Related Experiment Video
Updated: Jun 17, 2025
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
10.1K
Development of a Predictive Nomogram for Intra-Hospital Mortality in Acute Ischemic Stroke Patients Using LASSO
Li Zhou1, Youlin Wu1,2, Jiani Wang1
1Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Clinical Interventions in Aging
|August 14, 2024
Summary
This study developed a nomogram to predict intra-hospital mortality in ischemic stroke patients, identifying age, NIHSS score, COPD, and WBC as key predictors for improved patient care.
Area of Science:
- Neurology
- Cardiology
- Public Health
Background:
- Ischemic stroke is a major global cause of death and disability.
- Accurate prediction of intra-hospital mortality (IHM) is crucial for effective patient management.
- Current prediction tools may lack personalization for ischemic stroke patients.
Purpose of the Study:
- To develop a practical nomogram for personalized IHM risk prediction in ischemic stroke patients.
- To identify independent predictors of IHM in this population.
- To validate the nomogram's predictive performance and clinical utility.
Main Methods:
- Retrospective analysis of 422 ischemic stroke patients.
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression for predictor selection.
- Constructed and validated a nomogram using ROC curves, calibration curves, and decision curve analysis.
Main Results:
- Identified four independent predictors of IHM: age, admission NIHSS score, chronic obstructive pulmonary disease (COPD), and white blood cell count (WBC).
- The nomogram achieved high predictive accuracy with AUCs of 0.958 (training) and 0.962 (validation).
- Demonstrated excellent sensitivity and specificity, confirming clinical applicability.
Conclusions:
- Age, admission NIHSS, COPD history, and WBC are significant predictors of IHM in ischemic stroke.
- The developed nomogram offers a highly accurate and practical tool for mortality risk estimation.
- Further external validation and prospective studies are recommended to confirm clinical efficacy.
