Preterm Birth: Screening and Prediction.
Lyndsay CreswellDaniel Lorber RolnikStephen W LindowNeil O'GormanPublished in: International journal of women's health (2023)
Preterm birth (PTB) affects approximately 10% of births globally each year and is the most significant direct cause of neonatal death and of long-term disability worldwide. Early identification of women at high risk of PTB is important, given the availability of evidence-based, effective screening modalities, which facilitate decision-making on preventative strategies, particularly transvaginal sonographic cervical length (CL) measurement. There is growing evidence that combining CL with quantitative fetal fibronectin (qfFN) and maternal risk factors in the extensively peer-reviewed and validated QUanititative Innovation in Predicting Preterm birth (QUiPP) application can aid both the triage of patients who present as emergencies with symptoms of preterm labor and high-risk asymptomatic women attending PTB surveillance clinics. The QUiPP app risk of delivery thus supports shared decision-making with patients on the need for increased outpatient surveillance, in-patient treatment for preterm labor or simply reassurance for those unlikely to deliver preterm. Effective triage of patients at preterm gestations is an obstetric clinical priority as correctly timed administration of antenatal corticosteroids will maximise their neonatal benefits. This review explores the predictive capacity of existing predictive tests for PTB in both singleton and multiple pregnancies, including the QUiPP app v.2. and discusses promising new research areas, which aim to predict PTB through cervical stiffness and elastography measurements, metabolomics, extracellular vesicles and artificial intelligence.
Keyphrases
- preterm birth
- gestational age
- artificial intelligence
- low birth weight
- birth weight
- pregnancy outcomes
- risk factors
- emergency department
- polycystic ovary syndrome
- public health
- end stage renal disease
- pregnant women
- big data
- machine learning
- decision making
- newly diagnosed
- deep learning
- primary care
- ejection fraction
- multidrug resistant
- peritoneal dialysis
- type diabetes
- metabolic syndrome
- prognostic factors
- skeletal muscle
- smoking cessation
- breast cancer risk
- liver fibrosis