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Clin J Pain. 2008 Mar-Apr;24(3):265-72. doi: 10.1097/AJP.0b013e31816111a5.

Decision analysis for epidural labor analgesia with Multiattribute Utility (MAU) model.

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Division of Biostatistics, National Taiwan University, Taiwan, Republic of China.



Epidural analgesia (EA) is an effective and safe method to relieve labor pain. Little is known about the factors associated with decision on EA. We applied Multiattribute Utility (MAU) theory to ascertain possible factors on which we based to predict whether parturients would receive EA or not (non-EA) during their labor.


A hierarchical questionnaire on the basis of MAU theory was designed by experts to include individual attributes, knowledge and attitude toward EA and cue factors. Items in the questionnaire were compared between the EA and the non-EA groups. Receiver operating characteristics curve was used to assess predictive validity of the MAU model.


Of 167 parturients responding to the questionnaire, 151 participants (75 EA and 76 non-EA groups) completed all questions. Parturients in the EA group had significantly higher education level (rate of junior college or above: 88% vs. 67%, P=0.002). There were also more primiparae in the EA group compared with non-EA group (76% vs. 46%, P<0.001). For items in MAU model, 12 out of 20 items revealed significant differences between the 2 groups. Among them, "fear of side effects," "fear of severe complications," and "fear of needle" had the most remarkable differences. The area under receiver operating characteristics equaled to 0.91 (95% confidence interval=0.86, 0.96) for pre-labor decision and 0.83 (95% confidence interval=0.76, 0.89) for final decision.


These findings suggest that our MAU model can predict pre-labor decision and final decision of parturients by the incorporation of correlates with respect to knowledge and attitude.

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