title: Nuchal thickness, urine beta-core fragment level, and maternal age for down syndrome screening.
abstract: OBJECTIVE
  Our purpose was to report the midtrimester Down syndrome screening efficiency of a 2-analyte algorithm, urine beta-core fragment (a metabolite of human chorionic gonadotropin) and nuchal thickness, along with maternal age in a high-risk population undergoing genetic amniocentesis.
  
  
  METHODS
  Nuchal thickness, humerus length, and maternal urine beta-core fragment levels were measured prospectively before genetic amniocentesis in 1360 singleton pregnancies, 21 (1.5%) of which had fetal Down syndrome. All analyte levels were expressed as multiples of the normal medians based on biparietal diameter. Backward-stepwise logistic regression was used to determine whether the markers were significant independent predictors of fetal Down syndrome. Matrix analysis was used to calculate an adjusted Down syndrome likelihood ratio for each patient based on the significant screening markers. Multiplication by age-related midtrimester risk gave the adjusted Down syndrome risk. The sensitivity and false-positive rates at different Down syndrome screening thresholds were used to generate a receiver-operator characteristics curve. The area under the curve was used to assess the value of this screening test.
  
  
  RESULTS
  On the basis of logistic regression, beta-core fragment level (P 1/60 the sensitivity and false-positive rate for Down syndrome were 85.7% and 4.9%, respectively, when beta-core fragment level, nuchal thickness, and maternal age were used. Correspondence screening values at a risk threshold > 1/150 were 95.2% and 10.8%, respectively. The area under the receiver-operator characteristics curve was 0.9357 (SE = 0. 0137), indicating that the algorithm is excellent for Down syndrome screening.
  
  
  CONCLUSIONS
  In this study, a combination algorithm consisting of nuchal thickness, urine beta-core fragment level, and maternal age had a high screening efficiency for Down syndrome. This algorithm should be investigated as a new option for women at high risk of having a fetus with Down syndrome.
authors.affiliation: Department of Obstetrics and Gynecology, Yale University School of Medicine, New Haven, Connecticut, USA.
authors.forename: R O
authors.initials: RO
authors.lastname: Bahado-Singh
authors.forename: A U
authors.initials: AU
authors.lastname: Oz
authors.forename: D
authors.initials: D
authors.lastname: Flores
authors.forename: D
authors.initials: D
authors.lastname: Cermik
authors.forename: E
authors.initials: E
authors.lastname: Acuna
authors.forename: M J
authors.initials: MJ
authors.lastname: Mahoney
authors.forename: L
authors.initials: L
authors.lastname: Cole
mesh_terms: D000293:Adolescent; D000328:Adult; D000649:Amniocentesis; D018997:Chorionic Gonadotropin, beta Subunit, Human / Q000652:urine*; D004314:Down Syndrome / Q000175:diagnosis*; D005260:Female; D005865:Gestational Age; D006801:Humans; D007621:Karyotyping; D016015:Logistic Models; D008423:Maternal Age; D008875:Middle Aged; D009333:Neck / Q000000981:diagnostic imaging*; D011247:Pregnancy; D011296:Prenatal Diagnosis; D012372:ROC Curve; D012307:Risk Factors; D016216:Ultrasonography, Prenatal; D000509:alpha-Fetoproteins / Q000032:analysis