使用机器学习预测年轻急性缺血性脑卒中患者 3 个月的不良功能结局。

Predicting 3-month poor functional outcomes of acute ischemic stroke in young patients using machine learning.

机构信息

Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.

出版信息

Eur J Med Res. 2024 Oct 10;29(1):494. doi: 10.1186/s40001-024-02056-3.

DOI:10.1186/s40001-024-02056-3

PMID:39385211

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11466038/

Abstract

BACKGROUND

Prediction of short-term outcomes in young patients with acute ischemic stroke (AIS) may assist in making therapy decisions. Machine learning (ML) is increasingly used in healthcare due to its high accuracy. This study aims to use a ML-based predictive model for poor 3-month functional outcomes in young AIS patients and to compare the predictive performance of ML models with the logistic regression model.

METHODS

We enrolled AIS patients aged between 18 and 50 years from the Third Chinese National Stroke Registry (CNSR-III), collected between 2015 and 2018. A modified Rankin Scale (mRS) ≥ 3 was a poor functional outcome at 3 months. Four ML tree models were developed: The extreme Gradient Boosting (XGBoost), Light Gradient Boosted Machine (lightGBM), Random Forest (RF), and The Gradient Boosting Decision Trees (GBDT), compared with logistic regression. We assess the model performance based on both discrimination and calibration.

RESULTS

A total of 2268 young patients with a mean age of 44.3 ± 5.5 years were included. Among them, (9%) had poor functional outcomes. The mRS at admission, living alone conditions, and high National Institutes of Health Stroke Scale (NIHSS) at discharge remained independent predictors of poor 3-month outcomes. The best AUC in the test group was XGBoost (AUC = 0.801), followed by GBDT, RF, and lightGBM (AUCs of 0.795, 0, 794, and 0.792, respectively). The XGBoost, RF, and lightGBM models were significantly better than logistic regression (P < 0.05).

CONCLUSIONS

ML outperformed logistic regression, where XGBoost the boost was the best model for predicting poor functional outcomes in young AIS patients. It is important to consider living alone conditions with high severity scores to improve stroke prognosis.

摘要

背景

预测年轻急性缺血性脑卒中（AIS）患者的短期结局有助于制定治疗决策。机器学习（ML）由于其准确性高，在医疗保健中得到了越来越多的应用。本研究旨在使用基于 ML 的预测模型预测年轻 AIS 患者 3 个月时的不良功能结局，并比较 ML 模型与逻辑回归模型的预测性能。

方法

我们纳入了 2015 年至 2018 年中国第三次国家卒中登记研究（CNSR-III）中年龄在 18 至 50 岁之间的 AIS 患者。改良 Rankin 量表（mRS）评分≥3 为 3 个月时的不良功能结局。我们开发了四种 ML 树模型：极端梯度提升（XGBoost）、轻梯度提升机（lightGBM）、随机森林（RF）和梯度提升决策树（GBDT），并与逻辑回归进行比较。我们根据判别能力和校准能力来评估模型性能。

结果

共纳入 2268 名平均年龄为 44.3±5.5 岁的年轻患者，其中 9%的患者功能结局不良。入院时的 mRS、独居情况和出院时的国立卫生研究院卒中量表（NIHSS）评分较高仍然是 3 个月不良结局的独立预测因素。在测试组中，XGBoost 的 AUC 最高（AUC=0.801），其次是 GBDT、RF 和 lightGBM（AUC 分别为 0.795、0、0.794 和 0.792）。XGBoost、RF 和 lightGBM 模型均显著优于逻辑回归（P<0.05）。