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Simulation model of the relationship between cesarean section rates and labor duration

  • Karen T. Hicklin
  • Julie S. Ivy
  • James R. Wilson
  • Fay Cobb Payton
  • Meera Viswanathan
  • Evan R. Myers
Article
  • 20 Downloads

Abstract

Cesarean delivery is the most common major abdominal surgery in many parts of the world, and it accounts for nearly one-third of births in the United States. For a patient who requires a C-section, allowing prolonged labor is not recommended because of the increased risk of infection. However, for a patient who is capable of a successful vaginal delivery, performing an unnecessary C-section can have a substantial adverse impact on the patient’s future health. We develop two stochastic simulation models of the delivery process for women in labor; and our objectives are (i) to represent the natural progression of labor and thereby gain insights concerning the duration of labor as it depends on the dilation state for induced, augmented, and spontaneous labors; and (ii) to evaluate the Friedman curve and other labor-progression rules, including their impact on the C-section rate and on the rates of maternal and fetal complications. To use a shifted lognormal distribution for modeling the duration of labor in each dilation state and for each type of labor, we formulate a percentile-matching procedure that requires three estimated quantiles of each distribution as reported in the literature. Based on results generated by both simulation models, we concluded that for singleton births by nulliparous women with no prior complications, labor duration longer than two hours (i.e., the time limit for labor arrest based on the Friedman curve) should be allowed in each dilation state; furthermore, the allowed labor duration should be a function of dilation state.

Keywords

Medical decision making Mode of delivery Birth Simulation Dystocia Percentile matching 

References

  1. 1.
    Alagoz O, Bryce CL, Shechter S, Schaefer A, Chang C-CH, Angus DC, Roberts MS (2005) Incorporating biological natural history in simulation models: empirical estimates of the progression of end-stage liver disease. Med Decis Making Int J Soc Med Decis Making 25:620–632CrossRefGoogle Scholar
  2. 2.
    American College of Obstetricians and Gynecologists, Medicine SfM-F, Caughey AB, Cahill AG, Guise JM, Rouse DJ (2014) Safe prevention of the primary cesarean delivery. Amer J Obstet Gynecol 210:179–93. http://www.ncbi.nlm.nih.gov/pubmed/24565430 CrossRefGoogle Scholar
  3. 3.
    Barber EL, Lundsberg LS, Belanger K, Pettker CM, Funai EF, Illuzzi JL (2011) Indications contributing to the increasing cesarean delivery rate. Obstet Gynecol 118:29–38. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3751192&tool=pmcentrez&rendertype=abstract CrossRefGoogle Scholar
  4. 4.
    Caruana R, Niculescu RS, Rao RB, Simms C (2003) Evaluating the C-section rate of different physician practices: using machine learning to model standard practice. In: AMIA Annual Symposium proceedings, pp 135–9. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1480028&tool=pmcentrez&rendertype=abstract
  5. 5.
    Cesario SK (2004) Reevaluation of Friedman’s labor curve: a pilot study. J Obstetric, Gynecol Neonatal Nursing: JOGNN / NAACOG 33:713–22. http://www.ncbi.nlm.nih.gov/pubmed/15561659 CrossRefGoogle Scholar
  6. 6.
    Cheng YW, Delaney SS, Hopkins LM, Caughey AB (2009) The association between the length of first stage of labor, mode of delivery, and perinatal outcomes in women undergoing induction of labor. Amer J Obstet Gynecol 201(477):e1—7. http://www.ncbi.nlm.nih.gov/pubmed/19608153 Google Scholar
  7. 7.
    Cochran JK, Bharti A (2006) Stochastic bed balancing of an obstetrics hospital. Health Care Manag Sci 9:31–45CrossRefGoogle Scholar
  8. 8.
    Costa AX, Ridley SA, Shahani AK, Harper PR, De Senna V, Nielsen MS (2003) Mathematical modelling and simulation for planning critical care capacity. Anaesthesia 58:320–327CrossRefGoogle Scholar
  9. 9.
    Culligan PJ, Myers Ja, Goldberg RP, Blackwell L, Gohmann SF, Abell TD (2005) Elective cesarean section to prevent anal incontinence and brachial plexus injuries associated with macrosomia–a decision analysis. Int. Urogynecol. J. Pelvic Floor Dysfunct 16:19–28. discussion 28, http://www.ncbi.nlm.nih.gov/pubmed/15647962 CrossRefGoogle Scholar
  10. 10.
    Dahan MH, Dahan S (2005) Fetal weight, maternal age and height are poor predictors of the need for caesarean section for arrest of labor. Arch Gynecol Obstet 273:20–5. http://www.ncbi.nlm.nih.gov/pubmed/16001202 CrossRefGoogle Scholar
  11. 11.
    Dugas M, Shorten A, Dubé E, Wassef M, Bujold E, Chaillet N (2012) Decision aid tools to support women’s decision making in pregnancy and birth: a systematic review and meta-analysis. Soc Sci Med 74:1968–78. http://www.ncbi.nlm.nih.gov/pubmed/22475401 CrossRefGoogle Scholar
  12. 12.
    Emmett CL, Murphy DJ, Patel RR, Fahey T, Jones C, Ricketts IW, Gregor P, Macleod M, Montgomery Aa (2007) Decision-making about mode of delivery after previous caesarean section: development and piloting of two computer-based decision aids. Health Expect Int J Public Participation Health Care Health Polic 10:161–72. http://www.ncbi.nlm.nih.gov/pubmed/17524009 Google Scholar
  13. 13.
    Feghali M, Timofeev J, Huang C-C, Driggers R, Miodovnik M, Landy HJ, Umans JG (2015) Preterm induction of labor: predictors of vaginal delivery and labor curves. Amer J Obstet Gynecol 212(91):e1—7. http://www.sciencedirect.com/science/article/pii/S0002937814007844 Google Scholar
  14. 14.
    Friedman EA (1954) A graphic analysis of labor. Amer J Obstet Gynecol 68:1568–1575CrossRefGoogle Scholar
  15. 15.
    Friedman EA (1955) Primigravid labor a graphicostatistical analysis. Obstet Gynecol 6:567–589CrossRefGoogle Scholar
  16. 16.
    Friedman EA (1956) Labor in multiparas a graphicostatistical analysis. Obstet Gynecol 8:691–703Google Scholar
  17. 17.
    Gestel AV, Severens JL, Webers CAB, Beckers HJM, Jansonius NM, Schouten JSAG (2010) Modeling complex treatment strategies: construction and validation of a discrete event simulation model for glaucoma. Value Health 13:358–367CrossRefGoogle Scholar
  18. 18.
    Getahun D, Oyelese Y, Salihu HM, Ananth CV (2006) Previous cesarean delivery and risks of placenta previa and placental abruption. Obstet Gynecol 107:771–8. http://www.ncbi.nlm.nih.gov/pubmed/16582111 CrossRefGoogle Scholar
  19. 19.
    Ghaffarzadegan N, Epstein AJ, Martin EG (2013) Practice variation, bias, and experiential learning in cesarean delivery: a data-based system dynamics approach. Health Serv Res 48:713–34. http://www.ncbi.nlm.nih.gov/pubmed/23398502 CrossRefGoogle Scholar
  20. 20.
    Gombolay M, Golen T, Shah N, Shah J (2017) Queueing theoretic analysis of labor and delivery Understanding management styles and C-section rates. Health Care Manag Sci, 1–18Google Scholar
  21. 21.
    Grobman WA, Lai Y, Landon MB, Spong CY (2007) Development of a nomogram for prediction. Obstet Gynecol 109:806–812CrossRefGoogle Scholar
  22. 22.
    Guise J-M, Eden K, Emeis C, Denman MA, Marshall N, Fu RR, Janik R, Nygren P, Walker M, McDonagh M (2010) Vaginal birth after cesarean: new insights. Technical Report 19. http://www.ncbi.nlm.nih.gov/pubmed/20869552
  23. 23.
    Günal MM, Pidd M (2010) Discrete event simulation for performance modelling in health care: a review of the literature. J Simul 4:42–51. http://www.palgrave-journals.com/doifinder/10.1057/jos.2009.25 CrossRefGoogle Scholar
  24. 24.
    Hamilton BE, Martin JA, Osterman MJK, Driscoll AK, Rossen LM (2017) Births: provisional data for 2016. NVSS Vital Statist Rapid Release 2:1–21. https://www.cdc.gov/nchs/data/vsrr/report002.pdf Google Scholar
  25. 25.
    Harlow BL, Frigoletto FD, Cramer DW, Evans JK, Bain RP, Ewigman B, McNellis D (1995) Epidemiologic predictors of cesarean section in nulliparous patients at low risk. Amer J Obstet Gynecol 172:156–162. http://linkinghub.elsevier.com/retrieve/pii/000293789590106X CrossRefGoogle Scholar
  26. 26.
    Harper LM, Caughey AB, Odibo AO, Roehl Ka, Zhao Q, Cahill AG (2012) Normal progress of induced labor. Obstet Gynecol 119:1113–8. http://www.ncbi.nlm.nih.gov/pubmed/22569121 CrossRefGoogle Scholar
  27. 27.
    Herbst A, Wolner-Hanssen P, Ingemarsson I (1995) Risk factors for fever in labor. Obstet Gynecol 86:790–794CrossRefGoogle Scholar
  28. 28.
    Hill JL, Campbell MK, Zou GY, Challis JRG, Reid G, Chisaka H, Bocking AD (2008) Prediction of preterm birth in symptomatic women using decision tree modeling for biomarkers. Amer J Obstet Gynecol 198:468.e1–7. discussion 468.e7—9, http://www.ncbi.nlm.nih.gov/pubmed/18395044 CrossRefGoogle Scholar
  29. 29.
    Thorp JA, Eckert L0, Ang MS, Johnston DA, Peaceman AM, P VM (1991) Epidural analgesia and cesarean section for dystocia: risk factors in nulliparas. Amer J Perinatol 8:402–410CrossRefGoogle Scholar
  30. 30.
    Kaimal AJ, Kuppermann M (2010) Understanding risk, patient and provider preferences, and obstetrical decision making: approach to delivery after cesarean. Seminars Perinatol 34:331–6. http://www.ncbi.nlm.nih.gov/pubmed/20869549 CrossRefGoogle Scholar
  31. 31.
    King JT, Justice AC, Roberts MS, Chang C-CH, Fusco JS (2003) Long-term HIV/AIDS survival estimation in the highly active antiretroviral therapy era. Med Decis Making Int J Soc Med Decis Mak 23:9–20CrossRefGoogle Scholar
  32. 32.
    Klein JP, Moeschberger ML (2003) Survival analysis, techniques for censored and truncated data. Springer Science & Business Media, https://books.google.com/books?hl=en&lr=&id=KSq0e-6VFJ0C&pgis=1
  33. 33.
    Kreke J, Schaefer AJ, Angus DC, Bryce CL, Roberts MS (2002) Incorporating biology into discrete event simulation models of organ allocation. In: Proceedings of the 2002 Winter simulation conference, pp 532–536Google Scholar
  34. 34.
    Le Lay A, Despiegel N, Franco̧is C, Duru G (2006) Can discrete event simulation be of use in modelling major depression? Cost effectiveness and resource allocation: C/E 4:19. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1762026&tool=pmcentrez&rendertype=abstract CrossRefGoogle Scholar
  35. 35.
    Lindell G, Maršál K, Källén K (2013) Predicting risk for large-for-gestational age neonates at term: a population-based Bayesian theorem study. Ultrasound Obstet Gynecol Official J Int Soc Ultrasound Obstet Gynecol 41:398–405. http://www.ncbi.nlm.nih.gov/pubmed/23505150 CrossRefGoogle Scholar
  36. 36.
    Mankuta DD, Leshno MM, Menasche MM, Brezis MM (2003) Vaginal birth after cesarean section: Trial of labor or repeat cesarean section? A decision analysis. Amer J Obstet Gynecol 189:714–719. http://linkinghub.elsevier.com/retrieve/pii/S0002937803008330 CrossRefGoogle Scholar
  37. 37.
    Matchar DB, Duncan PW, Samsa GP, Whisnant JP, DeFriese GH, Ballard DJ, Paul JE, Witter DM, Mitchell JP (1993) The stroke prevention patient outcomes research team. Goals and methods. Stroke; J Cerebral Circul 24:2135–2142CrossRefGoogle Scholar
  38. 38.
    Mathews TJ, Hamilton BE (2016) Mean age of mothers is on the rise: United States, 2000-2014. NCHS Data Brief, 1–8, http://www.ncbi.nlm.nih.gov/pubmed/26828319
  39. 39.
    Miller Da, Chollet Ja, Goodwin TM (1997) Clinical risk factors for placenta previa-placenta accreta. Amer J Obstet Gynecol 177:210–4. http://www.ncbi.nlm.nih.gov/pubmed/9240608 CrossRefGoogle Scholar
  40. 40.
    Molina RL, Gombolay M, Jonas J, Modest AM, Shah J, Golen TH, Shah NT (2018) Association between labor and delivery unit census and delays in patient management: findings from a computer simulation module. Obstet Gynecol Publish Ah:545–552. https://journals.lww.com/greenjournal/Fulltext/publishahead/Association_Between_Labor_and_Delivery_Unit_Census.98153.aspx CrossRefGoogle Scholar
  41. 41.
    Montgomery AA, Emmett CL (2007) Two decision aids for mode of delivery among women with previous caesarean section: randomised controlled trial, BMJ (Clinical research ed.), 67101955Google Scholar
  42. 42.
    Pegden CD (2017) Simio LLC. https://www.simio.com/index.php
  43. 43.
    Rouse DJ, Weiner SJ, Bloom SL, Varner MW, Spong CY, Ramin SM, Caritis SN, Peaceman AM, Sorokin Y, Sciscione A, Carpenter MW, Mercer BM, Thorp JM, Malone FD, Harper M, Iams JD, Anderson GD (2009) Second-stage labor duration in nulliparous women: relationship to maternal and perinatal outcomes. Amer J Obstet Gynecol 201(357):e1—7. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2768280&tool=pmcentrez&rendertype=abstract Google Scholar
  44. 44.
    Say R, Robson S, Thomson R (2011) Helping pregnant women make better decisions: A systematic review of the benefits of patient decision aids in obstetrics. BMJ Open 1:1–16CrossRefGoogle Scholar
  45. 45.
    Schmeiser B (1982) Batch size effects in the analysis of simulation output. Oper Res 30:556–568. http://or.journal.informs.org/cgi/doi/10.1287/opre.30.3.556%5Cnpapers3://publication/doi/10.1287/opre.30.3.556 CrossRefGoogle Scholar
  46. 46.
    Serfling RJ (1980) Asymptotic representation theory for sample quantiles, order statistics, and sample distribution functions. In: Approximation theorems of mathematical statistics, chapter Chapter, vol 2, pp 91–102Google Scholar
  47. 47.
    Shechter SM, Bryce CL, Alagoz O, Kreke JE, Stahl JE, Schaefer AJ, Angus DC, Roberts MS (2005) A clinically based discrete-event simulation of end-stage liver disease and the organ allocation process. Med Decis Making Int J Soc Med Decis Making 25:109–209Google Scholar
  48. 48.
    Shorten A, Shorten B, Keogh J, West S, Morris J (2005) Making choices for childbirth: a randomized controlled trial of a decision-aid for informed birth after cesarean. Birth (Berkeley, Calif.) 32:252–61. http://www.ncbi.nlm.nih.gov/pubmed/16336366 CrossRefGoogle Scholar
  49. 49.
    Sims CJ, Meyn L, Caruana R, Rao RB, Mitchell T, Krohn M (2000) Predicting cesarean delivery with decision tree models. Amer J Obstet Gynecol 183:1198–1206CrossRefGoogle Scholar
  50. 50.
    Smith GCS, Dellens M, White IR, Pell JP (2004) Combined logistic and Bayesian modeling of cesarean section risk. Amer J Obstet Gynecol 191:2029–34. http://www.ncbi.nlm.nih.gov/pubmed/15592287 CrossRefGoogle Scholar
  51. 51.
    Smith GCS, White IR, Pell JP, Dobbie R (2005) Predicting cesarean section and uterine rupture among women attempting vaginal birth after prior cesarean section. PLoS Med 2:0871–0878. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1201366&tool=pmcentrez&rendertype=abstract Google Scholar
  52. 52.
    Solheim KN, Esakoff TF, Little SE, Cheng YW, Sparks TN, Caughey AB (2011) The effect of cesarean delivery rates on the future incidence of placenta previa, placenta accreta, and maternal mortality. The Journal of Maternal-Fetal & Neonatal Medicine: The Official Journal of the European Association of Perinatal Medicine, The Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians 24:1341–6. http://informahealthcare.com/doi/abs/10.3109/14767058.2011.553695 CrossRefGoogle Scholar
  53. 53.
    Takagi H, Kanai Y, Misue K (2017) Queueing network model for obstetric patient flow in a hospital. Health Care Manag Sci 20:433–451CrossRefGoogle Scholar
  54. 54.
    Tejada JJ, Ivy JS, King RE, Wilson JR, Ballan MJ, Kay MG, Diehl KM, Yankaskas BC (2014a) Combined DES/SD model of breast cancer screening for older women, II: screening-and-treatment simulation. IIE Trans 46:707–727. http://www.tandfonline.com/doi/abs/10.1080/0740817X.2013.851436 CrossRefGoogle Scholar
  55. 55.
    Tejada JJ, Ivy JS, Wilson JR, Ballan MJ, Diehl KM, Yankaskas BC (2014b) Combined DES/SD model of breast cancer screening for older women, I: Natural-history simulation. IIE Trans 47:600–619. http://www.tandfonline.com/doi/full/10.1080/0740817X.2014.959671 CrossRefGoogle Scholar
  56. 56.
    Therneau TM, Atkinson EJ (2015) An introduction to recursive partitioning using the RPART routines. Technical reportGoogle Scholar
  57. 57.
    Vahratian A, Troendle JF, Siega-Riz AM, Zhang J (2006) Methodological challenges in studying labour progression in contemporary practice. Paediatric Perinatal Epidemiol 20:72–78CrossRefGoogle Scholar
  58. 58.
    Vahratian A, Zhang J, Hasling J, Troendle JF, Klebanoff MA, Thorp JM (2004) The effect of early epidural versus early intravenous analgesia use on labor progression: a natural experiment. Amer J Obstet Gynecol 191:259–65. http://www.ncbi.nlm.nih.gov/pubmed/15295376 CrossRefGoogle Scholar
  59. 59.
    Vlemmix F, Warendorf JK, Rosman AN, Kok M, Mol BW, Morris JM, Nassar N (2013) Decision aids to improve informed decision-making in pregnancy care: a systematic review. BJOG: Int J Obstet Gynaecol 120:257–266CrossRefGoogle Scholar
  60. 60.
    Weinstein MC, Coxson PG, Williams LW, Pass TM, Stason WB, Goldman L (1987) Forecasting coronary heart disease incidence, mortality, and cost: the coronary heart disease policy model. Amer J Public Health 77:1417–1426CrossRefGoogle Scholar
  61. 61.
    Xu X, Ivy JS, Patel DA, Patel SN, Smith DG, Ransom SB, Fenner D, DeLancey JOL (2010) Pelvic floor consequences of cesarean delivery on maternal request in women with a single birth. J Women’s Health 19:147–160CrossRefGoogle Scholar
  62. 62.
    Yang YS, Hur MH, Kim SY (2013) Risk factors of cesarean delivery at prenatal care, admission and during labor in low-risk pregnancy: multivariate logistic regression analysis. J Obstet Gynaecol Res 39:96–104. http://www.ncbi.nlm.nih.gov/pubmed/22672671 CrossRefGoogle Scholar
  63. 63.
    Zhang J, Landy HJ, Branch DW, Burkman R, Haberman S, Gregory KD, Hatjis CG, Ramirez MM, Bailit JL, Gonzalez-Quintero VH, Hibbard JU, Hoffman MK, Kominiarek M, Learman LA, VanVeldhuisen P, Troendle J, Reddy UM (2010) Contemporary patterns of spontaneous labor with normal neonatal outcomes. Obstet Gynecol 116:1281–1287CrossRefGoogle Scholar
  64. 64.
    Zhang J, Troendle J, Mikolajczyk R, Sundaran R, Beaver J, Fraser W (2010) The natural history of the normal first stage of labor. Obstet Gynecol 115:705–710CrossRefGoogle Scholar
  65. 65.
    Zhang J, Troendle J, Reddy UM, Laughon SK, Branch DW, Burkman R, Landy HJ, Hibbard JU, Haberman S, Ramirez MM, Bailit JL, Hoffman MK, Gregory KD, Gonzalez-Quintero VH, Kominiarek M, Learman La, Hatjis CG, van Veldhuisen P (2010c) Contemporary cesarean delivery practice in the United States. Amer J Obstet Gynecol 203:326.e1–326.e10. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2947574&tool=pmcentrez&rendertype=abstract CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Health Policy and Management, Gillings School of Global Public HealthUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Edward P. Fitts Department of Industrial and Systems EngineeringNorth Carolina State UniversityRaleighUSA
  3. 3.Department of Industrial and Systems EngineeringNorth Carolina State UniversityRaleighUSA
  4. 4.College of ManagementNorth Carolina State UniversityRaleighUSA
  5. 5.RTI InternationalResearch Triangle ParkUSA
  6. 6.Department of Obstetrics and GynecologyDuke University School of MedicineDurhamUSA

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