Predicting Treatment Choice for Patients With Pelvic Organ Prolapse

Michael Heit, Chris Rosenquist, Patrick Culligan, Carol Graham, Miles Murphy, Susan Shott
2003 Obstetrical and Gynecological Survey  
OBJECTIVE: To evaluate which clinical factors were predictive of treatment choice for patients with pelvic organ prolapse. METHODS: One hundred fifty-two patients were enrolled in this cross-sectional study to collect clinical data on potential predictors of treatment choice. Continuous parametric, continuous nonparametric (ordinal), and categoric data were compared with chosen management plan (expectant, pessary, surgery) using analysis of variance, the Kruskal-Wallis test, and the 2 test for
more » ... and the 2 test for association, respectively. All significant predictors (P < .05) of treatment choice for pelvic organ prolapse identified during univariate analysis were entered into a backward elimination polytomous logistic regression analysis for predicting surgery versus pessary versus expectant management, with surgery as the reference group. RESULTS: The probability of choosing expectant management rather than surgery 1) increases as the preoperative pelvic pain score increases (odds ratio [OR] 1.6; 95% confidence interval [CI] 1.07, 2.40; P ‫؍‬ .024) and 2) decreases as the prolapse severity increases (OR 0.46; 95% CI 0.29, 0.72; P ‫؍‬ .001). The probability of choosing pessary rather than surgery 1) increases as age increases (OR 1.1; 95% CI 1.05, 1.16; P < .001), 2) decreases as the prolapse severity increases (OR 0.77; 95% CI 0.60, 0.99; P ‫؍‬ .042), and 3) is less if the participant had prior prolapse surgery (OR 0.23; 95% CI 0.07, 0.76; P ‫؍‬ .017). CONCLUSION: Age, prior prolapse surgery, preoperative pelvic pain scores, and pelvic organ prolapse severity were independently associated with treatment choices in a predictable way and provide physicians with medical evidence necessary to support a patient's decision. (Obstet Gynecol 2003;101:1279 -84.
doi:10.1097/01.ogx.0000090197.42885.24 fatcat:qa2a5cgsvfb5taosdl7hsv2qfu