- •Uterine atony is the most common cause of postpartum hemorrhage.
- •Accurately predicting uterine atony may decrease associated morbidity.
- •We developed risk prediction models to quantify patients’ risk of atony.
- •Antepartum model had moderate discriminatory ability, C-statistic 0.61 (0.60–0.62)
- •Intrapartum model had improved discriminatory ability, C-statistic 0.68 (0.67–0.69)
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:Subscribe to International Journal of Obstetric Anesthesia
- The epidemiology of postpartum hemorrhage in a large, nationwide sample of deliveries.Anesth Analg. 2010; 110: 1368-1373https://doi.org/10.1213/ANE.0b013e3181d74898
Centers for Disease Control and Prevention. Pregnancy Mortality Surveillance System. 2020. https://www.cdc.gov/reproductivehealth/maternal-mortality/pregnancy-mortality-surveillance-system.htm. Accessed January 19, 2021.
- Trends in postpartum hemorrhage in the United States from 2010 to 2014.Anesth Analg. 2020; 130: e119-e122https://doi.org/10.1213/ANE.0000000000004424
- Trends in postpartum hemorrhage: United States, 1994–2006.Am J Obstet Gynecol. 2010; 202: 353.e1-353.e6https://doi.org/10.1016/j.ajog.2010.01.011
- Postpartum hemorrhage resulting from uterine atony after vaginal delivery: factors associated with severity.Obstet Gynecol. 2011; 117: 21-31https://doi.org/10.1097/AOG.0b013e318202c845
- CMQCC obstetric hemorrhage hospital level implementation guide. The California Maternal Quality Care Collaborative (CMQCC).Stanford University, Palo Alto, CA2010: 93-145
American College of Obstetricians and Gynecologists. Maternal safety bundle for obstetric hemorrhage. https://www.acog.org/community/districts-and-sections/district-ii/programs-and-resources/safe-motherhood-initiative/obstetric-hemorrhage. Accessed May 26, 2020.
- Clinical validation of risk stratification criteria for peripartum hemorrhage.Obstet Gynecol. 2013; 122: 120-126https://doi.org/10.1097/AOG.0b013e3182941c78
- Evaluation of risk-assessment tools for severe postpartum hemorrhage in women undergoing cesarean delivery.Obstet Gynecol. 2019; 134: 1308-1316https://doi.org/10.1097/AOG.0000000000003574
- Predicting risk of postpartum haemorrhage: a systematic review.BJOG. 2021; 128: 46-53https://doi.org/10.1111/1471-0528.16379
- Machine learning and statistical models to predict postpartum hemorrhage.Obstet Gynecol. 2020; 135: 935-944https://doi.org/10.1097/AOG.0000000000003759
- Risk factors for atonic postpartum hemorrhage: a systematic review and meta-analysis.Obstet Gynecol. 2021; 137: 305-323https://doi.org/10.1097/AOG.0000000000004228
- Automated early detection of obstetric complications: theoretic and methodologic considerations.Am J Obstet Gynecol. 2019; 220: 297-307https://doi.org/10.1016/j.ajog.2019.01.208
- Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.BMJ. 2015; 350g7594https://doi.org/10.1136/bmj.g7594
- An enhanced method for identifying obstetric deliveries: implications for estimating maternal morbidity.Matern Child Health J. 2008; 12: 469-477https://doi.org/10.1007/s10995-007-0256-6
- Racial and ethnic disparities in maternal morbidity and obstetric care.Obstet Gynecol. 2015; 125: 1460-1467https://doi.org/10.1097/AOG.0000000000000735
- Sample size for binary logistic prediction models: beyond events per variable criteria.Stat Methods Med Res. 2019; 28: 2455-2474https://doi.org/10.1177/0962280218784726
- Decision curve analysis: a novel method for evaluating prediction models.Med Decis Making. 2006; 26: 565-574https://doi.org/10.1177/0272989X06295361
R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available at: https://www.R-project.org/.
Harrell FE, Jr. rms: Regression Modeling Strategies. R package version 6.0-1. 20https://CRAN.R-project.org/package=rms.
Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: Visualizing classifier performance in R. Bioinformatics. 2005;21:3940-1. http://rocr.bioinf.mpi-sb.mpg.de.
Thiele C. cutpointr: Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks. R package version 1.0.32. 2020. https://CRAN.R-project.org/package=cutpointr.
Sjoberg DD. Dcurves: Decision curve analysis for model evaluation. R package version 0.2.0. 2021. https://CRAN.R-project.org/package=dcurves.
- Hidden in plain sight - reconsidering the use of race correction in clinical algorithms.N Engl J Med. 2020; 383: 874-882https://doi.org/10.1056/NEJMms2004740
- Recalibrating the use of race in medical research.JAMA. 2021; 325: 623-624https://doi.org/10.1001/jama.2021.0003