Hysterectomy versus uterine preservation for pelvic organ prolapse surgery: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: One in 5 females will have surgery to treat pelvic organ prolapse in their lifetime. Uterine-preserving surgery involving suspension of the uterus is an increasingly popular alternative to the traditional use of hysterectomy with vaginal vault suspension to treat pelvic organ prolapse; however, comparative evidence with native tissue repairs remains limited in scope and quality. OBJECTIVE: To compare 1-year outcomes between hysterectomy-based and uterine-preserving native tissue prolapse surgeries performed through minimally invasive approaches. STUDY DESIGN: We used a nonrandomized design with patients self-selecting their surgical group to integrate a pragmatic, patient-centered, and autonomy-focused approach. Participants chose between uterine-preserving surgery or hysterectomy-based surgery, guided by neutral evidence-based discussions and individualized decision-making, with support from fellowship-trained urogynecologists. Inverse probability of treatment weighting based on high-dimensional propensity scores was used to balance baseline differences across surgical groups in an effort to resemble a randomized clinical trial. A prospective cohort study of 321 participants with stage ≥2 prolapse involving the uterus who desired surgical treatment were recruited between 2020 and 2022 and followed to 1 year (retention >90%). Patients chose to receive uterine-preserving pelvic organ prolapse surgery through hysteropexy (n=151) or hysterectomy with vaginal vault suspension (n=170; reference group), with repair of anterior and/or posterior prolapse if indicated. The primary outcome was anatomic prolapse recurrence within 1 year, defined as apical descent ≥50% of the total vaginal length. Secondary outcomes were perioperative, functional, clinical, and healthcare outcomes measured at 6 weeks and 1 year. Inverse probability of treatment weighted linear regression and modified Poisson regression were used to estimate adjusted mean differences and relative risks, respectively. RESULTS: Apical anatomic recurrence rates at 1 year were 17.2% following hysterectomy and 7.5% following uterine-preservation, resulting in an adjusted relative risk of 0.35 (95% CI 0.15, 0.83). Uterine-preserving surgery was associated with shorter length of surgery (adjusted mean difference -0.68 hours [-0.80, -0.55]) and hospitalization (adjusted mean difference -4.34 hours [-7.91, -0.77]), less use of any opioids within 24 hours (adjusted relative risk 0.79 [0.65, 0.97]), and fewer procedural complications (adjusted relative risk 0.19 [0.04, 0.83]) than hysterectomy. Up to 1 year, uterine-preserving surgery was associated with lower risk of composite recurrence (stage ≥2 prolapse in any compartment or retreatment; adjusted relative risk 0.47 [0.32, 0.69]) than hysterectomy, driven by anatomic outcomes. There were no clinically meaningful differences in functional or healthcare outcomes between surgical groups. CONCLUSION: This study adds real-world evidence to the growing body of research supportive of uterine-preserving surgery as a safe, efficient, and effective alternative to hysterectomy during native tissue prolapse repair. Given mounting evidence on safety, efficiency, and effectiveness of uterine-preserving surgery and its alignment with the preferences of approximately half of patients to keep their uterus, the standard of care should include routine offering and patient choice between uterine-preserving and hysterectomy-based surgery for pelvic organ prolapse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".