The sFlt-1/PlGF ratio as predictor of Maternal adverse outcome in patients with suspected or placental insufficiency

The sFlt-1/PlGF ratio as predictor of Maternal adverse outcome in patients with suspected or placental insufficiency | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The sFlt-1/PlGF ratio as predictor of Maternal adverse outcome in patients with suspected or placental insufficiency Alfredo Perales-Marín, Francis Fernández-de-la-Cruz, Marisa Martínez-Triguero, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3166567/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background sFlt-1/PlGF ratio has been proposed to herald adverse Pregnancy outcome (APO). Several authors have proposed the use of a continuous scale but most use specific cut-offs to evaluate the risk of APO, but the proposed range varies widely. The aim, is to evaluate if the Maternal adverse outcome (AO) prediction occurs in a stepwise manner, and if this was the case, which would be the point yielding the highest accuracy. Methods This is an observational retrospective cohort study. Singleton pregnancies, between 20 to 40 weeks were selected according the levels of sFlt-1/PlGF; three groups (n = 60 each): High ≥ 655, Intermediate ≥ 85 to < 655 and Low < 85. From hospital records we retrieve data, and outcome, also we quantitate the PE risk. We compared the groups, to determine the clinical utility of the sFlt1/PlGF ratio in prediction of Maternal AO, we used ROC analysis, and to develop a predictive model of Maternal AO a binary multivariate logistic regression was used. Results At entry a different degree of severity is observed for groups: High > Intermediate > Low, for: Systolic Blood pressure, Mean Blood pressure, Angiogenic markers, AST, ALT. Also a positive correlation was found between the sFlt-1/PlGF and the afore mentioned variables. We observed a potency order for the Maternal AO, from more to less manifestations High > Intermediate > Low (p < 0,001). sFlt1/PlGF ratio has the highest AUC for predicting Maternal AO than any other single parameter. sFlt-1/PlGF ratio Placental insufficiency Adverse maternal outcome Preeclampsia Prediction Biomarker Figures Figure 1 Figure 2 Introduction Preeclampsia and intrauterine growth restriction (IUGR), are closely interlinked with placental malfunction. In both conditions the ratio of soluble fms-like tyrosine kinase 1 (sFlt-1) to placental growth factor (PlGF), sFlt-1/PlGF ratio, has emerged as an important surrogated determinant, able to herald adverse Pregnancy outcome (APO) and improve maternal and fetal prognosis ( 1 – 11 ). Despite several authors have proposed the use of a continuous scale ( 12 , 13 ), currently, most authors use specific cut-offs to evaluate the risk of APO, with values that range among 85 ( 6 , 13 – 15 ), 178 ( 16 ), 201 ( 7 , 10 , 17 ), 377 ( 18 ), 655 ( 7 – 9 , 17 ), 871 ( 19 ) and 1000 ( 20 ). Strikingly, when studying APO, most authors consider indistinctly and at unison the possibility of both maternal and fetal events. However, from a physiological point of view it would be interesting to distinguish whether the reasons that increase this ratio are related with complications at the maternal or conversely at fetal side, and whether the sFlt-1/PlGF ratio is in fact predicting adverse maternal or fetal events. In addition, it would be interesting to evaluate whether this prediction occurs in a stepwise manner, and if this was the case, which would be the point yielding the highest accuracy. Finally, it would be interesting to ascertain how the ratio compares with other parameters in the prediction of Maternal AO, and also whether the addition of clinical data by mean of multivariable analysis might be able to improve the accuracy as an isolate parameter ( 9 , 16 , 21 – 23 ). In this work, we evaluate how the sFlt-1/PlGF ratio works in the prediction of maternal adverse outcome, and evaluate the questions above mentioned related with this prediction. Material and methods Design and Setting. This is observational retrospective cohort study was conducted in Hospital Universitari i Politènic La FE Valencia, Spain. Inclusion criteria. Pregnant patients bearing singleton live fetuses between the 20 to 40 gestational week were selected according the levels of sFlt-1/PlGF, three groups: High ≥ 655, Intermediate ≥ 85 to < 655 and Low < 85. Patients were included, when the first determination was between the boundaries, but in the cases of the intermediate and the low provided that the following determinations did no crossed to the next upper or lower group. Exclusion criteria: Patients who did not comply with the inclusion criteria; when the information contained in the clinical history was insufficient. The study was approved by the Institutional Review Board of Instituto de Investigación Sanitaria La Fe (2018/0202), informed consent was obtained from all women. Definitions Pre-eclampsia as well as severe preeclampsia was defined according the ACOG ( 24 ), we also include as criteria the concomitant hypertension with Uteroplacental dysfunction (such as fetal growth restriction) ( 25 , 26 ). Fetal growth restriction was defined according the FIGO criteria ( 27 ). Risk factors for preeclampsia and placental dysfunction related disorders, were the ones proposed by the ACOG and NICE ( 24 , 28 ). We arbitrarily quantitate the PE risk by giving points: 1 to the low risk, 2 points if two or more low risk factors or one high risk, 3 when there is a diagnosis of PE or high risk plus a low or more low risk, 4 when a Maternal AO is already documented, based on the basic published estimations ( 29 , 30 ). Reasons for assessing the sFlt-1/PlGF ratio were clinical suspicion of placental dysfunction disorders ( 24 , 28 ) Data Collection, and Outcome Measures: data was retrieved from hospital records, gestational characteristics, including parity, number of gestations, and maternal ethnicity, age, weight and height… The Maternal AO was maternal mortality, pre-eclampsia with severe features, and any maternal condition precluding expectant management as defined by the ACOG ( 24 ). Maternal serum levels of sFlt-1 and PlGF were determined using the fully automated Elecsys sFlt-1 and Elecsys PlGF assays on the cobas® electrochemiluminescence immunoassay platform (Roche Diagnostics GmbH, Mannheim, Germany) ( 1 , 2 ). Statistical Analysis: Test selection was based on the evaluation of the variables for normal distribution. To determine the clinical utility of the sFlt1/PlGF ratio in prediction of Maternal AO, we used ROC analysis to determine the AUC of sFlt1/PlGF that would correctly classify the maximum number of participants and after obtaining a cut-off (Youden’s index) we calculate the sensitivity, specificity, positive and negative likelihood ratio. To develop a predictive model of Maternal AO, binary multivariate logistic regression was used considering maternal characteristics medical history and biomarkers as potential predictors. All statistical analyses were performed using IBM SPSS statistics 22 (SPSS Inc; Chicago, Illinois) significance was set at p < 0.05. Results A total of 180 women were chosen, 60 per group. The reason to request the angiogenic markers are depicted in Table 1 A, established PE was the main reason in Intermediate and High group, both different from the Low (p < 0.001). Maternal epidemiological and medical characteristics are shown in Table 1 B. The younger mothers were in the a priori defined as Intermediate and High sFlt-1/PlGF ratio levels vs Low level (p < 0,05). Table 1 Descriptive analysis of maternal characteristics. Parameter Low (A) intermediate (B) High (C) A-Indication for sFlt1/PlGF testing Preeclampsia 6 (10) 27 27 Hypertension 26 8 14 HELLP syndrome 0 0 2 Suspicion of preeclampsia 23 16 8 Thrombophilia 0 0 1 FGR 5 9 8 B-Maternal medical history (Mean ± SD / N (%) Maternal age 35,65 ± 5,59 33,17 ± 5,30 33,88 ± 5,81 Parity 0 25 (41.7) 25 (41.7) 29 (48.3%) 1 12 (20) 19 (31.7) 15 (25) 2 10 (16.7) 12 (20) 10 (16.7) ≥ 3 13 (21.7) 4 (6.7) 6 (10) Smoking 9 (15) 4 (6.7) 9 (15) Ethnicity Caucasic 50 (83.3) 45 (75) 46 (76.7) Latin 7 (11.7) 10 (16.7) 13 (21.7) Black 2 (3.3) 2 (3.3) 0 (0) North-African 1 (1.7) 3 (5) 1 (1.7) Assisted reproduction None IVF 7 (11.7) 9 (15) 8 (13.3) Ovodonation 7 (11.7) 1 (1.7) 1 (1.7) Diabetes None 53 51 52 Pregestational 7 (11.7) 8 (13.3) 4 (6.7) Gestational 0 (0) 1 (1.7) 4 (6.7) Thrombophilia 1 (1.7) 2 (3.3) 2 (3.3) Lupus 4 (6.7) 0 (0) 1 (1.7) Kidney disease 2 (3.3) 1 (1.7) 5 (8.3) Previous FGR 7 (11.7) 4 (6.7) 3 (5) Obesity 1 (1.7) 4 (6.7) 4 (6.7) COVID in pregnancy 1 (1.7) 1 (1.7) 1 (1.7) At entry the maternal medical parameter is shown in Table 2 , overall, a different statistical degree of severity is observed, High > Intermediate > Low, for: Systolic Blood pressure, Mean Blood pressure, Angiogenic markers, AST, ALT. Also a positive significant correlation coefficient was found between the sFlt-1/PlGF and the before mentioned variables. The association was not observed for Uric Acid or platelets (Table 3 ). Table 2 Biophical and biochemical parameters at study entry Parameter Low (A) intermediate (B) High (C) Maternal examination Blood test (week) 34,04 ± 4,12 32,31 ± 3,40 28,50 ± 3,47 Systolic BP 149,87 ± 17,43 153,18 ± 13,02 167,80 ± 27,45 Dyastolic BP 94,95 ± 16,14 98,38 ± 10,27 99,60 ± 14,70 MAP 113,26 ± 15,01 116,65 ± 9,70 122,23 ± 17,64 sFlt1/PlGF 37,17 ± 23,81 237,10 ± 143,15 996,08 ± 414,04 sFlt1 4813,22 ± 2785,36 11738,12 ± 6744,52 18881,28 ± 11566,23 PlGF 199,08 ± 168,15 58,26 ± 31,96 21,04 ± 14,94 Uric Acid 6,81 ± 6,16 5,95 ± 1,57 6,05 ± 1,23 AST 17,39 ± 10,55 28,10 ± 16,81 59,62 ± 107,39 ALT 17,69 ± 17,96 31,08 ± 32,30 67,59 ± 151,69 Platelets 225,34 ± 57,81 211,18 ± 69,04 201,82 ± 75,85 Table 3 Pearson correlation coefficients among angiogenic factor and clinical variables sFlt1/PlGF sFlt1 PlGF sFlt1/PlGF, N = 180 - 0.78, p < 0,001 -0.88, p < 0,001 sFlt1, N = 180 0.78, p < 0,001 - -0.39, p < 0,001 PIGF, N = 180 -0.88, p < 0,001 -0.39, p < 0,001 - Proteinuria, N = 151 0.3, p < 0,001 0.32 p < 0,001 -0.214, p < 0,001 Uric Acid, N = 118 -0.04, NS -0.1, NS -0.04, NS AST, N = 160 0.229, p < 0.01 0.28, p < 0.01 -0.11, NS ALT, N = 162 0.190, p < 0.05 0.26, p < 0.01 -0.07, NS Platelets, N = 163 -0.13 (NS) -0.21, p < 0.01 0.02, NS Systolic blood pressure, N = 179 0.34 p < 0.001 0.18, p < 0.05 -0.37, p < 0.001 Diastolic blood pressure, N = 179 0.18, p < 0.05 0.120, NS -0.17, p < 0.05 Mean arterial pressure, N = 179 0.28, p < 0.001 0.160, p < 0.05 -0.28, p < 0.001 Delivery week, N = 180 -0.71, p < 0.001 -0.45, p < 0.001 0.71, p < 0.001 Interval to delivery, N = 180 -0.53, p < 0.001 -0.47, p < 0.001 0.43, p < 0.001 Risk of preeclampsia, N = 180 0.28, p < 0.001 0.18, p < 0.05 -0.28, p Intermediate > Low (p Intermediate > High (p < 0,001) Table 4 . Table 4 Maternal outcome. Subsequent maternal evolution. Interval exam-delivery (days) 23,22 ± 26,88 13,00 ± 13,32 4,58 ± 8,02 Normal outcome 34 21 4 Chronic hypertension 6 2 0 Gestational hypertension 8 0 0 Preeclampsia 10 14 9 Severe preeclampsia* 2 17 34 HELLP syndrome* 0 4 13 Abruptio placentae* 0 2 0 Note : * The presence of any of these parameters defined the existence of adverse maternal outcome The sFlt-1/PlGF ratio median and QI (25 and 75) was (352,5–927) for all the analysed samples associated to Maternal AO and 71 (29–211) for those not associated to Maternal AO (Mann-Whitney, p < 0.0001). We compared the performance for prediction of maternal adverse outcome of the sFlt1/PlGF ratio, with other single parameters; all parameters studied had statistically lower AUC: PlGF, sFlt1, AST, ALT, Platelets, Systolic BP, PE Risk, Proteinuria, MAP, Diastolic BP, Uric Acid (Table 5 ). Furthermore, by binary logistic regression we explore several models for prediction of the Maternal AO, been the sFlt1/PlGF ratio AUC not significantly different from the best models obtained: Model 1, (PE risk, sFlt1/PlGF, Platelets) AUC = 0.84; 95% CI = 0.76–0.91, and Model 2, (PE Risk, sFlt1/PlGF, Uric Acid and AST) AUC = 0.82; 95% CI = 0.73–0.90), (Table 5 and Fig. 1 ), these models did not improve the accuracy of the sFlt1/PlGF ratio. Interestingly, PE risk and sFlt1/PlGF were independent factors in the two models obtained by logistic regression. Table 5 Receiver operating characteristic curves for prediction of maternal adverse outcome. The AUC and the 95% CI is given for each potential predictor. AUC 95% CI *p-value sFlt1/PlGF 0,88 0,82 to 0,92 - 1/PlGF 0,82 0,76 to 0,88 0,01 sFlt1 0,80 0,73 to 0,86 0,001 AST 0,69 0,58 to 0,80 0,01 ALT 0,64 0,53 to 0,75 0,001 Preeclampsia risk 0,64 0,57 to 0,71 0,0001 Platelets 0,61 0,49 to 0,73 0,001 Systolic blood pressure 0,63 0,54 to 0,71 0,0001 Proteinuria 0,60 0,49 to 0,71 0,0001 Mean arterial pressure 0,59 0,50 to 0,67 0,0001 Diastolic blood pressure 0,54 0,45 to 0,63 0,0001 Uric Acid 0,51 0,40 to 0,63 0,0001 Model 1 0,84 0,76 to 0,91 NS Model 2 0,82 0,73 to 0,90 NS Notes : Model 1 = Preeclampsia risk + sFlt1/PlGF ratio + Platelets, Model 2 = Preeclampsia risk + sFlt1/PlGF ratio + Uric acid + AST. *p-values represent the difference with the sFlt1/PlGF which is represents the control parameter. Among all our patients, to ascertain the diagnostic performance for maternal AO, we built a ROC for sFlt1/PlGF ratio, been the AUC = 0,88, 95% CI (0,82 − 0,92): the cut-off value with the highest accuracy (Younden’s Index, J) corresponded to a sFlt1/PlGF of 137. At this cut-off point the test showed a sensitivity of 93,15% (95% CI = 84,7–97,7), a specificity of 70,09 (95% CI = 60,5–78,6), a positive likehood ratio (+ LR) of 3.11 (95% CI = 2,3–4,2) and a negative likehood ratio 0,098 (0,04 − 0,2). Assuming a pre-test probability of 2%, the negative likehood ratio of 0.098, and the positive LR of 3.11, yielded a probability of 0.2% in case of a negative result and a probability of 7 in case of a positive result (Fig. 2 ) Comment Main finding. Our study shows that sFlt1/PlGF ratio can help in the estimation of Maternal AO. We observed a difference between the three sFlt-1/PlGF groups on the biochemical and clinical parameters studied, and a correlation between sFlt1/PlGF and the different parameters. As a unique marker, sFlt1/PlGF has the best diagnostic performance for maternal AO. Furthermore, in the two models obtained by logistic regression the PE risk and sFlt1/PlGF were in the equations outlining the importance of the angiogenic marker as well as the clinical findings. The cut point proposed 137, could be used to rule out Maternal AO since is negative LR is very low 0.098 but not for rule in, Positive LR 3.11. Results in the context of what is known. Two systematic reviews studies that explored univariate and multivariate predictors of adverse outcomes of preeclampsia, revealed inconsistent results, likely due to heterogeneity in study populations, outcome…. ( 31 , 32 ). Our results, are consistent with prior published data, that found the higher sFlt-1/PlGF the shorter the time to delivery ( 12 , 14 , 18 , 21 , 23 , 33 – 36 ), as well as worst Maternal AO ( 6 , 12 , 14 , 21 – 23 , 33 – 36 ). Some discrepancies can arise due to different methodological approaches, such the time considered the extend of pregnancy considered, before 34 weeks( 18 ), 35 weeks ( 14 ), 37 weeks ( 12 , 22 ) or during all length of pregnancy ( 6 , 16 , 21 – 23 , 33 – 36 ), what is considered maternal complications: PE severe features from ACOG, FullPIERS or others( 6 , 12 , 14 , 18 , 22 , 23 , 34 – 36 ), inclusion criteria risk suspicion ( 6 , 16 , 21 , 23 , 33 ) confirmed diagnosis ( 12 , 14 , 18 ) a combination of suspected and confirmed ( 22 , 34 , 36 ). Similarly to us, others have shown sFlt-1/PlGF ratio appears to be a better maker of AO than any isolated parameter either clinical or analytical, as shown by the greater AUC, ( 6 , 14 ). No agreeing with our results, several authors observed multivariate models, including sFlt-1/PlGF, clinical and other analytical markers, better predictor of AO ( 6 , 21 – 23 ), despite having similar AUC values to us. The source of the discrepancies has been pointed in the anterior paragraph. The consistency of these findings strongly indicate angiogenic biomarkers will be useful for risk stratification in the triage setting It has been proposed that women with sFlt1/PlGF values 85 women are very likely to have PE or another form of placental insufficiency; values > 655 (described as the third quartile sFlt1/PlGF of values in singleton pregnancies < 34 weeks)( 17 ) are associated closely with the need to deliver within 48 h as well as are associated with both maternal and neonatal AO( 1 , 7 , 8 , 17 ). Some authors have chosen this number “85” to ascertain the AO ( 12 , 14 , 34 ). In women presenting at less than 34 weeks, a cut point of 85 gave a sensitivity of 72.9% and specificity of 94.0% ( 6 ), our data give figures somehow different, our threshold is 137, with a sensitivity of 93.15, specificity 70.09, positive LR 3.11 and Negative LR 0.098. This Negative LR, based on Bayesian Fagan estimations ( 37 , 38 ) would decrease the post-test probability about ten times, assuming the incidence of AO about 2% ( 1 ). This is under this sFlt1/PlGF cut point 137, the sFlt1/PlGF low NLR makes the cut point very good at ruling out the maternal AO, but no for prognosis of Maternal AO. This to some extend support the statement of the NICE ( 39 ) the value of sFlt1/PlGF for ruling out the presence of the disease, but in our case to rule out maternal AO. The performance of our analysis is in the high range of the one observed in the meta analysis in which In nine studies that assessed adverse maternal or perinatal outcomes, the sFlt-1/PlGF ratio has a sensitivity and specificity of 55–92% and 70–98%, respectively, depending on the cut off value used and the population sampled, and the reported four studies reported AUC values that ranged from 0.65–0.95 ( 32 ). Our AUC were 0,88 (0,82 − 0,92), for maternal AO. Not including the sFlt1/PlGF von Dadelszen et al( 40 ) with the full PIERS model predicted adverse maternal outcomes within 48 h of study eligibility (AUC 0·88, 95% CI 0·84–0·92). The question is that we strongly suggest a value for rule out of severe maternal complications whereas von Dadelszen et al ( 40 ) is just for rule in. To date the addition of sFlt1:PlGF to full Piers remains to be more deeply studied, just one study has done it ( 18 ) concluding that sFlt-1/PlGF correlated more closely with number of adverse maternal outcomes than PIERS, and was a superior predictor of maternal complications, and the complementary use of the sFlt-1/PlGF ratio and PIERS was not found to improve accuracy of the prediction of maternal complications. Extreme high sFlt1/PlGF ratio values have reported also by Leanos Miranda et al 2020 ( 41 ), 610 ± 378 for severe PE and 764 ± 415 for HELLP syndrome and or eclampsia both different from mild or severe gestational hypertension, and mild PE. A controversial point is the point in which we can set the rule in, this is the point at which Maternal AO should be strongly suspected. Very high sFlt-1/PlGF ratios (of > 655 for early-onset and > 201 for late-onset preeclampsia) are thought to indicate a risk of short-term complications and need for delivery( 8 , 42 , 43 ), in fact they found above 655, these pregnancies developed any severe morbidity in 41.8% of cases, been HELLP 12% and Abruptio placentae 11%. Others proposed as cut point of 178 for predicting complications such as imminent delivery or fetal/neonatal death ( 16 , 33 ), Stolz et al ( 20 ) suggested as more useful a sFlt-1:PlGF ratio above 1000 for perinatal AO associated to preeclampsia. Interestingly Mirkovic et al ( 18 ) predicting maternal AO, sFlt-1/PlGF AUC 0.853 (95% CI 0⋅733–0⋅972;) been their proposed sFlt-1/PlGF cut-off of 377 optimal for predicting complications (75⋅0% sensitivity; 92⋅3% specificity). Although our sFlt-1/PlGF AUC are quite similar, the points we proposed are somehow different, the differences, although we both just analyzed Maternal AO, may be explained by several points: they analysed the outcome like the ones of the PIERS study, the clinical AO manifestation should occur within the seven days, but the patients they included were with diagnosis of early severe preeclampsia and between 24 and 34 weeks whereas we included suspected and diagnosis of placental insufficiency and from 20 weeks to term. Clinical implications. Despite consensus guidelines outlining indications for delivery in patients with preeclampsia, risk assessment remains challenging. This is because no one sign, symptom, or laboratory test has yet been shown to predict adverse outcomes with high accuracy. Nevertheless, the highest association of sFlt-1/PlGF compared with any other single marker for maternal serious morbidity as well as the increase performance when associated to clinical markers makes the sFlt-1/PlGF ratio an important tool in the prediction of adverse maternal outcome associated with the risk or established preeclampsia. But clinical markers should be taken into account as shown for the different studies. Due the differences in methodology, there are wide discrepancy in the cut points proposed for estimating maternal AO, it appears that for ruling out our data, 137 seems to be an optimum cut point, whereas the upper limit, this is, to rule in more studies should be undertaken. Research implications. Since many sFlt-1/PlGF cut point have been proposed to diagnose preeclampsia as well as their complications view as the changed according to gestational age ( 33 ) it will be of interest to normalized the values converting it to MoM as well as to standardize the outcomes, to have more robust cut points. As previously stated, the differences in methodology hampers to find stable values for estimation the risk of AO, and since the case of placental insufficiency the maternal and fetal interest may not go in parallel, studies should analyse both separate and together for better estimate the AO. Due to the heterogeneity of the outcomes analysed, the lack of power because the limited sample size and low prevalence of some adverse outcomes and in order to define a reliable cut point for Maternal AO an individual patient metanalysis of observational cohort’s studies should be undertaken. Strengths. - The stratification of the population into three groups and the wide values in the sample analysed of sFlt-1/PlGF, allowed us to identify more clearly the association of different Maternal AO as a function of the sFlt-1/PlGF. Including women with suspicion or confirmed placental dysfunction, enabled us to mimic the clinical real setting where the sFlt-1/PlGF ratio will be used. The consistent results with those obtained in the literature and not least the importance of including in the clinical manifestation or risk in the models predicting AO. Limitations. - This is a single-centre study with a relatively small sample size, due to the low prevalence of the maternal AO manifestation, our study was not powered to fully assess all of them. The problem of the low prevalence has been also proposed by others ( 16 ). A potential bias in this study is that clinicians were not blinded to the sFlt- 1/PlGF values; therefore, management could have been influenced by its result and by their experience with angiogenic biomarkers. This study as all the ones in the literature relies on absolute sFlt-1/PlGF ratio values, since the distribution of the values across pregnancy are curvilinear ( 44 , 45 ) it seems that it should be more appropriate to have been express in terms of MoM for gestational age Conclusion. The sFlt-1/PlGF ratio has a dose dependent relation with the severity of the Maternal AO and can be used in the clinical setting for the prognosis of preeclampsia but linked to the clinical profile. sFlt-1/PlGF ratio appears to be a better maker of AO than any isolated parameter either clinical or analytical. We propose the 137 cut off as the new cut off for ruling out the maternal AO in patients with placental insufficiency. Abbreviations 1 sFlt-1. soluble fms-like tyrosine kinase ACOG. American College of Obstetricians and Gynecologists AO. Adverse outcome APO. Adverse Pregnancy outcome AUC. Area under the Curve FIGO. International Federation of Gynecology and Obstetrics IUGR. Intrauterine growth restriction NICE. The National Institute for Health and Care Excellence PE. Preeclampsia PlGF. Placental growth factor ROC. Receiver operating characteristic curve Declarations Ethics approval and consent to participate. This study adhered to the Guidelines for Good Clinical Practice, complied with the guidelines for human studies and were conducted ethically in accordance with the World Medical Association Declaration of Helsinki, and approved by the Institutional Review Board of Instituto de Investigación Sanitaria La Fe (2018/0202) 07 11 2018. Informed consent was obtained from all women. Consent for publication. Not applicable. Availability of data and materials. data from the present study are available upon appropriate request to the corresponding author Competing interests A.P-M and M.M-T. Reports receiving lecture fees and serving on advisory boards from Roche Diagnostics All other authors, report no conflicts of interest. Funding. This study was supported by any funding Authors' contributions. All authors contributed to the conception and design of this study, were involved in the interpretation of the data, and the development and approval of the manuscript. F.F-C, M.M-T and A.A-R, performed the analytical test A.P-M, F.F-C, A.A-R, R.M-O, B.N-A, and B.M-P, extracted the data from the medical records A.P-M and J. M-R wrote and performed the data analyses Acknowledgements. The authors would like to thank all the women who participated in the study, the faculty, midwives and midwifery staff who supported the study. Authors' information (optional). This study is part of the doctoral thesis project of F.F-C References Zeisler H, Llurba E, Chantraine F, Vatish M, Staff AC, Sennstrom M, et al. Predictive Value of the sFlt-1:PlGF Ratio in Women with Suspected Preeclampsia. N Engl J Med. 2016;374(1):13–22. Perales A, Delgado JL, de la Calle M, Garcia-Hernandez JA, Escudero AI, Campillos JM, et al. sFlt-1/PlGF for prediction of early-onset pre-eclampsia: STEPS (Study of Early Pre-eclampsia in Spain). Ultrasound Obstet Gynecol. 2017;50(3):373–82. Raia-Barjat T, Prieux C, Gris JC, Chapelle C, Laporte S, Chauleur C. Angiogenic factors for prediction of preeclampsia and intrauterine growth restriction onset in high-risk women: AngioPred study. J Matern Fetal Neonatal Med. 2017:1–10. Zeisler H, Llurba E, Chantraine F, Vatish M, Staff A, Sennstrom M, et al. Soluble fms-Like Tyrosine Kinase-1-to-Placental Growth Factor Ratio and Time to Delivery in Women With Suspected Preeclampsia. Obstet Gynecol. 2016;128(2):261–9. Zeisler H, Llurba E, Chantraine FJ, Vatish M, Staff AC, Sennstrom M, et al. Soluble fms-like tyrosine kinase-1 to placental growth factor ratio: ruling out pre-eclampsia for up to 4 weeks and value of retesting. Ultrasound Obstet Gynecol. 2019;53(3):367–75. Rana S, Powe CE, Salahuddin S, Verlohren S, Perschel FH, Levine RJ, et al. Angiogenic factors and the risk of adverse outcomes in women with suspected preeclampsia. Circulation. 2012;125(7):911–9. Stepan H, Herraiz I, Schlembach D, Verlohren S, Brennecke S, Chantraine F, et al. Implementation of the sFlt-1/PlGF ratio for prediction and diagnosis of pre-eclampsia in singleton pregnancy: implications for clinical practice. Ultrasound Obstet Gynecol. 2015;45(3):241–6. Villalaín C, Herraiz I, Valle L, Mendoza M, Delgado JL, Vázquez-Fernández M, et al. Maternal and Perinatal Outcomes Associated With Extremely High Values for the sFlt-1 (Soluble fms-Like Tyrosine Kinase 1)/PlGF (Placental Growth Factor) Ratio. J Am Heart Assoc. 2020;9(7):e015548. Herraiz I, Llurba E, Verlohren S, Galindo A. Update on the Diagnosis and Prognosis of Preeclampsia with the Aid of the sFlt-1/ PlGF Ratio in Singleton Pregnancies. Fetal Diagn Ther. 2018;43(2):81–9. Herraiz I, Llurba E, Verlohren S, Galindo A. Update on the Diagnosis and Prognosis of Preeclampsia with the Aid of the sFlt-1/ PlGF Ratio in Singleton Pregnancies. Fetal Diagn Ther. 2017. Levine RJ, Maynard SE, Qian C, Lim KH, England LJ, Yu KF, et al. Circulating angiogenic factors and the risk of preeclampsia. N Engl J Med. 2004;350(7):672–83. Baltajian K, Bajracharya S, Salahuddin S, Berg AH, Geahchan C, Wenger JB, et al. Sequential plasma angiogenic factors levels in women with suspected preeclampsia. Am J Obstet Gynecol. 2016;215(1):89. .e1-.e10 . Saleh L, Vergouwe Y, van den Meiracker AH, Verdonk K, Russcher H, Bremer HA et al. Angiogenic Markers Predict Pregnancy Complications and Prolongation in Preeclampsia: Continuous Versus Cutoff Values. Hypertens. 2017. De Oliveira L, Peraçoli JC, Peraçoli MT, Korkes H, Zampieri G, Moron AF, et al. sFlt-1/PlGF ratio as a prognostic marker of adverse outcomes in women with early-onset preeclampsia. Pregnancy Hypertens. 2013;3(3):191–5. Verlohren S, Herraiz I, Lapaire O, Schlembach D, Zeisler H, Calda P, et al. New gestational phase-specific cutoff values for the use of the soluble fms-like tyrosine kinase-1/placental growth factor ratio as a diagnostic test for preeclampsia. Hypertension. 2014;63(2):346–52. Álvarez-Fernández I, Prieto B, Rodríguez V, Ruano Y, Escudero AI, Álvarez FV. N-terminal pro B-type natriuretic peptide and angiogenic biomarkers in the prognosis of adverse outcomes in women with suspected preeclampsia. Clin Chim Acta. 2016;463:150–7. Verlohren S, Herraiz I, Lapaire O, Schlembach D, Moertl M, Zeisler H, et al. The sFlt-1/PlGF ratio in different types of hypertensive pregnancy disorders and its prognostic potential in preeclamptic patients. Am J Obstet Gynecol. 2012;206(1):58e1–8. Mirkovic L, Tulic I, Stankovic S, Soldatovic I. Prediction of adverse maternal outcomes of early severe preeclampsia. Pregnancy Hypertens. 2020;22:144–50. Leaños-Miranda A, Campos-Galicia I, Ramírez-Valenzuela KL, Chinolla-Arellano ZL, Isordia-Salas I. Circulating angiogenic factors and urinary prolactin as predictors of adverse outcomes in women with preeclampsia. Hypertension. 2013;61(5):1118–25. Stolz M, Zeisler H, Heinzl F, Binder J, Farr A. sFlt-1:PlGF ratio of 655 is not a reliable cut-off value for predicting perinatal outcomes in women with preeclampsia. Pregnancy Hypertens. 2018;11:54–60. Moore AG, Young H, Keller JM, Ojo LR, Yan J, Simas TA, et al. Angiogenic biomarkers for prediction of maternal and neonatal complications in suspected preeclampsia. J Matern Fetal Neonatal Med. 2012;25(12):2651–7. Saleh L, Alblas MM, Nieboer D, Neuman RI, Vergouwe Y, Brussé IA, et al. Prediction of pre-eclampsia‐related complications in women with suspected or confirmed pre‐eclampsia: development and internal validation of clinical prediction model. Ultrasound Obstet Gynecol. 2021;58(5):698–704. Dröge LA, Perschel FH, Stütz N, Gafron A, Frank L, Busjahn A, et al. Prediction of Preeclampsia-Related Adverse Outcomes With the sFlt-1 (Soluble fms-Like Tyrosine Kinase 1)/PlGF (Placental Growth Factor)-Ratio in the Clinical Routine: A Real-World Study. Hypertension. 2021;77(2):461–71. ACOG Practice Bulletin No. 202: Gestational Hypertension and Preeclampsia. Obstet Gynecol. 2019;133(1):e1–e25. Brown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, et al. The hypertensive disorders of pregnancy: ISSHP classification, diagnosis & management recommendations for international practice. Pregnancy Hypertens. 2018;13:291–310. 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. Melamed N, Baschat A, Yinon Y, Athanasiadis A, Mecacci F, Figueras F, et al. FIGO (international Federation of Gynecology and obstetrics) initiative on fetal growth: best practice advice for screening, diagnosis, and management of fetal growth restriction. Int J Gynaecol Obstet. 2021;152(1):3–57. NICE. Hypertension in pregnancy: diagnosis and management NICE guideline. 2019 (www.nice.org.uk/guidance/ng133 ). National Collaborating Centre for Women's and Children's Health (UK). Hypertension in Pregnancy: The Management of Hypertensive Disorders During Pregnancy. London: RCOG Press; 2010 Aug. p. 22220321. LeFevre ML. Low-dose aspirin use for the prevention of morbidity and mortality from preeclampsia: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med2014. p. 819–26. Ukah UV, De Silva DA, Payne B, Magee LA, Hutcheon JA, Brown H et al. Prediction of adverse maternal outcomes from pre-eclampsia and other hypertensive disorders of pregnancy: A systematic review. Pregnancy Hypertens. 2017. Lim S, Li W, Kemper J, Nguyen A, Mol BW, Reddy M. Biomarkers and the Prediction of Adverse Outcomes in Preeclampsia: A Systematic Review and Meta-analysis. Obstet Gynecol. 2021;137(1):72–81. Álvarez-Fernández I, Prieto B, Rodríguez V, Ruano Y, Escudero AI, Álvarez FV. New biomarkers in diagnosis of early onset preeclampsia and imminent delivery prognosis. Clin Chem Lab Med. 2014;52(8):1159–68. Saleh L, Verdonk K, Jan Danser AH, Steegers EA, Russcher H, van den Meiracker AH, et al. The sFlt-1/PlGF ratio associates with prolongation and adverse outcome of pregnancy in women with (suspected) preeclampsia: analysis of a high-risk cohort. Eur J Obstet Gynecol Reprod Biol. 2016;199:121–6. Chelli D, Fau - Hamdi A, Hamdi A, Fau - Saoudi S, Saoudi S, Fau - Jenayah AA, Jenayah Aa Fau -, Zagre A, Zagre A, Fau - Jguerim H, Jguerim H, Fau - Bedis C, et al. Clinical Assessment of Soluble FMS-Like Tyrosine Kinase-1/Placental Growth Factor Ratio for the Diagnostic and the Prognosis of Preeclampsia in the Second Trimester. Clin Lab. 2017;62(10):1927–32. Leanos-Miranda A, Mendez-Aguilar F, Ramirez-Valenzuela KL, Serrano-Rodriguez M, Berumen-Lechuga G, Molina-Perez CJ, et al. Circulating angiogenic factors are related to the severity of gestational hypertension and preeclampsia, and their adverse outcomes. Medicine. 2017;96(4):e6005. Fagan TJ. Letter: Nomogram for Bayes theorem. N Engl J Med. 1975;293(5):257. Jaeschke R, Guyatt G, Sackett DL. Users' guides to the medical literature. III. How to use an article about a diagnostic test. A. Are the results of the study valid? Evidence-Based Medicine Working Group. JAMA. 1994;271(5):389–91. NICE. PlGF-based testing to help diagnose suspected preeclampsia (Triage PlGF test, Elecsys immunoassay sFlt-1/PlGF ratio, DELFIA Xpress PlGF 1-2-3 test, and BRAHMS sFlt-1 Kryptor/BRAHMS PlGF plus Kryptor PE ratio) Diagnostics guidance Published: 11 May 2016 wwwniceorguk/guidance/dg23. 2016. von Dadelszen P, Payne B, Li J, Ansermino JM, Broughton Pipkin F, Cote AM, et al. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model. Lancet. 2011;377(9761):219–27. Leaños-Miranda A, Graciela Nolasco-Leaños A, Ismael Carrillo-Juárez R, José Molina-Pérez C, Janet Sillas-Pardo L, Manuel Jiménez-Trejo L, et al. Usefulness of the sFlt-1/PlGF (Soluble fms-Like Tyrosine Kinase-1/Placental Growth Factor) Ratio in Diagnosis or Misdiagnosis in Women With Clinical Diagnosis of Preeclampsia. Hypertension. 2020;76(3):892–900. Verlohren S, Herraiz I, Lapaire O, Schlembach D, Zeisler H, Calda P, et al. Risk Stratification of Hypertensive Pregnancy Disorders. Eur Obst Gynaecol. 2012;7(1):14–7. Herraiz I, Simon E, Gomez-Arriaga PI, Quezada MS, Garcia-Burguillo A, Lopez-Jimenez EA, et al. Clinical implementation of the sFlt-1/PlGF ratio to identify preeclampsia and fetal growth restriction: A prospective cohort study. Pregnancy Hypertensi. 2018;13:279–85. Verlohren S, Galindo A, Schlembach D, Zeisler H, Herraiz I, Moertl MG, et al. An automated method for the determination of the sFlt-1/PIGF ratio in the assessment of preeclampsia. Am J Obstet Gynecol. 2010;202(2):161. e1- e11. De La Calle M, Delgado JL, Verlohren S, Escudero AI, Bartha JL, Campillos JM, et al. Gestational Age-Specific Reference Ranges for the sFlt-1/PlGF Immunoassay Ratio in Twin Pregnancies. Fetal Diagn Ther. 2021;48(4):288–96. Additional Declarations Competing interest reported. A.P-M and M.M-T. Reports receiving lecture fees and serving on advisory boards from Roche Diagnostics. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3166567","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":228542148,"identity":"a5d84818-bee7-470c-a7f0-2f3760ba2b38","order_by":0,"name":"Alfredo Perales-Marín","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACdgglA8SMD4jTwgyheEBMA5K1sEkQpYO/mYHtwcc9djz8/KfTKn5UbEtsYG/H70CJwwzshjOeJfNIzsjddrPnzO3EBp4zBBx4mIFNmucAM4/BDd5ttxnbbhszSOTg1yEP0vLnQD2P/fmz24rBWuSf43eYAUgLw4HDPAYMuduYgVrkGCQY8DvM8DBju2HPgeM8EjdyN0sC/SLHxpODX4vc8eZjD34cqJbj7z+78cOPits8/OzH8TsMGOdtqHw2AuqJVTMKRsEoGAUjGgAA+wlA9tD6ViQAAAAASUVORK5CYII=","orcid":"","institution":"Hospital Universitari i Politècnic La Fe","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Alfredo","middleName":"","lastName":"Perales-Marín","suffix":""},{"id":228542149,"identity":"5f9fab92-3b99-42ae-978d-c30691f0d9fd","order_by":1,"name":"Francis Fernández-de-la-Cruz","email":"","orcid":"","institution":"Hospital Universitari i Politècnic La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Francis","middleName":"","lastName":"Fernández-de-la-Cruz","suffix":""},{"id":228542150,"identity":"beff11e4-b85b-4554-882c-9e5e11d7aed5","order_by":2,"name":"Marisa Martínez-Triguero","email":"","orcid":"","institution":"Hospital Universitari i Politècnic La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marisa","middleName":"","lastName":"Martínez-Triguero","suffix":""},{"id":228542151,"identity":"ef55c9f2-14e6-4963-b0b4-71e49cc85da0","order_by":3,"name":"Amparo Alba-Redondo","email":"","orcid":"","institution":"Hospital Universitari i Politècnic La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Amparo","middleName":"","lastName":"Alba-Redondo","suffix":""},{"id":228542152,"identity":"75125272-26cb-44fb-a99b-ed3506285741","order_by":4,"name":"Rogelio Monfort-Ortiz","email":"","orcid":"","institution":"Hospital Universitari i Politècnic La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rogelio","middleName":"","lastName":"Monfort-Ortiz","suffix":""},{"id":228542154,"identity":"9ef53ebb-fad6-42b6-9e56-f629995b722d","order_by":5,"name":"Blanca Novillo-del-Álamo","email":"","orcid":"","institution":"Hospital Universitari i Politècnic La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Blanca","middleName":"","lastName":"Novillo-del-Álamo","suffix":""},{"id":228542157,"identity":"63d4b4de-fe98-4c97-aed6-93634778f776","order_by":6,"name":"Beatriz Marcos-Puig","email":"","orcid":"","institution":"Hospital Universitari i Politècnic La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Beatriz","middleName":"","lastName":"Marcos-Puig","suffix":""},{"id":228542160,"identity":"0cfa58c0-e1ee-4968-b8f9-913c4fccd9ca","order_by":7,"name":"José Morales-Roselló","email":"","orcid":"","institution":"Hospital Universitari i Politècnic La Fe","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"José","middleName":"","lastName":"Morales-Roselló","suffix":""}],"badges":[],"createdAt":"2023-07-13 09:29:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3166567/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3166567/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42247994,"identity":"91da3d75-c969-4a39-8e0d-2715417e3fca","added_by":"auto","created_at":"2023-08-28 14:27:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14413,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve for the diagnosis of adverse maternal outcome using the sFlt1/PlGF ratio (AUC=0,85; 95% CI=0,82-0,92) and the two multivariable models described in the text.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3166567/v1/89d3325842960b2d52fc2ace.png"},{"id":42247995,"identity":"a5d8f34e-2352-4b8a-b69e-affed9134dfd","added_by":"auto","created_at":"2023-08-28 14:27:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":350604,"visible":true,"origin":"","legend":"\u003cp\u003eFagan nomogram using a sFlt/PlGF cut-off value of 137. Assuming a pre-test probability of 2%, the negative likehood ratio of 0.098, and the positive LR of 3.11, yielded a probability of 0.2 % in case of a negative result and a probability of 7 in case of a positive result.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3166567/v1/7d8b82c680738531ea63f783.png"},{"id":44845511,"identity":"9c7aacb7-b570-4c6e-9e2c-a8eca0ca5581","added_by":"auto","created_at":"2023-10-18 11:23:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":635956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3166567/v1/4b3ecad1-4cea-42af-8811-19adc5d9a735.pdf"}],"financialInterests":"Competing interest reported. A.P-M and M.M-T. Reports receiving lecture fees and serving on advisory boards from Roche Diagnostics.","formattedTitle":"The sFlt-1/PlGF ratio as predictor of Maternal adverse outcome in patients with suspected or placental insufficiency","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePreeclampsia and intrauterine growth restriction (IUGR), are closely interlinked with placental malfunction. In both conditions the ratio of soluble fms-like tyrosine kinase 1 (sFlt-1) to placental growth factor (PlGF), sFlt-1/PlGF ratio, has emerged as an important surrogated determinant, able to herald adverse Pregnancy outcome (APO) and improve maternal and fetal prognosis (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite several authors have proposed the use of a continuous scale (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), currently, most authors use specific cut-offs to evaluate the risk of APO, with values that range among 85 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), 178 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), 201 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), 377 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), 655 (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), 871 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and 1000 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStrikingly, when studying APO, most authors consider indistinctly and at unison the possibility of both maternal and fetal events. However, from a physiological point of view it would be interesting to distinguish whether the reasons that increase this ratio are related with complications at the maternal or conversely at fetal side, and whether the sFlt-1/PlGF ratio is in fact predicting adverse maternal or fetal events.\u003c/p\u003e \u003cp\u003eIn addition, it would be interesting to evaluate whether this prediction occurs in a stepwise manner, and if this was the case, which would be the point yielding the highest accuracy.\u003c/p\u003e \u003cp\u003eFinally, it would be interesting to ascertain how the ratio compares with other parameters in the prediction of Maternal AO, and also whether the addition of clinical data by mean of multivariable analysis might be able to improve the accuracy as an isolate parameter (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this work, we evaluate how the sFlt-1/PlGF ratio works in the prediction of maternal adverse outcome, and evaluate the questions above mentioned related with this prediction.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eDesign and Setting. This is observational retrospective cohort study was conducted in Hospital Universitari i Polit\u0026egrave;nic La FE Valencia, Spain.\u003c/p\u003e \u003cp\u003eInclusion criteria. Pregnant patients bearing singleton live fetuses between the 20 to 40 gestational week were selected according the levels of sFlt-1/PlGF, three groups: High\u0026thinsp;\u0026ge;\u0026thinsp;655, Intermediate\u0026thinsp;\u0026ge;\u0026thinsp;85 to \u0026lt;\u0026thinsp;655 and Low\u0026thinsp;\u0026lt;\u0026thinsp;85. Patients were included, when the first determination was between the boundaries, but in the cases of the intermediate and the low provided that the following determinations did no crossed to the next upper or lower group. Exclusion criteria: Patients who did not comply with the inclusion criteria; when the information contained in the clinical history was insufficient. The study was approved by the Institutional Review Board of Instituto de Investigaci\u0026oacute;n Sanitaria La Fe (2018/0202), informed consent was obtained from all women.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDefinitions\u003c/strong\u003e \u003cp\u003ePre-eclampsia as well as severe preeclampsia was defined according the ACOG (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), we also include as criteria the concomitant hypertension with Uteroplacental dysfunction (such as fetal growth restriction) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Fetal growth restriction was defined according the FIGO criteria (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Risk factors for preeclampsia and placental dysfunction related disorders, were the ones proposed by the ACOG and NICE (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). We arbitrarily quantitate the PE risk by giving points: 1 to the low risk, 2 points if two or more low risk factors or one high risk, 3 when there is a diagnosis of PE or high risk plus a low or more low risk, 4 when a Maternal AO is already documented, based on the basic published estimations (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Reasons for assessing the sFlt-1/PlGF ratio were clinical suspicion of placental dysfunction disorders (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eData Collection, and Outcome Measures: data was retrieved from hospital records, gestational characteristics, including parity, number of gestations, and maternal ethnicity, age, weight and height\u0026hellip; The Maternal AO was maternal mortality, pre-eclampsia with severe features, and any maternal condition precluding expectant management as defined by the ACOG (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Maternal serum levels of sFlt-1 and PlGF were determined using the fully automated Elecsys sFlt-1 and Elecsys PlGF assays on the cobas\u0026reg; electrochemiluminescence immunoassay platform (Roche Diagnostics GmbH, Mannheim, Germany) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStatistical Analysis: Test selection was based on the evaluation of the variables for normal distribution. To determine the clinical utility of the sFlt1/PlGF ratio in prediction of Maternal AO, we used ROC analysis to determine the AUC of sFlt1/PlGF that would correctly classify the maximum number of participants and after obtaining a cut-off (Youden\u0026rsquo;s index) we calculate the sensitivity, specificity, positive and negative likelihood ratio. To develop a predictive model of Maternal AO, binary multivariate logistic regression was used considering maternal characteristics medical history and biomarkers as potential predictors. All statistical analyses were performed using IBM SPSS statistics 22 (SPSS Inc; Chicago, Illinois) significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 180 women were chosen, 60 per group. The reason to request the angiogenic markers are depicted in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, established PE was the main reason in Intermediate and High group, both different from the Low (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eMaternal epidemiological and medical characteristics are shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB. The younger mothers were in the a priori defined as Intermediate and High sFlt-1/PlGF ratio levels vs Low level (p\u0026thinsp;\u0026lt;\u0026thinsp;0,05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive analysis of maternal characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eintermediate (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh (C)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA-Indication for sFlt1/PlGF testing\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHELLP syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspicion of preeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombophilia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB-Maternal medical history (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD / N (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35,65\u0026thinsp;\u0026plusmn;\u0026thinsp;5,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,17\u0026thinsp;\u0026plusmn;\u0026thinsp;5,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33,88\u0026thinsp;\u0026plusmn;\u0026thinsp;5,81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaucasic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (76.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (21.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-African\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssisted reproduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (13.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOvodonation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePregestational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombophilia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLupus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (8.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious FGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (6.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOVID in pregnancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt entry the maternal medical parameter is shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, overall, a different statistical degree of severity is observed, High\u0026thinsp;\u0026gt;\u0026thinsp;Intermediate\u0026thinsp;\u0026gt;\u0026thinsp;Low, for: Systolic Blood pressure, Mean Blood pressure, Angiogenic markers, AST, ALT. Also a positive significant correlation coefficient was found between the sFlt-1/PlGF and the before mentioned variables. The association was not observed for Uric Acid or platelets (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiophical and biochemical parameters at study entry\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eintermediate (B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh (C)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMaternal examination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood test (week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e34,04\u0026thinsp;\u0026plusmn;\u0026thinsp;4,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e32,31\u0026thinsp;\u0026plusmn;\u0026thinsp;3,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e28,50\u0026thinsp;\u0026plusmn;\u0026thinsp;3,47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e149,87\u0026thinsp;\u0026plusmn;\u0026thinsp;17,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e153,18\u0026thinsp;\u0026plusmn;\u0026thinsp;13,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e167,80\u0026thinsp;\u0026plusmn;\u0026thinsp;27,45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyastolic BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e94,95\u0026thinsp;\u0026plusmn;\u0026thinsp;16,14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e98,38\u0026thinsp;\u0026plusmn;\u0026thinsp;10,27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e99,60\u0026thinsp;\u0026plusmn;\u0026thinsp;14,70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e113,26\u0026thinsp;\u0026plusmn;\u0026thinsp;15,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e116,65\u0026thinsp;\u0026plusmn;\u0026thinsp;9,70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e122,23\u0026thinsp;\u0026plusmn;\u0026thinsp;17,64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esFlt1/PlGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e37,17\u0026thinsp;\u0026plusmn;\u0026thinsp;23,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e237,10\u0026thinsp;\u0026plusmn;\u0026thinsp;143,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e996,08\u0026thinsp;\u0026plusmn;\u0026thinsp;414,04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esFlt1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4813,22\u0026thinsp;\u0026plusmn;\u0026thinsp;2785,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11738,12\u0026thinsp;\u0026plusmn;\u0026thinsp;6744,52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e18881,28\u0026thinsp;\u0026plusmn;\u0026thinsp;11566,23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e199,08\u0026thinsp;\u0026plusmn;\u0026thinsp;168,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e58,26\u0026thinsp;\u0026plusmn;\u0026thinsp;31,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e21,04\u0026thinsp;\u0026plusmn;\u0026thinsp;14,94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6,81\u0026thinsp;\u0026plusmn;\u0026thinsp;6,16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5,95\u0026thinsp;\u0026plusmn;\u0026thinsp;1,57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6,05\u0026thinsp;\u0026plusmn;\u0026thinsp;1,23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17,39\u0026thinsp;\u0026plusmn;\u0026thinsp;10,55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e28,10\u0026thinsp;\u0026plusmn;\u0026thinsp;16,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e59,62\u0026thinsp;\u0026plusmn;\u0026thinsp;107,39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17,69\u0026thinsp;\u0026plusmn;\u0026thinsp;17,96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e31,08\u0026thinsp;\u0026plusmn;\u0026thinsp;32,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e67,59\u0026thinsp;\u0026plusmn;\u0026thinsp;151,69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e225,34\u0026thinsp;\u0026plusmn;\u0026thinsp;57,81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e211,18\u0026thinsp;\u0026plusmn;\u0026thinsp;69,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e201,82\u0026thinsp;\u0026plusmn;\u0026thinsp;75,85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation coefficients among angiogenic factor and clinical variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003esFlt1/PlGF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003esFlt1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlGF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esFlt1/PlGF, N\u0026thinsp;=\u0026thinsp;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esFlt1, N\u0026thinsp;=\u0026thinsp;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIGF, N\u0026thinsp;=\u0026thinsp;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinuria, N\u0026thinsp;=\u0026thinsp;151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32 p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.214, p\u0026thinsp;\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid, N\u0026thinsp;=\u0026thinsp;118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04, NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1, NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04, NS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, N\u0026thinsp;=\u0026thinsp;160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.229, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11, NS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, N\u0026thinsp;=\u0026thinsp;162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.190, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07, NS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, N\u0026thinsp;=\u0026thinsp;163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.13 (NS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02, NS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, N\u0026thinsp;=\u0026thinsp;179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34 p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure, N\u0026thinsp;=\u0026thinsp;179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.120, NS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean arterial pressure, N\u0026thinsp;=\u0026thinsp;179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.160, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelivery week, N\u0026thinsp;=\u0026thinsp;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval to delivery, N\u0026thinsp;=\u0026thinsp;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk of preeclampsia, N\u0026thinsp;=\u0026thinsp;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Maternal AO observed in our study were: Severe PE, HELLP and Abruptio. We can establish a potency order for the Maternal AO, from more to less AO manifestations High\u0026thinsp;\u0026gt;\u0026thinsp;Intermediate\u0026thinsp;\u0026gt;\u0026thinsp;Low (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001 for each comparison). The interval between the exam (sFlt-1/PlGF determinarion) to delivery was inverse to the severity of the sFlt-1/PlGF risk level, Low\u0026thinsp;\u0026gt;\u0026thinsp;Intermediate\u0026thinsp;\u0026gt;\u0026thinsp;High (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001) Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaternal outcome. Subsequent maternal evolution.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval exam-delivery (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,22\u0026thinsp;\u0026plusmn;\u0026thinsp;26,88\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,00\u0026thinsp;\u0026plusmn;\u0026thinsp;13,32\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,58\u0026thinsp;\u0026plusmn;\u0026thinsp;8,02\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal outcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere preeclampsia*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHELLP syndrome*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbruptio placentae*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNote\u003c/span\u003e: * The presence of any of these parameters defined the existence of adverse maternal outcome\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe sFlt-1/PlGF ratio median and QI (25 and 75) was (352,5\u0026ndash;927) for all the analysed samples associated to Maternal AO and 71 (29\u0026ndash;211) for those not associated to Maternal AO (Mann-Whitney, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eWe compared the performance for prediction of maternal adverse outcome of the sFlt1/PlGF ratio, with other single parameters; all parameters studied had statistically lower AUC: PlGF, sFlt1, AST, ALT, Platelets, Systolic BP, PE Risk, Proteinuria, MAP, Diastolic BP, Uric Acid (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Furthermore, by binary logistic regression we explore several models for prediction of the Maternal AO, been the sFlt1/PlGF ratio AUC not significantly different from the best models obtained: Model 1, (PE risk, sFlt1/PlGF, Platelets) AUC\u0026thinsp;=\u0026thinsp;0.84; 95% CI\u0026thinsp;=\u0026thinsp;0.76\u0026ndash;0.91, and Model 2, (PE Risk, sFlt1/PlGF, Uric Acid and AST) AUC\u0026thinsp;=\u0026thinsp;0.82; 95% CI\u0026thinsp;=\u0026thinsp;0.73\u0026ndash;0.90), (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), these models did not improve the accuracy of the sFlt1/PlGF ratio. Interestingly, PE risk and sFlt1/PlGF were independent factors in the two models obtained by logistic regression.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReceiver operating characteristic curves for prediction of maternal adverse outcome. The AUC and the 95% CI is given for each potential predictor.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%\u0026nbsp;CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esFlt1/PlGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,82 to 0,92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1/PlGF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,76\u0026nbsp;to\u0026nbsp;0,88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esFlt1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,73\u0026nbsp;to\u0026nbsp;0,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,58\u0026nbsp;to\u0026nbsp;0,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,53\u0026nbsp;to\u0026nbsp;0,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreeclampsia risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,57 to 0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,49\u0026nbsp;to\u0026nbsp;0,73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,54\u0026nbsp;to\u0026nbsp;0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,49\u0026nbsp;to\u0026nbsp;0,71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean arterial pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,50\u0026nbsp;to\u0026nbsp;0,67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,45\u0026nbsp;to\u0026nbsp;0,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric Acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,40\u0026nbsp;to\u0026nbsp;0,63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,76 to 0,91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,73 to 0,90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNotes\u003c/span\u003e: Model 1\u0026thinsp;=\u0026thinsp;Preeclampsia risk\u0026thinsp;+\u0026thinsp;sFlt1/PlGF ratio\u0026thinsp;+\u0026thinsp;Platelets, Model 2\u0026thinsp;=\u0026thinsp;Preeclampsia risk\u0026thinsp;+\u0026thinsp;sFlt1/PlGF ratio\u0026thinsp;+\u0026thinsp;Uric acid\u0026thinsp;+\u0026thinsp;AST. *p-values represent the difference with the sFlt1/PlGF which is represents the control parameter.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong all our patients, to ascertain the diagnostic performance for maternal AO, we built a ROC for sFlt1/PlGF ratio, been the AUC\u0026thinsp;=\u0026thinsp;0,88, 95% CI (0,82\u0026thinsp;\u0026minus;\u0026thinsp;0,92): the cut-off value with the highest accuracy (Younden\u0026rsquo;s Index, J) corresponded to a sFlt1/PlGF of 137. At this cut-off point the test showed a sensitivity of 93,15% (95% CI\u0026thinsp;=\u0026thinsp;84,7\u0026ndash;97,7), a specificity of 70,09 (95% CI\u0026thinsp;=\u0026thinsp;60,5\u0026ndash;78,6), a positive likehood ratio (+\u0026thinsp;LR) of 3.11 (95% CI\u0026thinsp;=\u0026thinsp;2,3\u0026ndash;4,2) and a negative likehood ratio 0,098 (0,04\u0026thinsp;\u0026minus;\u0026thinsp;0,2). Assuming a pre-test probability of 2%, the negative likehood ratio of 0.098, and the positive LR of 3.11, yielded a probability of 0.2% in case of a negative result and a probability of 7 in case of a positive result (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Comment","content":"\u003cp\u003eMain finding. Our study shows that sFlt1/PlGF ratio can help in the estimation of Maternal AO. We observed a difference between the three sFlt-1/PlGF groups on the biochemical and clinical parameters studied, and a correlation between sFlt1/PlGF and the different parameters. As a unique marker, sFlt1/PlGF has the best diagnostic performance for maternal AO. Furthermore, in the two models obtained by logistic regression the PE risk and sFlt1/PlGF were in the equations outlining the importance of the angiogenic marker as well as the clinical findings. The cut point proposed 137, could be used to rule out Maternal AO since is negative LR is very low 0.098 but not for rule in, Positive LR 3.11.\u003c/p\u003e \u003cp\u003eResults in the context of what is known. Two systematic reviews studies that explored univariate and multivariate predictors of adverse outcomes of preeclampsia, revealed inconsistent results, likely due to heterogeneity in study populations, outcome\u0026hellip;. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results, are consistent with prior published data, that found the higher sFlt-1/PlGF the shorter the time to delivery (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), as well as worst Maternal AO (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Some discrepancies can arise due to different methodological approaches, such the time considered the extend of pregnancy considered, before 34 weeks(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), 35 weeks (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), 37 weeks (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) or during all length of pregnancy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), what is considered maternal complications: PE severe features from ACOG, FullPIERS or others(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), inclusion criteria risk suspicion (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) confirmed diagnosis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) a combination of suspected and confirmed (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly to us, others have shown sFlt-1/PlGF ratio appears to be a better maker of AO than any isolated parameter either clinical or analytical, as shown by the greater AUC, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). No agreeing with our results, several authors observed multivariate models, including sFlt-1/PlGF, clinical and other analytical markers, better predictor of AO (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), despite having similar AUC values to us. The source of the discrepancies has been pointed in the anterior paragraph. The consistency of these findings strongly indicate angiogenic biomarkers will be useful for risk stratification in the triage setting\u003c/p\u003e \u003cp\u003eIt has been proposed that women with sFlt1/PlGF values\u0026thinsp;\u0026lt;\u0026thinsp;85 are without AO risk of PE, whereas if\u0026thinsp;\u0026gt;\u0026thinsp;85 women are very likely to have PE or another form of placental insufficiency; values\u0026thinsp;\u0026gt;\u0026thinsp;655 (described as the third quartile sFlt1/PlGF of values in singleton pregnancies\u0026thinsp;\u0026lt;\u0026thinsp;34 weeks)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) are associated closely with the need to deliver within 48 h as well as are associated with both maternal and neonatal AO(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome authors have chosen this number \u0026ldquo;85\u0026rdquo; to ascertain the AO (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). In women presenting at less than 34 weeks, a cut point of 85 gave a sensitivity of 72.9% and specificity of 94.0% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), our data give figures somehow different, our threshold is 137, with a sensitivity of 93.15, specificity 70.09, positive LR 3.11 and Negative LR 0.098. This Negative LR, based on Bayesian Fagan estimations (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) would decrease the post-test probability about ten times, assuming the incidence of AO about 2% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This is under this sFlt1/PlGF cut point 137, the sFlt1/PlGF low NLR makes the cut point very good at ruling out the maternal AO, but no for prognosis of Maternal AO. This to some extend support the statement of the NICE (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) the value of sFlt1/PlGF for ruling out the presence of the disease, but in our case to rule out maternal AO.\u003c/p\u003e \u003cp\u003eThe performance of our analysis is in the high range of the one observed in the meta analysis in which In nine studies that assessed adverse maternal or perinatal outcomes, the sFlt-1/PlGF ratio has a sensitivity and specificity of 55\u0026ndash;92% and 70\u0026ndash;98%, respectively, depending on the cut off value used and the population sampled, and the reported four studies reported AUC values that ranged from 0.65\u0026ndash;0.95 (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Our AUC were 0,88 (0,82\u0026thinsp;\u0026minus;\u0026thinsp;0,92), for maternal AO.\u003c/p\u003e \u003cp\u003eNot including the sFlt1/PlGF von Dadelszen et al(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) with the full PIERS model predicted adverse maternal outcomes within 48 h of study eligibility (AUC 0\u0026middot;88, 95% CI 0\u0026middot;84\u0026ndash;0\u0026middot;92). The question is that we strongly suggest a value for rule out of severe maternal complications whereas von Dadelszen et al (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) is just for rule in. To date the addition of sFlt1:PlGF to full Piers remains to be more deeply studied, just one study has done it (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) concluding that sFlt-1/PlGF correlated more closely with number of adverse maternal outcomes than PIERS, and was a superior predictor of maternal complications, and the complementary use of the sFlt-1/PlGF ratio and PIERS was not found to improve accuracy of the prediction of maternal complications.\u003c/p\u003e \u003cp\u003eExtreme high sFlt1/PlGF ratio values have reported also by Leanos Miranda et al 2020 (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), 610\u0026thinsp;\u0026plusmn;\u0026thinsp;378 for severe PE and 764\u0026thinsp;\u0026plusmn;\u0026thinsp;415 for HELLP syndrome and or eclampsia both different from mild or severe gestational hypertension, and mild PE. A controversial point is the point in which we can set the rule in, this is the point at which Maternal AO should be strongly suspected. Very high sFlt-1/PlGF ratios (of \u0026gt;\u0026thinsp;655 for early-onset and \u0026gt;\u0026thinsp;201 for late-onset preeclampsia) are thought to indicate a risk of short-term complications and need for delivery(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), in fact they found above 655, these pregnancies developed any severe morbidity in 41.8% of cases, been HELLP 12% and Abruptio placentae 11%. Others proposed as cut point of 178 for predicting complications such as imminent delivery or fetal/neonatal death (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), Stolz et al (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) suggested as more useful a sFlt-1:PlGF ratio above 1000 for perinatal AO associated to preeclampsia.\u003c/p\u003e \u003cp\u003eInterestingly Mirkovic et al (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) predicting maternal AO, sFlt-1/PlGF AUC 0.853 (95% CI 0\u0026sdot;733\u0026ndash;0\u0026sdot;972;) been their proposed sFlt-1/PlGF cut-off of 377 optimal for predicting complications (75\u0026sdot;0% sensitivity; 92\u0026sdot;3% specificity). Although our sFlt-1/PlGF AUC are quite similar, the points we proposed are somehow different, the differences, although we both just analyzed Maternal AO, may be explained by several points: they analysed the outcome like the ones of the PIERS study, the clinical AO manifestation should occur within the seven days, but the patients they included were with diagnosis of early severe preeclampsia and between 24 and 34 weeks whereas we included suspected and diagnosis of placental insufficiency and from 20 weeks to term.\u003c/p\u003e \u003cp\u003eClinical implications. Despite consensus guidelines outlining indications for delivery in patients with preeclampsia, risk assessment remains challenging. This is because no one sign, symptom, or laboratory test has yet been shown to predict adverse outcomes with high accuracy. Nevertheless, the highest association of sFlt-1/PlGF compared with any other single marker for maternal serious morbidity as well as the increase performance when associated to clinical markers makes the sFlt-1/PlGF ratio an important tool in the prediction of adverse maternal outcome associated with the risk or established preeclampsia. But clinical markers should be taken into account as shown for the different studies.\u003c/p\u003e \u003cp\u003eDue the differences in methodology, there are wide discrepancy in the cut points proposed for estimating maternal AO, it appears that for ruling out our data, 137 seems to be an optimum cut point, whereas the upper limit, this is, to rule in more studies should be undertaken.\u003c/p\u003e \u003cp\u003eResearch implications. Since many sFlt-1/PlGF cut point have been proposed to diagnose preeclampsia as well as their complications view as the changed according to gestational age (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) it will be of interest to normalized the values converting it to MoM as well as to standardize the outcomes, to have more robust cut points.\u003c/p\u003e \u003cp\u003eAs previously stated, the differences in methodology hampers to find stable values for estimation the risk of AO, and since the case of placental insufficiency the maternal and fetal interest may not go in parallel, studies should analyse both separate and together for better estimate the AO.\u003c/p\u003e \u003cp\u003eDue to the heterogeneity of the outcomes analysed, the lack of power because the limited sample size and low prevalence of some adverse outcomes and in order to define a reliable cut point for Maternal AO an individual patient metanalysis of observational cohort\u0026rsquo;s studies should be undertaken.\u003c/p\u003e \u003cp\u003eStrengths. - The stratification of the population into three groups and the wide values in the sample analysed of sFlt-1/PlGF, allowed us to identify more clearly the association of different Maternal AO as a function of the sFlt-1/PlGF. Including women with suspicion or confirmed placental dysfunction, enabled us to mimic the clinical real setting where the sFlt-1/PlGF ratio will be used. The consistent results with those obtained in the literature and not least the importance of including in the clinical manifestation or risk in the models predicting AO.\u003c/p\u003e \u003cp\u003eLimitations. - This is a single-centre study with a relatively small sample size, due to the low prevalence of the maternal AO manifestation, our study was not powered to fully assess all of them. The problem of the low prevalence has been also proposed by others (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). A potential bias in this study is that clinicians were not blinded to the sFlt- 1/PlGF values; therefore, management could have been influenced by its result and by their experience with angiogenic biomarkers. This study as all the ones in the literature relies on absolute sFlt-1/PlGF ratio values, since the distribution of the values across pregnancy are curvilinear (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) it seems that it should be more appropriate to have been express in terms of MoM for gestational age\u003c/p\u003e \u003cp\u003eConclusion. The sFlt-1/PlGF ratio has a dose dependent relation with the severity of the Maternal AO and can be used in the clinical setting for the prognosis of preeclampsia but linked to the clinical profile. sFlt-1/PlGF ratio appears to be a better maker of AO than any isolated parameter either clinical or analytical. We propose the 137 cut off as the new cut off for ruling out the maternal AO in patients with placental insufficiency.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003e1 sFlt-1.\u0026nbsp;\u003c/strong\u003esoluble fms-like tyrosine kinase\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACOG.\u0026nbsp;\u003c/strong\u003eAmerican College of Obstetricians and Gynecologists\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAO.\u0026nbsp;\u003c/strong\u003eAdverse outcome\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAPO.\u0026nbsp;\u003c/strong\u003eAdverse Pregnancy outcome\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUC.\u0026nbsp;\u003c/strong\u003eArea under the Curve\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFIGO.\u0026nbsp;\u003c/strong\u003eInternational Federation of Gynecology and Obstetrics\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIUGR.\u0026nbsp;\u003c/strong\u003eIntrauterine growth restriction\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNICE.\u0026nbsp;\u003c/strong\u003eThe National Institute for Health and Care Excellence\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePE.\u0026nbsp;\u003c/strong\u003ePreeclampsia\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlGF.\u0026nbsp;\u003c/strong\u003ePlacental growth factor\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC.\u0026nbsp;\u003c/strong\u003eReceiver operating characteristic curve\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e This study adhered to the Guidelines for Good Clinical Practice, complied with the guidelines for human studies and were conducted ethically in accordance with the World Medical Association Declaration of Helsinki, and approved by the Institutional Review Board of Instituto de Investigaci\u0026oacute;n Sanitaria La Fe (2018/0202) 07 11 2018. Informed consent was obtained from all women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials.\u0026nbsp;\u003c/strong\u003edata from the present study are available upon appropriate request to the corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.P-M and M.M-T. Reports receiving lecture fees and serving on advisory boards from Roche Diagnostics\u003c/p\u003e\n\u003cp\u003eAll other authors, report no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003eThis study was supported by any funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions.\u003c/strong\u003e All authors contributed to the conception and design of this study, were involved in the interpretation of the data, and the development and approval of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eF.F-C, M.M-T and A.A-R, performed the analytical test\u003c/p\u003e\n\u003cp\u003eA.P-M, F.F-C, A.A-R, R.M-O, B.N-A, and B.M-P, extracted the data from the medical records\u003c/p\u003e\n\u003cp\u003eA.P-M and J. M-R wrote and performed the data analyses\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eThe authors would like to thank all the women who participated in the study, the faculty, midwives and midwifery staff who supported the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional).\u0026nbsp;\u003c/strong\u003eThis study is part of the doctoral thesis project of F.F-C\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZeisler H, Llurba E, Chantraine F, Vatish M, Staff AC, Sennstrom M, et al. Predictive Value of the sFlt-1:PlGF Ratio in Women with Suspected Preeclampsia. N Engl J Med. 2016;374(1):13\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerales A, Delgado JL, de la Calle M, Garcia-Hernandez JA, Escudero AI, Campillos JM, et al. sFlt-1/PlGF for prediction of early-onset pre-eclampsia: STEPS (Study of Early Pre-eclampsia in Spain). Ultrasound Obstet Gynecol. 2017;50(3):373\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaia-Barjat T, Prieux C, Gris JC, Chapelle C, Laporte S, Chauleur C. Angiogenic factors for prediction of preeclampsia and intrauterine growth restriction onset in high-risk women: AngioPred study. J Matern Fetal Neonatal Med. 2017:1\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeisler H, Llurba E, Chantraine F, Vatish M, Staff A, Sennstrom M, et al. Soluble fms-Like Tyrosine Kinase-1-to-Placental Growth Factor Ratio and Time to Delivery in Women With Suspected Preeclampsia. Obstet Gynecol. 2016;128(2):261\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeisler H, Llurba E, Chantraine FJ, Vatish M, Staff AC, Sennstrom M, et al. Soluble fms-like tyrosine kinase-1 to placental growth factor ratio: ruling out pre-eclampsia for up to 4 weeks and value of retesting. Ultrasound Obstet Gynecol. 2019;53(3):367\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRana S, Powe CE, Salahuddin S, Verlohren S, Perschel FH, Levine RJ, et al. Angiogenic factors and the risk of adverse outcomes in women with suspected preeclampsia. Circulation. 2012;125(7):911\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStepan H, Herraiz I, Schlembach D, Verlohren S, Brennecke S, Chantraine F, et al. Implementation of the sFlt-1/PlGF ratio for prediction and diagnosis of pre-eclampsia in singleton pregnancy: implications for clinical practice. Ultrasound Obstet Gynecol. 2015;45(3):241\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillala\u0026iacute;n C, Herraiz I, Valle L, Mendoza M, Delgado JL, V\u0026aacute;zquez-Fern\u0026aacute;ndez M, et al. Maternal and Perinatal Outcomes Associated With Extremely High Values for the sFlt-1 (Soluble fms-Like Tyrosine Kinase 1)/PlGF (Placental Growth Factor) Ratio. J Am Heart Assoc. 2020;9(7):e015548.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerraiz I, Llurba E, Verlohren S, Galindo A. Update on the Diagnosis and Prognosis of Preeclampsia with the Aid of the sFlt-1/ PlGF Ratio in Singleton Pregnancies. Fetal Diagn Ther. 2018;43(2):81\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerraiz I, Llurba E, Verlohren S, Galindo A. Update on the Diagnosis and Prognosis of Preeclampsia with the Aid of the sFlt-1/ PlGF Ratio in Singleton Pregnancies. Fetal Diagn Ther. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevine RJ, Maynard SE, Qian C, Lim KH, England LJ, Yu KF, et al. Circulating angiogenic factors and the risk of preeclampsia. N Engl J Med. 2004;350(7):672\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaltajian K, Bajracharya S, Salahuddin S, Berg AH, Geahchan C, Wenger JB, et al. Sequential plasma angiogenic factors levels in women with suspected preeclampsia. Am J Obstet Gynecol. 2016;215(1):89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.e1-.e10\u003c/span\u003e\u003cspan address=\"http://.e1-.e10\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleh L, Vergouwe Y, van den Meiracker AH, Verdonk K, Russcher H, Bremer HA et al. Angiogenic Markers Predict Pregnancy Complications and Prolongation in Preeclampsia: Continuous Versus Cutoff Values. Hypertens. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Oliveira L, Pera\u0026ccedil;oli JC, Pera\u0026ccedil;oli MT, Korkes H, Zampieri G, Moron AF, et al. sFlt-1/PlGF ratio as a prognostic marker of adverse outcomes in women with early-onset preeclampsia. Pregnancy Hypertens. 2013;3(3):191\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerlohren S, Herraiz I, Lapaire O, Schlembach D, Zeisler H, Calda P, et al. New gestational phase-specific cutoff values for the use of the soluble fms-like tyrosine kinase-1/placental growth factor ratio as a diagnostic test for preeclampsia. Hypertension. 2014;63(2):346\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aacute;lvarez-Fern\u0026aacute;ndez I, Prieto B, Rodr\u0026iacute;guez V, Ruano Y, Escudero AI, \u0026Aacute;lvarez FV. N-terminal pro B-type natriuretic peptide and angiogenic biomarkers in the prognosis of adverse outcomes in women with suspected preeclampsia. Clin Chim Acta. 2016;463:150\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerlohren S, Herraiz I, Lapaire O, Schlembach D, Moertl M, Zeisler H, et al. The sFlt-1/PlGF ratio in different types of hypertensive pregnancy disorders and its prognostic potential in preeclamptic patients. Am J Obstet Gynecol. 2012;206(1):58e1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirkovic L, Tulic I, Stankovic S, Soldatovic I. Prediction of adverse maternal outcomes of early severe preeclampsia. Pregnancy Hypertens. 2020;22:144\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLea\u0026ntilde;os-Miranda A, Campos-Galicia I, Ram\u0026iacute;rez-Valenzuela KL, Chinolla-Arellano ZL, Isordia-Salas I. Circulating angiogenic factors and urinary prolactin as predictors of adverse outcomes in women with preeclampsia. Hypertension. 2013;61(5):1118\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStolz M, Zeisler H, Heinzl F, Binder J, Farr A. sFlt-1:PlGF ratio of 655 is not a reliable cut-off value for predicting perinatal outcomes in women with preeclampsia. Pregnancy Hypertens. 2018;11:54\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore AG, Young H, Keller JM, Ojo LR, Yan J, Simas TA, et al. Angiogenic biomarkers for prediction of maternal and neonatal complications in suspected preeclampsia. J Matern Fetal Neonatal Med. 2012;25(12):2651\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleh L, Alblas MM, Nieboer D, Neuman RI, Vergouwe Y, Bruss\u0026eacute; IA, et al. Prediction of pre-eclampsia‐related complications in women with suspected or confirmed pre‐eclampsia: development and internal validation of clinical prediction model. Ultrasound Obstet Gynecol. 2021;58(5):698\u0026ndash;704.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDr\u0026ouml;ge LA, Perschel FH, St\u0026uuml;tz N, Gafron A, Frank L, Busjahn A, et al. Prediction of Preeclampsia-Related Adverse Outcomes With the sFlt-1 (Soluble fms-Like Tyrosine Kinase 1)/PlGF (Placental Growth Factor)-Ratio in the Clinical Routine: A Real-World Study. Hypertension. 2021;77(2):461\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eACOG Practice Bulletin No. 202: Gestational Hypertension and Preeclampsia. Obstet Gynecol. 2019;133(1):e1\u0026ndash;e25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, et al. The hypertensive disorders of pregnancy: ISSHP classification, diagnosis \u0026amp; management recommendations for international practice. Pregnancy Hypertens. 2018;13:291\u0026ndash;310.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\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\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelamed N, Baschat A, Yinon Y, Athanasiadis A, Mecacci F, Figueras F, et al. FIGO (international Federation of Gynecology and obstetrics) initiative on fetal growth: best practice advice for screening, diagnosis, and management of fetal growth restriction. Int J Gynaecol Obstet. 2021;152(1):3\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNICE. Hypertension in pregnancy: diagnosis and management NICE guideline. 2019\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e(www.nice.org.uk/guidance/ng133\u003c/span\u003e\u003cspan address=\"http://(www.nice.org.uk/guidance/ng133\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Collaborating Centre for Women's and Children's Health (UK). Hypertension in Pregnancy: The Management of Hypertensive Disorders During Pregnancy. London: RCOG Press; 2010 Aug. p. 22220321.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeFevre ML. Low-dose aspirin use for the prevention of morbidity and mortality from preeclampsia: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med2014. p. 819\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUkah UV, De Silva DA, Payne B, Magee LA, Hutcheon JA, Brown H et al. Prediction of adverse maternal outcomes from pre-eclampsia and other hypertensive disorders of pregnancy: A systematic review. Pregnancy Hypertens. 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim S, Li W, Kemper J, Nguyen A, Mol BW, Reddy M. Biomarkers and the Prediction of Adverse Outcomes in Preeclampsia: A Systematic Review and Meta-analysis. Obstet Gynecol. 2021;137(1):72\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aacute;lvarez-Fern\u0026aacute;ndez I, Prieto B, Rodr\u0026iacute;guez V, Ruano Y, Escudero AI, \u0026Aacute;lvarez FV. New biomarkers in diagnosis of early onset preeclampsia and imminent delivery prognosis. Clin Chem Lab Med. 2014;52(8):1159\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleh L, Verdonk K, Jan Danser AH, Steegers EA, Russcher H, van den Meiracker AH, et al. The sFlt-1/PlGF ratio associates with prolongation and adverse outcome of pregnancy in women with (suspected) preeclampsia: analysis of a high-risk cohort. Eur J Obstet Gynecol Reprod Biol. 2016;199:121\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChelli D, Fau - Hamdi A, Hamdi A, Fau - Saoudi S, Saoudi S, Fau - Jenayah AA, Jenayah Aa Fau -, Zagre A, Zagre A, Fau - Jguerim H, Jguerim H, Fau - Bedis C, et al. Clinical Assessment of Soluble FMS-Like Tyrosine Kinase-1/Placental Growth Factor Ratio for the Diagnostic and the Prognosis of Preeclampsia in the Second Trimester. Clin Lab. 2017;62(10):1927\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeanos-Miranda A, Mendez-Aguilar F, Ramirez-Valenzuela KL, Serrano-Rodriguez M, Berumen-Lechuga G, Molina-Perez CJ, et al. Circulating angiogenic factors are related to the severity of gestational hypertension and preeclampsia, and their adverse outcomes. Medicine. 2017;96(4):e6005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFagan TJ. Letter: Nomogram for Bayes theorem. N Engl J Med. 1975;293(5):257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaeschke R, Guyatt G, Sackett DL. Users' guides to the medical literature. III. How to use an article about a diagnostic test. A. Are the results of the study valid? Evidence-Based Medicine Working Group. JAMA. 1994;271(5):389\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNICE. PlGF-based testing to help diagnose suspected preeclampsia (Triage PlGF test, Elecsys immunoassay sFlt-1/PlGF ratio, DELFIA Xpress PlGF 1-2-3 test, and BRAHMS sFlt-1 Kryptor/BRAHMS PlGF plus Kryptor PE ratio) Diagnostics guidance Published: 11 May 2016 wwwniceorguk/guidance/dg23. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Dadelszen P, Payne B, Li J, Ansermino JM, Broughton Pipkin F, Cote AM, et al. Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model. Lancet. 2011;377(9761):219\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLea\u0026ntilde;os-Miranda A, Graciela Nolasco-Lea\u0026ntilde;os A, Ismael Carrillo-Ju\u0026aacute;rez R, Jos\u0026eacute; Molina-P\u0026eacute;rez C, Janet Sillas-Pardo L, Manuel Jim\u0026eacute;nez-Trejo L, et al. Usefulness of the sFlt-1/PlGF (Soluble fms-Like Tyrosine Kinase-1/Placental Growth Factor) Ratio in Diagnosis or Misdiagnosis in Women With Clinical Diagnosis of Preeclampsia. Hypertension. 2020;76(3):892\u0026ndash;900.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerlohren S, Herraiz I, Lapaire O, Schlembach D, Zeisler H, Calda P, et al. Risk Stratification of Hypertensive Pregnancy Disorders. Eur Obst Gynaecol. 2012;7(1):14\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerraiz I, Simon E, Gomez-Arriaga PI, Quezada MS, Garcia-Burguillo A, Lopez-Jimenez EA, et al. Clinical implementation of the sFlt-1/PlGF ratio to identify preeclampsia and fetal growth restriction: A prospective cohort study. Pregnancy Hypertensi. 2018;13:279\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerlohren S, Galindo A, Schlembach D, Zeisler H, Herraiz I, Moertl MG, et al. An automated method for the determination of the sFlt-1/PIGF ratio in the assessment of preeclampsia. Am J Obstet Gynecol. 2010;202(2):161. e1- e11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe La Calle M, Delgado JL, Verlohren S, Escudero AI, Bartha JL, Campillos JM, et al. Gestational Age-Specific Reference Ranges for the sFlt-1/PlGF Immunoassay Ratio in Twin Pregnancies. Fetal Diagn Ther. 2021;48(4):288\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sFlt-1/PlGF ratio, Placental insufficiency, Adverse maternal outcome, Preeclampsia, Prediction, Biomarker","lastPublishedDoi":"10.21203/rs.3.rs-3166567/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3166567/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003esFlt-1/PlGF ratio has been proposed to herald adverse Pregnancy outcome (APO). Several authors have proposed the use of a continuous scale but most use specific cut-offs to evaluate the risk of APO, but the proposed range varies widely. The aim, is to evaluate if the Maternal adverse outcome (AO) prediction occurs in a stepwise manner, and if this was the case, which would be the point yielding the highest accuracy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis is an observational retrospective cohort study. Singleton pregnancies, between 20 to 40 weeks were selected according the levels of sFlt-1/PlGF; three groups (n\u0026thinsp;=\u0026thinsp;60 each): High\u0026thinsp;\u0026ge;\u0026thinsp;655, Intermediate\u0026thinsp;\u0026ge;\u0026thinsp;85 to \u0026lt;\u0026thinsp;655 and Low\u0026thinsp;\u0026lt;\u0026thinsp;85. From hospital records we retrieve data, and outcome, also we quantitate the PE risk. We compared the groups, to determine the clinical utility of the sFlt1/PlGF ratio in prediction of Maternal AO, we used ROC analysis, and to develop a predictive model of Maternal AO a binary multivariate logistic regression was used.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAt entry a different degree of severity is observed for groups: High\u0026thinsp;\u0026gt;\u0026thinsp;Intermediate\u0026thinsp;\u0026gt;\u0026thinsp;Low, for: Systolic Blood pressure, Mean Blood pressure, Angiogenic markers, AST, ALT. Also a positive correlation was found between the sFlt-1/PlGF and the afore mentioned variables. We observed a potency order for the Maternal AO, from more to less manifestations High\u0026thinsp;\u0026gt;\u0026thinsp;Intermediate\u0026thinsp;\u0026gt;\u0026thinsp;Low (p\u0026thinsp;\u0026lt;\u0026thinsp;0,001). sFlt1/PlGF ratio has the highest AUC for predicting Maternal AO than any other single parameter.\u003c/p\u003e","manuscriptTitle":"The sFlt-1/PlGF ratio as predictor of Maternal adverse outcome in patients with suspected or placental insufficiency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-28 14:27:52","doi":"10.21203/rs.3.rs-3166567/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f923b46-39d9-4610-914c-a715b21e953f","owner":[],"postedDate":"August 28th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-18T11:15:21+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-28 14:27:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3166567","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3166567","identity":"rs-3166567","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}