English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Objective  To generate a clinical prediction tool for stillbirth that combines maternal risk factors to provide an evidence based approach for the identification of women who will benefit most from antenatal testing for stillbirth prevention.    Design  Retrospective cohort study    Setting  Midwestern United States quaternary referral center    Population  Singleton pregnancies undergoing second trimester anatomic survey from 1999–2009. Pregnancies with incomplete follow-up were excluded.    Methods  Candidate predictors were identified from the literature and univariate analysis. Backward stepwise logistic regression with statistical comparison of model discrimination, calibration and clinical performance was used to generate final models for the prediction of stillbirth. Internal validation was performed using bootstrapping with 1,000 repetitions. A stillbirth risk calculator and stillbirth risk score were developed for the prediction of stillbirth at or beyond 32 weeks excluding fetal anomalies and aneuploidy. Statistical and clinical cut-points were identified and the tools compared using the Integrated Discrimination Improvement.    Main outcome measures  Antepartum stillbirth    Results  64,173 women met inclusion criteria. The final stillbirth risk calculator and score included maternal age, black race, nulliparity, body mass index, smoking, chronic hypertension and pre-gestational diabetes. The stillbirth calculator and simple risk score demonstrated modest discrimination but clinically significant performance with no difference in overall performance between the tools [(AUC 0.66 95% CI 0.60–0.72) and (AUC 0.64 95% CI 0.58–0.70), (p = 0.25)].    Conclusion  A stillbirth risk score was developed incorporating maternal risk factors easily ascertained during prenatal care to determine an individual woman’s risk for stillbirth and provide an evidenced based approach to the initiation of antenatal testing for the prediction and prevention of stillbirth.

A stillbirth calculator: Development and internal validation of a clinical prediction model to quantify stillbirth risk