Classification trees: A possible method for maternity risk grouping

Abstract

Pregnancy, although being one of the most natural processes in our evolution, still remains subject to numerous complications and potential high risk. Complications at birth, such as the need for a caesarean section or the use of forceps, are not uncommon. An early warning of possible complications would greatly benefit both medical professionals and the expectant mother. Classification tree analysis uses selected independent variables to group pregnant women according to a dependent variable in a way that reduces variation. In this study, data on 3902 births were analysed to create risk groups for a number of complications, including the risk of a non-spontaneous delivery (a complicated birth) and premature delivery. From an overall risk of 23% of a non-spontaneous delivery, the classification tree was able to find statistically significant risk groups ranging from 7% to 65%. The resulting classification rules have been incorporated into a developed database tool to help quantify associated risks and act as an early warning system of possible complications to individual pregnant women.

Introduction

Childbirth is a natural, fundamental process for human survival. Like any other natural process, it is governed by social, biological and environmental factors that can have adverse effects on the health of mothers and their babies. Over recent years, there has been considerable debate concerning the provision of maternity care in the UK. The government document Changing Childbirth [1] outlines new principles for good maternity care, suggesting that women should be the focus of the process and should be fully informed about all the possible options of care during pregnancy and at delivery.

Pregnancy still remains subject to numerous complications and risks. Although many procedures, such as caesarean section, are increasingly becoming more routine, there still remains an element of risk to both mother and child. It would be impossible to totally eliminate all risks and so different women receive different types of care based on the clinician’s assessment of the pregnancy. There is an obvious benefit to both midwife/obstetrician and expectant mother if an early warning system could flag up potential risks to individual women. Such risks might include a preterm (premature) delivery, the need for a caesarean section and a non-spontaneous delivery.¹ Such an early warning system would allow for more suitable types of care to be offered given the likely risks involved with a particular pregnancy.

Classification and Regression Tree (CART) analysis [2] may offer help in producing easy to use statistically and clinically meaningful maternity risk groups. CART investigates the effects of selected independent variables on a dependent variable. Starting with all observations in a single group and a set of independent variables, the observations are split into two groups on the basis of the independent variable that is judged to be the most important in reducing the total variation or deviance in the continuous or categorical (respectfully) dependent variable. The two resulting groups are split further by the same or an alternative independent variable that continues to reduce the total variation within the dependent variable. The partitioning is repeated until further splitting will not reduce the variation of the dependent variable.

The partitioning may be unrestricted, i.e. the total variation within the dependent variable is minimised by mathematical means alone, or fixed by the investigator, i.e. the effect of a selected independent variable on splitting is explored. As a result, either the statistical method or clinical judgement, or a combination of both, can be used. A statistical approach helps to minimise subjective clinical bias about which patients ought to be grouped together and the classification rules used to group the patients.

CART has been successfully used in other areas of healthcare such as case mix groups [3], hospital bed capacities [4], cancer survival groups [5] and intensive care [6], but has yet to be used to create risk groups for maternity care.

This paper describes a study of 3902 data records from an obstetric database. Consultant obstetricians and midwives from the Princess Anne Hospital, part of the Southampton University Hospitals NHS Trust, provided clinical guidance during the study. The study evaluated the statistical effectiveness and user-interpretability of the CART algorithm for the creation of risk groups for a number of childbirth complications. We found that in many cases, the resulting trees reinforce clinical judgement, highlight the heterogeneous nature of the observations, and help to quantify the associated risks through the construction of homogeneous patient groupings.