Individualised risk assessment may predict PPH likelihood during intrapartum period
Obstetric haemorrhage is a leading cause of maternal mortality worldwide, accounting for nearly-one quarter of all maternal deaths globally, while it is also associated with severe maternal morbidity including long-term psychological trauma, multi-organ failure and peripartum hysterectomy. Early diagnosis is essential in the effective management of obstetric haemorrhage. It typically refers to any kind of excessive pregnancy related bleeding during the antepartum period, childbirth, or in the postpartum period.
The definition of postpartum haemorrhage (PPH) was traditionally defined as a blood loss in excess of 500 ml after vaginal delivery or > 1000 ml after a caesarean delivery. However, the. American College of Obstetricians and Gynaecologists (ACOG) now define PPH as cumulative blood loss ≥ 1000 ml regardless of route of delivery, while the Royal College of Obstetricians and Gynaecologists (RCOG) divide PPH into minor (blood loss 500–1000 ml) or major (blood loss > 1000 ml).
G.M. Maher et al. using data from the National Maternal and Newborn Clinical Management System (MN-CMS) carried out a study to develop and validate a prediction model examining a combination of risk factors in order to predict PPH in a general obstetric Irish population of singleton pregnancies.
Candidate predictors included maternal age, maternal body mass index, parity, previous caesarean section, assisted fertility, gestational age, fetal macrosomia, mode of delivery and history of PPH. Discrimination was assessed using the area under the receiver operating characteristic curve (ROC) C-statistic.
Out of 6,077 women, 5,807 with complete data were included in the analyses, and there were 270 (4.65%) cases of PPH.
Four variables were considered the best combined predictors of PPH, including
- 1.parity (specifically nulliparous),
- 2.macrosomia,
- 3.mode of delivery (specifically operative vaginal delivery, emergency caesarean section and prelabour caesarean section), and
- 4.history of PPH.
These predictors were used to develop a nomogram to provide individualised risk assessment for PPH. The original apparent C-statistic was 0.751 (95% CI: 0.721, 0.779) suggesting good discriminative performance. There was minimal optimism adjustment to the Cstatistic after bootstrapping, indicating good internal performance (optimism adjusted C-statistic: 0.748). Results of external validation were comparable with the development model suggesting good reproducibility.
Prediction of adverse maternal events is critical to allow services allocate appropriate resources to those most at risk. This study developed and validated a prediction model for PPH in a general obstetric Irish population of singleton pregnancies. Authors identified four routinely collected variables when predicting PPH in this population. These included parity (specifically nulliparous), fetal macrosomia, mode of delivery (specifically operative vaginal delivery, emergency caesarean section and prelabour caesarean section), and history of PPH. The latter three of these predictors, in particular, are in line with the four basic processes of PPH, namely tone, trauma, tissue and thrombin.
Accurate risk prediction models can provide an individualised risk assessment and assist clinical decision-making and effective planning of care. Therefore, integration of a risk prediction model into an electronic health record may support timely clinical decisions.
Authors concluded, "Four routinely collected variables (parity, fetal macrosomia, mode of delivery and history of PPH) were identified when predicting PPH in a general obstetric Irish population of singleton pregnancies. Use of our nomogram could potentially assist with individualised risk assessment of PPH and inform clinical decision-making allowing those at highest risk of PPH be actively managed."
Source: G.M. Maher et al; European Journal of Obstetrics & Gynecology and Reproductive Biology 276 (2022) 168–173
