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Sökning: onr:"swepub:oai:gup.ub.gu.se/335384" > The Role of cfDNA B...

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FältnamnIndikatorerMetadata
00005138naa a2200517 4500
001oai:gup.ub.gu.se/335384
003SwePub
008240528s2024 | |||||||||||000 ||eng|
0247 a https://gup.ub.gu.se/publication/3353842 URI
0247 a https://doi.org/10.1016/j.ajog.2024.02.2992 DOI
040 a (SwePub)gu
041 a eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Khalil, Asma4 aut
2451 0a The Role of cfDNA Biomarkers and Patient Data in the Early Prediction of Preeclampsia: Artificial Intelligence Model.
264 1c 2024
520 a Accurate individualized assessment of preeclampsia risk enables the identification of patients most likely to benefit from initiation of low-dose aspirin at 12-16 weeks' gestation when there is evidence for its effectiveness, as well as guiding appropriate pregnancy care pathways and surveillance. The primary objective of this study was to evaluate the performance of artificial neural network models for the prediction of preterm preeclampsia (<37 weeks' gestation) using patient characteristics available at the first antenatal visit and data from prenatal cell-free DNA (cfDNA) screening. Secondary outcomes were prediction of early onset preeclampsia (<34 weeks' gestation) and term preeclampsia (≥37 weeks' gestation).This secondary analysis of a prospective, multicenter, observational prenatal cfDNA screening study (SMART) included singleton pregnancies with known pregnancy outcomes. Thirteen patient characteristics that are routinely collected at the first prenatal visit and two characteristics of cfDNA, total cfDNA and fetal fraction (FF), were used to develop predictive models for early-onset (<34 weeks), preterm (<37 weeks), and term (≥37 weeks) preeclampsia. For the models, the 'reference' classifier was a shallow logistic regression (LR) model. We also explored several feedforward (non-linear) neural network (NN) architectures with one or more hidden layers and compared their performance with the LR model. We selected a simple NN model built with one hidden layer and made up of 15 units.Of 17,520 participants included in the final analysis, 72 (0.4%) developed early onset, 251 (1.4%) preterm, and 420 (2.4%) term preeclampsia. Median gestational age at cfDNA measurement was 12.6 weeks and 2,155 (12.3%) had their cfDNA measurement at 16 weeks' gestation or greater. Preeclampsia was associated with higher total cfDNA (median 362.3 versus 339.0 copies/ml cfDNA; p<0.001) and lower FF (median 7.5% versus 9.4%; p<0.001). The expected, cross-validated area under the curve (AUC) scores for early onset, preterm, and term preeclampsia were 0.782, 0.801, and 0.712, respectively for the LR model, and 0.797, 0.800, and 0.713, respectively for the NN model. At a screen-positive rate of 15%, sensitivity for preterm preeclampsia was 58.4% (95% CI 0.569, 0.599) for the LR model and 59.3% (95% CI 0.578, 0.608) for the NN model.The contribution of both total cfDNA and FF to the prediction of term and preterm preeclampsia was negligible. For early-onset preeclampsia, removal of the total cfDNA and FF features from the NN model was associated with a 6.9% decrease in sensitivity at a 15% screen positive rate, from 54.9% (95% CI 52.9-56.9) to 48.0% (95% CI 45.0-51.0).Routinely available patient characteristics and cfDNA markers can be used to predict preeclampsia with performance comparable to other patient characteristic models for the prediction of preterm preeclampsia. Both LR and NN models showed similar performance.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Reproduktionsmedicin och gynekologi0 (SwePub)302202 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Obstetrics, Gynaecology and Reproductive Medicine0 (SwePub)302202 hsv//eng
7001 a Bellesia, Giovanni4 aut
7001 a Norton, Mary E4 aut
7001 a Jacobsson, Bo,d 1960u Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för obstetrik och gynekologi,Institute of Clinical Sciences, Department of Obstetrics and Gynecology4 aut0 (Swepub:gu)xjacbo
7001 a Haeri, Sina4 aut
7001 a Egbert, Melissa4 aut
7001 a Malone, Fergal D4 aut
7001 a Wapner, Ronald J4 aut
7001 a Roman, Ashley4 aut
7001 a Faro, Revital4 aut
7001 a Madankumar, Rajeevi4 aut
7001 a Strong, Noel4 aut
7001 a Silver, Robert M4 aut
7001 a Vohra, Nidhi4 aut
7001 a Hyett, Jon4 aut
7001 a Macpherson, Cora4 aut
7001 a Prigmore, Brittany4 aut
7001 a Ahmed, Ebad4 aut
7001 a Demko, Zach4 aut
7001 a Ortiz, J Bryce4 aut
7001 a Souter, Vivienne4 aut
7001 a Dar, Pe'er4 aut
7102 a Göteborgs universitetb Institutionen för kliniska vetenskaper, Avdelningen för obstetrik och gynekologi4 org
7730 t American journal of obstetrics and gynecologyx 1097-6868
8564 8u https://gup.ub.gu.se/publication/335384
8564 8u https://doi.org/10.1016/j.ajog.2024.02.299

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