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The Robson classification for caesarean section-A proposed method based on routinely collected health data.

ABSTRACT: BACKGROUND:With an increasing rate of caesarean sections as well as rising numbers of multiple pregnancies, valid classifications for benchmarking are needed. The Robson classification provides a method to group cases with caesarean section in order to assess differences in outcome across regions and sites. In this study we set up a novel method of classification by using routinely collected health data. We hypothesize i that routinely collected health data can be used to apply complex medical classifications and ii that the Robson classification is capable of classifying mothers and their corresponding newborn into meaningful groups with regard to outcome. METHODS AND FINDINGS:The study was conducted at the coding department and the department of obstetrics and gynecology Inselspital, University Hospital of Bern, Switzerland. The study population contained inpatient cases from 2014 until 2017. Administrative and health data were extracted from the Data Warehouse. Cases were classified by a Structured Query Language code according to the Robson criteria using data from the administrative system, the electronic health record and from the laboratory system. An automated query to classify the cases according to Robson could be implemented and successfully validated. A linkage of the mother's class to the corresponding newborn could be established. The distribution of clinical indicators was described. It could be shown that the Robson classes are associated to outcome parameters and case related costs. CONCLUSIONS:With this study it could be demonstrated, that a complex query on routinely collected health data would serve for medical classification and monitoring of quality and outcome. Risk-stratification might be conducted using this data set and should be the next step in order to evaluate the Robson criteria and outcome. This study will enhance the discussion to adopt an automated classification on routinely collected health data for quality assurance purposes.

SUBMITTER: Triep K

PROVIDER: S-EPMC7703923 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The Robson classification for caesarean section-A proposed method based on routinely collected health data.

Triep Karen K Torbica Nenad N Raio Luigi L Surbek Daniel D Endrich Olga O

PloS one 20201130 11

<h4>Background</h4>With an increasing rate of caesarean sections as well as rising numbers of multiple pregnancies, valid classifications for benchmarking are needed. The Robson classification provides a method to group cases with caesarean section in order to assess differences in outcome across regions and sites. In this study we set up a novel method of classification by using routinely collected health data. We hypothesize i that routinely collected health data can be used to apply complex m ...[more]

PMID: 33253262

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Project description:ObjectiveTo analyse the current situation of caesarean section in Palestine using the Robson Ten Group Classification System (TGCS).DesignA population-based birth cohort study.SettingObstetrical departments in three governmental hospitals in Gaza.ParticipantsAll women (18 908) who gave birth between 1 January 2016 and 30 April 2017.MethodsThe contributions of each group to the study population and to the overall rate of caesarean section were calculated, as well as the rate of caesarean section in each TGCS group. Differences in proportions between study hospitals were assessed by ?2 test.Main outcome measuresThe main outcome was the contributions of each group to the overall caesarean section rate.ResultsThe overall rate of caesarean section was 22.9% (4337 of 18 908), ranging from 20.6% in hospital 1 to 24.6% in hospital 3. The largest contributors to the overall caesarean section rate were multiparous women with single cephalic full-term pregnancy who had undergone at least one caesarean section (group 5, 42.6%), women with multiple pregnancies (group 8, 11.6%) and those with single cephalic preterm labour (group 10, 8.1%). Statistically significant differences in caesarean section rates between the study hospitals were observed in group 1 (nulliparous women with single cephalic full-term pregnancy and spontaneous labour), group 4 (multiparous with single cephalic full-term pregnancy with induced labour or prelabour caesarean section), group 5 (multiparous with single cephalic full-term pregnancy with previous caesarean section) and in group 7 (multiparous with breech presentation).ConclusionWomen in groups 5, 8 and 10 were the largest contributors to the overall caesarean section rate in the study hospitals. Efforts to reduce the differences in obstetrical care between hospitals need to be directed towards increasing the proportion of vaginal births after caesarean section and by reducing primary caesarean section in multiple pregnancies and preterm labour.

Dataset Information

The Robson classification for caesarean section-A proposed method based on routinely collected health data.

Publications

The Robson classification for caesarean section-A proposed method based on routinely collected health data.

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