Comparing WHO C-Model generated C-Section probabilities to actual delivery outcomes in a tertiary care centre

Authors

  • Amita Ray Department of Obstetrics and Gynecology, DM Wayanad Institute of Medical Sciences, Wayanad, Kerala, India
  • Arun Gopi Department of Community Medicine, DM Wayanad Institute of Medical Sciences, Wayanad, Kerala, India
  • Sujoy Ray Department of Psychiatry, St. John’s Medical College, Bangalore, Karnataka, India

DOI:

https://doi.org/10.18203/2320-1770.ijrcog20175851

Keywords:

C-Model, Negative predictive value, Positive predictive value, Robson’s Groups

Abstract

Background: A model which takes into account several relevant factors and gives the probability of C-Section in a woman would have the advantage of preparing for such an event. The identification of women at high risk of C-Section (>50% risk) would provide the opportunity for understanding risks involved in pursuing a vaginal delivery whereas if the risk of C-Section was less (<50%) it would prove useful in counselling for a vaginal delivery. We used the WHO C-Model with the aim to find the predictability of this model in our facility and the overuse of C-section in the 10 Robson’s Groups.

Methods: A retrospective observational study in which all women who gave birth at our hospital from June 2016 to May 2017 were included and C-Section probability was calculated using the C-Model. Comparison with the actual mode of delivery was done to find the sensitivity, specificity, positive and negative predictive value of the model and the overuse of C-Section in the Robson’s Groups.

Results: Out of the 314 C-Sections done only 45 women had a >50% probability, giving the model a sensitivity of 14.33%, specificity of 98.8%, positive predictive value of 90% and negative predictive value of 60.56%. Robson’s Groups 5 and 3 demonstrated the greatest overuse of C-Sections.

Conclusions: The WHO Model when applied to this centre showed a high positive predictive value for C-Sections but the negative predictive value or the ability to correctly predict a vaginal delivery was much less.

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Published

2017-12-25

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Original Research Articles