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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Developing and Validating a Nomogram Model for Predicting Ischemic Stroke Risk
Li Zhou1, Youlin Wu1,2, Jiani Wang1
1Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.
Insights
A new nomogram model effectively predicts acute ischemic stroke risk using eight key factors. This tool aids in identifying individuals at high risk for better clinical management and stroke prevention strategies.
Area of Science:
- Neurology
- Medical Informatics
- Biostatistics
Background:
- Identifying individuals at risk for ischemic stroke is a significant clinical challenge.
- Current methods for risk stratification have limitations in clinical practice.
Purpose of the Study:
- To develop and validate a nomogram model for predicting the risk of acute ischemic stroke.
- To identify key predictive variables for ischemic stroke risk.
Main Methods:
- Retrospective analysis of patient data from a neurology department.
- Multivariate logistic regression and LASSO regression for variable selection.
- Nomogram construction and validation using training, internal, and external datasets.
Main Results:
- Eight predictors identified: age, smoking, hypertension, diabetes, atrial fibrillation, stroke history, white blood cell count, and vitamin B12.
- The nomogram demonstrated good predictive performance with an AUC-ROC of 0.760.
- Internal and external validation confirmed the model's predictive efficacy and clinical applicability.
Conclusions:
- A nomogram based on eight variables was successfully constructed for quantifying ischemic stroke risk.
- The developed nomogram offers a valuable tool for clinical risk assessment and patient management.
- Further validation and implementation in clinical settings are warranted.
Abstract:
Background and purpose: Clinically, the ability to identify individuals at risk of ischemic stroke remains limited. This study aimed to develop a nomogram model for predicting the risk of acute ischemic stroke. Methods: In this study, we conducted a retrospective analysis on patients who visited the Department of Neurology, collecting important information including clinical records, demographic characteristics, and complete hematological tests. Participants were randomly divided into training and internal validation sets in a 7:3 ratio. Based on their diagnosis, patients were categorized as having or not having ischemic stroke (ischemic and non-ischemic stroke groups). Subsequently, in the training set, key predictive variables were identified through multivariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods, and a nomogram model was constructed accordingly. The model was then evaluated on the internal validation set and an independent external validation set through area under the receiver operating characteristic curve (AUC-ROC) analysis, a Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) to verify its predictive efficacy and clinical applicability. Results: Eight predictors were identified: age, smoking status, hypertension, diabetes, atrial fibrillation, stroke history, white blood cell count, and vitamin B12 levels. Based on these factors, a nomogram with high predictive accuracy was constructed. The model demonstrated good predictive performance, with an AUC-ROC of 0.760 (95% confidence interval [CI]: 0.736-0.784). The AUC-ROC values for internal and external validation were 0.768 (95% CI: 0.732-0.804) and 0.732 (95% CI: 0.688-0.777), respectively, proving the model's capability to predict the risk of ischemic stroke effectively. Calibration and DCA confirmed its clinical value. Conclusions: We constructed a nomogram based on eight variables, effectively quantifying the risk of ischemic stroke.
