Unknown

Dataset Information

0

A prognostic index model for predicting long-term recurrence of uterine leiomyoma after myomectomy.


ABSTRACT:

Introduction

Uterine leiomyoma (UL) is a common benign pelvic tumor in women that has a high recurrence rate. Our aim is to propose a prognostic index (PI) model for predicting the long-term recurrence risk of uterine leiomyoma (UL).

Methods

A total of 725 women who underwent myomectomy were enrolled in this retrospective multicenter study. Patients were contacted for follow-up. A PI model was proposed based on the multivariate Cox regression analysis in the model group. The predictive value of this model was tested in both internal and external validation group.

Results

PI formula = 1.5(if 3-5 leiomyomas) or 2(if >5 leiomyomas)+1(if residue)+1(if not submucosal)+1(if combined endometriosis). The PI value was divided into low-risk, intermediate-risk, and high-risk group by cut-off values 1.25 and 3.75. In the model group, the high-risk group had a significantly 4.55 times greater recurrence risk of UL than that in the low-risk group [cumulative recurrence rate (CR): 82.1% vs 29.5%, HR = 4.55, 95% CI 2.821-7.339]; the intermediate-risk group had a significantly 2.81 times greater recurrence risk of UL than that in the low-risk group (CR: 62.3% vs 29.5%, HR = 2.81, 95% CI 2.035-3.878). The differences between any two risk groups were also significant (P< 0.05) in both internal and external validation groups.

Conclusion

The model was proved to be effective in predicting recurrence of UL after myomectomy.

SUBMITTER: Ming X 

PROVIDER: S-EPMC8248613 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC5836951 | biostudies-other
| S-EPMC8875914 | biostudies-literature
| S-EPMC8568503 | biostudies-literature
| S-EPMC5995841 | biostudies-literature
| S-EPMC3754434 | biostudies-literature
| S-EPMC8640913 | biostudies-literature
| S-EPMC8978059 | biostudies-literature
| S-EPMC6289531 | biostudies-literature
| S-EPMC4901881 | biostudies-literature
| S-EPMC10961099 | biostudies-literature