Development and validation of a machine learning-based nomogram for preoperative prediction of laparoscopic surgical difficulty in gallstone patients.

ABSTRACT:

Background

Preoperative prediction of laparoscopic surgical difficulty in gallstone patients is crucial for improving surgical outcomes. This study aimed to develop and validate a nomogram based on advanced machine learning algorithms, incorporating key clinical and systemic inflammatory response indicators, such as the C-reactive protein to albumin ratio (CAR).

Methods

A retrospective analysis was conducted on 362 eligible patients who underwent laparoscopic cholecystectomy (LC) for gallstones between 2013 and 2019. A total of 420 patients were initially identified, with 58 excluded based on predefined criteria such as age and incomplete records. The remaining patients were divided into a training set (n=253) and a validation set (n=109). The development of the nomogram inv

SUBMITTER: Huang K 

PROVIDER: S-EPMC12314664 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature