Related Experiment Video
Updated: Jul 17, 2025
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Machine learning-based nomogram for 30-day mortality prediction for patients with unresectable malignant biliary
Zongdong Zhu1,2, Kaixin Hu1,2, Fengqing Zhao2
1Jiaxing University Master Degree Cultivation Base, Zhejiang Chinese Medical University, Jiaxing, Zhejiang, China.
Background:
This study aimed to investigate the risk factors for 30-day mortality in patients with malignant biliary obstruction (MBO) after endoscopic retrograde cholangiopancreatography (ERCP) with endobiliary metal stent placement. Furthermore, we aimed to construct and visualize a prediction model based on LASSO-logistic regression.
Methods:
Data were collected from 245 patients who underwent their first ERCP with endobiliary metal stent placement for unresectable MBO between June 1, 2013, and August 31, 2021. Univariable and multivariable logistic regression analyses were conducted to identify the risk factors for 30-day mortality. We subsequently developed a logistic regression model that incorporated multiple parameters identified by LASSO regression. The model was visualized and the nomogram was plotted. Risk stratification was performed based on nomogram-derived scores.
Results:
The 30-day mortality rate was 10.7% (23/245 patients). Distant metastasis, total bilirubin, post-ERCP complications, and successful drainage were independent risk factors of 30-day mortality. The variables screened by LASSO regression, including distant metastasis, total bilirubin, post-ERCP complications, and successful drainage, were incorporated into the logistic model. The results were visualized through a nomogram based on the model. To assess the model's performance, discrimination was evaluated using the area-under-the-curve values obtained from receiver operating characteristic analyses with 10-fold cross-validation in the training group and validated in the testing group. The calibration curve showed the good predictive ability of the model. Decision curve analysis is used to evaluate the clinical application of nomogram. Finally, we performed risk stratification based on the risk calculated using the nomogram. Patients were assigned to the low-, moderate-, and high-risk groups based on their probability scores. The Kaplan-Meier survival curves for the different nomogram-based groups were significantly different (p < 0.001).
Conclusions:
We developed a nomogram using the LASSO-logistic regression model to forecast the 30-day mortality rate in patients who had undergone ERCP with endobiliary metal stent placement due to MBO. This nomogram can assist in identifying individuals at high-risk of 30-day mortality following ERCP.
