Models for Predicting Recurrence, Complications, and Health Status in Women After Pelvic Organ Prolapse Surgery

Abstract

Objective: To develop statistical models predicting recurrent pelvic organ prolapse, surgical complications, and change in health status 12 months after apical prolapse surgery.

Methods: Logistic regression models were developed using a combined cohort from three randomized trials and two prospective cohort studies from 1,301 participants enrolled in surgical studies conducted by the Pelvic Floor Disorders Network. Composite recurrent prolapse was defined as prolapse beyond the hymen; the presence of bothersome bulge symptoms; or prolapse reoperation or retreatment within 12 months after surgery. Complications were defined as any serious adverse event or Dindo grade III complication within 12 months of surgery. Significant change in health status was defined as a minimum important change of SF-6D utility score (±0.035 points) from baseline. Thirty-two candidate risk factors were considered for each model and model accuracy was measured using concordance indices. All indices were internally validated using 1,000 bootstrap resamples to correct for bias.

Results: The models accurately predicted composite recurrent prolapse (concordance index=0.72, 95% CI 0.69-0.76), bothersome vaginal bulge (concordance index=0.73, 95% CI 0.68-0.77), prolapse beyond the hymen (concordance index=0.74, 95% CI 0.70-0.77), serious adverse event (concordance index=0.60, 95% CI 0.56-0.64), Dindo grade III or greater complication (concordance index=0.62, 95% CI 0.58-0.66), and health status improvement (concordance index=0.64, 95% CI 0.62-0.67) or worsening (concordance index=0.63, 95% CI 0.60-0.67). Calibration curves demonstrated all models were accurate through clinically useful predicted probabilities.

Conclusion: These prediction models are able to provide accurate and discriminating estimates of prolapse recurrence, complications, and health status 12 months after prolapse surgery.

Figure 1

Calibration curves for three models predicting probability of developing recurrent pelvic organ prolapse one year after surgery for pelvic organ prolapse.

Figure 2

Calibration curves for two models predicting probability of developing one or more serious adverse event and Dindo grade III or higher complication one year after surgery for pelvic organ prolapse.

Figure 3

Calibration curves for two models predicting probability of overall health status improvement and worsening from baseline one year after surgery for pelvic organ prolapse.

Figure 4

Comparing accuracy of the composite recurrence model’s predictions to all expert predictions (A) using receiver operating characteristic curves and each expert (B) using the concordance index for 50 random patients selected from the Colpopexy and Urinary Reduction Efforts (CARE), Outcomes Following Vaginal Prolapse Repair and Midurethral Sling (OPUS), and Operations and Pelvic Muscle Training in the Management of Apical Support Loss (OPTIMAL) trials, and the prospective cohort study Colpocleisis.

Figure 5

Comparing accuracy of the serious adverse event model’s predictions to all expert predictions (A) using receiver operating characteristic curves and each expert (B) using the concordance index for 50 random patients selected from the Colpopexy and Urinary Reduction Efforts (CARE), Outcomes Following Vaginal Prolapse Repair and Midurethral Sling (OPUS), and Operations and Pelvic Muscle Training in the Management of Apical Support Loss (OPTIMAL) trials, and the prospective cohort study Colpocleisis.