A Retrospective Analysis of the Robson Classification Implementation in a Tertiary Care Hospital in Serbia

TL;DR

This study analyzed cesarean section rates in Serbia using the Robson classification to identify which patient groups contribute most to high rates.

Contribution

The study provides the first reported implementation of the Robson classification in Serbia for analyzing cesarean section rates.

Findings

The overall cesarean section rate was 30.5%.

Robson groups 1, 3, and 5 had the highest cesarean section rates.

Group 5 contributed the most to the absolute cesarean section rate.

Abstract

Background/Objectives: Cesarean section (CS) is an essential intervention in obstetric care, significantly contributing to reducing the rate of maternal and neonatal mortality and morbidity. It has been recommended that the acceptable CS rate should not go beyond 10–15% across all deliveries. Nonetheless, the CS rate has escalated over the past decades. To understand the factors contributing to the rise in CS rates, the Robson classification that relies on pre-labor, intrapartum, and postpartum parameters has been proposed. As no journal-reported data are currently available on the implementation of the Robson classification in Serbia, we aimed to identify trends in CS rates, as well as the Robson groups with the highest risk for CS at our tertiary care clinic. Methods: We conducted a retrospective, cross-sectional analysis of 6574 women who gave birth to live fetuses weighing a minimum of 500 g and with a gestational age of at least 22 weeks. Results: The overall CS rate was 30.5%, with a statistically significant difference in CS rates between different Robson groups (X2 = 2703.9, p < 0.001). Robson groups 1 (31.9%), 3 (30.4%), and 5 (10.3%) were the largest, and groups 9 (0.9%) and 7 (1.3%) were the smallest. The CS rate in group 5 was the highest (30.3%), followed by groups 1 (20.3%) and 2 (13.2%). Group 5 was the largest contributor to the absolute CS rate (9.25%), followed by groups 1 (6.21%) and 2 (4.03%). Conclusions: We effectively implemented Robson classification for monitoring CS rates and distinguishing specific groups that individually contribute to these rates.

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