title: A predictive model for cesarean section in low risk pregnancies.
abstract: OBJECTIVE
  A small number of women with low risk pregnancies undergo cesarean section. A model that can predict this risk and therefore identify these women will be of help in several hospitals where personnel and resources are limited.
  
  
  METHODS
  The study consisted of 2 parts. All charts of women with low risk singleton pregnancies admitted to labor room over a 5-month period were analyzed. Adjusted odds ratios were calculated to find out relative importance of each risk factor and likelihood ratios were obtained. These were prospectively applied to 1010 consecutive low risk women and the post test probability calculated. Finally the actual incidence of cesarean section was compared with posttest probability derived from predictors.
  
  
  RESULTS
  A combination of maternal age >24 years, primiparity and height <150 cm or a combination of any 2 of the 3 variables is significantly associated with increased cesarean section rate. Individually, primiparity, height <150 cm or age >24 years also significantly increased the chances of cesarean section.
  
  
  CONCLUSIONS
  A predictive model consisting of maternal age, parity and height can be used to identify low risk pregnant women who are likely to require cesarean section.
authors.affiliation: Department of Obstetrics and Gynecology, Christian Medical College and Hospital, Vellore 632004, India. og2@cmcvellore.ac.in
authors.forename: L
authors.initials: L
authors.lastname: Seshadri
authors.forename: B
authors.initials: B
authors.lastname: Mukherjee
mesh_terms: D000328:Adult; D001827:Body Height; D002585:Cesarean Section; D005260:Female; D005544:Forecasting; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D008423:Maternal Age; D010298:Parity; D011247:Pregnancy; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors