Abstract:
Introduction: Recurrence of pelvic organ prolapse is one of the most common complications after
prolapse surgery. In Ethiopia, the burden and prediction model of recurrence was not studied. So,
this study stratified the risk of recurrence of pelvic organ prolapse based on baseline characteristics
of patient factors. It helps the clinician to plan the most durable surgical procedures based on
patient's risk factors.
Objective: to develop and validate a risk prediction model for postoperative recurrence of pelvic
organ prolapse at the University of Gondar Comprehensive Specialized Hospital, Northwest
Ethiopia, from January 2019 to January 2023.
Methods: A retrospective follow-up study was undertaken at Gondar Comprehensive Specialized
Hospital on 611 patients who had received pelvic organ prolapse surgery between January 1, 2019,
and June 30, 2023, to predict the recurrence risk of pelvic organ prolapse within one year of
primary surgery. Data were gathered using the open-source freeware KoboToolBox. Finally, the
data were analyzed with STATA version 17.0. Predictors were selected using Lasso regression,
and nomograms were developed using multivariable logistic regression with a P-value < 0.05. The
model's internally validated using bootstrapping, and its performance was assessed using
discrimination and calibration plots.
Result: In this investigation, the prevalence of recurrent pelvic organ prolapse was determined to
be 19.6%. The woman's age, residency, occupation, marital status, parity, mode of delivery,
duration of prolapse symptoms, presence of a decubitus ulcer, stage of POP, and presence of
postoperative complications were all substantially linked with recurrent POP. With this predictor
nomogram was constructed with a calibration test p-value of 0.861 and a discriminating power of
96.3% (95% CI: 93.77, 97.13). The model had sensitivity of 87.5%, specificity of 86.35%, positive
predictive value of 61.46%, negative predictive value of 96.58%, and accuracy of 86.58%. The
nomogram had a significant net benefit between the 0.2 and 0.8 threshold probabilities.
Conclusion: The generated model has high performance and good calibration; it is used to predict
the recurrence of pelvic organ prolapse following surgery. The model proved beneficial in clinical
settings, as validated by a net benefit analysis. The clinicians can use it to stratify cases and select
the most durable surgical techniques based on risks
