Objective: This study aimed to compare the predictive values of the American College of Obstetricians and Gynecologists (ACOG), the National Institute for Health and Care Excellence (NICE), and the Society of Obstetricians and Gynecologists of Canada (SOGC) factor-based models for preeclampsia (PE) screening.
Study Design: We conducted a secondary analysis of maternal and birth data from 32 hospitals. For each delivery, we calculated the risk of PE according to the ACOG, the NICE, and the SOGC models. Our primary outcomes were PE and preterm PE (PE combined with preterm birth) using the ACOG criteria. We calculated the detection rate (DR or sensitivity), the false positive rate (FPR or 1 - specificity), the positive (PPV) and negative (NPV) predictive values of each model for PE and for preterm PE using receiver operator characteristic (ROC) curves.
Results: We used 130,939 deliveries including 4,635 (3.5%) cases of PE and 823 (0.6%) cases of preterm PE. The ACOG model had a DR of 43.6% for PE and 50.3% for preterm PE with FPR of 15.6%; the NICE model had a DR of 36.2% for PE and 41.3% for preterm PE with FPR of 12.8%; and the SOGC model had a DR of 49.1% for PE and 51.6% for preterm PE with FPR of 22.2%. The PPV for PE of the ACOG (9.3%) and NICE (9.4%) models were both superior than the SOGC model (7.6%; < 0.001), with a similar trend for the PPV for preterm PE (1.9 vs. 1.9 vs. 1.4%, respectively; < 0.01). The area under the ROC curves suggested that the ACOG model is superior to the NICE for the prediction of PE and preterm PE and superior to the SOGC models for the prediction of preterm PE (all with < 0.001).
Conclusion: The current ACOG factor-based model for the prediction of PE and preterm PE, without considering race, is superior to the NICE and SOGC models.
Key Points: · Clinical factor-based model can predict PE in approximately 44% of the cases for a 16% false positive.. · The ACOG model is superior to the NICE and SOGC models to predict PE.. · Clinical factor-based models are better to predict PE in parous than in nulliparous..
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1055/s-0044-1782676 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Assessment of intraventricular hemorrhage risk in preterm infants using mathematically simulated cerebral blood flow.
Front Neurol
October 2024
Department of Clinical Medicine, Center for Digital Health and Technology, Orthopedic Department, Research Unit for Pediatric Neuroorthopedics and Cerebral Palsy of the Buhl-Strohmaier Foundation, Klinikum rechts der Isar, School of Medicine and Health, Technical University of Munich, Munich, Germany.
Intraventricular hemorrhage (IVH)4 is one of the most threatening neurological complications associated with preterm birth which can lead to long-term sequela such as cerebral palsy. Early recognition of IVH risk may prevent its occurrence and/or reduce its severity. Using multivariate logistic regression analysis, risk factors significantly associated with IVH were identified and integrated into risk scales.
View Article and Find Full Text PDFA new screening of preterm birth in gestation with short cervix after pessary plus progesterone.
Rev Bras Ginecol Obstet
October 2024
Department of Women, Children and Adolescents Health Universidade Federal do Ceará FortalezaCE Brazil Department of Women, Children and Adolescents Health, Universidade Federal do Ceará, Fortaleza, CE, Brazil.
Objective: This study aims to create a new screening for preterm birth < 34 weeks after gestation with a cervical length (CL) ≤ 30 mm, based on clinical, demographic, and sonographic characteristics.
Methods: This is a analysis of a randomized clinical trial (RCT), which included pregnancies, in middle-gestation, screened with transvaginal ultrasound. After observing inclusion criteria, the patient was invited to compare pessary plus progesterone (PP) versus progesterone only (P) (1:1).
First-trimester predictive models for adverse pregnancy outcomes-a base for implementation of strategies to prevent cardiovascular disease development.
Front Cell Dev Biol
September 2024
Institute for the Care of the Mother and Child, Third Faculty of Medicine, Charles University, Prague, Czechia.
Introduction: This study aimed to establish efficient, cost-effective, and early predictive models for adverse pregnancy outcomes based on the combinations of a minimum number of miRNA biomarkers, whose altered expression was observed in specific pregnancy-related complications and selected maternal clinical characteristics.
Methods: This retrospective study included singleton pregnancies with gestational hypertension (GH, n = 83), preeclampsia (PE, n = 66), HELLP syndrome (n = 14), fetal growth restriction (FGR, n = 82), small for gestational age (SGA, n = 37), gestational diabetes mellitus (GDM, n = 121), preterm birth in the absence of other complications (n = 106), late miscarriage (n = 34), stillbirth (n = 24), and 80 normal term pregnancies. MiRNA gene expression profiling was performed on the whole peripheral venous blood samples collected between 10 and 13 weeks of gestation using real-time reverse transcription polymerase chain reaction RT-PCR).
Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China.
Front Endocrinol (Lausanne)
June 2024
Department of Obstetrics and Gynecology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Introduction: Preeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11-13 + weeks' gestation, providing an effective tool for early screening and prediction of preeclampsia.
View Article and Find Full Text PDFMachine learning model with output correction: Towards reliable bradycardia detection in neonates.
Comput Biol Med
July 2024
CSIRO Australian e-Health Research Centre, Australia. Electronic address:
Bradycardia is a commonly occurring condition in premature infants, often causing serious consequences and cardiovascular complications. Reliable and accurate detection of bradycardia events is pivotal for timely intervention and effective treatment. Excessive false alarms pose a critical problem in bradycardia event detection, eroding trust in machine learning (ML)-based clinical decision support tools designed for such detection.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!