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Related Experiment Video

Updated: Jun 29, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Nomogram for predicting difficult total laparoscopic hysterectomy: a multi-institutional, retrospective model

Yin Chen¹, Jiahong Jiang¹, Min He¹

¹Department of Obstetrics and Gynecology, The 958th Army Hospital of the Chinese People's Liberation Army (958th Hospital).

International Journal of Surgery (London, England)

|March 27, 2024

Summary

This study developed a nomogram to predict the operative difficulty of total laparoscopic hysterectomy (TLH). The tool aids in preoperative planning, patient counseling, and surgical training for improved outcomes.

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Area of Science:

Gynaecology
Surgical Oncology

Background:

Total laparoscopic hysterectomy (TLH) is a common gynaecological procedure with variable difficulty.
Operative difficulty impacts patient outcomes and surgical efficiency.
Predicting TLH difficulty is crucial for optimizing surgical planning.

Purpose of the Study:

To develop and validate a preoperative nomogram for predicting operative difficulty in patients undergoing TLH.
To identify key predictors influencing TLH operative difficulty.

Main Methods:

Retrospective analysis of 663 patients from Southwest Hospital and 102 from 958th Hospital.
Multivariate logistic regression to identify independent predictors of operative difficulty.
Internal and external validation of the developed nomogram.

Main Results:

Key predictors identified: uterine weight, pelvic surgery history, adenomyosis, surgeon's experience, and annual hysterectomy volume.
Nomogram showed good predictive performance with AUCs of 0.827 (training), 0.793 (internal validation), and 0.756 (external validation).
Calibration curves indicated good agreement between predicted and observed operative difficulty.

Conclusions:

The developed nomogram accurately predicts TLH operative difficulty.
The nomogram can enhance preoperative planning, patient counseling, and surgical training.
Further prospective multicenter studies are recommended for model optimization and validation.