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Validation and Recalibration of a Model for Predicting Surgical-Site Infection After Pelvic Organ Prolapse Surgery
Stephen Rhodes1, Amine Sahmoud2, J Eric Jelovsek3
1Division of Urogynecology, Urology Institute, University Hospitals Cleveland, Cleveland, OH, USA.
Introduction And Hypothesis:
The objective was to externally validate and recalibrate a previously developed model for predicting postoperative surgical-site infection (SSI) after pelvic organ prolapse (POP) surgery.
Methods:
This study utilized a previously validated model for predicting post-POP surgery SSI within 90 days of surgery using a Medicare population. For this study, the model was externally validated and recalibrated using the Premier Healthcare Database (PHD) and the National Surgical Quality Improvement Project (NSQIP) database. Discriminatory performance was assessed via the c-statistic and calibration was assessed using calibration curves. Methods of recalibration in the large and logistic recalibration were used to update the models.
Results:
The PHD contained 420,277 POP procedures meeting the inclusion criteria and 1.6% resulted in SSI. The NSQIP dataset contained 62,553 POP surgeries and 1.4% resulted in SSI. Discrimination of the original model was comparable with that seen in the initial validation (c-statistic = 0.57 in PHD, 0.59 in NSQIP vs 0.60 in the original Medicare data). Recalibration greatly improved model calibration when evaluated in NSQIP data.
Conclusion:
A previously developed model for predicting SSI after POP surgery demonstrated stable discriminatory ability when externally validated on the PHD and NSQIP databases. Model recalibration was necessary to improve prediction. Prospective studies are needed to validate the clinical utility of such a model.
Insights
A predictive model for surgical-site infection (SSI) after pelvic organ prolapse (POP) surgery showed stable accuracy but required recalibration for improved clinical use. Further studies are needed to confirm its utility.
Area of Science:
- Urology
- Gynecology
- Infectious Disease
Background:
- Surgical-site infection (SSI) is a significant complication following pelvic organ prolapse (POP) surgery.
- Accurate prediction of SSI risk is crucial for patient management and resource allocation.
Purpose of the Study:
- To externally validate and recalibrate a previously developed model for predicting postoperative SSI after POP surgery.
- To assess the model's performance using large, independent healthcare databases.
Main Methods:
- Utilized a previously validated SSI prediction model.
- Externally validated the model on the Premier Healthcare Database (PHD) and the National Surgical Quality Improvement Project (NSQIP) database.
- Assessed discriminatory performance using the c-statistic and calibration using calibration curves; employed large and logistic recalibration methods.
Main Results:
- The PHD included 420,277 POP procedures (1.6% SSI), and NSQIP included 62,553 (1.4% SSI).
- The original model's discrimination remained comparable (c-statistic 0.57 in PHD, 0.59 in NSQIP).
- Recalibration significantly improved model calibration, particularly in the NSQIP data.
Conclusions:
- The existing SSI prediction model for POP surgery demonstrated stable discriminatory ability across external datasets.
- Recalibration of the model is essential to enhance its predictive accuracy.
- Prospective research is warranted to confirm the clinical utility of the recalibrated model.
