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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Developing and Validating a Nomogram Model for Predicting Ischemic Stroke Risk.

Li Zhou¹, Youlin Wu^1,2, Jiani Wang¹

¹Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China.

Journal of Personalized Medicine

|July 27, 2024

Summary

This summary is machine-generated.

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.

Keywords:

LASSO ischemic stroke nomogram predictors

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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.