title: Prediction of location of a symptomatic early gestation based solely on clinical presentation.
abstract: OBJECTIVE
  To evaluate three strategies for diagnosis of women at risk for ectopic pregnancy based on information collected at initial presentation.
  
  
  METHODS
  Strength of association for risk factors, signs, and symptoms obtained at initial presentation of women with pain, bleeding, or both in a first-trimester pregnancy and a nondiagnostic ultrasound examination were calculated using a cohort of 2,026 women. Three models (logistic regression, a numeric scoring system, and a Classification and Regression Tree) were created to predict final outcome and tested on a second cohort of 1,634 women. Accuracy was assessed using 2x2 tables evaluating the decision of send home compared with do not send home (combination of monitor or intervene) and intervene compared with do not intervene (monitor or send home). Sensitivity, specificity, and predictive values were calculated.
  
  
  RESULTS
  The ultimate diagnosis of women in the test population was 304 (18.6%) patients with an ectopic pregnancy, 834 (51.0%) with miscarriage, and 494 (30.2%) with an ongoing intrauterine pregnancy. A total of 95.9% of patients with ectopic pregnancy or miscarriage were correctly assigned to the strategy to monitor or intervene upon based on the scoring system, and 97.6% based on the Classification and Regression Tree. The specificity of the decision to send a patient home with a likely intrauterine pregnancy was greater than 95% for all three methods. The sensitivity of all strategies in the decision to intervene for an ectopic pregnancy was greater than 98%.
  
  
  CONCLUSIONS
  A simplified scoring system based on five factors (age, ectopic history, bleeding, prior miscarriage, and human chorionic gonadotropin level) was as effective as a Classification and Regression Tree or logistic regression modes in predicting outcome of women at risk for ectopic pregnancy. Prediction of location of a symptomatic first-trimester pregnancy based on clinical symptoms and risk factors is possible, but must be used in conjunction with outpatient surveillance.
  
  
  METHODS
  II.
authors.affiliation: From the Department of Obstetrics and Gynecology, Division of Reproductive Endocrinology, and Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Kurt T
authors.initials: KT
authors.lastname: Barnhart
authors.forename: Bruno
authors.initials: B
authors.lastname: Casanova
authors.forename: Mary D
authors.initials: MD
authors.lastname: Sammel
authors.forename: Kelly
authors.initials: K
authors.lastname: Timbers
authors.forename: Karine
authors.initials: K
authors.lastname: Chung
authors.forename: J L
authors.initials: JL
authors.lastname: Kulp
mesh_terms: D000022:Abortion, Spontaneous / Q000175:diagnosis*; D000293:Adolescent; D000328:Adult; D018997:Chorionic Gonadotropin, beta Subunit, Human / Q000097:blood; D015331:Cohort Studies; D003663:Decision Trees; D005260:Female; D006801:Humans; D016015:Logistic Models; D011247:Pregnancy; D011271:Pregnancy, Ectopic / Q000175:diagnosis*; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D016216:Ultrasonography, Prenatal; D055815:Young Adult
chemical_list: D018997:Chorionic Gonadotropin, beta Subunit, Human