Subjective risk vs. objective risk can lead to different post-cesarean birth decisions based on multiattribute modeling Poonam S. Sharma a, * , Karen B. Eden a , Jeanne-Marie Guise a,b , Holly B. Jimison a , James G. Dolan c a Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239-3098, USA b Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland, OR 97239-3098, USA c Department of Community and Preventive Medicine, University of Rochester, Rochester, NY 14642, USA Accepted 13 February 2010 Abstract Objective: To compare birth recommendations for pregnant women with a prior cesarean produced from a decision model using ab- solute risks vs. one using subjective interpretation of the same risks: (1) a multiattribute decision model based on patient prioritization of risks (subjective risk) and (2) a hybrid model that used absolute risks (objective risk). Study Design and Setting: The subjective risk multiattribute model used the Analytic Hierarchy Process to elicit priorities for maternal risks, neonatal risks, and the delivery experience from 96 postnatal women with a prior cesarean. The hybrid model combined the priorities for delivery experience obtained in the first model with the unadjusted absolute risk values. Results: The multiattribute model generated more recommendations for repeat cesarean delivery than the hybrid model: 73% vs. 18%, (P-value !0.001). The multiattribute model favored repeat cesarean because women heavily prioritized avoiding any risk (even rare risk) to the infant. The hybrid model favored the trial of labor because of lower probabilities of risk to the mother and its high success rate of vaginal birth after cesarean. Conclusion: This study highlights the importance of patients and clinicians discussing the patient’s priorities regarding the risks and other nonclinical considerations that may be important to her in the birthing decision. Ó 2011 Elsevier Inc. All rights reserved. Keywords: Childbirth; VBAC; Risk communication; Decision tree; Analytic Hierarchy Process; Shared decision making 1. Introduction The question of whether a woman should attempt a vag- inal delivery or plan for an elective repeat cesarean after a prior cesarean section has been an area of active research. A successful vaginal birth after cesarean (VBAC) is gener- ally associated with shorter hospital stays for the mother and infant, more rapid postpartum recovery, and thrombo- embolism when compared with cesarean [1e3]. However, a failed VBAC is associated with higher rates of infection and hemorrhage, increased risk of symptomatic uterine rup- ture, and increased neonatal morbidity [1,2]. The other birthing optiondan elective repeat cesareandis more pre- dictable in scheduling and is associated with reduced risk of symptomatic uterine rupture. However, there are risks of operative injury, scarring, and increased risk of placental abnormalities in future pregnancies. For both the birthing options, the risk of asymptomatic uterine rupture and hysterectomy are similar [1,2]. Moreover, the birth decision is also affected by several nonclinical patient priorities, such as the patient’s desire for a vaginal delivery, her desire to experience (or avoid) labor, her need for scheduling a de- livery, and her positive or negative feelings about a previous cesarean delivery [4]. Previously, investigators created decision tree models for childbirth decisions using utilities assigned by clinical ex- perts [5,6]. However, these models did not integrate patient priorities, an important component for making a decision shared by providers and patients. Understanding and inte- grating the priorities of pregnant women and their families regarding childbirth is especially important given the very personal nature of childbirth. Even decision analysis ex- perts highlight the necessity for ‘‘deciders’’ to include their preferences and judgment of decision choices in the decision-making process [7,8]. Moreover, these conditions are ‘‘essential requirements’’ in decision modelingd without which decision models are futile [8]. Decision models that include patient priorities can be useful in understanding what decision factors are important * Corresponding author: 11700 SW Grenoble St, Wilsonville, OR 97070. Tel.:(630) 242-0459. E-mail address: poonam.sharma@gmail.com (P.S. Sharma). 0895-4356/$ - see front matter Ó 2011 Elsevier Inc. All rights reserved. doi: 10.1016/j.jclinepi.2010.02.011 Journal of Clinical Epidemiology 64 (2011) 67e78