maternal ethnicity and amount of residual sample.\u003c/p\u003e \u003cp\u003eDuring the study period, most of the women were screened using Integrated prenatal screening. Others were screened using first trimester screening, enhanced first trimester screening or second trimester serum screening (QUAD). Integrated prenatal screening consisted of NT and PAPP-A measured between 11\u0026thinsp;+\u0026thinsp;0 and 13\u0026thinsp;+\u0026thinsp;6 weeks, and alpha fetoprotein (AFP), unconjugated estriol (uE3) and intact human chorionic gonadotrophin (hCG) measured between 15\u0026thinsp;+\u0026thinsp;0 and 18\u0026thinsp;+\u0026thinsp;6 weeks. Integrated prenatal screening results were reported after the second blood test was completed. First trimester screening consisted of NT, PAPP-A and free β-hCG measured between 11\u0026thinsp;+\u0026thinsp;0 and 13\u0026thinsp;+\u0026thinsp;6 weeks. Enhanced first trimester screening was introduced in April 2016 which includes all markers used for first trimester screening and at the same gestational window with the addition of PlGF and AFP. Second trimester QUAD consisted of AFP, uE3, total hCG and inhibin A measured between 15\u0026thinsp;+\u0026thinsp;0 to 20\u0026thinsp;+\u0026thinsp;6 weeks. As all first trimester samples had PAPP-A measured and all second trimester samples had AFP, uE3 and total hCG measured, the concentrations of these markers from routine aneuploidy screening records were used in this study. Other markers were measured using frozen residual samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical Assays\u003c/h2\u003e \u003cp\u003eAll samples were processed and assayed in a local genetic laboratory. Frozen serum samples had been stored at -20\u0026deg;C immediately after an initial assay for routine screening and at -70\u0026deg;C four weeks later for long term storage. All except 33 samples had one freeze-thaw cycle, and none had been used in other studies. Samples having two freeze-thaw cycles were from women who had originally enrolled for Integrated prenatal screening but did not provide a second sample in time to complete the test. Therefore, the first sample was converted to first trimester screening and was measured for free β-hCG -A. All assays were performed on the AutoDELFIA analyzer (PerkinElmer). The AutoDELFIA routine dictates a solid phase, two-site fluorometric assay using two monoclonal antibodies directed against two separate antigenic sites on the molecule. One antibody is labelled with a fluorescent marker, and after incubation, the europium or samarium forms fluorescent chelates. The fluorescence is proportional to the concentration of analyte in the sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe differences in the median of continuous variables including maternal age at (EDD, maternal weight and body mass index (BMI), gestational age at birth, gestational age at first and second sample among cases and controls were tested using Mann-Whitney U-tests. Categorical variables such as ethnicity, pregnancy outcome, parity, gravidity, chronic hypertension, Type 1 diabetes, Type 2 diabetes, smoking and in vitro fertilization (IVF) were compared using chi-square or Fisher\u0026rsquo;s exact tests.\u003c/p\u003e \u003cp\u003eThe concentrations of biochemical markers in this study were expressed as Multiple of the Median (MoM) calculated by dividing marker concentrations by the expected median values at a particular gestational age. The expected median was generated by weighted regression analysis on marker concentrations of all the controls. The MoM values were then adjusted for maternal weight and were further corrected for ethnicity, parity and smoking status where applicable. The adjustment factors for ethnicity, parity and smoking status were generated based on MoM values calculated from the current study. Multiple of the median values of markers that were taken from the routine screening records were already adjusted for ethnicity, parity and smoking status using factors that were implemented in the screening software Alpha 8.0 (Logical Medical Systems Ltd, London). All analyses going forward were based on adjusted MoM values, unless otherwise specified. To assess if there were significant variations in biochemical markers in pregnancies with adverse outcomes, median MoM values of biochemical markers from cases and controls were compared using Mann-Whitney U-tests.\u003c/p\u003e \u003cp\u003eOf women who had both the first and second trimester serum samples available, a subsequent data set was created to assess the change in biochemical marker concentrations and MoM values between the second and first trimester samples in the case and control groups. The changes were calculated by subtracting the values of the first trimester sample from the second trimester sample. The changes in cases and controls were compared using Mann-Whitney U-tests.\u003c/p\u003e \u003cp\u003eLogistic regression was used to assess if the risk of developing PE, gestational hypertension, and preterm birth can be predicted using a combination of maternal biochemical markers and maternal characteristics. Logistic regression analysis with backward selection were run with all first trimester biochemical markers to determine the best predictive models for each case group using only first trimester markers. This was repeated using second trimester biochemical markers. For models with maternal characteristics, an initial logistic regression with backward selection was run to identify significant maternal characteristics for each case group. Varying biochemical marker combinations were then added to significant maternal characteristics to develop predictive models with a combination of maternal characteristics and biochemical markers in each case group. Receiver operating characteristic (ROC) curves were created to determine detection rates (DR) at a 5%, 10%, and 20% FPR, respectively. All statistical analysis was carried out using SAS 9.4.\u003c/p\u003e \u003cp\u003e The study was approved by the Research Ethics Board of North York General Hospital on May 17, 2017. All methods were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki).\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":"\u003cp\u003eIn total, 608 cases and 1,815 controls were identified from 15,640 singleton pregnancies. The case group included 147 pregnancies with PE (81 term, 49 preterm and 17 early-onset), 295 with gestational hypertension, and 166 preterm birth. All but eight cases were matched to three controls. Eight cases had 1\u0026ndash;2 controls because the control samples were either unusable or unavailable. 459 cases and 1358 controls had samples available from both the first and second trimesters.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e compares the maternal characteristics, medical history, and pregnancy outcomes among case and control groups. Maternal age and ethnicity were similar among case and control groups. Women with PE delivered two weeks earlier, and those with gestational hypertension delivered one week earlier than the control group. The PE group included a greater proportion of women with a history of chronic hypertension, higher BMI and nulliparous women compared to controls. Live birth pregnancies were lowest among preterm birth cases (72%), followed by early-onset PE (94%), preterm PE (98%), and all PE cases (99%). Median gestational age was 88 days for first trimester samples and 114 days for second trimester samples in both case and control groups. The smoking and IVF status were similar in cases and controls (not shown in the table).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMaternal characteristics, medical history, and pregnancy outcomes among case and control groups\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMaternal\u003c/p\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003ePreeclampsia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGestational Hypertension (n\u0026thinsp;=\u0026thinsp;295)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePreterm birth\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;166)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eControls\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1815)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll (n\u0026thinsp;=\u0026thinsp;147)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePreterm (n\u0026thinsp;=\u0026thinsp;49)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEarly-onset ( n\u0026thinsp;=\u0026thinsp;17)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e(5th,95th percentile)\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e/Proportion(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e(5th,95th percentile)\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e/ Proportion (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e(5th,95th percentile)\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e/ Proportion(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e(5th,95th percentile)\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e/ Proportion(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e(5th,95th percentile)\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e/ Proportion(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u003cstrong\u003e(5th,95th percentile)\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e/ Proportion(%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal age, years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.1\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(26.5\u0026ndash;40.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.9\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(26.0-42.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.4\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(28.1\u0026ndash;41.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.5\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(26.4\u0026ndash;41.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.9\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(24.9\u0026ndash;41.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1815\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.5\u003c/p\u003e\n\u003cp\u003e(26.3\u0026ndash;39.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal weight, kg\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(48.6-113.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.4\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(47.7\u0026ndash;98.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.5\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(42.3\u0026ndash;97.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.5\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(50.8\u0026ndash;110.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.5\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(45.5\u0026ndash;92.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1772\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60.5\u003c/p\u003e\n\u003cp\u003e(46.8\u0026ndash;88.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal BMI, kg/M2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e124\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.7\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(18.7\u0026ndash;43.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.2\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(18.6\u0026ndash;36.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.8\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(17.9\u0026ndash;32.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e264\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.3\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(19.5\u0026ndash;38.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e134\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23.3\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(17.6\u0026ndash;35.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.4\u003c/p\u003e\n\u003cp\u003e(17.9\u0026ndash;32.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eGestational age at birth, weeks\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(32.0\u0026ndash;40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(28.0\u0026ndash;36.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(23.0\u0026ndash;33.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(36.0\u0026ndash;40.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e166\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.0\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(20.0\u0026ndash;33.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1815\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39.0\u003c/p\u003e\n\u003cp\u003e(37.0\u0026ndash;41.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEthnicity\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCaucasian\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.5\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.3\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e--- \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e111\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.6\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.1\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e589\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAsian\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e154\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e983\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eBlack\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e137\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOther/\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnknown\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e106\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eChronic hypertension\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.2\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.4\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e--- \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.8\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e \u003csup\u003e\u003cstrong\u003ea, c\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNulliparous\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.5\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.4\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.9\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.6\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.6 \u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePregnancy Outcome\u003c/strong\u003e \u003csup\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eLive Birth\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.3\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.0\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94.1 \u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.3\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e1815\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"19\"\u003e\u003csup\u003e*\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003csup\u003e^\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003csup\u003e\u0026para;\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e%\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003eNS\u003c/sup\u003e Not significant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"19\"\u003eComparison between case and control groups: Chi-Square or Fisher\u0026rsquo;s exact test for categorical variables; Mann-Whithey U test for continuous variables\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"19\"\u003eBMI, Body Mass Index\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"19\"\u003e\u003csup\u003ea\u003c/sup\u003e Parity is a binary variable categorized as nulliparous or parous\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"19\"\u003e\u003csup\u003ec\u003c/sup\u003e Parity and Gravidity were missing among cases and controls: 3 missing among all preeclampsia cases(n\u0026thinsp;=\u0026thinsp;144), 2 missing among preterm preeclampsia cases (n\u0026thinsp;=\u0026thinsp;47), 5 missing among gestational hypertension cases(n\u0026thinsp;=\u0026thinsp;290), 11 missing among preterm birth cases(n\u0026thinsp;=\u0026thinsp;155), and 35 missing among controls(n\u0026thinsp;=\u0026thinsp;1780).\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"19\"\u003e\u003csup\u003ed\u003c/sup\u003e Pregnancy Outcome is a binary variable categorized as live birth or stillbirth/pregnancy loss\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e gives the median MoM of first trimester and second trimester biochemical markers seen in case and control groups. The median MoM of first trimester PAPP-A, PlGF, and free-\u0026beta; hCG were significantly reduced among cases compared to controls. Of women who developed PE, the lowest median MoM of PAPP-A and PlGF were seen in those with preterm PE (0.63 and 0.73), and early-onset PE (0.54 and 0.72), respectively. First trimester median MoM values of AFP increased in the overall PE, gestational hypertension and preterm birth groups. Free-\u0026beta; hCG decreased in PE, gestational hypertension and preterm birth groups. No change in the first trimester inhibin A median MoM was observed in case groups.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMedian multiple of the medians (MoMs) of biochemical markers among case and control groups\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMarker\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTested\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003ePreeclampsia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGestational Hypertension\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePreterm birth\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eControls\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEarly-onset\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian (MoM)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian (MoM)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian (MoM)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(MoM)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian (MoM)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedian (MoM)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eFirst trimester (T1)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePAPP-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRoutine\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.77 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.63 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.54 \u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e283\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.93\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.86 \u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.10\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efree-\u0026beta; hCG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81 \u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82 \u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88 \u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.88 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.73 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72 \u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.91 \u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.16 \u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.11 \u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.27 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInhibin A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.07 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e284\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.08 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1751\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eSecond trimester (T2)\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRoutine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.03 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e223\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.04 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e130\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.18 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1402\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003euE\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRoutine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.00 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92 \u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ehCG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRoutine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.17 \u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.30 \u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.62 \u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.14 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePAPP-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82 \u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80 \u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e189\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStudy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.75 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.42\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.89 \u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e103\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79 \u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1187\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInhibin A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRoutine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.45 \u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.51 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.95 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.36 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"14\"\u003e\u003csup\u003e*\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003csup\u003e^\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003csup\u003e\u0026para;\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e%\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003eNS\u003c/sup\u003e Not significant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"14\"\u003e\u003csup\u003ea\u003c/sup\u003eRoutine- marker measurement from routine screening tests\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"14\"\u003e\u003csup\u003eb\u003c/sup\u003eStudy- marker (re)/measured specifically for this study\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the second trimester, median MoM values of PlGF were lower in overall PE, gestational hypertension and preterm birth groups compared to controls. MoM values of PAPP-A were lower in PE group but not gestational hypertension and preterm birth groups. Median MoM of hCG was higher in PE groups. The lowest median MoM of PlGF (0.42) and PAPP-A (0.80) was seen in women with early-onset PE and preterm PE, respectively. Median MoM of PlGF was lower and AFP was higher in women with preterm birth, compared to controls.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the increase in PlGF concentrations between first and second trimester samples; revealing smaller increases among all PE (43.70 pg/ml), preterm PE (32.85 pg/ml), early-onset PE (21.40 pg/ml), and preterm birth cases (51.50 pg/ml) compared to controls (64.40 pg/ml, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Trends among other biomarkers were not statistically or clinically significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e gives the coefficients of logistic regressions which indicate the degree of the associations between biochemical markers, maternal characteristics and the risk of developing PE, gestational hypertension, and preterm birth. When maternal characteristics were not included in the model, first and second trimester PlGF was associated with all PE, preterm PE and early-onset PE. First trimester PAPP-A, free-\u0026beta; hCG and inhibin A were associated with all PE and preterm PE. When maternal characteristics were included in the model, the associations between PlGF and all PE, preterm PE and early-onset PE remained statistically significant.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic regression results for the prediction of adverse pregnancy outcomes using maternal characteristics and biochemical markers\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMarker\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ePreeclampsia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGestational hypertension\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePreterm birth\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEarly-onset\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eT1 Biochemical Markers\u003c/span\u003e\u003csup\u003e\u003cspan class=\"BoldItalicUnderline\"\u003ea\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.1692\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.1523\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.6729\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.1012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.9447\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePAPP-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.7193\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.5537\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.4694\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.3024\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003efree-\u0026beta; hCG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.4488\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.6470\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.2657\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePlGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.9420\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.3867\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.3491\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.3503\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.5498\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1868\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3657\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInhibin A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5241\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8701\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4195\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eT2 Biochemical Markers\u003c/span\u003e\u003csup\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eb\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.5231\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.5203\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.8837\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.2967*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.9344\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7503\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.2020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003euE\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ehCG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5895\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5332\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6212\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePAPP-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.6405\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.3987\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePlGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.0464\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.9338\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.1643\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eMaternal Characteristics\u003c/span\u003e\u003csup\u003e\u003cspan class=\"BoldItalicUnderline\"\u003ec\u003c/span\u003e\u003c/sup\u003e \u003cspan class=\"BoldItalicUnderline\"\u003e+ T1 PlGF\u0026thinsp;+\u0026thinsp;T1 PAPP-A\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.9419\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.2600\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.7611\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.2080\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.7145\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePlGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.9925\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.7875\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.8433\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.3273\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.4626\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePAPP-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.9265\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.5843\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.1291\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.6066\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.4302\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eMaternal Characteristics\u003c/span\u003e\u003csup\u003e\u003cspan class=\"BoldItalicUnderline\"\u003ec\u003c/span\u003e\u003c/sup\u003e \u003cspan class=\"BoldItalicUnderline\"\u003e+ All T1 Biochemical Markers\u003c/span\u003e \u003csup\u003e\u003cspan class=\"BoldItalicUnderline\"\u003ea\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.4496\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.9671\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.2326\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-6.5085\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.0900\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePAPP-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.9335\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.6698\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.1970\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.6177\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.3836\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003efree-\u0026beta; hCG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.3955\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.5637\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.2477\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.1426\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0709\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePlGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.7713\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.2278\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.4894\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.2676\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.4342\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2180\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1505\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0942\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1697\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3204\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eInhibin A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4649\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8114\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5278\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2345\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.00160\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eMaternal Characteristics\u003c/span\u003e\u003csup\u003e\u003cspan class=\"BoldItalicUnderline\"\u003ec\u003c/span\u003e\u003c/sup\u003e \u003cspan class=\"BoldItalicUnderline\"\u003e+ All T2 Biochemical Markers\u003c/span\u003e \u003csup\u003e\u003cspan class=\"BoldItalicUnderline\"\u003eb\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eIntercept\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-5.5285\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.1855\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.1794\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7.2465\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2.9484\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAFP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.4117\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.7740\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.1304\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4014\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9651\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003euE\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.4744\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.9717\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.9243\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.0260\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.5001\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ehCG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6030\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9158\u003csup\u003e^\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7432\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2084\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1450\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePAPP-A\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.8083\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.8946\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3250\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.6117\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.5368 \u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePlGF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.0086\u003csup\u003e\u0026para;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.4560\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-3.0874\u003csup\u003e%\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.3119\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.3736\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; \u003csup\u003e^\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003csup\u003e\u0026para;\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e%\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003eNS\u003c/sup\u003e Not significant\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003ePAPP-A, pregnancy-associated plasma protein A; hCG, human chorionic gonadotropin; PlGF, placental growth factor; AFP, alpha feto-protein\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e First Trimester (T1) Biochemical Markers: PAPP-A, free-\u0026beta; hCG ,PlGF, AFP, Inhibin A\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003e Second Trimester (T2) Biochemical Markers: AFP, uE3, hCG, PAPP-A, PlGF\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003e Through backward selection significant maternal characteristics for each case group were identified. Significant maternal characteristics for each case group include - All PE: age, weight, ethnicity, nulliparous; Preterm PE: age, weight, ethnicity, gravidity; Early-onset PE: age, gravidity; Gestational Hypertension: age, weight, ethnicity, nulliparous; Preterm Birth: nulliparous\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the estimated DR for PE, gestational hypertension and preterm birth at a FPR of 5%, 10%, and 20% using different biochemical marker combinations with and without maternal characteristics. Detection rates for preterm PE and early-onset PE were higher than for all PE cases. Adding maternal characteristics to any combinations yielded better DR values than maternal characteristics or biochemical marker combination alone. Detection rates for both gestational hypertension and preterm birth, using different biochemical marker combinations with and without maternal characteristics, were low.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eModel-predicted screening performance using maternal characteristics and different serum marker combinations\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMarker\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003ePreeclampsia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGestational hypertension\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePreterm birth\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEarly-onset\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eT1 Biochemical Markers\u003c/span\u003e \u003csup\u003e\u003cspan class=\"BoldItalic\"\u003ea\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 5% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e23%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e35%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e13%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e17%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 10% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e33%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e42%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e35%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e19%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 20% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e55%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e73%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e65%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e35%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e40%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eT2 Biochemical Markers\u003c/span\u003e \u003csup\u003e\u003cspan class=\"BoldItalic\"\u003eb\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 5% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e23%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e29%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e50%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e12%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e19%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 10% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e31%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e42%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e63%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e20%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e29%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 20% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e44%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e58%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e88%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e28%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e38%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eMaternal Characteristics Only\u003c/span\u003e \u003csup\u003e\u003cspan class=\"BoldItalic\"\u003ec\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 5% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e18%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e15%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e18%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e22%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e7%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 10% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e31%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e18%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e34%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e15%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 20% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e45%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e47%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e41%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e54%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e25%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eMaternal Characteristics\u003c/span\u003e \u003csup\u003e\u003cspan class=\"BoldItalic\"\u003ec\u003c/span\u003e\u003c/sup\u003e \u003cspan class=\"BoldItalic\"\u003e+ T1 PlGF\u0026thinsp;+\u0026thinsp;T1 PAPP-A\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 5% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e35%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e41%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e23%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e15%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 10% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e42%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e52%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e65%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e39%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e21%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 20% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e60%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e67%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e76%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e58%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e36%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cspan class=\"BoldItalic\"\u003eMaternal Characteristics\u003c/span\u003e \u003csup\u003e\u003cspan class=\"BoldItalic\"\u003ec\u003c/span\u003e\u003c/sup\u003e \u003cspan class=\"BoldItalic\"\u003e+ All T1 Biochemical Markers\u003c/span\u003e \u003csup\u003e\u003cspan class=\"BoldItalic\"\u003ea\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 5% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e30%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e37%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e47%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e24%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e19%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 10% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e45%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e59%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e71%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e40%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e29%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 20% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e59%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e72%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e82%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e59%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e40%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\u003cspan class=\"BoldItalic\"\u003eMaternal Characteristics\u003c/span\u003e\u0026nbsp;\u003csup\u003e\u003cspan class=\"BoldItalic\"\u003ec\u003c/span\u003e\u003c/sup\u003e\u0026nbsp;\u003cspan class=\"BoldItalic\"\u003e+ All T2 Biochemical Markers\u003csup\u003eb\u003c/sup\u003e\u003c/span\u003e\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 5% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e27%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e39%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e29%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e18%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 10% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e38%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e48%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e40%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e37%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDR for 20% FPR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e63%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e65%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e75%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e56%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e58%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003eDR, Detection Rate; FPR, False Positive Rate; AUC, Area under the Curve\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003e First Trimester (T1) Biochemical Markers: PAPP-A, free-\u0026beta; hCG ,PlGF, AFP, Inhibin A. Refer to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for specific markers used in each case group\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003e\u003csup\u003eb\u003c/sup\u003e Second Trimester (T2) Biochemical Markers: AFP, uE3, hCG, PAPP-A, PlGF. Refer to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for specific markers used in each case group\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"11\"\u003e\u003csup\u003ec\u003c/sup\u003e Significant maternal characteristics for each case group include- All PE: age, weight, ethnicity, nulliparous; Preterm PE: age, weight, ethnicity, gravidity; Early-onset PE: age, gravidity; Gestational Hypertension: age, weight, ethnicity, nulliparous; Preterm Birth: nulliparous\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the ROC curves for PE, gestational hypertension and preterm birth using maternal characteristics and PlGF and PAPP-A in the first trimester. The area under the ROC curve (AUC) was largest among preterm PE (0.83) and early-onset PE (0.82) cases followed by all PE (0.78), gestational hypertension (0.75) and preterm birth cases (0.61). The best screening performance for a FPR\u0026thinsp;\u0026lt;\u0026thinsp;25% was seen for early-onset PE followed by preterm PE and all PE.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eOur study assessed whether first and second trimester biochemical markers used in prenatal aneuploidy screening could accurately identify pregnancies at risk of developing adverse pregnancy outcomes, mainly PE. We found that the combination of maternal characteristics with biochemical markers in both trimesters can provide reasonable accuracy in identifying women at risk of developing preterm and early-onset PE but not women at risk for gestational hypertension or preterm birth. PAPP-A and PlGF were the most reliable biochemical markers to be associated with and to predict adverse pregnancy outcomes, particularly early-onset PE.\u003c/p\u003e \u003cp\u003eOur results have confirmed previously reported associations between individual biochemical markers and PE and preterm birth.\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Similar to previous studies, we found the performance of PE prediction to be improved by using multiple biochemical markers in combination with maternal characteristics .\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,12\u003c/sup\u003eThe most commonly used marker combination in the first trimester PAPP-A and PlGF along with maternal characteristics can predict 65% and 76% of early-onset PE with a FPR of 10% and 20% respectively. Second trimester biochemical markers can identify 88% of the pregnancies at risk of developing early-onset PE with a FPR of 20%. However, this will require the addition of PAPP-A and PlGF which are not currently used in second trimester aneuploidy screening. In addition, we found that the increase of PlGF with gestational age was smaller in women affected by PE compared to the control group.\u003c/p\u003e \u003cp\u003eLow first trimester PAPP-A and PlGF have been associated with adverse pregnancy outcomes, especially PE in multiple studies.\u003csup\u003e5,11\u0026minus;13\u003c/sup\u003e The case-control study by Keikkala et al. (2016) investigating maternal serum samples from 8\u0026ndash;13 weeks of gestation found lower median MoM values for PlGF and PAPP-A in women with PE compared to control. \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Consistent with our study, the lowest median MoM values were seen in cases with preterm and early-onset PE. As with previous studies, our study found median first trimester AFP MoM values to be higher and median free β-hCG MoM values to be lower in women with PE, although the significance of these changes was smaller in comparison to those of PAPP-A and PlGF. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The previously described association between first trimester inhibin A and PE was not confirmed in our study. \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSecond trimester PlGF and PAPP-A median MoM values, were lower in our study than those reported by other studies.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e However, this was mainly true for PlGF and not significant for PAPP-A in women with preterm or early-onset PE, likely due to the small number of pregnancies in this group. We found an increase in total hCG in PE pregnancies, as reported in previous studies.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e In contrast, no change in Inhibin A was seen in our study although reported as an optimal second trimester marker by others.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e We suspect this is due to the small number of early PE pregnancies involved in our study. Lastly, the change in the first and second trimester markers in gestational hypertension and preterm birth cases was consistent with other studies in general although the magnitude of the change was variable.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn recent years, the results of several large clinical trials have suggested that the performance of PE screening can be improved by using multiple biochemical markers together with maternal characteristics and biophysical markers.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e A prospective study by Akolekar et al. (2013) on 58,884 singleton pregnancies at 11\u0026ndash;13 weeks reported a 50.5% DR at a 10% FPR with maternal characteristics alone for early-onset PE, 74.3% with the addition of PlGF and PAPP-A and 89.7% with the addition of biophysical markers, MAP and UTPI. The DR improved to 96.3% when maternal characteristics and biochemical and biophysical markers were combined. \u003csup\u003e12\u003c/sup\u003eSimilarly, in a prospective study of 35,948 singleton pregnancies at 11\u0026ndash;13 weeks\u0026rsquo; gestation, O\u0026rsquo;Gorman et al. (2016) found that combined screening obtained a DR of 75% and 47% at a 10% FPR for preterm PE and term PE pregnancies, respectively. \u003csup\u003e12\u003c/sup\u003eWhen such modelling combinations were applied to the ASPRE trial, similar DR values were observed. First-trimester screening for preterm PE with a risk cut-off of 1 in 100 detected 76.7% of preterm PE and 43.1% of term PE pregnancies, at a screen-positive rate of 10.5% and a FPR of 9.2%.\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhile the best performance can be achieved by combining multiple variables (e.g., maternal characteristics, medical and family history, biochemical and biophysical markers), MAP and especially UTPI might not be readily accessible, especially for women in remote areas. If the combination of maternal characteristics and biochemical markers can predict the risk of PE with reasonable accuracy, the current aneuploidy screening could be expanded to include PE screening to identify women at increased risk of developing PE, with biophysical markers to be followed as a second line or contingent screen. In our study, the PE screening performance was comparable to previous studies when using maternal characteristics in combination with first trimester PAPP-A and PlGF. At a FPR of 20%, the DR was 76%, 67% and 60% for early-onset PE, preterm PE and all PE, respectively. This suggests that for 20% of women who screen positive for early-onset PE, a contingent PE screening strategy using MAP and UTPI can predict up to 76% of early-onset PE. As with the one-time screening approach, our contingent screen can yield results before 16 weeks of gestation in order to initiate prophylactic therapy with Aspirin.\u003c/p\u003e \u003cp\u003eIf first trimester screening is aiming to initiate Aspirin treatment, second trimester screening is useful for patient triage by identifying pregnancies needing close surveillance and more urgent medical attention. For example, numerous studies have focused on second trimester PlGF or soluble fms-like tyrosine kinase-1 (sFlt-1)/PlGF ratio.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In our study, in addition to the PE screening performance using second trimester markers, we compared the changes in biochemical markers between the first and second trimester samples. For women who developed PE, the increase in PlGF concentrations was significantly smaller compared to controls. These results have been confirmed by the findings from other studies.\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Theoretically, for women who are identified to be at high risk for PE in the first trimester, a repeat test for PlGF and PAPP-A in the second trimester might provide useful information which can be used for risk modification. Nevertheless, a calculation of biomarker changes between trimesters did not yield a better prediction for PE than using second trimester markers alone in our study. Further investigation of this trend may provide clinicians with valuable information for monitoring and early detection of high-risk women.\u003c/p\u003e \u003cp\u003eIn our study, the preterm birth screening performance was less optimal than previously reported.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e A possible reason for the lower DR is that unlike some previous studies, our preterm birth group excluded all women with PE or gestational hypertension. Incomplete maternal characteristics data may have also contributed to the lower-than-expected DR values for preterm birth.\u003c/p\u003e \u003cp\u003eThe strengths of our study include the identification of cases and controls from a routine unselected screening population, representing a true sample of women undergoing prenatal screening in Ontario. Also unique to our study is the availability of both first and second trimester serum samples for most cases and controls. Having samples from both trimesters enabled us to investigate the change between the first and second trimesters, which might provide additional information for PE screening and monitoring. The limitations of this study include, the transfer of some women to other obstetrical centres, potentially lowering the incidence of PE in our population. Additionally, our local population includes a greater proportion of women of Asian ancestry compared with other studies. Varying ethnicity between studies may impact PE prevalence; in particular, a lower prevalence of PE among Asian women has been noted previously.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Our study lacked complete information on all maternal characteristics and as such, the accuracy of PE screening using maternal characteristics cannot be directly compared to studies where maternal characteristics were explicitly collected for PE screening. However, the contribution of biochemical markers and overall PE screening performance using the combination of maternal characteristics and biochemical markers were consistent with previous studies. In a real-life clinical setting, it will be practical to collect all maternal characteristics missed in this study. Since information on MAP and UTPI was not available to our study, we were not able to assess the final performance of a contingent PE screening strategy. Nevertheless, we achieved our goal of assessing the first tier of a contingent screening strategy to provide reasonable performance and warrant the expansion of current aneuploidy screening to include preeclampsia.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eOur study, based on a routine aneuploidy screening, showed that the combination of maternal characteristics and first trimester serum PAPP-A and PlGF can provide reasonable performance for PE screening. If our study results can be validated by prospective studies, the current aneuploidy screening program can be expanded to include identification of women at risk of developing PE, particularly early-onset PE. As a contingent strategy, it can provide first-tier PE screening with minimal associated costs and minimal change to the current workflow.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eAFP: \u0026alpha;-fetoprotein\u003c/p\u003e\n\u003cp\u003eAUC: Area Under the Curve\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u003c/p\u003e\n\u003cp\u003ecfDNA: cell-free fetal DNA\u003c/p\u003e\n\u003cp\u003eDR: detection rate\u003c/p\u003e\n\u003cp\u003eEDD: expected date of delivery\u003c/p\u003e\n\u003cp\u003eFPR: false positive rate\u003c/p\u003e\n\u003cp\u003ehCG: human chorionic gonadotrophin\u003c/p\u003e\n\u003cp\u003eIUGR: intrauterine growth restriction\u003c/p\u003e\n\u003cp\u003eIVF: in vitro fertilization\u003c/p\u003e\n\u003cp\u003eMAP: mean arterial blood pressure\u003c/p\u003e\n\u003cp\u003eMoM: multiple of the median\u003c/p\u003e\n\u003cp\u003eNT: nuchal translucency\u003c/p\u003e\n\u003cp\u003ePAPP-A: pregnancy-associated plasma protein A\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePE: preeclampsia\u003c/p\u003e\n\u003cp\u003ePlGF: placental growth factor\u003c/p\u003e\n\u003cp\u003eQUAD: second trimester serum screening\u003c/p\u003e\n\u003cp\u003eROC: Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003esFlt-1: soluble fms-like tyrosine kinase-1\u003c/p\u003e\n\u003cp\u003eT1: first trimester\u003c/p\u003e\n\u003cp\u003eT2: second trimester\u003c/p\u003e\n\u003cp\u003euE3: unconjugated estriol\u003c/p\u003e\n\u003cp\u003eUTPI: uterine artery pulsatility index\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe Research Ethics Board of North York General Hospital has approved the study on May 17, 2017. The Informed consent was waived by NYGH\u0026rsquo;s REB. The informed consent was waived because our study meets the requirements specified in Canada\u0026rsquo;s Tri-council policy statements C, Article 12.3 (https://ethics.gc.ca/eng/tcps2-eptc2_chapter12-chapitre12.html) for following reasons:\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;1) The study is retrospective in nature. It is impracticable to seek consent from each individual patient. At the time of the sample collection, we did not know if a pregnancy will have an adverse pregnancy outcome (therefore will be include in the study). As screening tests were ordered by thousands of physicians across the province of Ontario, Canada, we do not have direct contact with patients.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;2) This study cannot be carried out without using residual samples.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;3) Using residual serum samples without patient consent will not have an adverse effect on patients because all subjects in the study have been managed using current standard of care guidelines for detection, follow-up, confirmation and treatment.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;4). We have de-identified the study dataset and study samples to protect the privacy of individuals and safeguard residual blood samples. No patients will be contacted. Only aggregate data will be published.\u003c/p\u003e\n\u003cp\u003eAll methods were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki).\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe reagent kits used in the study were provided by PerkinElmer free of charge. Professor Howard Cuckle is a consultant of PerkinElmer. Dr. Elad Mei-Dan is an Editorial Board Member of BMC Pregnancy \u0026amp; Childbirth. The rest of the authors declare to have no competing interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eFunding: The study was supported by Imperial Order Daughters of the Empire (IODE) Genetics Program Research Fund of North York General Hospital.\u003c/p\u003e\n\u003ch2\u003eAuthors Contributions\u003c/h2\u003e\n\u003cp\u003eTH contributed to conception and design, performed data analysis, interpreted the data, drafted the manuscript and agreed to act as guarantors of the work presented in this article. SR performed laboratory assay and revised the manuscript critically. HC performed data analysis, interpreted the data, and revised the manuscript critically. ER performed data analysis and drafted the manuscript. EM, HMB, MP, EMT, CG and WSM and contributed substantially to the interpretation of data, and revised the manuscript critically. All authors have given the final approval of version to be published.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors thank PerkinElmer for providing assay kits used in this study free of charge. We wish to acknowledge staff of the Maternal Multiple Marker Screening Laboratory at North York General Hospital for preparing study samples and performing laboratory assays.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCommittee Opinion No. 640: Cell-Free DNA Screening For Fetal Aneuploidy. \u003cem\u003eObstet Gynecol\u003c/em\u003e. 2015;126(3):e31-e37\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Dur\u0026aacute;n M\u0026Aacute;, Bernabeu Garc\u0026iacute;a A, Calero I,Ramis Fossas J, Illescac T, Aviles MT et al. Clinical application of a contingent screening strategy for trisomies with cell-free DNA: A pilot study. \u003cem\u003eBMC Pregnancy Childbirth\u003c/em\u003e. 2019;19:274\u003c/li\u003e\n\u003cli\u003eLitwińska E, Litwińska M, Oszukowski P, Szaflik K, Litwinski W, Korcz M, et al. Biochemical markers in screening for preeclampsia and intrauterine growth restriction. \u003cem\u003eGinekol Pol\u003c/em\u003e. 2015;86(8):611-5\u003c/li\u003e\n\u003cli\u003eCohen JL, Smilen KE, Bianco AT, Moshier EL, Ferrara LA, L. Stone J. Predictive value of combined serum biomarkers for adverse pregnancy outcomes. \u003cem\u003eEur J Obstet Gynecol Reprod Biol\u003c/em\u003e. 2014;181:89-94\u003c/li\u003e\n\u003cli\u003eAkolekar R, Syngelaki A, Poon L, Wright D, Nicolaides KH. Competing risks model in early screening for preeclampsia by biophysical and biochemical markers.[Erratum appears in Fetal Diagn Ther. 2013;34(1):43].\u003c/li\u003e\n\u003cli\u003ePark F, Russo K, Williams P, Pelosi M, Puddephatt R, Walter M, et al. Prediction and prevention of early-onset pre-eclampsia: Impact of aspirin after first-trimester screening. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2015;46:419-23.\u003c/li\u003e\n\u003cli\u003eTsiakkas A, Duvdevani N, Wright A, Wright D, Nicolaides KH. Serum placental growth factor in the three trimesters of pregnancy: effects of maternal characteristics and medical history. Ultrasound Obstet Gynecol. 2015;45:591\u0026ndash;8\u003c/li\u003e\n\u003cli\u003eBredaki FE, Mataliotakis M, Wright A, Wright D, Nicolaides KH. Maternal serum alpha-fetoprotein at 12, 22 and 32 weeks\u0026rsquo; gestation in screening for pre-eclampsia. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2016;50:807.\u003c/li\u003e\n\u003cli\u003eWright A, Guerra L, Pellegrino M, Wright D, Nicolaides KH. Maternal serum PAPP-A and free \u0026beta;-hCG at 12, 22 and 32 weeks\u0026rsquo; gestation in screening for pre-eclampsia. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2016;47:762-7\u003c/li\u003e\n\u003cli\u003ePoon LC, Shennan A, Hyett JA, Kapur A, Hadar E, Divakar H et al. The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention. Int J Gynaecol Obstet. 2019;145 Suppl 1:1-33. Erratum in: Int J Gynaecol Obstet. 2019;146:390-391\u003c/li\u003e\n\u003cli\u003ePoon LC, Nicolaides KH. First-trimester maternal factors and biomarker screening for preeclampsia. \u003cem\u003ePrenat Diagn\u003c/em\u003e. 2014;34:618-27\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Gorman N, Wright D, Syngelaki A, Akolekar R, Wright A, Poon LC, et al. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks gestation. \u003cem\u003eAm J Obstet Gynecol\u003c/em\u003e. 2016;214:103.e1-103.e12\u003c/li\u003e\n\u003cli\u003eKeikkala E, Koskinen S, Vuorela P, Laivuori H, Romppanen J, Heinonen S, et al First trimester serum placental growth factor and hyperglycosylated human chorionic gonadotropin are associated with pre-eclampsia: a case control study. BMC Pregnancy Childbirth. 2016;16:378.\u003c/li\u003e\n\u003cli\u003eCuckle H, Sehmi I, Jones R. Maternal serum inhibin A can predict pre-eclampsia. \u003cem\u003eBJOG An Int J Obstet Gynaecol\u003c/em\u003e. 1998;105:1101-3\u003c/li\u003e\n\u003cli\u003eSpencer K, Cowans NJ, Nicolaides KH. Maternal serum inhibin-A and activin-A levels in the first trimester of pregnancies developing pre-eclampsia. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2008;32:622-6\u003c/li\u003e\n\u003cli\u003eWright D, Silva M, Papadopoulos S, Wright A, Nicolaides KH. Serum pregnancy-associated plasma protein-A in the three trimesters of pregnancy: Effects of maternal characteristics and medical history. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2015;46:42-50\u003c/li\u003e\n\u003cli\u003eBarjaktarovic M, Korevaar TIM, Jaddoe VWV, de Rijke YB, Peeters RP, Steegers EAP. Human chorionic gonadotropin and risk of pre-eclampsia: prospective population-based cohort study. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2019;54:477-483\u003c/li\u003e\n\u003cli\u003eBroumand F, Salari Lak S, Nemati F, Mazidi A. A study of the diagnostic value of Inhibin A Tests for occurrence of preeclampsia in pregnant women. \u003cem\u003eElectron Physician\u003c/em\u003e. 2018;10:6186-6192\u003c/li\u003e\n\u003cli\u003eLakhi N, Govind A, Moretti M, Jones J. Maternal serum analytes as markers of adverse obstetric outcome. \u003cem\u003eObstet Gynaecol\u003c/em\u003e. 2012;14(4):267-273\u003c/li\u003e\n\u003cli\u003eJelliffe-Pawlowski LL, Baer RJ, Blumenfeld YJ, et al. Maternal characteristics and mid-pregnancy serum biomarkers as risk factors for subtypes of preterm birth. \u003cem\u003eBJOG An Int J Obstet Gynaecol\u003c/em\u003e. 2015;122:1484-93\u003c/li\u003e\n\u003cli\u003eRolnik DL, Wright D, Poon LCY, et al. ASPRE trial: performance of screening for preterm pre-clampsia. \u003cem\u003eUltrasound Obstet Gynecol\u003c/em\u003e. 2017;50:492-495\u003c/li\u003e\n\u003cli\u003eCaillon H, Tardif C, Dumontet E, Winer N, Masson D. Evaluation of sFlt-1/PlGF ratio for predicting and improving clinical management of pre-eclampsia: Experience in a specialized perinatal care center. \u003cem\u003eAnn Lab Med\u003c/em\u003e. 2018;38:95-101\u003c/li\u003e\n\u003cli\u003eMyatt L, Clifton RG, Roberts JM, Spong CY, Wapner RJ, Thorp Jr JM, et al. Can changes in angiogenic biomarkers between the first and second trimesters of pregnancy predict development of pre-eclampsia in a low-risk nulliparous patient population? BJOG An Int J Obstet Gynaecol. 2013;120:1183\u0026ndash;91\u003c/li\u003e\n\u003cli\u003eVatten LJ, Eskild A, Nilsen TIL, Jeansson S, Jenum PA, Staff AC. Changes in circulating level of angiogenic factors from the first to second trimester as predictors of preeclampsia. Am J Obstet Gynecol. 2007;196:239.e1-6\u003c/li\u003e\n\u003cli\u003eErez O, Romero R, Espinoza J, Fu W, Todem D, Kusanovic JP, et al. The change in concentrations of angiogenic and anti-angiogenic factors in maternal plasma between the first and second trimesters in risk assessment for the subsequent development of preeclampsia and small-for-gestational age. J Matern Fetal Neonatal Med. 2008;21:279\u0026ndash;87\u003c/li\u003e\n\u003cli\u003eXiao J, Shen F, Xue Q, et al. Is ethnicity a risk factor for developing preeclampsia? An analysis of the prevalence of preeclampsia in China. \u003cem\u003eJ Hum Hypertens\u003c/em\u003e. 2014;28:694-8\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multiple marker screening, preeclampsia, gestational hypertension, preterm birth, pregnancy-associated plasma protein A, placental growth factor, human chorionic gonadotropin, alpha feto-protein, unconjugated estriol and Inhibin A","lastPublishedDoi":"10.21203/rs.3.rs-492997/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-492997/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eMaternal biochemical markers used in multiple marker aneuploidy screening have been associated with adverse pregnancy outcomes. \u0026nbsp;This study aims to assess if a combination of maternal characteristics and biochemical markers in the first and second trimesters can be used to screen for preeclampsia (PE), gestational hypertension and preterm birth.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis case-control study used information on maternal characteristics and residual blood samples from pregnant women who have undergone multiple marker aneuploidy screening. \u0026nbsp;The median multiple of the median (MoM) of first and second trimester biochemical markers in cases (women with PE, gestational hypertension and preterm birth) and controls were compared. \u0026nbsp;Biochemical markers included pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PlGF), human chorionic gonadotropin (hCG), alpha feto-protein (AFP), unconjugated estriol (uE3) and Inhibin A. \u0026nbsp;Logistic regression analysis was used to estimate screening performance using different marker combinations. \u0026nbsp;Screening performance was defined as detection rate (DR) and false positive rate (FPR). Preterm and early-onset preeclampsia PE were defined as women with PE delivered \u0026lt; 37 and \u0026lt; 34 weeks of gestation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThere were 147 pregnancies with PE (81 term, 49 preterm and 17 early-onset), 295 with gestational hypertension, and 166 preterm birth. \u0026nbsp;Compared to controls, PE cases had significantly lower median MoM of PAPP-A (0.77 vs 1.10, p\u0026lt;0.0001), PlGF (0.76 vs 1.01, p\u0026lt;0.0001) and free-β hCG (0.81 vs. 0.98, p\u0026lt;0.001) in the first trimester along with PAPP-A (0.82 vs 0.99, p\u0026lt;0.01) and PlGF (0.75 vs 1.02, p\u0026lt;0.0001) in the second trimester. \u0026nbsp;The lowest first trimester PAPP-A, PlGF and free β-hCG were seen in those with preterm and early-onset PE. \u0026nbsp;At a 20% FPR, 67% of preterm and 76% of early-onset PE cases can be predicted using a combination of maternal characteristics with PAPP-A and PlGF in the first trimester.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eMaternal characteristics with first trimester PAPP-A and PlGF measured for aneuploidy screening provided reasonable accuracy in identifying women at risk of developing early onset PE, allowing triage of high-risk women for further investigation and risk-reducing therapy.\u003c/p\u003e","manuscriptTitle":"Modified Multiple Marker Aneuploidy Screening as a Primary Screening Test for Preeclampsia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-06-02 15:35:53","doi":"10.21203/rs.3.rs-492997/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-11-08T05:19:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-10-11T14:30:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-07-05T23:37:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"d01acc1a-4bae-4608-a8c8-a337a6aab75d","date":"2021-06-15T07:46:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2b8d9423-a1ad-4803-871c-514165fdacc5","date":"2021-06-12T18:08:52+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-06-08T22:26:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-05-31T22:17:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-05-31T21:57:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-05-31T21:48:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2021-05-04T13:19:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3657906e-d1b8-4153-8fb3-9f5985525b3b","owner":[],"postedDate":"June 2nd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4733669,"name":"Maternal \u0026 Fetal Medicine"},{"id":4733670,"name":"Sexual \u0026 Reproductive Medicine"}],"tags":[],"updatedAt":"2022-03-08T12:50:26+00:00","versionOfRecord":{"articleIdentity":"rs-492997","link":"https://doi.org/10.1186/s12884-022-04514-4","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2022-03-08 12:50:26","publishedOnDateReadable":"March 8th, 2022"},"versionCreatedAt":"2021-06-02 15:35:53","video":"","vorDoi":"10.1186/s12884-022-04514-4","vorDoiUrl":"https://doi.org/10.1186/s12884-022-04514-4","workflowStages":[]},"version":"v1","identity":"rs-492997","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-492997","identity":"rs-492997","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}