Add like
Add dislike
Add to saved papers

Nomogram for predicting difficult total laparoscopic hysterectomy: A multi-institutional, retrospective model development and validation study.

BACKGROUND: Total laparoscopic hysterectomy (TLH) is the most commonly performed gynecological surgery. However, the difficulty of the operation varies depending on the patient and surgeon. Subsequently, patient's outcomes and surgical efficiency are affected. We aimed to develop and validate a pre-operative nomogram to predict the operative difficulty in patients undergoing TLH.

METHODS: This retrospective study included 663 patients with TLH from XXX Hospital and 102 patients from YYY Hospital in Chongqing, China. A multivariate logistic regression analysis was used to identify the independent predictors of operative difficulty, and a nomogram was constructed. The performance of the nomogram was validated internally and externally.

RESULTS: The uterine weight, history of pelvic surgery, presence of adenomyosis, surgeon's years of practice, and annual hysterectomy volume were identified as significant independent predictors of operative difficulty. The nomogram demonstrated good discrimination in the training dataset (area under the receiver operating characteristic curve [AUC], 0.827 (95% confidence interval [CI], 0.783-0.872), internal validation dataset (AUC, 0.793 [95% CI, 0.714-0.872]), and external validation dataset (AUC, 0.756 [95% CI, 0.658-0.854]). The calibration curves showed good agreement between the predictions and observations for both internal and external validations.

CONCLUSION: The developed nomogram accurately predicted the operative difficulty of TLH, facilitated pre-operative planning and patient counseling, and optimized surgical training. Further prospective multicenter clinical studies are required to optimize and validate this model.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

Related Resources

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app