The Application of a Multidimensional Prediction Model in the Recurrence of Female Pelvic Organ Prolapse after Surgery.

Abstract

Objective The relationship between multiple indicators of women and postoperative recurrence of pelvic organ prolapse was analyzed to establish a model for predicting postoperative recurrence of female pelvic organ prolapse. Methods Three hundred patients with pelvic organ prolapse who underwent pelvic organ prolapse surgery at our hospital were monitored for 1-2 years to determine their prognosis. Whether there was a postoperative recurrence, they were divided into two groups. We collected the relevant data from the two groups of patients before and after surgery. Through single factor and logistic multivariate analysis, we selected the risk factors that may affect the recurrence of patients to construct a prediction model. We verified the identification ability, proofreading ability, and clinical applicability of the model. Results Eighty-four patients with pelvic organ prolapse who had postoperative recurrence were assigned to the recurrence group, and 216 patients were included in the nonrecurrence group. Based on the logistic multivariate analysis results, we constructed a nomogram model containing 5 dimensions of age, BMI, degree of prolapse, pubic fissure, and serum calcium to predict postoperative recurrence. The tests revealed that the model had an excellent identification ability (AUC = 0.910), and the expected recurrence rate was significantly in agreement with the actual recurrence rate (U = −0.007, Brief = 0.087). The Hosmer-Lemeshow goodness-of-fit test demonstrated that the model had good calibration (c2 = 29.352, P = 0.522), and the decision curve showed that the threshold probability was in the range of ~12% to 100%, having a high net benefit value. Conclusion Based on the present study findings, we concluded that the constructed nomogram model has suitable identification, calibration, and clinical applicability.