A novel use of a classification system to audit severe maternal morbidity
Introduction
Maternity care occupies a unique position within the patient safety field and within health services worldwide. The World Alliance for Patient Safety has recognised this uniqueness in their global set of research priorities in relation to maternal and newborn care (WHO Patient Safety, 2008). Maternal and newborn care is one of the top 20 research priorities. This includes developing a greater understanding of the burden of unsafe maternal and newborn care, and being able to track trends and changes in maternal morbidity and mortality using simple solutions and approaches.
One of the major causes of maternal morbidity and mortality and a disruption to patient safety is obstetric haemorrhage. Major obstetric haemorrhage accounts for a significant proportion of cases of severe maternal morbidity, up to 50% in some studies (Brace et al., 2004, Murphy et al., 2009). Classifying and auditing severe maternal morbidity, such as major obstetric haemorrhage, provides a measure of the safety of maternity services, particularly where maternal mortality is rare, and is a useful way to track trends and changes in incidence and burden (Brace et al., 2004, Brace et al., 2007). The use of a simple classification system that accounts for some of the known contributing factors to major morbidity would be helpful for the audit and monitoring of patient safety.
Haemorrhage-associated peripartum hysterectomy is an extreme procedure carried out for life-threatening severe obstetric haemorrhage (Flood et al., 2009). In the past decade, the rate of and occurrences surrounding the management of severe obstetric haemorrhage and peripartum hysterectomy have been subject to at least two government inquiries in relation to concerns around the quality and safety of services (Douglas et al., 2001, Government of Ireland, 2006), even though the event is rare. A population-based, matched case–control study using the UK Obstetric Surveillance System (UKOSS) reported a peripartum hysterectomy incidence of 0.41 cases per 1,000 births [95% confidence interval (CI) 0.36–0.45] (Knight et al., 2008). However, the rate is increasing in some studies, probably due to the increased caesarean section rate in many developed nations (Daskalakis et al., 2007, Kuklina et al., 2009). In the USA, a population-based study reported incidence rates ranging from 0.25 in 1987 to 0.82 per 1,000 births in 2006 (Bodelon et al., 2009). There are a number of factors that have been associated with an increased risk of peripartum hysterectomy, including previous caesarean section, maternal age over 35 years, parity of three or greater, previous placental removal, previous uterine surgery (myomectomy) and twin pregnancy (Knight et al., 2008, Bodelon et al., 2009, Flood et al., 2009). Previous caesarean section was associated with a population attributable risk of 28% in the UK study (Knight et al., 2008), thus accounting for this factor in any classification system is important.
Systems to classify and audit events such as peripartum hysterectomy often do not consider the differing characteristics or complexities (case mix) of women from different hospitals. One system that considers different factors, including previous caesarean section, is the Robson Ten Group Classification System (TGCS) which was first proposed in 2001 as a means to classify and audit caesarean sections (Robson, 2001). The classification system uses five variables – previous obstetric history (parity and previous caesarean section), type of pregnancy (single or multiple), presentation (cephalic or otherwise), onset of labour and gestation at birth. A combination of these variables defines the 10 groups (see Table 1 for a description of each group). The variables on which classification is made are often readily available in hospital databases or patient files (e.g. parity, previous caesarean section, plurality, presentation). The groups are mutually exclusive and totally inclusive; that is, each woman can, and must, be placed in one group. The grouping concepts are straightforward, and clinicians and managers identify easily with the classification concepts and the groups.
Although the TGCS is robust and clinically relevant, it has been used in a limited way in the published literature and only in relation to caesarean section (McCarthy et al., 2007, National Maternity Hospital, 2008). This study sought to investigate whether the TGCS might be a useful classification system to audit other obstetric outcomes, particularly cases of severe morbidity where previous caesarean section, parity, prematurity and multiple pregnancy are important factors. It could potentially provide a way for maternity units to monitor particular outcomes as it accounts for the case mix or complexity of the women attending the maternity service. As the classification groups are easy to explain, the system may also provide a framework to inform women about the risks of particular outcomes.
With these factors in mind, an exploratory analysis was undertaken to determine whether the TGCS could be extended in a novel way to audit peripartum hysterectomy. The aim was to calculate the proportion of women in each TGCS group and to estimate the odds and risks of peripartum hysterectomy for each group. This was a secondary analysis using data from UKOSS (Knight et al., 2008).
Section snippets
Methods
Data from a population-based frequency-matched case–control study were used (Knight et al., 2008). The cases were women who underwent peripartum hysterectomy between February 2005 and February 2006 and their frequency matched controls. A detailed description of UKOSS methods is presented elsewhere (Knight et al., 2005, Knight et al., 2008). All 229 eligible UK hospitals participated.
In brief, cases were defined as any woman giving birth and undergoing a hysterectomy in the same clinical
Findings
There were eight women with incomplete data which meant classification was not possible. Three hundred and seven of the 315 women who had a peripartum hysterectomy were classified into one of the 10 groups; 606 of the 608 control women were classified (Table 1). Women who underwent a peripartum hysterectomy were predominantly from the more complex classification groups. Almost 60% (n=188) of women who had a peripartum hysterectomy had either had a previous caesarean section (27%, n=85) or
Discussion
Severe maternal morbidity is 50 times more common than maternal death. Understanding and being able to monitor severe maternal morbidity could modify the safety and quality of care (Callaghan et al., 2008, WHO Patient Safety, 2008). The TGCS was created to explore and explain differences in caesarean section rates within, and between, institutions. This study extended its use in this secondary analysis of data from UKOSS to investigate whether it could be a simple classification system to audit
Conclusion
This exploratory analysis demonstrated that the TGCS could be used in a novel way, that is, to monitor an outcome other than caesarean section and could be an adjunct to monitoring patient safety. Population-based data on peripartum hysterectomy were used as an example of how an existing classification system could be used in a different way from that for which it was created. The TGCS may, however, not be sufficiently generalisable to use as a tool to audit severe maternal morbidity as a
Funding
This paper reports on an independent study which is funded by the Policy Research Programme in the Department of Health. The views expressed are not necessarily those of the Department.
Conflict of interest
We have no competing interests to declare.
Acknowledgements
The authors thank the UKOSS reporting clinicians who notified cases and completed the data collection forms. The authors also acknowledge the assistance of the UKOSS Steering Committee and the support of the Royal College of Obstetricians and Gynaecologists, Royal College of Midwives, Obstetric Anesthetists Association, Faculty of Public Health, National Childbirth Trust, and the Confidential Enquiry into Maternal and Child Health.
Caroline Homer is a Chief Investigator on the Australasian
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