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Updated: Jun 26, 2026
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram to predict mortality of patients with DIC in ICU
Qingbo Zeng1,2, Qingwei Lin1, Lincui Zhong1
1Intensive Care Unit, The 908th Hospital of Chinese PLA Logistic Support Force, Nanchang, China.
Background:
Disseminated intravascular coagulation (DIC) is a devastating condition, which always cause poor outcome of critically ill patients in intensive care unit. Studies concerning short-term mortality prediction in DIC patients is scarce. This study aimed to identify risk factors contributing to DIC mortality and construct a predictive nomogram.
Methods:
A total of 676 overt DIC patients were included. A Cox proportional hazards regression model was developed based on covariates identified using least absolute shrinkage and selection operator (LASSO) regression. The prediction performance was independently evaluated in the MIMIC-III and MIMIC-IV Clinical Database, as well as the 908th Hospital Database (908thH). Model performance was independently assessed using MIMIC-III, MIMIC-IV, and the 908th Hospital Clinical Database.
Results:
The Cox model incorporated variables identified by Lasso regression including heart failure, sepsis, height, SBP, lactate levels, HCT, PLT, INR, AST, and norepinephrine use. The model effectively stratified patients into different mortality risk groups, with a C-index of >0.65 across the MIMIC-III, MIMIC-IV, and 908th Hospital databases. The calibration curves of the model at 7 and 28 days demonstrated that the prediction performance was good. And then, a nomogram was developed to facilitate result visualization. Decision curve analysis indicated superior net benefits of the nomogram.
Conclusion:
This study provides a predictive nomogram for short-term overt DIC mortality risk based on a Lasso-Cox regression model, offering individualized and reliable mortality risk predictions.
