Original Article| Volume 51, 103550, August 2022

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Development of clinical risk-prediction models for uterine atony following vaginal and cesarean delivery

Published:April 21, 2022DOI:


      • 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)



      Uterine atony is the most common cause of postpartum hemorrhage and is associated with substantial morbidity. Prospectively identifying women at increased risk of atony may reduce the incidence of subsequent adverse events. We sought to develop and evaluate clinical risk-prediction models for uterine atony following vaginal and cesarean delivery, using prespecified risk factors identified from systematic review.


      Using retrospective data from vaginal and cesarean deliveries occurring at a single institution between 2010 and 2019, antepartum and intrapartum risk-prediction models for uterine atony, defined by supplementary uterotonic administration in addition to prophylactic oxytocin infusion, were developed using logistic regression. The C-statistic quantified the ability of the model to discriminate between cases and controls.


      Data were available for 4773 atony cases and 23 933 controls. The antepartum model included 20 risk factors and exhibited moderate discriminatory ability (C-statistic 0.61, 95% confidence interval 0.60 to 0.62). The intrapartum model included 27 risk factors and showed improved discriminatory ability (C-statistic 0.68, 95% confidence interval 0.67 to 0.69).


      We identified antepartum and intrapartum risk-prediction models to quantify patients’ risk of uterine atony. Models performed similarly for all delivery modes, races, and ethnic groups. Future work should further improve these models through inclusion of more comprehensive prediction data.


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        • Bateman B.T.
        • Berman M.F.
        • Riley L.E.
        • Leffert L.R.
        The epidemiology of postpartum hemorrhage in a large, nationwide sample of deliveries.
        Anesth Analg. 2010; 110: 1368-1373
      1. Centers for Disease Control and Prevention. Pregnancy Mortality Surveillance System. 2020. Accessed January 19, 2021.

        • Reale S.C.
        • Easter S.R.
        • Xu X.
        • Bateman B.T.
        • Farber M.K.
        Trends in postpartum hemorrhage in the United States from 2010 to 2014.
        Anesth Analg. 2020; 130: e119-e122
        • Callaghan W.M.
        • Kuklina E.V.
        • Berg C.J.
        Trends in postpartum hemorrhage: United States, 1994–2006.
        Am J Obstet Gynecol. 2010; 202: 353.e1-353.e6
        • Driessen M.
        • Bouvier-Colle M.H.
        • Dupont C.
        • et al.
        Postpartum hemorrhage resulting from uterine atony after vaginal delivery: factors associated with severity.
        Obstet Gynecol. 2011; 117: 21-31
        • Bingham D.
        • Melsop K.
        • Main E.
        CMQCC obstetric hemorrhage hospital level implementation guide. The California Maternal Quality Care Collaborative (CMQCC).
        Stanford University, Palo Alto, CA2010: 93-145
      2. American College of Obstetricians and Gynecologists. Maternal safety bundle for obstetric hemorrhage. Accessed May 26, 2020.

        • Dilla A.J.
        • Waters J.H.
        • Yazer M.H.
        Clinical validation of risk stratification criteria for peripartum hemorrhage.
        Obstet Gynecol. 2013; 122: 120-126
        • Kawakita T.
        • Mokhtari N.
        • Huang J.C.
        • Landy H.J.
        Evaluation of risk-assessment tools for severe postpartum hemorrhage in women undergoing cesarean delivery.
        Obstet Gynecol. 2019; 134: 1308-1316
        • Neary C.
        • Naheed S.
        • McLernon D.J.
        • Black M.
        Predicting risk of postpartum haemorrhage: a systematic review.
        BJOG. 2021; 128: 46-53
        • Venkatesh K.K.
        • Strauss R.A.
        • Grotegut C.A.
        • et al.
        Machine learning and statistical models to predict postpartum hemorrhage.
        Obstet Gynecol. 2020; 135: 935-944
        • Ende H.B.
        • Lozada M.J.
        • Chestnut D.H.
        • et al.
        Risk factors for atonic postpartum hemorrhage: a systematic review and meta-analysis.
        Obstet Gynecol. 2021; 137: 305-323
        • Escobar G.J.
        • Gupta N.R.
        • Walsh E.M.
        • Soltesz L.
        • Terry S.M.
        • Kipnis P.
        Automated early detection of obstetric complications: theoretic and methodologic considerations.
        Am J Obstet Gynecol. 2019; 220: 297-307
        • Collins G.S.
        • Reitsma J.B.
        • Altman D.G.
        • Moons K.G.
        Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement.
        BMJ. 2015; 350g7594
        • Kuklina E.V.
        • Whiteman M.K.
        • Hillis S.D.
        • et al.
        An enhanced method for identifying obstetric deliveries: implications for estimating maternal morbidity.
        Matern Child Health J. 2008; 12: 469-477
        • Grobman W.A.
        • Bailit J.L.
        • Rice M.M.
        • et al.
        Racial and ethnic disparities in maternal morbidity and obstetric care.
        Obstet Gynecol. 2015; 125: 1460-1467
        • van Smeden M.
        • Moons K.G.
        • de Groot J.A.
        • et al.
        Sample size for binary logistic prediction models: beyond events per variable criteria.
        Stat Methods Med Res. 2019; 28: 2455-2474
        • Vickers A.J.
        • Elkin E.B.
        Decision curve analysis: a novel method for evaluating prediction models.
        Med Decis Making. 2006; 26: 565-574
      3. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. Available at:

      4. Harrell FE, Jr. rms: Regression Modeling Strategies. R package version 6.0-1. 20

      5. Sing T, Sander O, Beerenwinkel N, Lengauer T. ROCR: Visualizing classifier performance in R. Bioinformatics. 2005;21:3940-1.

      6. Thiele C. cutpointr: Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks. R package version 1.0.32. 2020.

      7. Sjoberg DD. Dcurves: Decision curve analysis for model evaluation. R package version 0.2.0. 2021.

        • Vyas D.A.
        • Eisenstein L.G.
        • Jones D.S.
        Hidden in plain sight - reconsidering the use of race correction in clinical algorithms.
        N Engl J Med. 2020; 383: 874-882
        • Ioannidis J.P.A.
        • Powe N.R.
        • Yancy C.
        Recalibrating the use of race in medical research.
        JAMA. 2021; 325: 623-624