Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores.
Vesela P KovachevaBraden W EberhardRaphael Y CohenMatthew MaherRicha SaxenaKathryn J GrayPublished in: medRxiv : the preprint server for health sciences (2023)
Integrating clinical and genetic factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented in clinical practice to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.
Keyphrases
- pregnancy outcomes
- risk factors
- end stage renal disease
- clinical practice
- early onset
- randomized controlled trial
- ejection fraction
- genome wide
- newly diagnosed
- primary care
- healthcare
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- copy number
- preterm birth
- pregnant women
- emergency department
- gene expression
- dna methylation
- quality improvement
- current status
- insulin resistance
- patient reported
- weight loss
- drug induced