1
|
Kongprayoon P, Phupong V. Serum macrophage stimulating protein α-chain and uterine artery Doppler ultrasound in the first trimester for the prediction of preeclampsia. Sci Rep 2024; 14:21905. [PMID: 39300215 DOI: 10.1038/s41598-024-72304-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Accepted: 09/05/2024] [Indexed: 09/22/2024] Open
Abstract
To assess how effective macrophage stimulating protein α-chain (MSP-α) combined with uterine artery Doppler is in predicting preeclampsia in singleton pregnancies during 11-13+6 weeks of gestation. This prospective observational study included singleton pregnant women who attended antenatal care at King Chulalongkorn Memorial Hospital, Department of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University between December 2021 and April 2023, during 11-13+6 weeks of gestation. Serum MSP-α levels were collected and uterine artery Doppler ultrasound was performed. Pregnancy outcomes were recorded, and the predictive values of these tests were determined to predict preeclampsia. A total of 365 patients, with 21 cases of preeclampsia (5.8%), were analyzed. Serum MSP-α levels were higher in pregnant women who developed preeclampsia than those who did not (899.7 ± 550.1 ng/ml vs 642.5 ± 466.1 ng/ml, p = 0.016). The mean pulsatility index of the uterine artery and the presence of diastolic notching were not significantly different between the groups. As a cut-off value for predicting preeclampsia, using serum MSP-α levels higher than 1.0 multiple of median for gestational age, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 71.4%, 50.3%, 8.1%, and 96.7%, respectively. Additionally, when abnormal serum MSP-α levels were combined with a uterine artery Doppler pulsatility index above the 95th percentile and bilateral notching as predictive values for preeclampsia, the sensitivity was 85.7%, specificity was 18.3%, PPV was 6.0%, and NPV was 95.5%. Serum MSP-α alone at 11-13+6 weeks of gestation was effective in predicting preeclampsia. However, the use of serum MSP-α in combination with uterine artery Doppler increased sensitivity but reduced specificity for the prediction of preeclampsia.
Collapse
Grants
- GA65/15 Ratchadapiseksompotch Fund, Faculty of Medicine, Chulalongkorn University, study grant number GA65/15 and Grant for International Research Integration: Research Pyramid, Ratchadaphiseksomphot Endowment Fund, Chulalongkorn University, and Placental Related Disease Research Unit, Chulalongkorn University.
- GA65/15 Ratchadapiseksompotch Fund, Faculty of Medicine, Chulalongkorn University, study grant number GA65/15 and Grant for International Research Integration: Research Pyramid, Ratchadaphiseksomphot Endowment Fund, Chulalongkorn University, and Placental Related Disease Research Unit, Chulalongkorn University.
Collapse
Affiliation(s)
- Pimon Kongprayoon
- Placental Related Diseases Research Unit, Department of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University, Rama IV Road, Pathumwan, Bangkok, 10330, Thailand
| | - Vorapong Phupong
- Placental Related Diseases Research Unit, Department of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University, Rama IV Road, Pathumwan, Bangkok, 10330, Thailand.
| |
Collapse
|
2
|
Allotey J, Archer L, Coomar D, Snell KI, Smuk M, Oakey L, Haqnawaz S, Betrán AP, Chappell LC, Ganzevoort W, Gordijn S, Khalil A, Mol BW, Morris RK, Myers J, Papageorghiou AT, Thilaganathan B, Da Silva Costa F, Facchinetti F, Coomarasamy A, Ohkuchi A, Eskild A, Arenas Ramírez J, Galindo A, Herraiz I, Prefumo F, Saito S, Sletner L, Cecatti JG, Gabbay-Benziv R, Goffinet F, Baschat AA, Souza RT, Mone F, Farrar D, Heinonen S, Salvesen KÅ, Smits LJ, Bhattacharya S, Nagata C, Takeda S, van Gelder MM, Anggraini D, Yeo S, West J, Zamora J, Mistry H, Riley RD, Thangaratinam S. Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis. Health Technol Assess 2024; 28:1-119. [PMID: 39252507 PMCID: PMC11404361 DOI: 10.3310/dabw4814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/11/2024] Open
Abstract
Background Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes. Objectives To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data. Design Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis. Participants Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies). Predictors Maternal clinical characteristics, biochemical and ultrasound markers. Primary outcomes fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight. Analysis First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model. Results Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval -154.3 g to 173.8 g). Limitations We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data. Future work International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation. Conclusion The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management. Study registration This study is registered as PROSPERO CRD42019135045. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.
Collapse
Affiliation(s)
- John Allotey
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Lucinda Archer
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Dyuti Coomar
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Kym Ie Snell
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Melanie Smuk
- Blizard Institute, Centre for Genomics and Child Health, Queen Mary University of London, London, UK
| | - Lucy Oakey
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Sadia Haqnawaz
- The Hildas, Dame Hilda Lloyd Network, WHO Collaborating Centre for Global Women's Health, University of Birmingham, Birmingham, UK
| | - Ana Pilar Betrán
- Department of Reproductive and Health Research, World Health Organization, Geneva, Switzerland
| | - Lucy C Chappell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Wessel Ganzevoort
- Department of Obstetrics, Amsterdam UMC University of Amsterdam, Amsterdam, the Netherlands
| | - Sanne Gordijn
- Faculty of Medical Sciences, University Medical Center Groningen, Groningen, the Netherlands
| | - Asma Khalil
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Ben W Mol
- Department of Obstetrics and Gynaecology, Monash University, Monash Medical Centre, Clayton, Victoria, Australia
- Aberdeen Centre for Women's Health Research, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Rachel K Morris
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jenny Myers
- Maternal and Fetal Health Research Centre, Manchester Academic Health Science Centre, University of Manchester, Central Manchester NHS Trust, Manchester, UK
| | - Aris T Papageorghiou
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Basky Thilaganathan
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
- Tommy's National Centre for Maternity Improvement, Royal College of Obstetrics and Gynaecology, London, UK
| | - Fabricio Da Silva Costa
- Maternal Fetal Medicine Unit, Gold Coast University Hospital and School of Medicine, Griffith University, Gold Coast, Queensland, Australia
| | - Fabio Facchinetti
- Mother-Infant Department, University of Modena and Reggio Emilia, Emilia-Romagna, Italy
| | - Arri Coomarasamy
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Akihide Ohkuchi
- Department of Obstetrics and Gynecology, Jichi Medical University School of Medicine, Shimotsuke-shi, Tochigi, Japan
| | - Anne Eskild
- Akershus University Hospital, University of Oslo, Oslo, Norway
| | | | - Alberto Galindo
- Fetal Medicine Unit, Maternal and Child Health and Development Network (SAMID), Department of Obstetrics and Gynaecology, Hospital Universitario, Instituto de Investigación Hospital, Universidad Complutense de Madrid, Madrid, Spain
| | - Ignacio Herraiz
- Department of Obstetrics and Gynaecology, Hospital Universitario, Madrid, Spain
| | - Federico Prefumo
- Department of Clinical and Experimental Sciences, University of Brescia, Italy
| | - Shigeru Saito
- Department Obstetrics and Gynecology, University of Toyama, Toyama, Japan
| | - Line Sletner
- Deptartment of Pediatric and Adolescents Medicine, Akershus University Hospital, Sykehusveien, Norway
| | - Jose Guilherme Cecatti
- Obstetric Unit, Department of Obstetrics and Gynecology, University of Campinas, Campinas, Sao Paulo, Brazil
| | - Rinat Gabbay-Benziv
- Maternal Fetal Medicine Unit, Department of Obstetrics and Gynecology, Hillel Yaffe Medical Center Hadera, Affiliated to the Ruth and Bruce Rappaport School of Medicine, Technion, Haifa, Israel
| | - Francois Goffinet
- Maternité Port-Royal, AP-HP, APHP, Centre-Université de Paris, FHU PREMA, Paris, France
- Université de Paris, INSERM U1153, Equipe de recherche en Epidémiologie Obstétricale, Périnatale et Pédiatrique (EPOPé), Centre de Recherche Epidémiologie et Biostatistique Sorbonne Paris Cité (CRESS), Paris, France
| | - Ahmet A Baschat
- Department of Gynecology and Obstetrics, Johns Hopkins University School of Medicine, MD, USA
| | - Renato T Souza
- Obstetric Unit, Department of Obstetrics and Gynecology, University of Campinas, Campinas, Sao Paulo, Brazil
| | - Fionnuala Mone
- Centre for Public Health, Queen's University, Belfast, UK
| | - Diane Farrar
- Bradford Institute for Health Research, Bradford, UK
| | - Seppo Heinonen
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Kjell Å Salvesen
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Luc Jm Smits
- Care and Public Health Research Institute, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Sohinee Bhattacharya
- Aberdeen Centre for Women's Health Research, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Chie Nagata
- Center for Postgraduate Education and Training, National Center for Child Health and Development, Tokyo, Japan
| | - Satoru Takeda
- Department of Obstetrics and Gynecology, Juntendo University, Tokyo, Japan
| | - Marleen Mhj van Gelder
- Department for Health Evidence, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Dewi Anggraini
- Faculty of Mathematics and Natural Sciences, Lambung Mangkurat University, South Kalimantan, Indonesia
| | - SeonAe Yeo
- University of North Carolina at Chapel Hill, School of Nursing, NC, USA
| | - Jane West
- Bradford Institute for Health Research, Bradford, UK
| | - Javier Zamora
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Clinical Biostatistics Unit, Hospital Universitario Ramón y Cajal (IRYCIS), Madrid, Spain
| | - Hema Mistry
- Warwick Medical School, University of Warwick, Warwick, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK
| | - Shakila Thangaratinam
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| |
Collapse
|
3
|
Tiruneh SA, Vu TTT, Rolnik DL, Teede HJ, Enticott J. Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review. Curr Hypertens Rep 2024; 26:309-323. [PMID: 38806766 PMCID: PMC11199280 DOI: 10.1007/s11906-024-01297-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2024] [Indexed: 05/30/2024]
Abstract
PURPOSE OF REVIEW Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia. RECENT FINDINGS From 9382 studies retrieved, 82 were included. Sixty-six publications exclusively reported eighty-four classical regression models to predict variable timing of onset of pre-eclampsia. Another six publications reported purely ML algorithms, whilst another 10 publications reported ML algorithms and classical regression models in the same sample with 8 of 10 findings that ML algorithms outperformed classical regression models. The most frequent prognostic factors were age, pre-pregnancy body mass index, chronic medical conditions, parity, prior history of pre-eclampsia, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein A. Top performing ML algorithms were random forest (area under the curve (AUC) = 0.94, 95% confidence interval (CI) 0.91-0.96) and extreme gradient boosting (AUC = 0.92, 95% CI 0.90-0.94). The competing risk model had similar performance (AUC = 0.92, 95% CI 0.91-0.92) compared with a neural network. Calibration performance was not reported in the majority of publications. ML algorithms had better performance compared to classical regression models in pre-eclampsia prediction. Random forest and boosting-type algorithms had the best prediction performance. Further research should focus on comparing ML algorithms to classical regression models using the same samples and evaluation metrics to gain insight into their performance. External validation of ML algorithms is warranted to gain insights into their generalisability.
Collapse
Affiliation(s)
- Sofonyas Abebaw Tiruneh
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Tra Thuan Thanh Vu
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Daniel Lorber Rolnik
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
| | - Helena J Teede
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Joanne Enticott
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia.
| |
Collapse
|
4
|
Tiruneh SA, Vu TTT, Moran LJ, Callander EJ, Allotey J, Thangaratinam S, Rolnik DL, Teede HJ, Wang R, Enticott J. Externally validated prediction models for pre-eclampsia: systematic review and meta-analysis. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2024; 63:592-604. [PMID: 37724649 DOI: 10.1002/uog.27490] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 08/29/2023] [Accepted: 09/08/2023] [Indexed: 09/21/2023]
Abstract
OBJECTIVE This systematic review and meta-analysis aimed to evaluate the performance of existing externally validated prediction models for pre-eclampsia (PE) (specifically, any-onset, early-onset, late-onset and preterm PE). METHODS A systematic search was conducted in five databases (MEDLINE, EMBASE, Emcare, CINAHL and Maternity & Infant Care Database) and using Google Scholar/reference search to identify studies based on the Population, Index prediction model, Comparator, Outcome, Timing and Setting (PICOTS) approach until 20 May 2023. We extracted data using the CHARMS checklist and appraised the risk of bias using the PROBAST tool. A meta-analysis of discrimination and calibration performance was conducted when appropriate. RESULTS Twenty-three studies reported 52 externally validated prediction models for PE (one preterm, 20 any-onset, 17 early-onset and 14 late-onset PE models). No model had the same set of predictors. Fifteen any-onset PE models were validated externally once, two were validated twice and three were validated three times, while the Fetal Medicine Foundation (FMF) competing-risks model for preterm PE prediction was validated widely in 16 different settings. The most common predictors were maternal characteristics (prepregnancy body mass index, prior PE, family history of PE, chronic medical conditions and ethnicity) and biomarkers (uterine artery pulsatility index and pregnancy-associated plasma protein-A). The FMF model for preterm PE (triple test plus maternal factors) had the best performance, with a pooled area under the receiver-operating-characteristics curve (AUC) of 0.90 (95% prediction interval (PI), 0.76-0.96), and was well calibrated. The other models generally had poor-to-good discrimination performance (median AUC, 0.66 (range, 0.53-0.77)) and were overfitted on external validation. Apart from the FMF model, only two models that were validated multiple times for any-onset PE prediction, which were based on maternal characteristics only, produced reasonable pooled AUCs of 0.71 (95% PI, 0.66-0.76) and 0.73 (95% PI, 0.55-0.86). CONCLUSIONS Existing externally validated prediction models for any-, early- and late-onset PE have limited discrimination and calibration performance, and include inconsistent input variables. The triple-test FMF model had outstanding discrimination performance in predicting preterm PE in numerous settings, but the inclusion of specialized biomarkers may limit feasibility and implementation outside of high-resource settings. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
Collapse
Affiliation(s)
- S A Tiruneh
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - T T T Vu
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - L J Moran
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - E J Callander
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
- School of Public Health, Faculty of Health, University of Technology Sydney, Sydney, NSW, Australia
| | - J Allotey
- World Health Organization (WHO) Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - S Thangaratinam
- World Health Organization (WHO) Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
- Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - D L Rolnik
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
| | - H J Teede
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| | - R Wang
- Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia
| | - J Enticott
- Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia
| |
Collapse
|
5
|
Lee NMW, Chaemsaithong P, Poon LC. Prediction of preeclampsia in asymptomatic women. Best Pract Res Clin Obstet Gynaecol 2024; 92:102436. [PMID: 38056380 DOI: 10.1016/j.bpobgyn.2023.102436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/21/2023] [Accepted: 11/18/2023] [Indexed: 12/08/2023]
Abstract
Preeclampsia is a major cause of maternal and perinatal morbidity and mortality. It is important to identify women who are at high risk of developing this disorder in their first trimester of pregnancy to allow timely therapeutic intervention. The use of low-dose aspirin initiated before 16 weeks of gestation can significantly reduce the rate of preterm preeclampsia by 62 %. Effective screening recommended by the Fetal Medicine Foundation (FMF) consists of a combination of maternal risk factors, mean arterial pressure, uterine artery pulsatility index (UtA-PI) and placental growth factor (PLGF). The current model has detection rates of 90 %, 75 %, and 41 % for early, preterm, and term preeclampsia, respectively at 10 % false-positive rate. Similar risk assessment can be performed during the second trimester in all pregnant women irrespective of first trimester screening results. The use of PLGF, UtA-PI, sFlt-1 combined with other investigative tools are part of risk assessment.
Collapse
Affiliation(s)
- Nikki M W Lee
- Department of Obstetrics and Gynaecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
| | - Piya Chaemsaithong
- Department of Obstetrics and Gynecology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
| | - Liona C Poon
- Department of Obstetrics and Gynaecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China.
| |
Collapse
|
6
|
Chen Y, Huang X, Wu S, Guo P, Huang J, Zhou L, Tan X. Machine-learning predictive model of pregnancy-induced hypertension in the first trimester. Hypertens Res 2023; 46:2135-2144. [PMID: 37160966 DOI: 10.1038/s41440-023-01298-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/17/2023] [Accepted: 03/17/2023] [Indexed: 05/11/2023]
Abstract
In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (LASSO logistic regression, random forest, backpropagation neural network, and support vector machines) to predict the occurrence of PIH in a prospective cohort. Candidate features for predicting the occurrence of middle and late PIH were acquired using a LASSO algorithm. The performance of predictive models was assessed using receiver operating characteristic analysis. Finally, a nomogram was established with the model scores, age, and nulliparity. Calibration, clinical usefulness, and internal validation were used to assess the performance of the nomogram. In the training set (2258 pregnant women), eleven candidate factors in the first trimester were significantly associated with the occurrence of PIH (P < 0.001 in the training set). Four models showed AUCs from 0.780 to 0.816 in the training set. For the validation set (939 pregnant women), AUCs varied from 0.516 to 0.795. The nomogram showed good discrimination, with an AUC of 0.847 (95% CI: 0.805-0.889) in the training set and 0.753 (95% CI: 0.653-0.853) in the validation set. Decision curve analysis suggested that the model was clinically useful. The model developed using LASSO logistic regression achieved the best performance in predicting the occurrence of PIH. The derived nomogram, which incorporates the model score and maternal risk factors, can be used to predict PIH in clinical practice. We develop a model with good performance for clinical prediction of PIH in the first trimester.
Collapse
Affiliation(s)
- Yequn Chen
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, China
| | - Xiru Huang
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, China
- Shantou University Medical College, Shantou, Guangdong, 515041, China
| | - Shiwan Wu
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, China
| | - Pi Guo
- Shantou University Medical College, Shantou, Guangdong, 515041, China
| | - Ju Huang
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, China
| | - Li Zhou
- Cancer Hospital Of Shantou University Medical College, Shantou, Guangdong, 515041, China
| | - Xuerui Tan
- The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, China.
- Shantou University Medical College, Shantou, Guangdong, 515041, China.
| |
Collapse
|
7
|
Gunabalasingam S, De Almeida Lima Slizys D, Quotah O, Magee L, White SL, Rigutto-Farebrother J, Poston L, Dalrymple KV, Flynn AC. Micronutrient supplementation interventions in preconception and pregnant women at increased risk of developing pre-eclampsia: a systematic review and meta-analysis. Eur J Clin Nutr 2023; 77:710-730. [PMID: 36352102 PMCID: PMC10335932 DOI: 10.1038/s41430-022-01232-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 10/20/2022] [Accepted: 10/24/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Pre-eclampsia can lead to maternal and neonatal complications and is a common cause of maternal mortality worldwide. This review has examined the effect of micronutrient supplementation interventions in women identified as having a greater risk of developing pre-eclampsia. METHODS A systematic review was performed using the PRISMA guidelines. The electronic databases MEDLINE, EMBASE and the Cochrane Central Register of Controlled trials were searched for relevant literature and eligible studies identified according to a pre-specified criteria. A meta-analysis of randomised controlled trials (RCTs) was conducted to examine the effect of micronutrient supplementation on pre-eclampsia in high-risk women. RESULTS Twenty RCTs were identified and supplementation included vitamin C and E (n = 7), calcium (n = 5), vitamin D (n = 3), folic acid (n = 2), magnesium (n = 1) and multiple micronutrients (n = 2). Sample size and recruitment time point varied across studies and a variety of predictive factors were used to identify participants, with a previous history of pre-eclampsia being the most common. No studies utilised a validated prediction model. There was a reduction in pre-eclampsia with calcium (risk difference, -0.15 (-0.27, -0.03, I2 = 83.4%)), and vitamin D (risk difference, -0.09 (-0.17, -0.02, I2 = 0.0%)) supplementation. CONCLUSION Our findings show a lower rate of pre-eclampsia with calcium and vitamin D, however, conclusions were limited by small sample sizes, methodological variability and heterogeneity between studies. Further higher quality, large-scale RCTs of calcium and vitamin D are warranted. Exploration of interventions at different time points before and during pregnancy as well as those which utilise prediction modelling methodology, would provide greater insight into the efficacy of micronutrient supplementation intervention in the prevention of pre-eclampsia in high-risk women.
Collapse
Affiliation(s)
- Sowmiya Gunabalasingam
- Department of Women and Children's Health, School of Life Course and Population Sciences, King's College London, 10th Floor North Wing, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Daniele De Almeida Lima Slizys
- Department of Women and Children's Health, School of Life Course and Population Sciences, King's College London, 10th Floor North Wing, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Ola Quotah
- Department of Women and Children's Health, School of Life Course and Population Sciences, King's College London, 10th Floor North Wing, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Laura Magee
- Department of Women and Children's Health, School of Life Course and Population Sciences, King's College London, 10th Floor North Wing, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Sara L White
- Department of Women and Children's Health, School of Life Course and Population Sciences, King's College London, 10th Floor North Wing, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | | | - Lucilla Poston
- Department of Women and Children's Health, School of Life Course and Population Sciences, King's College London, 10th Floor North Wing, St Thomas' Hospital, Westminster Bridge Road, London, SE1 7EH, UK
| | - Kathryn V Dalrymple
- Department of Population Health Sciences, School of Life Course and Population Sciences, King's College London, 4th floor Addison House, Guy's Campus, London, SE1 1UL, UK
| | - Angela C Flynn
- Department of Nutritional Sciences, School of Life Course and Population Sciences, King's College London, Franklin-Wilkins Building, 150 Stamford Street, London, SE1 9NH, UK.
| |
Collapse
|
8
|
Siricharoenthai P, Phupong V. The first-trimester serum high-temperature requirement protease A4 and uterine artery Doppler for the prediction of preeclampsia. Sci Rep 2023; 13:8295. [PMID: 37217518 PMCID: PMC10202921 DOI: 10.1038/s41598-023-35243-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/15/2023] [Indexed: 05/24/2023] Open
Abstract
The objective of this study was to investigate the predictive value of serum high-temperature requirement protease A4 (HtrA4) and the first-trimester uterine artery in predicting preeclampsia in singleton pregnancy. Pregnant women at gestational age 11-13+6 weeks, who visited the antenatal clinic at King Chulalongkorn Memorial Hospital, Department of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University during April 2020-July 2021 were included. Serum HtrA4 levels and transabdominal uterine artery Doppler ultrasound were performed to evaluate this combination for calculating the predictive value of preeclampsia. While 371 singleton pregnant women enrolled in this study, 366 completed it. Thirty-four (9.3%) women had preeclampsia. Mean serum HtrA4 levels were higher in the preeclampsia group than in the control group (9.4 ± 3.9 vs 4.6 ± 2.2 ng/ml, p < 0.001). The mean uterine artery pulsatility index (UtA-PI) was higher in the group with early onset preeclampsia than in the control group (2.3 ± 0.5 vs 1.7 ± 0.5, p = 0.002). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 76.5%, 90.7%, 45.6%, and 97.4%, respectively, when using serum HtrA4 levels above 1.8 multiples of the median for the gestational age as a cut-off value for predicting preeclampsia. A combination of serum HtrA4 levels and UtA-PI > 95th percentile yielded sensitivity, specificity, PPV, and NPV of 79.4%, 86.1%, 37% and 97.6%, respectively, for the prediction of preeclampsia. A combination of serum HtrA4 levels and uterine artery Doppler in the first trimester had good sensitivity for predicting preeclampsia.
Collapse
Affiliation(s)
- Patcharaporn Siricharoenthai
- Placental Related Diseases Research Unit, Department of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University, Rama IV Road, Pathumwan, Bangkok, 10330, Thailand
| | - Vorapong Phupong
- Placental Related Diseases Research Unit, Department of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University, Rama IV Road, Pathumwan, Bangkok, 10330, Thailand.
| |
Collapse
|
9
|
Sedaghati F, Gleason RL. A mathematical model of vascular and hemodynamics changes in early and late forms of preeclampsia. Physiol Rep 2023; 11:e15661. [PMID: 37186372 PMCID: PMC10132946 DOI: 10.14814/phy2.15661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 03/15/2023] [Accepted: 03/16/2023] [Indexed: 05/17/2023] Open
Abstract
Preeclampsia-eclampsia syndrome is a leading cause of maternal mortality. The precise etiology of preeclampsia is still not well-defined and different forms exist, including early and late forms or preeclampsia, which may arise via distinctly different mechanisms. Low-dose aspirin administered at the end of the first trimester in women identified as high risk has been shown to reduce the incidence of early, but not late, preeclampsia; however, current risk factors show only fair predictive capability. There is a pressing need to develop accurate descriptions for the different forms of preeclampsia. This paper presents 1D fluid, solid, growth, and remodeling models for pregnancies complicated with early and late forms of preeclampsia. Simulations affirm a broad set of literature results that early forms of preeclampsia are characterized by elevated uterine artery pulsatility index (UA-PI) and total peripheral resistance (TPR) and lower cardiac output (CO), with modestly increased mean arterial blood pressure (MAP) in the first half of pregnancy, with elevation of TPR and MAP beginning at 20 weeks. Conversely, late forms of preeclampsia are characterized by only slightly elevated UA-PI and normal pre-term TPR, and slightly elevated MAP and CO throughout pregnancy, with increased TPR and MAP beginning after 34 weeks. Results suggest that preexisting arterial stiffness may be elevated in women that develop both early forms and late forms of preeclampsia; however, data that verify these results are lacking in the literature. Pulse wave velocity increases in early- and late-preeclampsia, coincident with increases in blood pressure; however, these increases are mainly due to the strain-stiffening response of larger arteries, rather than arterial remodeling-derived changes in material properties. These simulations affirm that early forms of preeclampsia may be associated with abnormal placentation, whereas late forms may be more closely associated with preexisting maternal cardiovascular factors; simulations also highlight several critical gaps in available data.
Collapse
Affiliation(s)
- Farbod Sedaghati
- The George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| | - Rudolph L. Gleason
- The George W. Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
- The Wallace H. Coulter Department of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA
| |
Collapse
|
10
|
Hantoushzadeh S, Ahangari R, Balaneji SS, Ghamari A, Hashemnejad M, Piri S. Correlation of Fetal Heart Rate, Uterine Artery Pulsatility Index, Pregnancy Associated Plasma Protein-A and Crown-Rump Length in Pre-eclampsia - a Prospective Cohort Study. MAEDICA 2023; 18:50-54. [PMID: 37266467 PMCID: PMC10231157 DOI: 10.26574/maedica.2023.18.1.50] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
BackgroundThe prevalence of pre-eclampsia (PE) as a systemic disease in pregnancy is about 3-5%, but it is still one of the most important causes of maternal and infant mortality worldwide. This study aimed to investigate the association between fetal heart rate (FHR) and uterine artery pulsatility index (UtA-PI) in Doppler. Methods:The current cohort study was carried out on 317 pregnant women with a gestational age of 11 to 13 weeks and six days. Mothers were followed up from the first trimester until the delivery between March 2019 and March 2020. Uterine artery pulsatility index, FHR and ductus venosus pulsatility index (DVPI) were recorded. Finally, the Doppler index of ductus venosus, FHR and other design variables were compared between the two groups with and without preeclampsia. Results: Subjects' mean body mass index (BMI) was 25.31±3.98 kg/m2. The UtA-PI was correlated with Crown rump length (CRL) (r=-0.207, p=0.001), pregnancy associated plasma protein-A (PAPP-A) (r=-0.167, p=0.003), FHR (r=0.14, p=0.011) and uterine artery multiples of the median (UA MoM) (r=0.990, p=0.001), with the last one showing a strong positive correlation with CRL; PAPP-A had a reverse correlation with UA MoM (r=-0.171, p=0.002) and UtA-PI (r=-0.167, p=0.003), while FHR had a poor correlation with UA MoM (r=0.118, p=0.035) and UtA-PI (r=0.142, p=0.011). Conclusions:Uterine artery multiples of the median (UA MoM) was found to have a strong correlation with UtA-PI and, but a reverse correlation with PAPP-A. Intrauterine growth restriction (IUGR) had a significant association with FHR and UtA-PI. These findings imply the necessity of further future follow-up of offspring with a history of increased UtA-PI or maternal PE for cardiac alteration.
Collapse
Affiliation(s)
- Sedigheh Hantoushzadeh
- Maternal, Fetal and Neonatal Research Center, Tehran University of Medical Science, Tehran, Iran
| | | | | | - Azin Ghamari
- Growth and Development Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Hashemnejad
- Department of Obstetrics and Gynecology, School of Medicine, Kamali Hospital, Alborz University of Medical Sciences, Alborz, Iran
| | - Solmaz Piri
- Obstetrician and Gynecologist, Perinatalogist, Director of International Affairs of National Association of Iranian Gynecologists and Obstetricians (NAIGO), Ambassador of ISUOG in Middle East and North Africa
| |
Collapse
|
11
|
Sheikh J, Allotey J, Kew T, Fernández-Félix BM, Zamora J, Khalil A, Thangaratinam S. Effects of race and ethnicity on perinatal outcomes in high-income and upper-middle-income countries: an individual participant data meta-analysis of 2 198 655 pregnancies. Lancet 2022; 400:2049-2062. [PMID: 36502843 DOI: 10.1016/s0140-6736(22)01191-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/16/2022] [Accepted: 06/17/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Existing evidence on the effects of race and ethnicity on pregnancy outcomes is restricted to individual studies done within specific countries and health systems. We aimed to assess the impact of race and ethnicity on perinatal outcomes in high-income and upper-middle-income countries, and to ascertain whether the magnitude of disparities, if any, varied across geographical regions. METHODS For this individual participant data (IPD) meta-analysis we used data from the International Prediction of Pregnancy Complications (IPPIC) Network of studies on pregnancy complications; the full dataset comprised 94 studies, 53 countries, and 4 539 640 pregnancies. We included studies that reported perinatal outcomes (neonatal death, stillbirth, preterm birth, and small-for-gestational-age babies) in at least two racial or ethnic groups (White, Black, south Asian, Hispanic, or other). For our two-step random-effects IPD meta-analysis, we did multiple imputations for confounder variables (maternal age, BMI, parity, and level of maternal education) selected with a directed acyclic graph. The primary outcomes were neonatal mortality and stillbirth. Secondary outcomes were preterm birth and a small-for-gestational-age baby. We estimated the association of race and ethnicity with perinatal outcomes using a multivariate logistic regression model and reported this association with odds ratios (ORs) and 95% CIs. We also did a subgroup analysis of studies by geographical region. FINDINGS 51 studies from 20 high-income and upper-middle-income countries, comprising 2 198 655 pregnancies, were eligible for inclusion in this IPD meta-analysis. Neonatal death was twice as likely in babies born to Black women than in babies born to White women (OR 2·00, 95% CI 1·44-2·78), as was stillbirth (2·16, 1·46-3·19), and babies born to Black women were at increased risk of preterm birth (1·65, 1·46-1·88) and being small for gestational age (1·39, 1·13-1·72). Babies of women categorised as Hispanic had a three-times increased risk of neonatal death (OR 3·34, 95% CI 2·77-4·02) than did those born to White women, and those born to south Asian women were at increased risk of preterm birth (OR 1·26, 95% CI 1·07-1·48) and being small for gestational age (1·61, 1·32-1·95). The effects of race and ethnicity on preterm birth and small-for-gestational-age babies did not vary across regions. INTERPRETATION Globally, among underserved groups, babies born to Black women had consistently poorer perinatal outcomes than White women after adjusting for maternal characteristics, although the risks varied for other groups. The effects of race and ethnicity on adverse perinatal outcomes did not vary by region. FUNDING National Institute for Health and Care Research, Wellbeing of Women.
Collapse
Affiliation(s)
- Jameela Sheikh
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - John Allotey
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK
| | - Tania Kew
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Borja M Fernández-Félix
- Clinical Biostatistics Unit, Hospital Universitario Ramón y Cajal, IRYCIS, Madrid, Spain; CIBER Epidemiology and Public Health, Madrid, Spain
| | - Javier Zamora
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Clinical Biostatistics Unit, Hospital Universitario Ramón y Cajal, IRYCIS, Madrid, Spain; CIBER Epidemiology and Public Health, Madrid, Spain.
| | - Asma Khalil
- Foetal Medicine Unit, Department of Obstetrics and Gynaecology, St George's University Hospitals NHS Foundation Trust, London, UK; Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Shakila Thangaratinam
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; Birmingham Women's Hospital, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| |
Collapse
|
12
|
Marić I, Contrepois K, Moufarrej MN, Stelzer IA, Feyaerts D, Han X, Tang A, Stanley N, Wong RJ, Traber GM, Ellenberger M, Chang AL, Fallahzadeh R, Nassar H, Becker M, Xenochristou M, Espinosa C, De Francesco D, Ghaemi MS, Costello EK, Culos A, Ling XB, Sylvester KG, Darmstadt GL, Winn VD, Shaw GM, Relman DA, Quake SR, Angst MS, Snyder MP, Stevenson DK, Gaudilliere B, Aghaeepour N. Early prediction and longitudinal modeling of preeclampsia from multiomics. PATTERNS (NEW YORK, N.Y.) 2022; 3:100655. [PMID: 36569558 PMCID: PMC9768681 DOI: 10.1016/j.patter.2022.100655] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 09/28/2022] [Accepted: 11/11/2022] [Indexed: 12/13/2022]
Abstract
Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.
Collapse
Affiliation(s)
- Ivana Marić
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Mira N. Moufarrej
- Departments of Bioengineering and Applied Physics, Stanford University and Chan Zuckerberg Biohub, Stanford, CA 94305, USA
| | - Ina A. Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xiaoyuan Han
- University of the Pacific, Arthur A. Dugoni School of Dentistry, San Francisco, CA 94103, USA
| | - Andy Tang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ronald J. Wong
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gavin M. Traber
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Mathew Ellenberger
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Alan L. Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Huda Nassar
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Maria Xenochristou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Davide De Francesco
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Mohammad S. Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Digital Technologies Research Centre, National Research Council Canada, Toronto, Canada
| | - Elizabeth K. Costello
- Departments of Medicine, and of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Xuefeng B. Ling
- Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Karl G. Sylvester
- Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gary L. Darmstadt
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Virginia D. Winn
- Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gary M. Shaw
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - David A. Relman
- Departments of Medicine, and of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Infectious Diseases Section, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA 94304, USA
| | - Stephen R. Quake
- Departments of Bioengineering and Applied Physics, Stanford University and Chan Zuckerberg Biohub, Stanford, CA 94305, USA
| | - Martin S. Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - David K. Stevenson
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Brice Gaudilliere
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Nima Aghaeepour
- Department of Pediatrics, Division of Neonatal and Developmental Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| |
Collapse
|
13
|
Li S, Wang Z, Vieira LA, Zheutlin AB, Ru B, Schadt E, Wang P, Copperman AB, Stone JL, Gross SJ, Kao YH, Lau YK, Dolan SM, Schadt EE, Li L. Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data. NPJ Digit Med 2022; 5:68. [PMID: 35668134 PMCID: PMC9170686 DOI: 10.1038/s41746-022-00612-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Accepted: 05/19/2022] [Indexed: 11/15/2022] Open
Abstract
Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preeclampsia across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted preeclampsia with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.82, and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts. While our machine learning approach identified known risk factors of preeclampsia (such as blood pressure, weight, and maternal age), it also identified other potential risk factors, such as complete blood count related characteristics for the antepartum period. Our model not only has utility for earlier identification of patients at risk for preeclampsia, but given the prediction accuracy exceeds what is currently achieved in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.
Collapse
Affiliation(s)
| | | | - Luciana A Vieira
- Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | | | - Pei Wang
- Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alan B Copperman
- Sema4, Stamford, CT, USA.,Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Reproductive Endocrinology and Infertility, Reproductive Medicine associates of New York, New York, NY, USA
| | - Joanne L Stone
- Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Susan J Gross
- Sema4, Stamford, CT, USA.,Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | - Siobhan M Dolan
- Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric E Schadt
- Sema4, Stamford, CT, USA. .,Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Li Li
- Sema4, Stamford, CT, USA. .,Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| |
Collapse
|
14
|
Bennett R, Mulla ZD, Parikh P, Hauspurg A, Razzaghi T. An imbalance-aware deep neural network for early prediction of preeclampsia. PLoS One 2022; 17:e0266042. [PMID: 35385525 PMCID: PMC8985991 DOI: 10.1371/journal.pone.0266042] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 03/12/2022] [Indexed: 11/18/2022] Open
Abstract
Preeclampsia (PE) is a hypertensive complication affecting 8-10% of US pregnancies annually. While there is no cure for PE, aspirin may reduce complications for those at high risk for PE. Furthermore, PE disproportionately affects racial minorities, with a higher burden of morbidity and mortality. Previous studies have shown early prediction of PE would allow for prevention. We approached the prediction of PE using a new method based on a cost-sensitive deep neural network (CSDNN) by considering the severe imbalance and sparse nature of the data, as well as racial disparities. We validated our model using large extant rich data sources that represent a diverse cohort of minority populations in the US. These include Texas Public Use Data Files (PUDF), Oklahoma PUDF, and the Magee Obstetric Medical and Infant (MOMI) databases. We identified the most influential clinical and demographic features (predictor variables) relevant to PE for both general populations and smaller racial groups. We also investigated the effectiveness of multiple network architectures using three hyperparameter optimization algorithms: Bayesian optimization, Hyperband, and random search. Our proposed models equipped with focal loss function yield superior and reliable prediction performance compared with the state-of-the-art techniques with an average area under the curve (AUC) of 66.3% and 63.5% for the Texas and Oklahoma PUDF respectively, while the CSDNN model with weighted cross-entropy loss function outperforms with an AUC of 76.5% for the MOMI data. Furthermore, our CSDNN model equipped with focal loss function leads to an AUC of 66.7% for Texas African American and 57.1% for Native American. The best results are obtained with 62.3% AUC with CSDNN with weighted cross-entropy loss function for Oklahoma African American, 58% AUC with DNN and balanced batch for Oklahoma Native American, and 72.4% AUC using either CSDNN with weighted cross-entropy loss function or CSDNN with focal loss with balanced batch method for MOMI African American dataset. Our results provide the first evidence of the predictive power of clinical databases for PE prediction among minority populations.
Collapse
Affiliation(s)
- Rachel Bennett
- School of Industrial and Systems Engineering, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Zuber D. Mulla
- Department of Obstetrics and Gynecology, and Office of Faculty Development, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, Texas, United States of America
- Department of Public Health, Texas Tech University Health Sciences Center, Lubbock, Texas, United States of America
| | - Pavan Parikh
- Division of Maternal Fetal Medicine, University of Oklahoma Health Science Center, Oklahoma City, Oklahoma, United States of America
| | - Alisse Hauspurg
- Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Talayeh Razzaghi
- School of Industrial and Systems Engineering, University of Oklahoma, Norman, Oklahoma, United States of America
| |
Collapse
|
15
|
Vasilache IA, Carauleanu A, Socolov D, Matasariu R, Pavaleanu I, Nemescu D. Predictive performance of first trimester serum galectin‑13/PP‑13 in preeclampsia screening: A systematic review and meta‑analysis. Exp Ther Med 2022; 23:370. [PMID: 35495605 PMCID: PMC9019605 DOI: 10.3892/etm.2022.11297] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/08/2022] [Indexed: 11/06/2022] Open
Affiliation(s)
- Ingrid-Andrada Vasilache
- Department of Obstetrics and Gynecology, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Alexandru Carauleanu
- Department of Obstetrics and Gynecology, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Demetra Socolov
- Department of Obstetrics and Gynecology, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Roxana Matasariu
- Department of Obstetrics and Gynecology, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Ioana Pavaleanu
- Department of Obstetrics and Gynecology, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Dragos Nemescu
- Department of Obstetrics and Gynecology, ‘Grigore T. Popa’ University of Medicine and Pharmacy, 700115 Iasi, Romania
| |
Collapse
|
16
|
Das E, Singh V, Agrawal S, Pati SK. Prediction of Preeclampsia Using First-Trimester Uterine Artery Doppler and Pregnancy-Associated Plasma Protein-A (PAPP-A): A Prospective Study in Chhattisgarh, India. Cureus 2022; 14:e22026. [PMID: 35340517 PMCID: PMC8913542 DOI: 10.7759/cureus.22026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/08/2022] [Indexed: 11/05/2022] Open
|
17
|
A mathematical model of maternal vascular growth and remodeling and changes in maternal hemodynamics in uncomplicated pregnancy. Biomech Model Mechanobiol 2022; 21:647-669. [PMID: 35112224 DOI: 10.1007/s10237-021-01555-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 12/26/2021] [Indexed: 11/02/2022]
Abstract
The maternal vasculature undergoes tremendous growth and remodeling (G&R) that enables a > 15-fold increase in blood flow through the uterine vasculature from conception to term. Hemodynamic metrics (e.g., uterine artery pulsatility index, UA-PI) are useful for the prognosis of pregnancy complications; however, improved characterization of the maternal hemodynamics is necessary to improve prognosis. The goal of this paper is to develop a mathematical framework to characterize maternal vascular G&R and hemodynamics in uncomplicated human pregnancies. A validated 1D model of the human vascular tree from the literature was adapted and inlet blood flow waveforms at the ascending aorta at 4 week increments from 0 to 40 weeks of gestation were prescribed. Peripheral resistances of each terminal vessel were adjusted to achieve target flow rates and mean arterial pressure at each gestational age. Vessel growth was governed by wall shear stress (and axial lengthening in uterine vessels), and changes in vessel distensibility were related to vessel growth. Uterine artery velocity waveforms generated from this model closely resembled ultrasound results from the literature. The literature UA-PI values changed significantly across gestation, increasing in the first month of gestation, then dramatically decreasing from 4 to 20 weeks. Our results captured well the time-course of vessel geometry, material properties, and UA-PI. This 1D fluid-G&R model captured the salient hemodynamic features across a broad range of clinical reports and across gestation for uncomplicated human pregnancy. While results capture available data well, this study highlights significant gaps in available data required to better understand vascular remodeling in pregnancy.
Collapse
|
18
|
Chaemsaithong P, Sahota DS, Poon LC. First trimester preeclampsia screening and prediction. Am J Obstet Gynecol 2022; 226:S1071-S1097.e2. [PMID: 32682859 DOI: 10.1016/j.ajog.2020.07.020] [Citation(s) in RCA: 127] [Impact Index Per Article: 63.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/30/2020] [Accepted: 07/14/2020] [Indexed: 12/16/2022]
Abstract
Preeclampsia is a major cause of maternal and perinatal morbidity and mortality. Early-onset disease requiring preterm delivery is associated with a higher risk of complications in both mothers and babies. Evidence suggests that the administration of low-dose aspirin initiated before 16 weeks' gestation significantly reduces the rate of preterm preeclampsia. Therefore, it is important to identify pregnant women at risk of developing preeclampsia during the first trimester of pregnancy, thus allowing timely therapeutic intervention. Several professional organizations such as the American College of Obstetricians and Gynecologists (ACOG) and National Institute for Health and Care Excellence (NICE) have proposed screening for preeclampsia based on maternal risk factors. The approach recommended by ACOG and NICE essentially treats each risk factor as a separate screening test with additive detection rate and screen-positive rate. Evidence has shown that preeclampsia screening based on the NICE and ACOG approach has suboptimal performance, as the NICE recommendation only achieves detection rates of 41% and 34%, with a 10% false-positive rate, for preterm and term preeclampsia, respectively. Screening based on the 2013 ACOG recommendation can only achieve detection rates of 5% and 2% for preterm and term preeclampsia, respectively, with a 0.2% false-positive rate. Various first trimester prediction models have been developed. Most of them have not undergone or failed external validation. However, it is worthy of note that the Fetal Medicine Foundation (FMF) first trimester prediction model (namely the triple test), which consists of a combination of maternal factors and measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor, has undergone successful internal and external validation. The FMF triple test has detection rates of 90% and 75% for the prediction of early and preterm preeclampsia, respectively, with a 10% false-positive rate. Such performance of screening is superior to that of the traditional method by maternal risk factors alone. The use of the FMF prediction model, followed by the administration of low-dose aspirin, has been shown to reduce the rate of preterm preeclampsia by 62%. The number needed to screen to prevent 1 case of preterm preeclampsia by the FMF triple test is 250. The key to maintaining optimal screening performance is to establish standardized protocols for biomarker measurements and regular biomarker quality assessment, as inaccurate measurement can affect screening performance. Tools frequently used to assess quality control include the cumulative sum and target plot. Cumulative sum is a sensitive method to detect small shifts over time, and point of shift can be easily identified. Target plot is a tool to evaluate deviation from the expected multiple of median and the expected median of standard deviation. Target plot is easy to interpret and visualize. However, it is insensitive to detecting small deviations. Adherence to well-defined protocols for the measurements of mean arterial pressure, uterine artery pulsatility index, and placental growth factor is required. This article summarizes the existing literature on the different methods, recommendations by professional organizations, quality assessment of different components of risk assessment, and clinical implementation of the first trimester screening for preeclampsia.
Collapse
|
19
|
Niveles séricos de PAPP-A y β-hCG en el primer trimestre del embarazo como predictores de resultados obstétricos desfavorables en el Hospital Universitario Nuestra Señora de Candelaria. CLINICA E INVESTIGACION EN GINECOLOGIA Y OBSTETRICIA 2022. [DOI: 10.1016/j.gine.2021.100711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
20
|
Wu Y, Liu Y, Ding Y. Predictive Performance of Placental Protein 13 for Screening Preeclampsia in the First Trimester: A Systematic Review and Meta-Analysis. Front Med (Lausanne) 2021; 8:756383. [PMID: 34869456 PMCID: PMC8640131 DOI: 10.3389/fmed.2021.756383] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/25/2021] [Indexed: 12/02/2022] Open
Abstract
Preeclampsia is a pregnancy-specific syndrome that affects maternal and neonatal mortality. Several serum biomarkers can be used to predict preeclampsia. Among these proteins, placental protein 13 (PP13) has received progressively more interest in recent studies. The decrease in PP13 expression is one of the earliest signs for the development of preeclampsia and has shown its predictive performance for preeclampsia. In this meta-analysis, we collected 17 observational studies with 40,474 pregnant women. The overall sensitivity of PP13 to predict preeclampsia was 0.62 [95% confidence interval (CI) = 0.49–0.74], the specificity was 0.84 (95%CI = 0.81–0.86), and the diagnostic odds ratio was nine (95%CI = 5–15). The area under the curve for summary receiver operating characteristic was 0.84. We then chose the early-onset preeclampsia as a subgroup. The sensitivity of early-onset subgroup was 0.63 (95%CI = 0.58–0.76), the specificity was 0.85 (95%CI = 0.82–0.88), and the diagnostic odds ratio was 10 (95%CI = 6–18). The findings of our meta-analysis indicate that PP13 may be an effective serum biomarker for the predictive screening of preeclampsia. Nonetheless, large prospective cohort studies and randomized controlled trials are expected to uncover its application in clinical practice. The heterogeneity of the original trials may limit the clinical application of PP13. Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=188948 The meta-analysis was registered in PROSPERO (CRD42020188948).
Collapse
Affiliation(s)
- Yifan Wu
- Department of Obstetrics, The Second Xiangya Hospital, Central South University, Changsha, China.,Department of Obstetrics, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yang Liu
- Department of Obstetrics, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yiling Ding
- Department of Obstetrics, The Second Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
21
|
Li YX, Shen XP, Yang C, Cao ZZ, Du R, Yu MD, Wang JP, Wang M. Novelelectronic health records applied for prediction of pre-eclampsia: Machine-learning algorithms. Pregnancy Hypertens 2021; 26:102-109. [PMID: 34739939 DOI: 10.1016/j.preghy.2021.10.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/16/2021] [Accepted: 10/22/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE To predict risk of pre-eclampsia (PE) in women using machine learning (ML) algorithms, based on electronic health records (EHR) collected at the early second trimester. STUDY DESIGN A total of 3759 cases of pregnancy who received antenatal care at Xinhua hospital Chongming branch Affiliated to Shanghai Jiaotong University were included in this retrospective EHR-based study. Thirty-eight candidate clinical parameters routinely available at the first visit in antenatal care were collected by manual chart review. Logistic regression (LR), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) were used to construct the prediction model. Features that contributed to the model predictions were identified using XGBoost. OUTCOME MEASURES The performance of ML models to predict women at risk of PE was quantified in terms of accuracy, precision, recall, false negative score, f1_score, brier score and the area under the receiver operating curve (auROC). RESULTS The XGboost model had the best prediction performance (accuracy = 0.920, precision = 0.447, recall = 0.789, f1_score = 0.571, auROC = 0.955). The most predictive feature of PE development was fasting plasma glucose, followed by mean blood pressure and body mass index. An easy-to-use model that a patient could answer independently still enabled accurate prediction, with auROC of 0.83. CONCLUSION risk of PE development can be predicted with excellent discriminative ability using ML algorithms based on EHR collected at the early second trimester. Future studies are needed to assess the real-world clinical utility of the model.
Collapse
Affiliation(s)
- Yi-Xin Li
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xiao-Ping Shen
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chao Yang
- Department of Scientific Research Centre, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zuo-Zeng Cao
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Rui Du
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Min-da Yu
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jun-Ping Wang
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mei Wang
- Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| |
Collapse
|
22
|
Allotey J, Snell KI, Smuk M, Hooper R, Chan CL, Ahmed A, Chappell LC, von Dadelszen P, Dodds J, Green M, Kenny L, Khalil A, Khan KS, Mol BW, Myers J, Poston L, Thilaganathan B, Staff AC, Smith GC, Ganzevoort W, Laivuori H, Odibo AO, Ramírez JA, Kingdom J, Daskalakis G, Farrar D, Baschat AA, Seed PT, Prefumo F, da Silva Costa F, Groen H, Audibert F, Masse J, Skråstad RB, Salvesen KÅ, Haavaldsen C, Nagata C, Rumbold AR, Heinonen S, Askie LM, Smits LJ, Vinter CA, Magnus PM, Eero K, Villa PM, Jenum AK, Andersen LB, Norman JE, Ohkuchi A, Eskild A, Bhattacharya S, McAuliffe FM, Galindo A, Herraiz I, Carbillon L, Klipstein-Grobusch K, Yeo S, Teede HJ, Browne JL, Moons KG, Riley RD, Thangaratinam S. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis. Health Technol Assess 2021; 24:1-252. [PMID: 33336645 DOI: 10.3310/hta24720] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. OBJECTIVES To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. DESIGN This was an individual participant data meta-analysis of cohort studies. SETTING Source data from secondary and tertiary care. PREDICTORS We identified predictors from systematic reviews, and prioritised for importance in an international survey. PRIMARY OUTCOMES Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia. ANALYSIS We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I 2 and τ2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. RESULTS The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia. LIMITATIONS Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data. CONCLUSION For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings. FUTURE WORK Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate. STUDY REGISTRATION This study is registered as PROSPERO CRD42015029349. FUNDING This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.
Collapse
|
23
|
Tianthong W, Phupong V. Serum hypoxia-inducible factor-1α and uterine artery Doppler ultrasound during the first trimester for prediction of preeclampsia. Sci Rep 2021; 11:6674. [PMID: 33758274 PMCID: PMC7988168 DOI: 10.1038/s41598-021-86073-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/23/2021] [Indexed: 11/09/2022] Open
Abstract
The objective of this study was to determine the predictive value of serum hypoxia-inducible factor-1α (HIF-1α) combined with uterine artery Doppler in singleton pregnancy during 11-13+6 weeks of gestation for preeclampsia. This prospective observational study was conducted in singleton pregnant women at 11-13+6 weeks of gestation who visited the King Chulalongkorn Memorial Hospital, Faculty of Medicine, Chulalongkorn University for antenatal care between February 2019 and May 2020. Serum HIF-1α levels and uterine artery Doppler ultrasound were performed. Pregnancy outcomes were recorded. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of these tests at the optimal cut-off values were determined to predict preeclampsia. A total of 385 participants were analyzed. Of these, 31 cases had preeclampsia (8.1%), and 6 cases of them had early-onset preeclampsia (1.6%). Preeclamptic women had significantly higher serum HIF-1α levels than normal pregnant women (median 1315.2 pg/ml vs. 699.5 pg/ml, p < 0.001). There was no difference in the mean pulsatility (PI) of the uterine artery. Serum HIF-1α levels were higher than 1.45 multiple of median for the gestational age as a cut-off value for predicting preeclampsia; the sensitivity, specificity, PPV, and NPV were 66.7%, 71.5%, 17.2%, and 96.2%, respectively. When a combination of abnormal serum HIF-1α levels and abnormal uterine artery Doppler PI (above the 95th percentile) were used as a predictive value to predict preeclampsia, the sensitivity, specificity, PPV, and NPV were 74.2%, 67.2%, 16.6%, and 96.8%, respectively. This study showed that the serum HIF-1α levels with or without uterine artery Doppler at 11-13+6 weeks of gestation were effective in predicting preeclampsia.
Collapse
Affiliation(s)
- Wasinee Tianthong
- Placental Related Diseases Research Unit, Department of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University, Rama IV Road, Pathumwan, Bangkok, 10330, Thailand
| | - Vorapong Phupong
- Placental Related Diseases Research Unit, Department of Obstetrics and Gynecology, Faculty of Medicine, Chulalongkorn University, Rama IV Road, Pathumwan, Bangkok, 10330, Thailand.
| |
Collapse
|
24
|
Liu N, Guo YN, Gong LK, Wang BS. Advances in biomarker development and potential application for preeclampsia based on pathogenesis. Eur J Obstet Gynecol Reprod Biol X 2021; 9:100119. [PMID: 33103113 PMCID: PMC7575783 DOI: 10.1016/j.eurox.2020.100119] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/27/2020] [Accepted: 10/08/2020] [Indexed: 02/06/2023] Open
Abstract
Preeclampsia (PE) is a pregnancy-specific complication that seriously threatens the health and safety of mothers and infants. The etiology of PE has not been fully elucidated, and no effective treatments are currently available. A pregnant woman with PE often has to make a tough choice on either endangering her own health to give a birth or being forced to terminate her pregnancy. It is recommended by the International Federation of Gynecology and Obstetrics that the combination of maternal high-risk factors and biomarkers could form a good strategy for predicting the risk of PE. Such a combination may also enable more effective monitoring and early clinical intervention in high-risk populations to reduce the risk of PE. Therefore, biomarkers validated by extensive clinical research may be formally applied for clinical PE risk prediction. In this review, we summarized data from clinical research on potential biomarkers and classified them according to the current four major hypotheses, namely placental or trophoblast ischemia and hypoxia, vascular endothelial injury, oxidative stress, and immune dysregulation. Additionally, we also discussed the underlying mechanisms by which these potential biomarkers may be involved in the pathogenesis of PE. Finally, we propose that multiple biomarkers reflecting different aspects of the disease pathogenesis should be used in combination to detect the high-risk PE population in support of clinically targeted intervention and prevention of PE. It is expected that tests made of more sensitive and reliable PE biomarkers based on the aforementioned major hypotheses could potentially improve the accuracy of PE prediction in the future.
Collapse
Affiliation(s)
- Nan Liu
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Drug Research, Center for Drug Safety Evaluation and Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Yu-Na Guo
- Department of Obstetrics, International Peace Maternity & Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China
| | - Li-Kun Gong
- School of Pharmacy, University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Drug Research, Center for Drug Safety Evaluation and Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Bing-Shun Wang
- Department of Biostatistics, Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, No. 227 South Chongqing Rd., Shanghai, 200025, China
| |
Collapse
|
25
|
Zhang N, Tan J, Yang H, Khalil RA. Comparative risks and predictors of preeclamptic pregnancy in the Eastern, Western and developing world. Biochem Pharmacol 2020; 182:114247. [PMID: 32986983 PMCID: PMC7686229 DOI: 10.1016/j.bcp.2020.114247] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 09/21/2020] [Accepted: 09/23/2020] [Indexed: 11/15/2022]
Abstract
Preeclampsia (PE) is a complication of pregnancy characterized by hypertension (HTN-Preg), and often proteinuria. If not managed promptly, PE could lead to eclampsia and seizures. PE could also lead to intrauterine growth restriction (IUGR) and prematurity at birth. Although PE is a major cause of maternal and fetal morbidity and mortality, the underlying mechanisms are unclear. Also, there is a wide variability in the incidence of PE, ranging between 2 and 8% of pregnancies in the Eastern, Western and Developing world, suggesting regional differences in the risk factors and predictors of the pregnancy-related disorder. Several demographic, genetic, dietary and environmental factors, as well as maternal circulating biomarkers have been associated with PE. Demographic factors such as maternal race and ethnicity could play a role in PE. Specific genetic polymorphisms have been identified in PE. Maternal age, parity, education and socioeconomic status could be involved in PE. Dietary fat, protein, calcium and vitamins, body weight, and environmental factors including climate changes and air pollutants could also play a role in PE. Several circulating cytoactive factors including anti-angiogenic factors and cytokines have also been associated with PE. Traditional midwifery care is a common practice in local maternity care units, while advanced perinatal care and new diagnostic tools such as uterine artery Doppler velocimetry have been useful in predicting early PE in major medical centers. These PE risk factors, early predictors and diagnostic tools vary vastly in different regions of the Eastern, Western and Developing world. Further understanding of the differences in the demographic, genetic, dietary and environmental factors among pregnant women in different world regions should help in designing a region-specific cluster of risk factors and predictors of PE, and in turn provide better guidance for region-specific tools for early detection and management of PE.
Collapse
Affiliation(s)
- Ning Zhang
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jing Tan
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - HaiFeng Yang
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Raouf A Khalil
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
| |
Collapse
|
26
|
Soongsatitanon A, Phupong V. Prediction of preeclampsia using first trimester placental protein 13 and uterine artery Doppler. J Matern Fetal Neonatal Med 2020; 35:4412-4417. [PMID: 33198548 DOI: 10.1080/14767058.2020.1849127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
OBJECTIVE To determine the predictive value for preeclampsia by using serum placental protein 13 (PP13) levels and uterine artery pulsatility index (PI) in the first trimester. METHODS This is a prospective observational study that was conducted in pregnant women with gestational age 11-13+6 weeks. Transabdominal uterine artery Doppler and serum PP13 level were performed at the first trimester aneuploidy screening visit. The predictive values of these tests were calculated. RESULTS Data from 353 pregnant women were analyzed. Twenty-nine cases developed preeclampsia. The sensitivity, specificity, positive predictive value and negative predictive value of serum PP13 levels in predicting preeclampsia were 51.7, 65.7, 11.9, and 93.8%, respectively. The sensitivity, specificity, positive predictive value and negative predictive value of the uterine artery PI in predicting preeclampsia were 10.3, 95.7, 17.7, and 92.3%, respectively. When a combination of serum PP13 levels and uterine artery PI were used to predict preeclampsia, the sensitivity, specificity, positive predictive value and negative predictive value were 58.6, 62.9, 12.4 and 94.4%, respectively. CONCLUSION This study demonstrated that the combination of serum PP13 level and uterine artery Doppler in the first trimester was increased the sensitivity for predicting preeclampsia.
Collapse
Affiliation(s)
- Adjima Soongsatitanon
- Department of Obstetrics and Gynecology, Faculty of Medicine, Placental Related diseases Research Unit & Division of Maternal-Fetal Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Vorapong Phupong
- Department of Obstetrics and Gynecology, Faculty of Medicine, Placental Related diseases Research Unit & Division of Maternal-Fetal Medicine, Chulalongkorn University, Bangkok, Thailand
| |
Collapse
|
27
|
Snell KIE, Allotey J, Smuk M, Hooper R, Chan C, Ahmed A, Chappell LC, Von Dadelszen P, Green M, Kenny L, Khalil A, Khan KS, Mol BW, Myers J, Poston L, Thilaganathan B, Staff AC, Smith GCS, Ganzevoort W, Laivuori H, Odibo AO, Arenas Ramírez J, Kingdom J, Daskalakis G, Farrar D, Baschat AA, Seed PT, Prefumo F, da Silva Costa F, Groen H, Audibert F, Masse J, Skråstad RB, Salvesen KÅ, Haavaldsen C, Nagata C, Rumbold AR, Heinonen S, Askie LM, Smits LJM, Vinter CA, Magnus P, Eero K, Villa PM, Jenum AK, Andersen LB, Norman JE, Ohkuchi A, Eskild A, Bhattacharya S, McAuliffe FM, Galindo A, Herraiz I, Carbillon L, Klipstein-Grobusch K, Yeo SA, Browne JL, Moons KGM, Riley RD, Thangaratinam S. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis. BMC Med 2020; 18:302. [PMID: 33131506 PMCID: PMC7604970 DOI: 10.1186/s12916-020-01766-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 08/26/2020] [Indexed: 01/05/2023] Open
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 .
Collapse
Affiliation(s)
- Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK.
| | - John Allotey
- Barts Research Centre for Women's Health (BARC), Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Melanie Smuk
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Richard Hooper
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Claire Chan
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Asif Ahmed
- MirZyme Therapeutics, Innovation Birmingham Campus, Birmingham, UK
| | - Lucy C Chappell
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Peter Von Dadelszen
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Marcus Green
- Action on Pre-eclampsia (APEC) Charity, Worcestershire, UK
| | - Louise Kenny
- Faculty Health & Life Sciences, University of Liverpool, Liverpool, UK
| | - Asma Khalil
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Khalid S Khan
- Barts Research Centre for Women's Health (BARC), Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Pragmatic Clinical Trials Unit, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ben W Mol
- Department of Obstetrics and Gynaecology, Monash University, Monash Medical Centre, Clayton, Victoria, Australia
| | - Jenny Myers
- Maternal and Fetal Health Research Centre, Manchester Academic Health Science Centre, University of Manchester, Central Manchester NHS Trust, Manchester, UK
| | - Lucilla Poston
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Basky Thilaganathan
- Fetal Medicine Unit, St George's University Hospitals NHS Foundation Trust and Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK
| | - Anne C Staff
- Division of Obstetrics and Gynaecology, Oslo University Hospital, and Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Gordon C S Smith
- Department of Obstetrics and Gynaecology, NIHR Biomedical Research Centre, Cambridge University, Cambridge, UK
| | - Wessel Ganzevoort
- Department of Obstetrics, Amsterdam UMC University of Amsterdam, Amsterdam, The Netherlands
| | - Hannele Laivuori
- Department of Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Obstetrics and Gynecology, Faculty of Medicine and Health Technology, Tampere University Hospital and Tampere University, Tampere, Finland
| | | | - Javier Arenas Ramírez
- Department of Obstetrics and Gynaecology, University Hospital de Cabueñes, Gijón, Spain
| | - John Kingdom
- Maternal-Fetal Medicine Division, Department OBGYN, Mount Sinai Hospital, University of Toronto, Toronto, Canada
| | - George Daskalakis
- Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Alexandra Hospital, Athens, Greece
| | - Diane Farrar
- Bradford Institute for Health Research, Bradford Teaching Hospitals, Bradford, UK
| | - Ahmet A Baschat
- Johns Hopkins Center for Fetal Therapy, Department of Gynecology & Obstetrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Paul T Seed
- Department of Women and Children's Health, School of Life Course Sciences, King's College London, London, UK
| | - Federico Prefumo
- Department of Obstetrics and Gynaecology, University of Brescia, Brescia, Italy
| | - Fabricio da Silva Costa
- Department of Gynecology and Obstetrics, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Henk Groen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Francois Audibert
- Department of Obstetrics and Gynecology, CHU Ste Justine, Université de Montréal, Montreal, Canada
| | - Jacques Masse
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, Canada
| | - Ragnhild B Skråstad
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology - NTNU, Trondheim, Norway
- Department of Clinical Pharmacology, St. Olav University Hospital, Trondheim, Norway
| | - Kjell Å Salvesen
- Department of Obstetrics and Gynecology, Trondheim University Hospital, Trondheim, Norway
- Department of Laboratory Medicine, Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Camilla Haavaldsen
- Department of Obstetrics and Gynaecology, Akershus University Hospital, Lørenskog, Norway
| | - Chie Nagata
- Department of Education for Clinical Research, National Center for Child Health and Development, Tokyo, Japan
| | - Alice R Rumbold
- South Australian Health and Medical Research Institute and Robinson Research Institute, The University of Adelaide, Adelaide, Australia
| | - Seppo Heinonen
- Department of Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Lisa M Askie
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
| | - Luc J M Smits
- Care and Public Health Research Institute, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Christina A Vinter
- Department of Gynecology and Obstetrics, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Per Magnus
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Kajantie Eero
- National Institute for Health and Welfare, Helsinki, Finland
- Children's Hospital, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Pia M Villa
- Department of Obstetrics and Gynaecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Anne K Jenum
- General Practice Research Unit (AFE), Department of General Practice, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Louise B Andersen
- Institute for Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Obstetrics and Gynecology, Odense University Hospital, Odense, Denmark
| | - Jane E Norman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Akihide Ohkuchi
- Department of Obstetrics and Gynecology, Jichi Medical University School of Medicine, Shimotsuke-shi, Tochigi, Japan
| | - Anne Eskild
- Department of Obstetrics and Gynaecology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sohinee Bhattacharya
- Obstetrics & Gynaecology, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Fionnuala M McAuliffe
- UCD Perinatal Research Centre, School of Medicine, University College Dublin, National Maternity Hospital, Dublin, Ireland
| | - Alberto Galindo
- Fetal Medicine Unit, Maternal and Child Health and Development Network (SAMID), Department of Obstetrics and Gynaecology, Hospital Universitario, Instituto de Investigación Hospital, Universidad Complutense de Madrid, Madrid, Spain
| | - Ignacio Herraiz
- Fetal Medicine Unit, Maternal and Child Health and Development Network (SAMID), Department of Obstetrics and Gynaecology, Hospital Universitario, Instituto de Investigación Hospital, Universidad Complutense de Madrid, Madrid, Spain
| | - Lionel Carbillon
- Department of Obstetrics and Gynecology, Assistance Publique-Hôpitaux de Paris Université Paris, Paris, France
| | - Kerstin Klipstein-Grobusch
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Seon Ae Yeo
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joyce L Browne
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, Utrecht, The Netherlands
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Shakila Thangaratinam
- Institute of Metabolism and Systems Research, WHO Collaborating Centre for Women's Health, University of Birmingham, Birmingham, UK
| |
Collapse
|
28
|
Qu H, Khalil RA. Vascular mechanisms and molecular targets in hypertensive pregnancy and preeclampsia. Am J Physiol Heart Circ Physiol 2020; 319:H661-H681. [PMID: 32762557 DOI: 10.1152/ajpheart.00202.2020] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Preeclampsia is a major complication of pregnancy manifested as hypertension and often intrauterine growth restriction, but the underlying pathophysiological mechanisms are unclear. Predisposing genetic and environmental factors cause placental maladaptations leading to defective placentation, apoptosis of invasive cytotrophoblasts, inadequate expansive remodeling of the spiral arteries, reduced uteroplacental perfusion pressure, and placental ischemia. Placental ischemia promotes the release of bioactive factors into the maternal circulation, causing an imbalance between antiangiogenic soluble fms-like tyrosine kinase-1 and soluble endoglin and proangiogenic vascular endothelial growth factor, placental growth factor, and transforming growth factor-β. Placental ischemia also stimulates the release of proinflammatory cytokines, hypoxia-inducible factor, reactive oxygen species, and angiotensin type 1 receptor agonistic autoantibodies. These circulating factors target the vascular endothelium, causing generalized endotheliosis in systemic, renal, cerebral, and hepatic vessels, leading to decreases in endothelium-derived vasodilators such as nitric oxide, prostacyclin, and hyperpolarization factor and increases in vasoconstrictors such as endothelin-1 and thromboxane A2. The bioactive factors also target vascular smooth muscle and enhance the mechanisms of vascular contraction, including cytosolic Ca2+, protein kinase C, and Rho-kinase. The bioactive factors could also target matrix metalloproteinases and the extracellular matrix, causing inadequate vascular remodeling, increased arterial stiffening, and further increases in vascular resistance and hypertension. As therapeutic options are limited, understanding the underlying vascular mechanisms and molecular targets should help design new tools for the detection and management of hypertension in pregnancy and preeclampsia.
Collapse
Affiliation(s)
- Hongmei Qu
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts
| | - Raouf A Khalil
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
29
|
Boutin A, Gasse C, Guerby P, Giguère Y, Tétu A, Bujold E. First-Trimester Preterm Preeclampsia Screening in Nulliparous Women: The Great Obstetrical Syndrome (GOS) Study. JOURNAL OF OBSTETRICS AND GYNAECOLOGY CANADA 2020; 43:43-49. [PMID: 32917539 DOI: 10.1016/j.jogc.2020.06.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 06/03/2020] [Accepted: 06/04/2020] [Indexed: 12/28/2022]
Abstract
OBJECTIVES To estimate the ability of a combination of first-trimester markers to predict preterm preeclampsia in nulliparous women. METHODS We conducted a prospective cohort study of nulliparous women with singleton gestations, recruited between 110 and 136 weeks gestation. Data on the following were collected: maternal age; ethnicity; chronic diseases; use of fertility treatment; body mass index; mean arterial blood pressure (MAP); serum levels of pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1), alpha fetoprotein (AFP), free beta human chorionic gonadotropin (ß-hCG); and mean uterine artery pulsatility index (UtA-PI). We constructed a proportional hazard model for the prediction of preterm preeclampsia selected based on the Akaike information criterion. A receiver operating characteristic curve was created with the predicted risk from the final model. Our primary outcome was preterm preeclampsia and our secondary outcome was a composite of preeclampsia, small for gestational age, intrauterine death, and preterm birth. RESULTS Among 4659 nulliparous women with singleton gestations, our final model included 4 variables: MAP MoM, log10PlGF MoM, log10AFP MoM and log10UtA-PI MoM. We obtained an area under the curve of 0.84 (95% CI 0.75-0.93) with a detection rate of preterm preeclampsia of 55% (95% CI 37%-73%) and a false-positive rate of 10%. Using a risk cut-off with a false-positive rate of 10%, the positive predictive value for our composite outcome was 33% (95% CI 29%-37%). CONCLUSIONS The combination of MAP, maternal serum PlGF and AFP, and UtA-PI are useful to identify nulliparous women at high risk of preterm preeclampsia but also at high risk of other great obstetrical syndromes.
Collapse
Affiliation(s)
- Amélie Boutin
- Reproduction, Mother and Child Health Unit, CHU de Québec-Université Laval Research Center, Québec City, QC
| | - Cédric Gasse
- Reproduction, Mother and Child Health Unit, CHU de Québec-Université Laval Research Center, Québec City, QC; Department of Social and Preventive Medicine, Université Laval, Québec City, QC
| | - Paul Guerby
- Reproduction, Mother and Child Health Unit, CHU de Québec-Université Laval Research Center, Québec City, QC; Department of Obstetrics, Gynecology, and Reproduction, Université Laval, Québec City, QC
| | - Yves Giguère
- Reproduction, Mother and Child Health Unit, CHU de Québec-Université Laval Research Center, Québec City, QC; Department of Molecular Biology, Medical Biochemistry, and Pathology, Université Laval, Québec City, QC
| | - Amélie Tétu
- Reproduction, Mother and Child Health Unit, CHU de Québec-Université Laval Research Center, Québec City, QC
| | - Emmanuel Bujold
- Reproduction, Mother and Child Health Unit, CHU de Québec-Université Laval Research Center, Québec City, QC; Department of Obstetrics, Gynecology, and Reproduction, Université Laval, Québec City, QC.
| |
Collapse
|
30
|
Antwi E, Amoakoh-Coleman M, Vieira DL, Madhavaram S, Koram KA, Grobbee DE, Agyepong IA, Klipstein-Grobusch K. Systematic review of prediction models for gestational hypertension and preeclampsia. PLoS One 2020; 15:e0230955. [PMID: 32315307 PMCID: PMC7173928 DOI: 10.1371/journal.pone.0230955] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 03/12/2020] [Indexed: 12/22/2022] Open
Abstract
INTRODUCTION Prediction models for gestational hypertension and preeclampsia have been developed with data and assumptions from developed countries. Their suitability and application for low resource settings have not been tested. This review aimed to identify and assess the methodological quality of prediction models for gestational hypertension and pre-eclampsia with reference to their application in low resource settings. METHODS Using combinations of keywords for gestational hypertension, preeclampsia and prediction models seven databases were searched to identify prediction models developed with maternal data obtained before 20 weeks of pregnancy and including at least three predictors (Prospero registration CRD 42017078786). Prediction model characteristics and performance measures were extracted using the CHARMS, STROBE and TRIPOD checklists. The National Institute of Health quality assessment tools for observational cohort and cross-sectional studies were used for study quality appraisal. RESULTS We retrieved 8,309 articles out of which 40 articles were eligible for review. Seventy-seven percent of all the prediction models combined biomarkers with maternal clinical characteristics. Biomarkers used as predictors in most models were pregnancy associated plasma protein-A (PAPP-A) and placental growth factor (PlGF). Only five studies were conducted in a low-and middle income country. CONCLUSIONS Most of the studies evaluated did not completely follow the CHARMS, TRIPOD and STROBE guidelines in prediction model development and reporting. Adherence to these guidelines will improve prediction modelling studies and subsequent application of prediction models in clinical practice. Prediction models using maternal characteristics, with good discrimination and calibration, should be externally validated for use in low and middle income countries where biomarker assays are not routinely available.
Collapse
Affiliation(s)
- Edward Antwi
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Ghana Health Service, Accra, Ghana
| | - Mary Amoakoh-Coleman
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Epidemiology Department, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Legon, Accra, Ghana
| | - Dorice L. Vieira
- New York University Health Sciences Library, New York University School of Medicine, New York, NY, United States of America
| | - Shreya Madhavaram
- New York University Health Sciences Library, New York University School of Medicine, New York, NY, United States of America
| | - Kwadwo A. Koram
- Epidemiology Department, Noguchi Memorial Institute for Medical Research, College of Health Sciences, University of Ghana, Legon, Accra, Ghana
| | - Diederick E. Grobbee
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | | | - Kerstin Klipstein-Grobusch
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Division of Epidemiology & Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| |
Collapse
|
31
|
Early prediction of preeclampsia via machine learning. Am J Obstet Gynecol MFM 2020; 2:100100. [PMID: 33345966 DOI: 10.1016/j.ajogmf.2020.100100] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 03/04/2020] [Accepted: 03/07/2020] [Indexed: 01/17/2023]
Abstract
BACKGROUND Early prediction of preeclampsia is challenging because of poorly understood causes, various risk factors, and likely multiple pathogenic phenotypes of preeclampsia. Statistical learning methods are well-equipped to deal with a large number of variables, such as patients' clinical and laboratory data, and to select the most informative features automatically. OBJECTIVE Our objective was to use statistical learning methods to analyze all available clinical and laboratory data that were obtained during routine prenatal visits in early pregnancy and to use them to develop a prediction model for preeclampsia. STUDY DESIGN This was a retrospective cohort study that used data from 16,370 births at Lucile Packard Children Hospital at Stanford, CA, from April 2014 to January 2018. Two statistical learning algorithms were used to build a predictive model: (1) elastic net and (2) gradient boosting algorithm. Models for all preeclampsia and early-onset preeclampsia (<34 weeks gestation) were fitted with the use of patient data that were available at <16 weeks gestational age. The 67 variables that were considered in the models included maternal characteristics, medical history, routine prenatal laboratory results, and medication intake. The area under the receiver operator curve, true-positive rate, and false-positive rate were assessed via cross-validation. RESULTS Using the elastic net algorithm, we developed a prediction model that contained a subset of the most informative features from all variables. The obtained prediction model for preeclampsia yielded an area under the curve of 0.79 (95% confidence interval, 0.75-0.83), sensitivity of 45.2%, and false-positive rate of 8.1%. The prediction model for early-onset preeclampsia achieved an area under the curve of 0.89 (95% confidence interval, 0.84-0.95), true-positive rate of 72.3%, and false-positive rate of 8.8%. CONCLUSION Statistical learning methods in a retrospective cohort study automatically identified a set of significant features for prediction and yielded high prediction performance for preeclampsia risk from routine early pregnancy information.
Collapse
|
32
|
Babic I, Mejia A, Wrobleski JA, Shen M, Wen SW, Moretti F. Intraplacental Villous Artery Doppler as an Independent Predictor for Placenta-Mediated Disease and Its Comparison with Uterine Artery Doppler and/or Placental Biochemical Markers in Predictive Models: A Prospective Cohort Study. Fetal Diagn Ther 2019; 47:292-300. [PMID: 31726454 DOI: 10.1159/000503963] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2019] [Accepted: 09/26/2019] [Indexed: 11/19/2022]
Abstract
OBJECTIVES To validate intraplacental villous artery (IPVA) Doppler as a predictor for placenta-mediated diseases (PMDs), to compare its predictive value with uterine artery (UtA) Doppler and placental biochemical markers, and to assess its value in predictive PMD models. METHODS IPVA and UtA indices (pulsatility index [PI] and resistance index [RI]) were recorded at 18-24 weeks of gestation in a cohort of 117 women. The predictive values of IPVA, UtA, and placental biochemical markers were analyzed and compared between the PMD group (the women who developed preeclampsia or intrauterine growth restriction) and the non-PMD group (the women who remained healthy throughout pregnancy and 3 months postpartum) using the receiver-operating characteristic curves. Logistic regression was used to compare predictive models for PMDs based on IPVA, UtA, and/or biochemical markers. RESULTS 31 (26.5%) women developed PMD (17 preeclampsia and 14 intrauterine growth restriction). IPVA PI was significantly higher in the PMD group than in the non-PMD group (p = 0.001). UtA PI and RI values remained nonsignificant between both groups (p = 0.066 and 0.104, respectively). IPVA PI from the 3 main branches of the placenta, and specifically the central main stem villi, showed a strong association with PMDs in comparison to UtA (p = 0.03 and 0.001 vs. 0.29). Model prediction including IPVA and UtA PI with or without placental biomarkers did not add any further significance to IPVA PI alone (p = 0.03, 0.41, and 0.36). CONCLUSIONS IPVA PI appears superior to UtA PI or RI and placental biomarkers in PMD prediction. Model prediction for PMDs including IPVA, UtA Doppler, and biochemical markers did not enhance prediction values compared to IPVA Doppler alone.
Collapse
Affiliation(s)
- Inas Babic
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Prince Sultan Military Medical City, Riyadh, Saudi Arabia, .,Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada,
| | - Alberto Mejia
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Julie-Anne Wrobleski
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Minxue Shen
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Shi Wu Wen
- Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.,Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
| | - Felipe Moretti
- Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| |
Collapse
|
33
|
Serum SHARP1 and uterine artery Doppler for the prediction of preeclampsia. Sci Rep 2019; 9:12266. [PMID: 31439869 PMCID: PMC6706446 DOI: 10.1038/s41598-019-48727-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 08/07/2019] [Indexed: 12/17/2022] Open
Abstract
The aim of this study was to identify the value of serum SHARP1 levels and Doppler of the uterine artery in singleton pregnancy at 11–13+6 weeks for predicting preeclampsia. A prospective observational study was conducted in pregnant women at 11–13+6 weeks of pregnancy who had antenatal care at King Chulalongkorn Memorial Hospital, Chulalongkorn University, Bangkok, Thailand, between January 2017 and January 2018. Serum SHARP1 measurement and transabdominal Doppler of the uterine artery were performed. The predictive values of these tests were determined. Data were obtained from 405 pregnant women. Thirty-five women had preeclampsia (8.6%), and six of these had early-onset preeclampsia (1.5%). Preeclamptic women had significantly lower serum SHARP1 levels than pregnant women without preeclampsia (3.6 ng/ml vs 4.7 ng/ml, p < 0.01). The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of serum SHARP1 levels of less than 3.89 ng/ml for predicting preeclampsia were 77.1%, 72.7%, 21.1% and 97.1%, respectively. For uterine artery Doppler, the sensitivity, specificity, PPV and NPV of the mean pulsatility index (PI) > 95th percentile for predicting preeclampsia were 5.7%, 95.4%, 10.5% and 91.5%, respectively. For the combination of serum SHARP1 levels with a cutoff value of less than 3.89 ng/ml and a mean PI > 95th percentile, the sensitivity, specificity, PPV and NPV were 77.1%, 70.3%, 19.7% and 97.0%, respectively. This study demonstrated that serum SHARP1 is a promising biomarker for predicting preeclampsia in the first trimester.
Collapse
|
34
|
Sunjaya AF, Sunjaya AP. Evaluation of Serum Biomarkers and Other Diagnostic Modalities for Early Diagnosis of Preeclampsia. J Family Reprod Health 2019; 13:56-69. [PMID: 31988641 PMCID: PMC6969892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Objective: Preeclampsia (PE) is a multi-systemic complication of pregnancy often characterised with the onset of hypertension and proteinuria after 20 weeks of gestation. Today, PE is the leading cause of maternal and perinatal morbidity and mortality worldwide. An early detection of PE would allow a chance to plan the appropriate monitoring and for clinical management to be immediately done following early detection thus making prophylactic strategies much more effective. Materials and methods: This systematic review aims to evaluate the potential of the various serum biomarkers and diagnostic modalities (uterine artery Doppler, MAP, and maternal history) available for early prediction of PE with articles included and obtained through MEDLINE Full Text, Pubmed, Science Direct, ProQuest, SAGE, Taylor and Francis Online, Google Scholar, HighWire and Elsevier ClinicalKey. Results: Ninety-five articles were found that fulfilled all of our inclusion criteria. Placental growth factor (PlGF), pregnancy associated plasma protein A (PAPP-A), soluble fms-like tyrosine kinase (sFLT) and placental protein 13 (PP-13) were the most commonly studied biomarkers. Whereas uterine Doppler scanning and Mean Arterial Pressure (MAP) were the most commonly studied out of other modalities. Conclusion: Current evidence shows serum biomarkers such as PIGF, PP-13 and sFlt yielded the best results for a single biomarker with others having conflicting results. However, a combination model with other diagnostic modalities performed better than a single biomarker. In the future, new techniques will hopefully provide sets of multiple markers, which will lead to a screening program with clinically relevant performance. However further studies are required to improve current methods.
Collapse
|
35
|
Drobnjak T, Jónsdóttir AM, Helgadóttir H, Runólfsdóttir MS, Meiri H, Sammar M, Osol G, Mandalà M, Huppertz B, Gizurarson S. Placental protein 13 (PP13) stimulates rat uterine vessels after slow subcutaneous administration. Int J Womens Health 2019; 11:213-222. [PMID: 30988643 PMCID: PMC6443218 DOI: 10.2147/ijwh.s188303] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Introduction Reduced concentrations of placental protein 13 (PP13) during the first trimester of human pregnancy are associated with elevated risk for the subsequent development of preeclampsia, which is one of the deadliest obstetrical complications of pregnancy. Previous studies by our group have shown that PP13 lowers blood pressure in pregnant rats, increases the size and weight of pups and placentas, and induces vasodilation of resistance arteries through endothelial signaling pathways involving endothelial nitric oxid synthase and prostaglandin. Methods In the present study, the effect of PP13 was investigated in nonpregnant female Sprague Dawley rats (n=27). Osmotic pumps were surgically implanted subcutaneously that released a constant dose of PP13 or saline over 7 days. Most animals were sacrificed 6 days after the end of PP13 release (on day 13), while some were sacrificed immediately at the end of day 7 (the last PP13 release day), to compare the short- and long-term impact of PP13 on vessels’ growth and size. Results The uterine vessels were significantly expanded in the group exposed to recombinant PP13 (rPP13) compared to the control (saline) group. Both veins and arteries were significantly expanded by rPP13 with a more pronounced effect after 13 days compared to the corresponding vessels after 7 days. Furthermore, the long-term effect of treatment by rPP13 was more pronounced in the veins compared to the corresponding arteries. The effect of a PP13 variant with a histidine-tag (His-PP13) remained the same between 7 and 13 days. Conclusion In conclusion, PP13 might play a key role in the expansive remodeling of the uterine vessels, reflecting what would happen if the rat was pregnant, preparing the uterine vascu-lature for the increase in uteroplacental blood flow, which is necessary for normal pregnancy. We suggest that PP13 could act by NO signaling pathways, a hypothesis that requires future study.
Collapse
Affiliation(s)
- Tijana Drobnjak
- Faculty of Pharmaceutical Sciences, School of Health Science, University of Iceland, Reykjavik, Iceland,
| | | | - Helga Helgadóttir
- Faculty of Pharmaceutical Sciences, School of Health Science, University of Iceland, Reykjavik, Iceland,
| | | | - Hamutal Meiri
- Hy Laboratories Ltd, Rehovot, Israel.,TeleMarpe Ltd., Tel Aviv, Israel
| | - Marei Sammar
- Ephraim Katzir Department of Biotechnology Engineering, ORT Braude College, Karmiel, Israel
| | - George Osol
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of Vermont College of Medicine, Burlington, VT, USA
| | - Maurizio Mandalà
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | - Berthold Huppertz
- Department of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria
| | - Sveinbjörn Gizurarson
- Faculty of Pharmaceutical Sciences, School of Health Science, University of Iceland, Reykjavik, Iceland,
| |
Collapse
|
36
|
Lamain-de Ruiter M, Kwee A, Naaktgeboren CA, Louhanepessy RD, De Groot I, Evers IM, Groenendaal F, Hering YR, Huisjes AJM, Kirpestein C, Monincx WM, Schielen PCJI, Van 't Zelfde A, Van Oirschot CM, Vankan-Buitelaar SA, Vonk MAAW, Wiegers TA, Zwart JJ, Moons KGM, Franx A, Koster MPH. External validation of prognostic models for preeclampsia in a Dutch multicenter prospective cohort. Hypertens Pregnancy 2019; 38:78-88. [PMID: 30892981 DOI: 10.1080/10641955.2019.1584210] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
OBJECTIVE To perform an external validation of all published prognostic models for first-trimester prediction of the risk of developing preeclampsia (PE). METHODS Women <14 weeks of pregnancy were recruited in the Netherlands. All systematically identified prognostic models for PE that contained predictors commonly available were eligible for external validation. RESULTS 3,736 women were included; 87 (2.3%) developed PE. Calibration was poor due to overestimation. Discrimination of 9 models for LO-PE ranged from 0.58 to 0.71 and of 9 models for all PE from 0.55 to 0.75. CONCLUSION Only a few easily applicable prognostic models for all PE showed discrimination above 0.70, which is considered an acceptable performance.
Collapse
Affiliation(s)
- Marije Lamain-de Ruiter
- a Department of Obstetrics, Division Woman and Baby , University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands
| | - Anneke Kwee
- a Department of Obstetrics, Division Woman and Baby , University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands
| | - Christiana A Naaktgeboren
- b Julius Center for Health Sciences and Primary Care , University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands
| | - Rebecca D Louhanepessy
- c Department of Medical Oncology , Netherlands Cancer Institute , Amsterdam , The Netherlands
| | - Inge De Groot
- d Livive, Center for Obstetrics , Tilburg , The Netherlands
| | - Inge M Evers
- e Department of Obstetrics , Meander Medical Center , Amersfoort , The Netherlands
| | - Floris Groenendaal
- f Department of Neonatology, Division Woman and Baby , University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands
| | - Yolanda R Hering
- g Department of Obstetrics , Zuwe Hofpoort Hospital , Woerden , The Netherlands
| | - Anjoke J M Huisjes
- h Department of Obstetrics , Gelre Hospital , Apeldoorn , The Netherlands
| | - Cornel Kirpestein
- i Department of Obstetrics , Hospital Rivierenland , Tiel , The Netherlands
| | - Wilma M Monincx
- j Department of Obstetrics , St. Antonius Hospital , Nieuwegein , The Netherland
| | - Peter C J I Schielen
- k Center for Infectious Diseases Research, Diagnostics and Screening (IDS) , National Institute for Public Health and the Environment (RIVM) , Bilthoven , The Netherlands
| | | | | | | | | | - Therese A Wiegers
- p Netherlands Institute for health services research (NIVEL) , Utrecht , The Netherlands
| | - Joost J Zwart
- q Department of Obstetrics , Deventer Hospital , Deventer , The Netherlands
| | - Karel G M Moons
- b Julius Center for Health Sciences and Primary Care , University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands
| | - Arie Franx
- a Department of Obstetrics, Division Woman and Baby , University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands
| | - Maria P H Koster
- a Department of Obstetrics, Division Woman and Baby , University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands.,r Department of Obstetrics and Gynecology, Erasmus Medical Center , University Medical Center Rotterdam , Rotterdam , the Netherlands
| |
Collapse
|
37
|
De Kat AC, Hirst J, Woodward M, Kennedy S, Peters SA. Prediction models for preeclampsia: A systematic review. Pregnancy Hypertens 2019; 16:48-66. [PMID: 31056160 DOI: 10.1016/j.preghy.2019.03.005] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/11/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND Preeclampsia is a disease specific to pregnancy that can cause severe maternal and foetal morbidity and mortality. Early identification of women at higher risk for preeclampsia could potentially aid early prevention and treatment. Although a plethora of preeclampsia prediction models have been developed in recent years, individualised prediction of preeclampsia is rarely used in clinical practice. OBJECTIVES The objective of this systematic review was to provide an overview of studies on preeclampsia prediction. STUDY DESIGN Relevant research papers were identified through a MEDLINE search up to 1 January 2017. Prognostic studies on the prediction of preeclampsia or preeclampsia-related disorders were included. Quality screening was performed with the Quality in Prognostic Studies (QUIPS) tool. RESULTS Sixty-eight prediction models from 70 studies with 425,125 participants were selected for further review. The number of participants varied and the gestational age at prediction varied widely across studies. The most frequently used predictors were medical history, body mass index, blood pressure, parity, uterine artery pulsatility index, and maternal age. The type of predictor (maternal characteristics, ultrasound markers and/or biomarkers) was not clearly associated with model discrimination. Few prediction studies were internally (4%) or externally (6%) validated. CONCLUSIONS To date, multiple and widely varying models for preeclampsia prediction have been developed, some yielding promising results. The high degree of between-study heterogeneity impedes selection of the best model, or an aggregated analysis of prognostic models. Before multivariable preeclampsia prediction can be clinically implemented universally, further validation and calibration of well-performing prediction models is needed.
Collapse
Affiliation(s)
- Annelien C De Kat
- The George Institute for Global Health, University of Oxford Le Gros Clark Building, South Parks Road, Oxford OX1 3QX, UK; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK.
| | - Jane Hirst
- The George Institute for Global Health, University of Oxford Le Gros Clark Building, South Parks Road, Oxford OX1 3QX, UK; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK
| | - Mark Woodward
- The George Institute for Global Health, University of Oxford Le Gros Clark Building, South Parks Road, Oxford OX1 3QX, UK; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Stephen Kennedy
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK
| | - Sanne A Peters
- The George Institute for Global Health, University of Oxford Le Gros Clark Building, South Parks Road, Oxford OX1 3QX, UK; Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, UK; The George Institute for Global Health, University of New South Wales, Sydney, Australia
| |
Collapse
|
38
|
Yu W, Gao W, Rong D, Wu Z, Khalil RA. Molecular determinants of microvascular dysfunction in hypertensive pregnancy and preeclampsia. Microcirculation 2018; 26:e12508. [PMID: 30338879 PMCID: PMC6474836 DOI: 10.1111/micc.12508] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 10/05/2018] [Accepted: 10/15/2018] [Indexed: 12/16/2022]
Abstract
Preeclampsia is a pregnancy-related disorder characterized by hypertension and often fetal intrauterine growth restriction, but the underlying mechanisms are unclear. Defective placentation and apoptosis of invasive cytotrophoblasts cause inadequate remodeling of spiral arteries, placental ischemia, and reduced uterine perfusion pressure (RUPP). RUPP causes imbalance between the anti-angiogenic factors soluble fms-like tyrosine kinase-1 and soluble endoglin and the pro-angiogenic vascular endothelial growth factor and placental growth factor, and stimulates the release of proinflammatory cytokines, hypoxia-inducible factor, reactive oxygen species, and angiotensin AT1 receptor agonistic autoantibodies. These circulating factors target the vascular endothelium, smooth muscle and various components of the extracellular matrix. Generalized endotheliosis in systemic, renal, cerebral, and hepatic vessels causes decreases in endothelium-derived vasodilators such as nitric oxide, prostacyclin and hyperpolarization factor, and increases in vasoconstrictors such as endothelin-1 and thromboxane A2. Enhanced mechanisms of vascular smooth muscle contraction, such as intracellular Ca2+ , protein kinase C, and Rho-kinase cause further increases in vasoconstriction. Changes in matrix metalloproteinases and extracellular matrix cause inadequate vascular remodeling and increased arterial stiffening, leading to further increases in vascular resistance and hypertension. Therapeutic options are currently limited, but understanding the molecular determinants of microvascular dysfunction could help in the design of new approaches for the prediction and management of preeclampsia.
Collapse
Affiliation(s)
- Wentao Yu
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Wei Gao
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Dan Rong
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Zhixian Wu
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Raouf A Khalil
- Vascular Surgery Research Laboratories, Division of Vascular and Endovascular Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
39
|
Gadde R, Cd D, Sheela SR. Placental protein 13: An important biological protein in preeclampsia. J Circ Biomark 2018; 7:1849454418786159. [PMID: 30023011 PMCID: PMC6047241 DOI: 10.1177/1849454418786159] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Accepted: 05/28/2018] [Indexed: 12/26/2022] Open
Abstract
Placental protein 13 (PP13), a glycan binding protein predominantly expressed in syncytiotrophoblast, dimeric in nature, lacks N-terminal signal peptide, bypasses the endoplasmic reticulum, and secretes into maternal circulation as exosomes or microvesicles. PP13 has jelly roll fold conformation with conserved carbohydrate recognition domain which specifically binds to β-galactosides of the glycan receptors during placentation. PP13 binds to glycosylated receptors on human erythrocytes and brings about hemagglutination by the property of lectin activity; other functions are immunoregulation and vasodilation during placentation and vascularization. The gene LGALS13 located on 19q13.2 comprising four exons expresses a 32-kDa protein with 139 amino acid residues, PP13. Impaired expression due to mutation in the gene leads to a nonfunctional truncated PP13. The low serum levels predict high risk for the onset of preeclampsia or obstetric complications. Hence, PP13 turned to be an early marker for risk assessment of preeclampsia. The recombinant PP13 and monoclonal antibodies availability help for replenishing PP13 in conditions with low serum levels and for detection and prevention of preeclampsia, respectively.
Collapse
Affiliation(s)
- Ranjeeta Gadde
- Department of Biochemistry, Sri Devaraj Urs Medical College, Kolar, India
| | - Dayanand Cd
- Department of Biochemistry, Sri Devaraj Urs Medical College, Kolar, India
| | - S R Sheela
- Department of Obstetrics and Gynecology, Sri Devaraj Urs Medical College, Kolar, India
| |
Collapse
|
40
|
Drobnjak T, Meiri H, Mandalá M, Huppertz B, Gizurarson S. Pharmacokinetics of placental protein 13 after intravenous and subcutaneous administration in rabbits. DRUG DESIGN DEVELOPMENT AND THERAPY 2018; 12:1977-1983. [PMID: 30013317 PMCID: PMC6037268 DOI: 10.2147/dddt.s167926] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Introduction Human placental protein 13 (PP13) is a galectin predominantly expressed by the placenta. Low serum concentrations of PP13 in early pregnancy indicate a higher risk of developing preeclampsia. Methods The pharmacokinetic disposition and bioavailability of PP13 were determined by single intravenous and subcutaneous administration to 12 healthy New Zealand White rabbits. The serum pharmacokinetic values were determined by enzyme-linked immunosorbent assay, and are best described by a two-compartment model. Results Both volume of distribution and the area under the curve were dose dependent for the intravenous group (p<0.01). PP13 elimination half-life was also found to be different between the groups (p<0.01). The bioavailability of PP13 following subcutaneous administration was found to be 57%. Conclusion This study shows that the concentration of total PP13 released into the maternal circulation during pregnancy might be much higher than previously estimated.
Collapse
Affiliation(s)
- Tijana Drobnjak
- Faculty of Pharmaceutical Sciences, School of Health Sciences, University of Iceland, Reykjavik, Iceland,
| | - Hamutal Meiri
- Hy Laboratories, Rehovot, Israel.,TeleMarpe Ltd., Tel Aviv, Israel
| | - Maurizio Mandalá
- Department of Biology, Ecology and Earth Sciences, University of Calabria, Rende, Italy
| | - Berthold Huppertz
- Department of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria
| | - Sveinbjörn Gizurarson
- Faculty of Pharmaceutical Sciences, School of Health Sciences, University of Iceland, Reykjavik, Iceland,
| |
Collapse
|
41
|
Eastabrook G, Aksoy T, Bedell S, Penava D, de Vrijer B. Preeclampsia biomarkers: An assessment of maternal cardiometabolic health. Pregnancy Hypertens 2018; 13:204-213. [PMID: 30177053 DOI: 10.1016/j.preghy.2018.06.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Revised: 05/09/2018] [Accepted: 06/09/2018] [Indexed: 12/15/2022]
Abstract
Preeclampsia is a serious pregnancy condition defined as new-onset hypertension and proteinuria, commonly characterized as either early, 'placental', or late onset, 'maternal', using a cut-off of 34 weeks gestation. However, it may be more useful to differentiate between the vascular remodelling and placental invasion vs. inflammation and metabolic pathophysiology that underlie these forms of preeclampsia. Due to rising rates of obesity, the late-onset, maternal form is increasingly occurring earlier in pregnancy. Predictive tests for preeclampsia typically include biophysical markers such as maternal body mass index and mean arterial pressure, indicating the importance of cardiovascular and metabolic health in its pathophysiology. In contrast, the placental, inflammatory, endothelial and/or metabolic biomarkers used in these tests are generally thought to indicate an abnormal response to placentation and predict the disease. However, many of these non-placental biomarkers are known to predict impaired metabolic health in non-pregnant subjects with obesity (metabolically unhealthy obesity) and coronary artery disease or stroke in people at risk for cardiovascular events. Similarities between the performance of these markers in the prediction of cardiovascular and metabolic health outside of pregnancy suggests that they may be more indicative of maternal health than predictive for preeclampsia. This paper reviews the biophysical and biochemical markers in preeclampsia prediction and compares their performance to tests assessing metabolic health and risk of cardiovascular disease, particularly in the obese population.
Collapse
Affiliation(s)
- Genevieve Eastabrook
- Department of Obstetrics and Gynaecology, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Children's Health Research Institute and Lawson Health Research Institute, London, Ontario, Canada.
| | - Tuba Aksoy
- Department of Obstetrics and Gynecology, Mackenzie Richmond Hill Hospital, Richmond Hill, Ontario, Canada.
| | - Samantha Bedell
- Department of Obstetrics and Gynaecology, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada.
| | - Debbie Penava
- Department of Obstetrics and Gynaecology, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Children's Health Research Institute and Lawson Health Research Institute, London, Ontario, Canada.
| | - Barbra de Vrijer
- Department of Obstetrics and Gynaecology, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Children's Health Research Institute and Lawson Health Research Institute, London, Ontario, Canada.
| |
Collapse
|
42
|
Wataganara T, Leetheeragul J, Pongprasobchai S, Sutantawibul A, Phatihattakorn C, Angsuwathana S. Prediction and prevention of pre-eclampsia in Asian subpopulation. J Obstet Gynaecol Res 2018; 44:813-830. [PMID: 29442407 DOI: 10.1111/jog.13599] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 12/31/2017] [Indexed: 12/20/2022]
Abstract
The benefit of the early administration of aspirin to reduce preterm pre-eclampsia among screened positive European women from multivariate algorithmic approach (ASPRE trial) has opened an intense debate on the feasibility of universal screening. This review aims to assess the new perspectives in the combined screening of pre-eclampsia in the first trimester of pregnancy and the chances for prevention using low-dose aspirin with special emphasis on the particularities of the Asian population. PubMed, CENTRAL and Embase databases were searched from inception until 15 November 2017 using combinations of the search terms: preeclampsia, Asian, prenatal screening, early prediction, ultrasonography, pregnancy, biomarker, mean arterial pressure, soluble fms-like tyrosine kinase-1, placental growth factor, pregnancy-associated plasma protein-A and pulsatility index. This is not a systematic review or meta-analysis, so the risk of bias of the selected published articles and heterogeneity among the studies need to be considered. The prevalence of pre-eclampsia and serum levels of biochemical markers in Asian are different from Caucasian women; hence, Asian ethnicity needs to be corrected for in the algorithmic assessment of multiple variables to improve the screening performance. Aspirin prophylaxis may still be viable in Asian women, but resource implication needs to be considered. Asian ethnicity should be taken into account before implementing pre-eclampsia screening strategies in the region. The variables included can be mixed and matched to achieve an optimal performance that is appropriate for economical restriction in individual countries.
Collapse
Affiliation(s)
- Tuangsit Wataganara
- Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital, Bangkok, Thailand
| | - Jarunee Leetheeragul
- Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital, Bangkok, Thailand
| | - Suchittra Pongprasobchai
- Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital, Bangkok, Thailand
| | - Anuwat Sutantawibul
- Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital, Bangkok, Thailand
| | - Chayawat Phatihattakorn
- Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital, Bangkok, Thailand
| | - Surasak Angsuwathana
- Department of Obstetrics and Gynecology, Faculty of Medicine Siriraj Hospital, Bangkok, Thailand
| |
Collapse
|
43
|
Sonek J, Krantz D, Carmichael J, Downing C, Jessup K, Haidar Z, Ho S, Hallahan T, Kliman HJ, McKenna D. First-trimester screening for early and late preeclampsia using maternal characteristics, biomarkers, and estimated placental volume. Am J Obstet Gynecol 2018; 218:126.e1-126.e13. [PMID: 29097177 DOI: 10.1016/j.ajog.2017.10.024] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 10/10/2017] [Accepted: 10/20/2017] [Indexed: 11/28/2022]
Abstract
BACKGROUND Preeclampsia is a major cause of perinatal morbidity and mortality. First-trimester screening has been shown to be effective in selecting patients at an increased risk for preeclampsia in some studies. OBJECTIVE We sought to evaluate the feasibility of screening for preeclampsia in the first trimester based on maternal characteristics, medical history, biomarkers, and placental volume. STUDY DESIGN This is a prospective observational nonintervention cohort study in an unselected US population. Patients who presented for an ultrasound examination between 11-13+6 weeks' gestation were included. The following parameters were assessed and were used to calculate the risk of preeclampsia: maternal characteristics (demographic, anthropometric, and medical history), maternal biomarkers (mean arterial pressure, uterine artery pulsatility index, placental growth factor, pregnancy-associated plasma protein A, and maternal serum alpha-fetoprotein), and estimated placental volume. After delivery, medical records were searched for the diagnosis of preeclampsia. Detection rates for early-onset preeclampsia (<34 weeks' gestation) and later-onset preeclampsia (≥34 weeks' gestation) for 5% and 10% false-positive rates using various combinations of markers were calculated. RESULTS We screened 1288 patients of whom 1068 (82.99%) were available for analysis. In all, 46 (4.3%) developed preeclampsia, with 13 (1.22%) having early-onset preeclampsia and 33 (3.09%) having late-onset preeclampsia. Using maternal characteristics, serum biomarkers, and uterine artery pulsatility index, the detection rate of early-onset preeclampsia for either 5% or 10% false-positive rate was 85%. With the same protocol, the detection rates for preeclampsia with delivery <37 weeks were 52% and 60% for 5% and 10% false-positive rates, respectively. Based on maternal characteristics, the detection rates for late-onset preeclampsia were 15% and 48% for 5% and 10%, while for preeclampsia at ≥37 weeks' gestation the detection rates were 24% and 43%, respectively. The detection rates for late-onset preeclampsia and preeclampsia with delivery at >37 weeks' gestation were not improved by the addition of biomarkers. CONCLUSION Screening for preeclampsia at 11-13+6 weeks' gestation using maternal characteristics and biomarkers is associated with a high detection rate for a low false-positive rate. Screening for late-onset preeclampsia yields a much poorer performance. In this study the utility of estimated placental volume and mean arterial pressure was limited but larger studies are needed to ultimately determine the effectiveness of these markers.
Collapse
Affiliation(s)
- Jiri Sonek
- Fetal Medicine Foundation USA, Dayton, OH; Wright State University, Dayton, OH.
| | | | | | - Cathy Downing
- Fetal Medicine Foundation USA, Dayton, OH; Wright State University, Dayton, OH
| | | | | | | | | | | | - David McKenna
- Fetal Medicine Foundation USA, Dayton, OH; Wright State University, Dayton, OH
| |
Collapse
|
44
|
Helmo FR, Lopes AMM, Carneiro ACDM, Campos CG, Silva PB, Dos Reis Monteiro MLG, Rocha LP, Dos Reis MA, Etchebehere RM, Machado JR, Corrêa RRM. Angiogenic and antiangiogenic factors in preeclampsia. Pathol Res Pract 2017; 214:7-14. [PMID: 29174227 DOI: 10.1016/j.prp.2017.10.021] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 10/23/2017] [Accepted: 10/25/2017] [Indexed: 12/17/2022]
Abstract
BACKGROUND Pre-eclampsia is a multifactorial hypertensive disorder that is triggered by placental insufficiency and that accounts for up to 15% of maternal deaths. In normal pregnancies, this process depends on the balance between the expression of angiogenic factors and antiangiogenic factors, which are responsible for remodeling the spiral arteries, as well as for neoangiogenesis and fetal development. PURPOSE The aim of this review is to discuss the main scientific findings regarding the role of angiogenic and antiangiogenic factors in the etiopathogenesis of preeclampsia. METHODS An extensive research was conducted in the Pubmed database in search of scientific manuscripts discussing potential associations between angiogenic and antiangiogenic factors and preeclampsia. Ninety-one papers were included in this review. RESULTS There is an increased expression of soluble fms-like tyrosine kinase receptor and soluble endoglin in pre-eclampsia, as well as reduced placental expression of vascular endothelial growth factor and placental growth factor. Systemic hypertension, proteinuria and kidney injury - such as enlargement and glomerular fibrin deposit, capillary occlusion due to edema, and hypertrophy of endocapillary cells - are some of these changes. The complex etiopathogenesis of preeclampsia instigates research of different biomarkers that allow for the early diagnosis of this entity, such as vascular endothelial growth factor, placental growth factor, soluble fms-like tyrosine kinase receptor, soluble endoglin, placental glycoprotein pregnancy-associated plasma protein-A and protein 13. CONCLUSION Even though it is possible to establish an efficient and effective diagnostic tool, three key principles must be observed in the management of preeclampsia: prevention, early screening and treatment.
Collapse
Affiliation(s)
- Fernanda Rodrigues Helmo
- Discipline of General Pathology, Institute of Biological and Natural Sciences, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | - Angela Maria Moed Lopes
- Oncology Research Institute, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | - Anna Cecília Dias Maciel Carneiro
- Discipline of Histology, Institute of Biological and Natural Sciences, Federal University of Triângulo Mineiro. Uberaba, Minas Gerais, Brazil
| | - Carolina Guissoni Campos
- Oncology Research Institute, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | - Polyana Barbosa Silva
- Oncology Research Institute, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | | | - Laura Penna Rocha
- Discipline of General Pathology, Institute of Biological and Natural Sciences, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | - Marlene Antônia Dos Reis
- Discipline of General Pathology, Institute of Biological and Natural Sciences, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | - Renata Margarida Etchebehere
- Surgical Pathology Service, Clinical Hospital, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil
| | - Juliana Reis Machado
- Discipline of General Pathology, Institute of Biological and Natural Sciences, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil; Department of General Pathology, Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Goiás, Brazil
| | - Rosana Rosa Miranda Corrêa
- Discipline of General Pathology, Institute of Biological and Natural Sciences, Federal University of Triângulo Mineiro, Uberaba, Minas Gerais, Brazil.
| |
Collapse
|
45
|
Allen R, Aquilina J. Prospective observational study to determine the accuracy of first-trimester serum biomarkers and uterine artery Dopplers in combination with maternal characteristics and arteriography for the prediction of women at risk of preeclampsia and other adverse pregnancy outcomes. J Matern Fetal Neonatal Med 2017; 31:2789-2806. [DOI: 10.1080/14767058.2017.1355903] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Rebecca Allen
- Barts Health NHS Trust, Royal London Hospital, Whitechapel, London
| | - Joseph Aquilina
- Barts Health NHS Trust, Royal London Hospital, Whitechapel, London
| |
Collapse
|
46
|
Drobnjak T, Gizurarson S, Gokina NI, Meiri H, Mandalá M, Huppertz B, Osol G. Placental protein 13 (PP13)-induced vasodilation of resistance arteries from pregnant and nonpregnant rats occurs via endothelial-signaling pathways. Hypertens Pregnancy 2017; 36:186-195. [PMID: 28524718 DOI: 10.1080/10641955.2017.1295052] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Placental protein 13 (PP13) induces hypotension in rats. This study aims to evaluate PP13 effects on isolated uterine arteries from nonpregnant and mid-pregnant rats. Vessels were isolated, cannulated, and pressurized to 50 mmHg within an arteriograph, preconstricted and exposed to increasing PP13 concentrations (10-13-10-8 M). PP13 elicited 38-50% arterial vasodilation with half-maximum response (EC50) = 1 pM. The relaxation was mediated by activating the endothelial-signaling pathways of prostaglandin and nitric oxide (NO). Accordingly, these results encourage evaluation of PP13 as a possible therapy for gestational diseases characterized by insufficient uteroplacental blood flow and/or maternal hypertension.
Collapse
Affiliation(s)
- Tijana Drobnjak
- a Faculty of Pharmaceutical Sciences , School of Health Science, University of Iceland , Reykjavik , Iceland
| | - Sveinbjörn Gizurarson
- a Faculty of Pharmaceutical Sciences , School of Health Science, University of Iceland , Reykjavik , Iceland
| | - Natalia I Gokina
- b Department of Obstetrics , Gynecology and Reproductive Sciences, University of Vermont , Burlington , Vermont , USA
| | - Hamutal Meiri
- c Hy Laboratories, Rehovot, and TeleMarpe , Tel Aviv , Israel
| | - Maurizio Mandalá
- d Department of Biology, Ecology and Earth Sciences , University of Calabria , Rende , Italy
| | - Berthold Huppertz
- e Institute of Cell Biology, Histology and Embryology , Medical University of Graz , Graz , Austria
| | - George Osol
- b Department of Obstetrics , Gynecology and Reproductive Sciences, University of Vermont , Burlington , Vermont , USA
| |
Collapse
|
47
|
Montagnana M, Danese E, Lippi G, Fava C. Blood laboratory testing for early prediction of preeclampsia: chasing the finish line or at the starting blocks? Ann Med 2017; 49:240-253. [PMID: 27791388 DOI: 10.1080/07853890.2016.1255350] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Preeclampsia (PE) affects 2-8% of pregnancies worldwide, thus representing an important cause of maternal and neonatal morbidity, up to death. Many studies have been designed to identify putative biomarkers for accurate and timely diagnosing PE, but only some of them were focused on specific and sensitive biomarkers for early prediction of this life-threatening condition. In particular, some prospective studies aimed to investigate the predictive role of circulating biomarkers before 20 weeks of gestation in the general pregnant population yielded conflicting results. This article is hence centered on results obtained in studies investigating the predictive performances of angiogenic, anti-angiogenic, inflammatory, endocrine, and epigenetic biomarkers. The available evidence suggests that angiogenic and anti-angiogenic molecules, in particular the sFlt1:PlGF ratio, may be considered the biomarkers with the best diagnostic performance in the second trimester. However, doubts remain about their use in clinical settings before the 20th gestational week. Even lower evidence is available for other biomarkers, due to the fact that some positive results have not been confirmed in ensuing investigations, whereas unresolved analytical issues still contribute to make their clinical reliability rather questionable. Differential expression of microRNAs seems also a promising evidence for early prediction of PE, but additional research and well-designed prospective studies are needed to identify and validate routine predictive tests. KEY MESSAGES Preeclampsia affects 2-8% of pregnant women worldwide, thus remaining one of the leading causes of maternal and neonatal morbidity and mortality. Several studies have investigated the predictive role of circulating biomarkers before 20th week of gestation with conflicting results. Additional research and well-designed prospective studies are needed to identify and validate predictive tests in clinical practice.
Collapse
Affiliation(s)
- Martina Montagnana
- a Sezione di Biochimica Clinica, Dipartimento di Neuroscienze , Biomedicina e Movimento Università di Verona , Italy
| | - Elisa Danese
- a Sezione di Biochimica Clinica, Dipartimento di Neuroscienze , Biomedicina e Movimento Università di Verona , Italy
| | - Giuseppe Lippi
- a Sezione di Biochimica Clinica, Dipartimento di Neuroscienze , Biomedicina e Movimento Università di Verona , Italy
| | - Cristiano Fava
- b Sezione di Medicina Interna C, Dipartimento di Medicina , Università di Verona , Italy
| |
Collapse
|
48
|
Evaluation of the predictive value of placental vascularisation indices derived from 3-Dimensional power Doppler whole placental volume scanning for prediction of pre-eclampsia: A systematic review and meta-analysis. Placenta 2017; 51:89-97. [DOI: 10.1016/j.placenta.2017.01.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 12/13/2016] [Accepted: 01/04/2017] [Indexed: 12/18/2022]
|
49
|
Scazzocchio E, Crovetto F, Triunfo S, Gratacós E, Figueras F. Validation of a first-trimester screening model for pre-eclampsia in an unselected population. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2017; 49:188-193. [PMID: 27257033 DOI: 10.1002/uog.15982] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2016] [Revised: 04/26/2016] [Accepted: 05/27/2016] [Indexed: 05/07/2023]
Abstract
OBJECTIVE To validate the performance of a previously constructed first-trimester predictive model for pre-eclampsia (PE) in routine care of an unselected population. METHODS A validation cohort of 4621 consecutive women attending their routine first-trimester ultrasound examination was used to test a prediction model for PE that had been developed previously in 5170 women. The prediction model included maternal factors, uterine artery Doppler, blood pressure and pregnancy-associated plasma protein-A. Model performance was evaluated using receiver-operating characteristics (ROC) curve analysis and ROC curves from both cohorts were compared unpaired. RESULTS Among the 4203 women included in the final analysis, 169 (4.0%) developed PE, including 141 (3.4%) cases of late-onset PE and 28 (0.7%) cases of early-onset PE. For early-onset PE, the model showed an area under the ROC curve of 0.94 (95% CI, 0.88-0.99), which did not differ significantly (P = 0.37) from that obtained in the construction cohort (0.88 (95% CI, 0.78-0.99)). For late-onset PE, the final model showed an area under the ROC curve of 0.72 (95% CI, 0.66-0.77), which did not differ significantly (P = 0.49) from that obtained in the construction cohort (0.75 (95% CI, 0.67-0.82)). CONCLUSION The prediction model for PE achieved a similar performance to that obtained in the construction cohort when tested on a subsequent cohort of women, confirming its validity as a predictive model for PE. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.
Collapse
Affiliation(s)
- E Scazzocchio
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
- Obstetrics, Gynecology and Reproductive Medicine Department, Quirón Dexeus Universitari Hospital, Barcelona, Spain
| | - F Crovetto
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - S Triunfo
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - E Gratacós
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - F Figueras
- BCNatal - Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| |
Collapse
|
50
|
Chang Y, Chen X, Cui HY, Li X, Xu YL. New Predictive Model at 11 +0 to 13 +6 Gestational Weeks for Early-Onset Preeclampsia With Fetal Growth Restriction. Reprod Sci 2016; 24:783-789. [PMID: 27678097 DOI: 10.1177/1933719116669053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The aim of the present study was to determine a predictive model for early-onset preeclampsia with fetal growth restriction (FGR) to be used at 11+0 to 13+6 gestational weeks, by combining the maternal serum level of pregnancy-associated plasma protein-A (PAPP-A), placental growth factor (PLGF), placental protein 13 (PP13), soluble endoglin (sEng), mean arterial pressure (MAP), and uterine artery Doppler. This was a retrospective cohort study of 4453 pregnant women. Uterine artery Doppler examination was conducted in the first trimester. Maternal serum PAPP-A, PLGF, PP13, and sEng were measured. Mean arterial pressure was obtained. Women were classified as with/without early-onset preeclampsia, and women with preeclampsia were classified as with/without FGR. Receiver operating characteristic analysis was performed to determine the value of the model. There were 30 and 32 pregnant women with early-onset preeclampsia with and without FGR. The diagnosis rate of early-onset preeclampsia with FGR was 67.4% using the predictive model when the false positive rate was set at 5% and 73.2% when the false positive rate was 10%. The predictive model (MAP, uterine artery Doppler measurements, and serum biomarkers) had some predictive value for the early diagnosis (11+0 to 13+6 gestational weeks) of early-onset preeclampsia with FGR.
Collapse
Affiliation(s)
- Ying Chang
- 1 Obstetrics Department, Tianjin Center Hospital of Obstetrics and Gynecology, Tianjin, China
| | - Xu Chen
- 1 Obstetrics Department, Tianjin Center Hospital of Obstetrics and Gynecology, Tianjin, China
| | - Hong-Yan Cui
- 1 Obstetrics Department, Tianjin Center Hospital of Obstetrics and Gynecology, Tianjin, China
| | - Xing Li
- 1 Obstetrics Department, Tianjin Center Hospital of Obstetrics and Gynecology, Tianjin, China
| | - Ya-Ling Xu
- 1 Obstetrics Department, Tianjin Center Hospital of Obstetrics and Gynecology, Tianjin, China
| |
Collapse
|