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A predictive model for cesarean section in low risk pregnancies
Author(s) -
Seshadri L.,
Mukherjee B.
Publication year - 2005
Publication title -
international journal of gynecology and obstetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.895
H-Index - 97
eISSN - 1879-3479
pISSN - 0020-7292
DOI - 10.1016/j.ijgo.2005.01.031
Subject(s) - singleton , medicine , obstetrics , odds ratio , odds , section (typography) , parity (physics) , incidence (geometry) , risk factor , predictive value , pregnancy , gynecology , logistic regression , mathematics , genetics , physics , geometry , pathology , particle physics , advertising , business , biology
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.