First-Trimester Fetal Crown–Rump Length as an Early Predictor of Pregnancy and Birth Outcomes: A Narrative Review

Abstract

First-trimester fetal crown–rump length (CRL) is the most accurate ultrasonographic parameter for estimating gestational age and serves as the cornerstone of early obstetric assessment. Emerging evidence suggests that CRL has prognostic significance beyond pregnancy dating, reflecting early embryonic growth, placental development, and fetal wellbeing. This narrative review summarizes current evidence regarding the association between first-trimester CRL and subsequent pregnancy, birth, and neonatal outcomes. A comprehensive literature review of peer-reviewed articles, systematic reviews, clinical guidelines, and standard obstetric textbooks published predominantly between 2000 and 2025 was undertaken using electronic databases including PubMed, Scopus, Google Scholar, and major international guidelines. The available evidence demonstrates that reduced first-trimester CRL is consistently associated with an increased risk of miscarriage, fetal growth restriction, low birth weight, small-for-gestational-age infants, and adverse neonatal outcomes, whereas its predictive value for spontaneous preterm birth remains inconsistent. International organizations, including the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) and the American Institute of Ultrasound in Medicine (AIUM), emphasize standardized CRL measurement to ensure accurate gestational age estimation and reliable fetal growth assessment. Recent studies also support the integration of CRL with maternal characteristics, biochemical markers, uterine artery Doppler indices, and advanced prediction models to improve early identification of high-risk pregnancies. Although CRL alone is not sufficient to predict all adverse pregnancy outcomes, it remains an important biomarker of early fetal development and contributes significantly to first-trimester risk stratification. Future research should focus on multicenter prospective studies and artificial intelligence-assisted prediction models to further enhance the clinical utility of CRL in precision obstetrics.