Predicting Postpartum Hemorrhage (PPH) during Cesarean Delivery Using the Leicester PPH Predict Tool: A Retrospective Cohort Study

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

Note

Retrospective service evaluation review was undertaken to analyze the complications of cesarean sections. Therefore, as per local protocol, ethical approval and individual subject consent were not required to analyze anonymized hospital data.

Authors' Contribution

S.E.D.: analysis and write up of article; Y.B.J.: idea conception and write up of article; E.B.: study design and data interpretation; N.W.: data analysis; T.S.: study design.

• References

• 1 Knight M, Tuffnell D, Kenyon S, Shakespeare J, Gray R, Kurinczuk J. Surveillance of maternal deaths in the UK 2011–13 and lessons learned to inform maternity care from the UK and Ireland confidential enquiries into maternal deaths and morbidity 2009–13 Updated 2015 . Available at: https://www.npeu.ox.ac.uk/downloads/files/mbrrace-uk/reports/MBRRACE-UK%20Maternal%20Report%202015.pdf . Accessed August 22, 2017
• 2 Prevention and management of postpartum haemorrhage: Green-top guideline no. 52. BJOG 2017; 124 (05) e106-e149
• 3 Bateman BT, Berman MF, Riley LE, Leffert LR. The epidemiology of postpartum hemorrhage in a large, nationwide sample of deliveries. Anesth Analg 2010; 110 (05) 1368-1373
• 4 Al-Zirqi I, Vangen S, Forsen L, Stray-Pedersen B. Prevalence and risk factors of severe obstetric haemorrhage. BJOG 2008; 115 (10) 1265-1272
• 5 Rahman MM, Abe SK, Kanda M. , et al. Maternal body mass index and risk of birth and maternal health outcomes in low- and middle-income countries: a systematic review and meta-analysis. Obes Rev 2015; 16 (09) 758-770
• 6 Hutcheon JA, Harper S, Strumpf EC, Lee L, Marquette G. Using inter-institutional practice variation to understand the risks and benefits of routine labour induction at 41(+0) weeks. BJOG 2015; 122 (07) 973-981
• 7 Pasupathy D, McCowan LM, Poston L, Kenny LC, Dekker GA, North RA. ; SCOPE consortium. Perinatal outcomes in large infants using customised birthweight centiles and conventional measures of high birthweight. Paediatr Perinat Epidemiol 2012; 26 (06) 543-552
• 8 Sung E, George J, Porter M. Sepsis in pregnancy. Fetal Matern Med Rev 2011; 22 (04) 287-305
• 9 Sheiner E, Sarid L, Levy A, Seidman DS, Hallak M. Obstetric risk factors and outcome of pregnancies complicated with early postpartum hemorrhage: a population-based study. J Matern Fetal Neonatal Med 2005; 18 (03) 149-154
• 10 Mhyre JM, Shilkrut A, Kuklina EV. , et al. Massive blood transfusion during hospitalization for delivery in New York State, 1998-2007. Obstet Gynecol 2013; 122 (06) 1288-1294
• 11 Radhika BH, Naik K, Shreelatha S, Vana H. Case series: Pregnancy outcome in patients with uterine fibroids. J Clin Diagn Res 2015; 9 (10) QR01-QR04
• 12 You WB, Zahn CM. Postpartum hemorrhage: abnormally adherent placenta, uterine inversion, and puerperal hematomas. Clin Obstet Gynecol 2006; 49 (01) 184-197
• 13 Suta J, Leungratsameerung S, Phaloprakarn C. A risk score for predicting post partum haemorrhage associated with caesarean delivery. Thai J Obstet Gynaec 2015; 23 (01) 3-11
• 14 Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: A conditional inference framework. J Comput Graph Stat 2006; 13 (03) 651-674
• 15 Podgorelec V, Kokol P, Stiglic B, Rozman I. Decision trees: an overview and their use in medicine. J Med Syst 2002; 26 (05) 445-463
• 16 Arlot S, Celisse A. A survey of cross-validation procedures for model selection. Stat Surv 2010; (04) 40-79
• 17 Parsonnet V, Dean D, Bernstein AD. A method of uniform stratification of risk for evaluating the results of surgery in acquired adult heart disease. Circulation 1989; 79 (6 Pt 2): I3-I12
• 18 Nashef SA, Roques F, Michel P, Gauducheau E, Lemeshow S, Salamon R. European system for cardiac operative risk evaluation (EuroSCORE). Eur J Cardiothorac Surg 1999; 16 (01) 9-13
• 19 Bhandari S, Raja EA, Shetty A, Bhattacharya S. Maternal and perinatal consequences of antepartum haemorrhage of unknown origin. BJOG 2014; 121 (01) 44-50 , discussion 50–52
• 20 Royal College of Obstetricians and Gynaecologists. Thrombosis and embolism during pregnancy and the puerperium, reducing the risk. Green-top Guideline No. 37a (April 2015). Available at: https://www.rcog.org.uk/globalassets/documents/guidelines/gtg-37a.pdf . Accessed June 24, 2017
• 21 Nogueira RG, Silva GS, Lima FO. , et al. The FAST-ED app: A smartphone platform for the field triage of patients with stroke. Stroke 2017; 48 (05) 1278-1284
• 22 Walkinshaw N, Dunkerton S. . Leicester PPH predict tool (2017). Available at: https://docs.google.com/forms/d/e/1FAIpQLSdzo0YaA7njlail0NtjnsLG8NzgO6Qv3xx8IFeizMDa3krJKg/viewform . Accessed July 04, 2017
• 23 Scheer JK, Osorio JA, Smith JS. , et al; International Spine Study Group. Development of validated computer based preoperative predictive model for proximal junction failure (PJK) or clinically significant PJK with 86% accuracy based on 510 ASD patients with 2 year follow up. Spine 2016; 41 (22) E1328-E1335
• 24 Inoue S, Masuyama H, Hiramatsu Y. Efficacy of transarterial embolisation in the management of post-partum haemorrhage and its impact on subsequent pregnancies multi-institutional study group of transarterial embolization for massive obstetric haemorrhage in chugoku & shikoku area society of obstetrics and gynecology. J Obstet Gyn 2014; 56 (06) 541-545
• 25 Doumouchtsis SK, Papageorghiou AT, Arulkumaran S. Systematic review of conservative management of postpartum hemorrhage: what to do when medical treatment fails. Obstet Gynecol Surv 2007; 62 (08) 540-547