To construct and validate a nomogram to predict symptomatic recurrence after laparoscopic adenomyomectomy based on retrospective clinical data.
Approach:
Study Design: Retrospective cohort study involving 484 patients who underwent primary laparoscopic adenomyomectomy.
Data Collection: Postoperative follow-up data were collected, and independent predictors of recurrence were identified using multivariate Cox regression.
Model Validation: The nomogram was evaluated using Harrell’s concordance index, receiver operating characteristic curve, calibration curves, and decision curve analysis.
Key Findings:
Independent predictors of symptomatic recurrence included previous surgical history of ovarian endometrioma, preoperative CA125 level, concomitant ovarian endometrioma, postoperative medication modality, and duration of postoperative therapy.
The nomogram demonstrated good discriminatory ability with an AUC of 0.776 (95% CI, 0.728–0.824).
Calibration curve performance was also good, and decision curve analysis indicated high net benefit for predicted probability thresholds between 0% and 60%.
Interpretation:
The nomogram provides accurate individualized risk estimation for symptomatic recurrence after laparoscopic adenomyomectomy.
Limitations:
The study requires multicenter external validation to confirm the clinical utility of the nomogram.
Conclusion:
The nomogram offers a tool for predicting symptomatic recurrence post-laparoscopic adenomyomectomy, pending further validation.
Federal prosecutors allege that a Florida physician and research staff fabricated clinical trial records that were submitted into database systems used to evaluate investigational drugs.
