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.