Browsing by Subject "Pre-eclampsia"

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  • Jääskeläinen, Tiina; Heinonen, Seppo; Hämäläinen, Esa; Pulkki, Kari; Romppanen, Jarkko; Laivuori, Hannele (2018)
    Objectives: To study first and second/third trimester levels of soluble fms-like tyrosine kinase 1 (sFlt1), placental growth factor (PlGF) and soluble endoglin (sEng) in FINNPEC case-control cohort. The participants were further divided into subgroups based on parity and onset of the disease. Recommended cut-off values in aid of pre-eclampsia (PE) prediction and diagnosis were also tested. Methods: First trimester serum samples were available from 221 women who later developed PE and 239 women who did not develop PE. Second/third trimester serum samples were available from 175 PE and 55 non-PE women. sFl1-1 and PlGF were measured electro-chemiluminescence immunoassays and sEng by ELISA. Results: In all timepoints PlGF, endoglin and the sFlt-1/PlGF ratio were increased in the PE group compared to the non-PE group. The serum concentrations of sFlt-1 were increased only a second/third trimester in PE women. Higher concentrations of s-Flt1, endoglin and higher sFlt/PlGF ratio were found a the third trimester in primiparous women compared to multiparous women. Primiparous PE women also had lower concentrations of PlGF a the third trimester. The proportion of women exceeding all cut-offs of the sFlt-1/PlGF ratio (>= 33, >= 38, >= 85 and >= 110) was greater in the PE group, but there were also pre-eclamptic women who met rule-out cut-off or did no meet rule-in cut-off. Conclusions: Primiparous pregnancies have more anti-angiogenic profile during second/third trimester compared with multiparous pregnancies. Our findings also suggest that certain maternal characteristics, e.g. BMI, smoking and pre-existing diseases, should be taken into account when different sFlt-1/PlGF ratio cut-offs are utilized.
  • Burke, Orlaith; Benton, Samantha; Szafranski, Pawel; von Dadelszen, Peter; Buhimschi, S. Catalin; Cetin, Irene; Chappell, Lucy; Figueras, Francesc; Galindo, Alberto; Herraiz, Ignacio; Holzman, Claudia; Hubel, Carl; Knudsen, Ulla; Kronborg, Camilla; Laivuori, Hannele; Lapaire, Olav; McElrath, Thomas; Moertl, Manfred; Myers, Jenny; Ness, Roberta B.; Oliveira, Leandro; Olson, Gayle; Poston, Lucilla; Ris-Stalpers, Carrie; Roberts, James M.; Schalekamp-Timmermans, Sarah; Schlembach, Dietmar; Steegers, Eric; Stepan, Holger; Tsatsaris, Vassilis; van der Post, Joris A.; Verlohren, Stefan; Villa, Pia M.; Williams, David; Zeisler, Harald; Redman, Christopher W. G.; Staff, Anne Cathrine; Global Pregnancy Collaboration (2016)
    Background: A common challenge in medicine, exemplified in the analysis of biomarker data, is that large studies are needed for sufficient statistical power. Often, this may only be achievable by aggregating multiple cohorts. However, different studies may use disparate platforms for laboratory analysis, which can hinder merging. Methods: Using circulating placental growth factor (PIGF), a potential biomarker for hypertensive disorders of pregnancy (HDP) such as preeclampsia, as an example, we investigated how such issues can be overcome by inter-platform standardization and merging algorithms. We studied 16,462 pregnancies from 22 study cohorts. PIGF measurements (gestational age >= 20 weeks) analyzed on one of four platforms: R & Systems, Alere (R) Triage, Roche (R) Elecsys or Abbott (R) Architect, were available for 13,429 women. Two merging algorithms, using Z-Score and Multiple of Median transformations, were applied. Results: Best reference curves (BRC), based on merged, transformed PIGF measurements in uncomplicated pregnancy across six gestational age groups, were estimated. Identification of HDP by these PIGF-BRCS was compared to that of platform-specific curves. Conclusions: We demonstrate the feasibility of merging PIGF concentrations from different analytical platforms. Overall BRC identification of HDP performed at least as well as platform-specific curves. Our method can be extended to any set of biomarkers obtained from different laboratory platforms in any field. Merged biomarker data from multiple studies will improve statistical power and enlarge our understanding of the pathophysiology and management of medical syndromes. (C) 2015 International Society for the Study of Hypertension in Pregnancy. Published by Elsevier B.V. All rights reserved.
  • IPPIC Collaborative Network; Snell, Kym I. E.; Allotey, John; Smuk, Melanie; Laivuori, Hannele; Heinonen, Seppo; Kajantie, Eero; Villa, Pia M. (2020)
    Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. Methods: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. Results: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. Conclusions: The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice.
  • Snell, Kym I E; Allotey, John; Smuk, Melanie; Hooper, Richard; Chan, Claire; Ahmed, Asif; Chappell, Lucy C; Von Dadelszen, Peter; Green, Marcus; Kenny, Louise; Khalil, Asma; Khan, Khalid S; Mol, Ben W; Myers, Jenny; Poston, Lucilla; Thilaganathan, Basky; Staff, Anne C; Smith, Gordon C S; Ganzevoort, Wessel; Laivuori, Hannele; Odibo, Anthony O; Arenas Ramírez, Javier; Kingdom, John; Daskalakis, George; Farrar, Diane; Baschat, Ahmet A; Seed, Paul T; Prefumo, Federico; da Silva Costa, Fabricio; Groen, Henk; Audibert, Francois; Masse, Jacques; Skråstad, Ragnhild B; Salvesen, Kjell Å; Haavaldsen, Camilla; Nagata, Chie; Rumbold, Alice R; Heinonen, Seppo; Askie, Lisa M; Smits, Luc J M; Vinter, Christina A; Magnus, Per; Eero, Kajantie; Villa, Pia M; Jenum, Anne K; Andersen, Louise B; Norman, Jane E; Ohkuchi, Akihide; Eskild, Anne; Bhattacharya, Sohinee; McAuliffe, Fionnuala M; Galindo, Alberto; Herraiz, Ignacio; Carbillon, Lionel; Klipstein-Grobusch, Kerstin; Yeo, Seon A; Browne, Joyce L; Moons, Karel G M; Riley, Richard D; Thangaratinam, Shakila (BioMed Central, 2020)
    Abstract Background Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. Methods IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. Results Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model’s calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. Conclusions The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice. Trial registration PROSPERO ID: CRD42015029349 .
  • Keikkala, Elina; Koskinen, Sini; Vuorela, Piia; Laivuori, Hannele; Romppanen, Jarkko; Heinonen, Seppo; Stenman, Ulf-Hakan (2016)
    Background: To study whether maternal serum hyperglycosylated human chorionic gonadotropin (hCG-h) improves first trimester prediction of pre-eclampsia when combined with placental growth factor (PlGF), pregnancy-associated plasma protein-A (PAPP-A) and maternal risk factors. Methods: Gestational-age-adjusted concentrations of hCG, hCG-h, PlGF and PAPP-A were analysed in serum samples by time-resolved immunofluorometric assays at 8-13 weeks of gestation. The case-control study included 98 women who developed pre-eclampsia, 25 who developed gestational hypertension, 41 normotensive women with small-for-gestational-age (SGA) infants and 177 controls. Results: Of 98 women with pre-eclampsia, 24 women developed preterm pre-eclampsia (diagnosis <37 weeks of gestation) and 13 of them had early-onset pre-eclampsia (diagnosis <34 weeks of gestation). They had lower concentrations of PlGF, PAPP-A and proportion of hCG-h to hCG (% hCG-h) than controls. In receiver-operating characteristics (ROC) curve analysis, the area under the curve (AUC) for the combination of PlGF, PAPP-A, % hCG-h, nulliparity and mean arterial blood pressure was 0.805 (95% confidence interval, CI, 0.699-0.912) for preterm pre-eclampsia and 0.870 (95% CI 0.750-0.988) for early-onset pre-eclampsia. Without % hCG-h the AUC values were 0.756 (95% CI 0.651-0.861) and 0.810 (95% CI 0.682-0.938) respectively. For prediction of gestational hypertension, the AUC for % hCG-h was 0.708 (95% CI 0.608-0.808), but for other markers the AUC values were not significant. None of the AUC values were significant for the prediction of SGA infants in normotensive women. Conclusions: First trimester maternal serum % hCG-h tended to improve prediction of preterm and early-onset pre-eclampsia when combined with PlGF, PAPP-A and maternal risk factors.
  • Keikkala, Elina; Koskinen, Sini; Vuorela, Piia; Laivuori, Hannele; Romppanen, Jarkko; Heinonen, Seppo; Stenman, Ulf-Håkan (BioMed Central, 2016)
    Abstract Background To study whether maternal serum hyperglycosylated human chorionic gonadotropin (hCG-h) improves first trimester prediction of pre-eclampsia when combined with placental growth factor (PlGF), pregnancy-associated plasma protein-A (PAPP-A) and maternal risk factors. Methods Gestational-age-adjusted concentrations of hCG, hCG-h, PlGF and PAPP-A were analysed in serum samples by time-resolved immunofluorometric assays at 8–13 weeks of gestation. The case–control study included 98 women who developed pre-eclampsia, 25 who developed gestational hypertension, 41 normotensive women with small-for-gestational-age (SGA) infants and 177 controls. Results Of 98 women with pre-eclampsia, 24 women developed preterm pre-eclampsia (diagnosis < 37 weeks of gestation) and 13 of them had early-onset pre-eclampsia (diagnosis < 34 weeks of gestation). They had lower concentrations of PlGF, PAPP-A and proportion of hCG-h to hCG (%hCG-h) than controls. In receiver-operating characteristics (ROC) curve analysis, the area under the curve (AUC) for the combination of PlGF, PAPP-A, %hCG-h, nulliparity and mean arterial blood pressure was 0.805 (95% confidence interval, CI, 0.699–0.912) for preterm pre-eclampsia and 0.870 (95% CI 0.750–0.988) for early-onset pre-eclampsia. Without %hCG-h the AUC values were 0.756 (95% CI 0.651–0.861) and 0.810 (95% CI 0.682–0.938) respectively. For prediction of gestational hypertension, the AUC for %hCG-h was 0.708 (95% CI 0.608–0.808), but for other markers the AUC values were not significant. None of the AUC values were significant for the prediction of SGA infants in normotensive women. Conclusions First trimester maternal serum %hCG-h tended to improve prediction of preterm and early-onset pre-eclampsia when combined with PlGF, PAPP-A and maternal risk factors.
  • Murtoniemi, Katja; Villa, Pia M.; Matomäki, Jaakko; Keikkala, Elina; Vuorela, Piia; Hämäläinen, Esa; Kajantie, Eero; Pesonen, Anu-Katriina; Räikkönen, Katri; Taipale, Pekka; Stenman, Ulf-Håkan; Laivuori, Hannele (2018)
    Background: The proportion of hyperglycosylated human chorionic gonadotropin (hCG-h) to total human chorionic gonadotropin (%hCG-h) during the first trimester is a promising biomarker for prediction of early-onset pre-eclampsia. We wanted to evaluate the performance of clinical risk factors, mean arterial pressure (MAP), %hCG-h, hCG beta, pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PIGF) and mean pulsatility index of the uterine artery (Uta-PI) in the first trimester in predicting pre-eclampsia (PE) and its subtypes early-onset, late-onset, severe and non-severe PE in a high-risk cohort. Methods: We studied a subcohort of 257 high-risk women in the prospectively collected Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) cohort Multivariate logistic regression was used to construct the prediction models. The first model included background variables and MAP. Additionally, biomarkers were included in the second model and mean Uta-PI was included in the third model. All variables that improved the model fit were included at each step. The area under the curve (AUC) was determined for all models. Results: We found that lower levels of serum PIGF concentration were associated with early-onset PE, whereas lower %hCG-h was associated with the late-onset PE. Serum PIGF was lower and hCG beta higher in severe PE, while %hCG-h and serum PAPP-A were lower in non-severe PE. By using multivariate regression analyses the best prediction for all PE was achieved with the third model: AUC was 0.66, and sensitivity 36% at 90% specificity. Third model also gave the highest prediction accuracy for late-onset, severe and non-severe PE: AUC 0.66 with 32% sensitivity, AUC 0.65, 24% sensitivity and AUC 0.60, 22% sensitivity at 90% specificity, respectively. The best prediction for early-onset PE was achieved using the second model: AUC 0.68 and 20% sensitivity at 90% specificity. Conclusions: Although the multivariate models did not meet the requirements to be clinically useful screening tools, our results indicate that the biomarker profile in women with risk factors for PE is different according to the subtype of PE. The heterogeneous nature of PE results in difficulty to find new, clinically useful biomarkers for prediction of PE in early pregnancy in high-risk cohorts.
  • Murtoniemi, Katja; Villa, Pia M; Matomäki, Jaakko; Keikkala, Elina; Vuorela, Piia; Hämäläinen, Esa; Kajantie, Eero; Pesonen, Anu-Katriina; Räikkönen, Katri; Taipale, Pekka; Stenman, Ulf-Håkan; Laivuori, Hannele (BioMed Central, 2018)
    Abstract Background The proportion of hyperglycosylated human chorionic gonadotropin (hCG-h) to total human chorionic gonadotropin (%hCG-h) during the first trimester is a promising biomarker for prediction of early-onset pre-eclampsia. We wanted to evaluate the performance of clinical risk factors, mean arterial pressure (MAP), %hCG-h, hCGβ, pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PlGF) and mean pulsatility index of the uterine artery (Uta-PI) in the first trimester in predicting pre-eclampsia (PE) and its subtypes early-onset, late-onset, severe and non-severe PE in a high-risk cohort. Methods We studied a subcohort of 257 high-risk women in the prospectively collected Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) cohort. Multivariate logistic regression was used to construct the prediction models. The first model included background variables and MAP. Additionally, biomarkers were included in the second model and mean Uta-PI was included in the third model. All variables that improved the model fit were included at each step. The area under the curve (AUC) was determined for all models. Results We found that lower levels of serum PlGF concentration were associated with early-onset PE, whereas lower %hCG-h was associated with the late-onset PE. Serum PlGF was lower and hCGβ higher in severe PE, while %hCG-h and serum PAPP-A were lower in non-severe PE. By using multivariate regression analyses the best prediction for all PE was achieved with the third model: AUC was 0.66, and sensitivity 36% at 90% specificity. Third model also gave the highest prediction accuracy for late-onset, severe and non-severe PE: AUC 0.66 with 32% sensitivity, AUC 0.65, 24% sensitivity and AUC 0.60, 22% sensitivity at 90% specificity, respectively. The best prediction for early-onset PE was achieved using the second model: AUC 0.68 and 20% sensitivity at 90% specificity. Conclusions Although the multivariate models did not meet the requirements to be clinically useful screening tools, our results indicate that the biomarker profile in women with risk factors for PE is different according to the subtype of PE. The heterogeneous nature of PE results in difficulty to find new, clinically useful biomarkers for prediction of PE in early pregnancy in high-risk cohorts. Trial registration International Standard Randomised Controlled Trial number ISRCTN14030412 , Date of registration 6/09/2007, retrospectively registered.
  • Keikkala, Elina; Forsten, Janina; Ritvos, Olli; Stenman, Ulf-Håkan; Kajantie, Eero; Hämäläinen, Esa; Räikkönen, Katri; Villa, Pia M.; Laivuori, Hannele (2021)
    Objectives: Maternal serum inhibin-A , pregnancy associated plasma protein-A (PAPP-A) and PAPP-A2 together with placental growth factor (PlGF), maternal risk factors and uterine arter y pulsatility inde x (UtA PI) were analysed to study thei r ability to predict pre-eclampsia (PE). Study design: Serial serum samples for the nested case-control study were collected prospectively at 12-14, 18-20 and 26-28 weeks of gestation from 11 women who later developed early-onset PE (EO PE , diagnosis < 34 + 0 weeks of gestation), 34 women who developed late-onset PE (LO PE , diagnosis 2 34 + 0 weeks) and 89 controls. Main outcome measures: Gestational age-adjusted multiples of the median (MoM) values were calculated for biomarker concentrations. Multivariate regression analyses were performed to combine first trimester bio-markers, previously reported results on PlGF, maternal risk factors and UtA PI. Area under cu r v e (AUC) values and 95% confidence intervals (CIs) for the prediction of PE and its subtypes were calculated . Results: A high first trimester inhibin-A predicted PE (AUC 0.618, 95%CI, 0.513-0.724), whereas PAPP-A and PlGF predicted only EO PE (0.701, 0.562-0.840 and 0.798, 0.686-0.909, respectively). At 26-28 weeks PAPP-A2 and inhibin-A predicted a l l PE subtypes. In the multivariate setting inhibin-A combined with maternal pre-pregnancy body mass index, prior PE and mean UtA PI predicted PE (0.811,0.726-0.896) and LO PE (0.824, 0.733-0.914). Conclusions: At first trimester inhibin-A show potential ability to predict not only EO PE but also LO PE whereas PlGF and PAPP-A predict only EO PE. At late second trimester inhibin-A and PAPP-A2 might be usef u l for short-term prediction of PE.
  • FullPIERS Grp; Ukah, U. Vivian; Payne, Beth; Karjalainen, Hanna; Kortelainen, Eija; Seed, Paul T.; Conti-Ramsden, Frances Inez; Cao, Vivien; Laivuori, Hannele; Hutcheon, Jennifer; Chappell, Lucy; Ansermino, J. Mark; Vatish, Manu; Redman, Christopher; Lee, Tang; Li, Larry; Magee, Laura A.; von Dadelszen, Peter (2019)
    The fullPIERS model is a risk prediction model developed to predict adverse maternal outcomes within 48 h for women admitted with pre-eclampsia. External validation of the model is required before implementation for clinical use. We assessed the temporal and external validity of the fullPIERS model in high income settings using five cohorts collected between 2003 and 2016, from tertiary hospitals in Canada, the United States of America, Finland and the United Kingdom. The cohorts were grouped into three datasets for assessing the primary external, and temporal validity, and broader transportability of the model. The predicted risks of developing an adverse maternal outcome were calculated using the model equation and model performance was evaluated based on discrimination, calibration, and stratification. Our study included a total of 2429 women, with an adverse maternal outcome rate of 6.7%, 6.6%, and 7.0% in the primary external, temporal, and combined (broader) validation cohorts, respectively. The model had good discrimination in all datasets: 0.81 (95%CI 0.75-0.86), 0.82 (95%CI 0.76-0.87), and 0.75 (95%CI 0.71-0.80) for the primary external, temporal, and broader validation datasets, respectively. Calibration was best for the temporal cohort but poor in the broader validation dataset The likelihood ratios estimated to rule in adverse maternal outcomes were high at a cut-off of >= 30% in all datasets. The fullPIERS model is temporally and externally valid and will be useful in the management of women with pre-eclampsia in high income settings although model recalibration is required to improve performance, specifically in the broader healthcare settings.
  • Kallela, Jenni; Jääskeläinen, Tiina; Kortelainen, Eija; Heinonen, Seppo; Kajantie, Eero; Kere, Juha; Kivinen, Katja; Pouta, Anneli; Laivuori, Hannele (2016)
    Background: The Finnish Pre-eclampsia Consortium (FINNPEC) case-control cohort consisting of 1447 pre-eclamptic and 1068 non-pre-eclamptic women was recruited during 2008-2011 to study genetic background of pre-eclampsia and foetal growth. Pre-eclampsia was defined by hypertension and proteinuria according to the American College of Obstetricians and Gynecologists (ACOG) 2002 classification. The ACOG Task Force Report on Hypertension in Pregnancy (2013) and The International Society for the Study of Hypertension in Pregnancy (ISSHP) (2014) have published new classifications, in which proteinuria is not necessary for diagnosis when specific symptoms are present. For diagnoses based on proteinuria, the ISSHP 2014 criteria raised its threshold to 2+ on dipstick. We studied how the new classifications would affect pre-eclampsia diagnoses in the FINNPEC cohort. Methods: We re-evaluated pre-eclampsia diagnosis using the ACOG 2013 and the ISSHP 2014 classifications in pre-eclamptic women whose proteinuria did not exceed 1+ on dipstick (n = 68), in women with gestational hypertension (n = 138) and in women with chronic hypertension (n = 66). Results: The number of women with pre-eclampsia increased 0.8 % (1459/1447) according to the ACOG 2013 criteria and 0.6 % (1455/1447) according to the ISSHP 2014 criteria. All 68 women with the amount of proteinuria not exceeding 1+ on dipstick diagnosed originally pre-eclamptic met the ACOG 2013 criteria but only 20 women (29.4 %) met the ISSHP 2014 criteria. Seven (5.1 %) and 35 (25.4 %) women with gestational hypertension were diagnosed with pre-eclampsia according to the ACOG 2013 and the ISSHP 2014 criteria, respectively. Correspondingly five (7.6 %) and 21 (31.8 %) women with chronic hypertension were diagnosed with pre-eclampsia according to the ACOG 2 013 and the ISSHP 2014 criteria. Conclusions: Only minor changes were observed in the total number of pre-eclamptic women in the FINNPEC cohort when comparing the ACOC 2002 classification with the ACOG 2013 and ISSHP 2014 classifications.
  • Kallela, Jenni; Jääskeläinen, Tiina; Kortelainen, Eija; Laivuori, Hannele (Helsingfors universitet, 2016)
    Background The Finnish Pre-eclampsia Consortium (FINNPEC) case-control cohort consisting of 1447 pre-eclamptic and 1068 non-pre-eclamptic women was recruited at the five Finnish university hospitals to study genetic background of pre-eclampsia and fetal growth. Pre-eclampsia was defined by hypertension and proteinuria according to the modified The American College of Obstetricians and Gynecologists (ACOG) 2002 classification. The ACOG Task Force Report on Hypertension in Pregnancy (2013) and The international Society for the Study of Hypertension in Pregnancy (ISSHP) (2014) have published new classifications, which change the paradigm that the diagnosis of preeclampsia always requires proteinuria. Here we studied how the new classifications would affect the pre-eclampsia diagnoses in the FINNPEC cohort. Methods We re-evaluated pre-eclampsia diagnosis using the ACOG 2013 and the ISSHP 2014 classifications in those pre-eclamptic women with the amount of proteinuria not exceeding 1+ in dipstick (N=68) and in women with gestational hypertension (N=138). Results Number of women with pre-eclampsia increased 0.5% (1454/1447) according to the ACOG 2013 criteria and decreased 0.9% (1434/1447) according to the ISSHP 2014 criteria. All 68 women with the amount of proteinuria not exceeding 1+ in dipstick diagnosed originally pre-eclamptic met the ACOG 2013 criteria but only 20 women (29.4%) met the ISSHP 2014 criteria. Seven (5.1%) and 35 (25.4%) women with gestational hypertension were diagnosed with pre-eclampsia according to the ACOG 2013 and the ISSHP 2014 criteria, respectively. Conclusions Only minor changes were observed in the total number of pre-eclamptic women in the FINNPEC cohort when comparing the modified ACOC 2002 classification with the ACOG 2013 and ISSHP 2014 classifications.
  • Murtoniemi, K.; Vahlberg, T.; Hämäläinen, E.; Kajantie, E.; Pesonen, A.K.; Räikkönen, K.; Taipale, P.; Villa, P.M.; Laivuori, H. (2018)
    Objectives: Our first aim was to study the longitudinal changes of serum placental growth factor (PlGF) concentration between 12(+0) and 28(+0) weeks of gestation in the prospective PREDO cohort. Our second aim was to study the effect of low-dose acetylsalicylic acid (LDA; 100 mg/day), started before the 14th week of gestation, on PlGF concentration. Study design: Blood samples were collected at 12(+0)-14(+0), 18(+0)-20(+0) and 26(+0)-28(+0) weeks of gestation in 101 women without and 309 with clinical risk factors for pre-eclampsia. Risk-women were divided into two groups: to those who had medium risk for pre-eclampsia and to those who had high risk for pre-eclampsia. Finally there were seven groups according to risk, treatment (no prevention/placebo/LDA) and outcome measure pre-eclampsia. Longitudinal changes in the PlGF concentration between groups were compared. To investigate the effect of LDA on serum PlGF concentration, placebo (N = 62) and LDA (N = 61) groups were compared. A repeated measures ANOVA was used to analyze differences in PlGF levels between the groups. Results: The increase in serum PlGF concentration was higher in LDA than in placebo group (time x group effect, p = 0.046). The increase in serum PlGF concentration during pregnancy was lower in high-risk women who had placebo and developed pre-eclampsia and in medium-risk women who developed pre-eclampsia compared to the other women (time x group effect, p <0.001). There were no differences in PlGF change between low-risk women, medium-risk women who did not develop pre-eclampsia, high-risk women in the placebo group without pre-eclampsia and high-risk women in the LDA group with and without pre-eclampsia (p = 0.15). Conclusions: Our finding suggests an association between LDA started before 14 weeks of gestation and higher increase in serum PlGF concentration.