Abstract
Objective The aim of the present study was to develop a toolkit combining various risk factors
to predict the risk of developing a postpartum hemorrhage (PPH) during a cesarean
delivery.
Study Design A retrospective cohort study of 24,230 women who had cesarean delivery between January
2003 and December 2013 at a tertiary care teaching hospital within the United Kingdom
serving a multiethnic population. Data were extracted from hospital databases, and
risk factors for PPH were identified. Hothorn et al recursive partitioning algorithm
was used to infer a conditional decision tree. For each of the identified combinations
of risk factors, two probabilities were calculated: the probability of a patient producing
≥1,000 and ≥ 2,000 mL blood loss.
Results The Leicester PPH predict score was then tested on the randomly selected remaining
25% (n = 6,095) of the data for internal validity. Reliability testing showed an intraclass
correlation of 0.98 and mean absolute error of 239.8 mL with the actual outcome.
Conclusion The proposed toolkit enables clinicians to predict the risk of postpartum hemorrhage.
As a result, preventative measures for postpartum hemorrhage could be undertaken.
Further external validation of the current toolkit is required.
Keywords
postpartum hemorrhage - score - cesarean delivery - risk assessment tool - machine
learning - recursive partitioning
