In case you missed it: The <i>Prenatal Diagnosis</i> editors bring you the most significant advances of 2019

Chitty, Lyn S.; Hui, Lisa; Ghidini, Alessandro; Levy, Brynn; Deprest, Jan; Mieghem, Tim Van; Bianchi, D. W.

doi:10.1002/pd.5632

Cited by 8 publications

(8 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning is a computer programming approach whereby computers learn from “big data” to make better predictions [ 17 ]. In 2019, machine learning was identified as one of the most advanced tools for prenatal diagnosis [ 18 ]. Morover, machine learning has been broadly applied in medicine, from cancer detection [ 19 , 20 ] to prediction of cardiovascular diseases [ 21 ], among others.…”

Section: Introductionmentioning

confidence: 99%

Prediction of preterm birth in nulliparous women using logistic regression and machine learning

2021

View full text Add to dashboard Cite

Objective To predict preterm birth in nulliparous women using logistic regression and machine learning. Design Population-based retrospective cohort. Participants Nulliparous women (N = 112,963) with a singleton gestation who gave birth between 20–42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014. Methods We used data during the first and second trimesters to build logistic regression and machine learning models in a “training” sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent “validation” sample. Results During the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23–5.42; Type II: AOR: 2.68; 95% CI: 2.05–3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80–2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58–62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21–13.90). During the second trimester, the AUC increased to 65% (95% CI: 63–66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79–81%) with artificial neural networks. All models yielded 94–97% negative predictive values for spontaneous PTB during the first and second trimesters. Conclusion Although artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of preterm birth in nulliparous women using logistic regression and machine learning

2021

View full text Add to dashboard Cite

show abstract

“…The benefits and risks of NIPS-M need to be carefully evaluated when patients are counselled based on current understanding of the diseases. 24 This study is aimed to address above important issues with a focus on the clinical validation of an innovative NIPS for concurrent screening of common aneuploidies, CNVs and monogenic disorders. The potential benefits and the limitations of this screening test will also be explored based on the pregnancy outcome data.…”

Section: The Development Of Nips For Monogenic Disordersmentioning

confidence: 99%

“…The benefits and risks of NIPS-M need to be carefully evaluated when patients are counselled based on current understanding of the diseases. 24 …”

Section: Introductionmentioning

confidence: 99%

Comprehensive non-invasive prenatal screening for pregnancies with elevated risks of genetic disorders: protocol for a prospective, multicentre study

Cai

Chen

et al. 2021

BMJ Open

View full text Add to dashboard Cite

IntroductionChromosomal abnormalities and monogenic disorders account for ~15%–25% of recognisable birth defects. With limited treatment options, preconception and prenatal screening were developed to reduce the incidence of such disorders. Currently, non-invasive prenatal screening (NIPS) for common aneuploidies is implemented worldwide with superiority over conventional serum or sonographic screening approaches. However, the clinical validity for the screening of frequent chromosome segmental copy number variations and monogenic disorders still awaits to be proved.Methods and analysisThis study is a multicentre, prospective study. The participants were recruited from three tertiary hospitals in China starting from 10 April 2021. The study is expected to conclude before 10 October 2022. Pregnant women with abnormal prenatal screening results indicated for invasive prenatal diagnosis or those who decide to terminate their pregnancies due to abnormal ultrasound findings will be evaluated for enrolment. Cell-free DNA extracted from the maternal plasma will be used for an analytically validated comprehensive NIPS test developed by Beijing BioBiggen Technology Co. (Beijing, China). The diagnostic results from prenatal or postnatal specimens as well as the pregnancy outcome data will be collected to examine the clinical sensitivity, specificity, positive and negative predictive values of the test.Ethics and disseminationThis study was approved by the Obstetrics and Gynecology Hospital of Fudan University (2020-178). Results of this study will be disseminated to public through scientific conferences and a peer-reviewed journal. Written informed consents will be obtained from participants.Trial registration numberChiCTR2100045739.

show abstract

“…At this meeting we discuss the key progress made in the specialties of prenatal diagnosis and fetal therapy and begin to put together our traditional end of year publication "In Case You Missed It" to keep you up to date with developments in our field. [1][2][3][4][5][6][7] We take turns to host the editors' meeting and have met in Washington, Leuven, Boston, London and Hertfordshire. This year, however, was different in more ways than one!…”

Section: Introductionmentioning

confidence: 99%

“…Usually, around mid‐October, the Associate Editors of Prenatal Diagnosis meet to make plans for the year to come and reflect on the year just gone. At this meeting we discuss the key progress made in the specialties of prenatal diagnosis and fetal therapy and begin to put together our traditional end of year publication “In Case You Missed It” to keep you up to date with developments in our field 1‐7 . We take turns to host the editors' meeting and have met in Washington, Leuven, Boston, London and Hertfordshire.…”

Section: Introductionmentioning

confidence: 99%

Right or wrong? Looking through the retrospectoscope to analyse predictions made a decade ago in prenatal diagnosis and fetal surgery

et al. 2020

Self Cite

View full text Add to dashboard Cite

In case you missed it: The Prenatal Diagnosis editors bring you the most significant advances of 2019

Cited by 8 publications

References 66 publications

Prediction of preterm birth in nulliparous women using logistic regression and machine learning

Prediction of preterm birth in nulliparous women using logistic regression and machine learning

Comprehensive non-invasive prenatal screening for pregnancies with elevated risks of genetic disorders: protocol for a prospective, multicentre study

Right or wrong? Looking through the retrospectoscope to analyse predictions made a decade ago in prenatal diagnosis and fetal surgery

Contact Info

Product

Resources

About