Nomogram-based prediction of pre-eclampsia in the first trimester of gestation

Wang, Wei; Wang, Yanyan; Tan, Yuan; Zhang, Hao; Li, Chunfang; Li, Xuelan; Han, Zhen

doi:10.1016/j.preghy.2020.04.011

Cited by 7 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al predicted the risk of early-onset preeclampsia for pregnant women with twin pregnancy, where the AUROC was 0.82 (95% CI: 0.76–0.88), and the detection rate was 40.7% (false positive 10%) ( 22 ). Wang et al developed a prediction model for pre-eclampsia using 31 blood flow related parameters such as vascularization index (VI), blood flow index (FI) and vascularization blood flow index (VFI) related to uterus and placenta, and the AUROC reached 0.877 ( 25 ). However, these studies aimed at specific high-risk groups, or used expensive indicators that were not within the scope of routine testing, so it may not be conducive to promotion.…”

Section: Discussionmentioning

confidence: 99%

“…However, these studies aimed at specific high-risk groups, or used expensive indicators that were not within the scope of routine testing, so it may not be conducive to promotion. In addition, these studies also have other limitations: not consider pre-eclampsia subtypes ( 23 – 26 ), possible bias caused by variable screening process ( 23 , 24 , 26 ), and insufficient number of outcome events ( 21 – 23 , 25 ).…”

Section: Discussionmentioning

confidence: 99%

“…Least Absolute Shrinkage And Selection Operator (LASSO) is a shrinking technique used to do variable selection. It has been used to do variable selection and avoid overfitting for development of pre-eclampsia prediction models in previous studies ( 25 , 32 ). LASSO was described as follows.…”

Section: Methodsmentioning

confidence: 99%

“…However, these studies primarily focused on specific high-risk groups or used expensive biomarkers beyond the scope of routine testing, which limits their generalizability. In addition, these studies did not consider pre-eclampsia subtypes ( 23 – 26 ), possible bias caused by the process of variable selection ( 23 , 24 , 26 ), and insufficient number of outcome events ( 21 – 23 , 25 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Development and Validation of Multi-Stage Prediction Models for Pre-eclampsia: A Retrospective Cohort Study on Chinese Women

Tang

Zhou

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

ObjectiveThe aim of this study is to develop multistage prediction models for pre-eclampsia (PE) covering almost the entire pregnancy period based on routine antenatal measurements and to propose a risk screening strategy.MethodsThis was a retrospective cohort study that included 20582 singleton pregnant women with the last menstruation between January 1, 2013 and December 31, 2019. Of the 20582 women, 717 (3.48%) developed pre-eclampsia, including 46 (0.22%) with early-onset pre-eclampsia and 119 (0.58%) preterm pre-eclampsia. We randomly divided the dataset into the training set (N = 15665), the testing set (N = 3917), and the validation set (N = 1000). Least Absolute Shrinkage And Selection Operator (LASSO) was used to do variable selection from demographic characteristics, blood pressure, blood routine examination and biochemical tests. Logistic regression was used to develop prediction models at eight periods: 5–10 weeks, 11–13 weeks, 14–18 weeks, 19–23 weeks, 24–27 weeks, 28–31 weeks, 32–35 weeks, and 36–39 weeks of gestation. We calculated the AUROC (Area Under the Receiver Operating Characteristic Curve) on the test set and validated the screening strategy on the validation set.ResultsWe found that uric acid tested from 5–10 weeks of gestation, platelets tested at 18–23 and 24–31 weeks of gestation, and alkaline phosphatase tested at 28–31, 32–35 and 36–39 weeks of gestation can further improve the prediction performance of models. The AUROC of the optimal prediction models on the test set gradually increased from 0.71 at 5–10 weeks to 0.80 at 24–27 weeks, and then gradually increased to 0.95 at 36–39 weeks of gestation. At sensitivity level of 0.98, our screening strategy can identify about 94.8% of women who will develop pre-eclampsia and reduce about 40% of the healthy women to be screened by 28–31 weeks of pregnancy.ConclusionWe developed multistage prediction models and a risk screening strategy, biomarkers of which were part of routine test items and did not need extra costs. The prediction window has been advanced to 5–10 weeks, which has allowed time for aspirin intervention and other means for PE high-risk groups.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development and Validation of Multi-Stage Prediction Models for Pre-eclampsia: A Retrospective Cohort Study on Chinese Women

Tang

Zhou

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Currently, nomograms are widely used in the prognosis of diseases to aid clinicians in decision makings. In recent years, this tool has garnered increased attention of obstetricians and gynecologists ( 6 , 7 ). To the best of our knowledge, there is currently no nomogram for predicting bleeding after vaginal delivery alone.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Prognostic Nomogram for Postpartum Hemorrhage After Vaginal Delivery: A Retrospective Cohort Study in China

et al. 2022

View full text Add to dashboard Cite

BackgroundPostpartum hemorrhage (PPH) is a common complication following vaginal delivery and in severe cases can lead to maternal death. A straightforward predictive model is required to enable prenatal evaluations by obstetricians to prevent PPH complications.MethodsData of patients who delivered vaginally after 37 weeks of gestation were retrospectively collected from the medical database at Shengjing Hospital of China Medical University for the period 2016 to 2020. PPH was defined as blood loss of 500 mL or more within 24 h of delivery, and important independent prognostic factors were determined using univariate and multivariate logistic regression analyses to construct nomograms regarding PPH.ResultsA total of 24,833 patients who delivered vaginally were included in this study. The training cohort included 22,302 patients who delivered between 2016 and 2019 and the external validation cohort included 2,531 patients who delivered during 2020. Nomogram was created using data such as age, race, occupation, parity, gestational weeks, labor time, neonatal weight, analgesic delivery, gestational diabetes mellitus, premature rupture of membranes, anemia, hypertension, adenomyosis, and placental adhesion. The nomogram has good predictive power and clinical practicality through the analysis of the area under the curve and decision curve analysis. Internal verification was performed on the nomogram for PPH, demonstrating consistency between the nomogram's predicted probability and actual probability.ConclusionsThe developed and validatable nomogram is a good predictor of PPH in vaginal delivery and can be used in clinical practice to guide obstetricians to administer preventive therapies before delivery.

show abstract

Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review

Tiruneh,

Vu,

Rolnik

et al. 2024

Curr Hypertens Rep

View full text Add to dashboard Cite

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. Summary 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.

show abstract

Nomogram-based prediction of pre-eclampsia in the first trimester of gestation

Cited by 7 publications

References 18 publications

Development and Validation of Multi-Stage Prediction Models for Pre-eclampsia: A Retrospective Cohort Study on Chinese Women

Development and Validation of Multi-Stage Prediction Models for Pre-eclampsia: A Retrospective Cohort Study on Chinese Women

Development and Validation of Prognostic Nomogram for Postpartum Hemorrhage After Vaginal Delivery: A Retrospective Cohort Study in China

Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review

Contact Info

Product

Resources

About