Identifying novel risk factors for aneurysmal subarachnoid haemorrhage using machine learning.

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-03-18 DOI:10.1038/s41598-025-88826-3

Jos P Kanning, Junfeng Wang, Shahab Abtahi, Mirjam I Geerlings, Ynte M Ruigrok

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Abstract

Aneurysmal subarachnoid haemorrhage (aSAH) is a type of stroke with high mortality and morbidity. This study aimed to identify novel aSAH risk factors by combining machine learning (ML) and traditional statistical methods. Using the UK Biobank, we identified aSAH cases via hospital-based ICD codes and analysed 618 baseline variables covering demographics, lifestyle, medical history, and physical measurements. The CatBoost ML algorithm and Shapley Additive Explanations (SHAP) identified the top 25 variables most influential in predicting aSAH. Logistic regression further described these variables while adjusting for established aSAH risk factors. Among 501,847 participants, 893 aSAH cases were identified. ML identified 214 variables with non-zero SHAP values. Logistic regression of the top 25 variables revealed four potential novel aSAH risk factors. Increased aSAH risk was associated with mean sphered cell volume (OR 1.02, 95% CI 1.00-1.03) and tea intake (OR 1.03, 95% CI 1.01-1.05). Decreased aSAH risk was associated with peak expiratory flow (OR 0.80, 95% CI 0.66-0.96), and haematocrit percentage (OR 0.97, 95% CI 0.95-1.00). Future research should validate these findings and explore the potential non-linear relationships and interactions indicated by the ML models.

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利用机器学习识别动脉瘤性蛛网膜下腔出血的新危险因素。

动脉瘤性蛛网膜下腔出血（aSAH）是一种死亡率和发病率都很高的中风类型。本研究旨在通过结合机器学习（ML）和传统统计方法来识别新型蛛网膜下腔出血风险因素。我们利用英国生物库，通过医院的 ICD 代码确定了 aSAH 病例，并分析了涵盖人口统计学、生活方式、病史和体格测量的 618 个基线变量。CatBoost ML 算法和 Shapley Additive Explanations (SHAP) 确定了对预测 ASAH 影响最大的前 25 个变量。逻辑回归进一步描述了这些变量，同时对已确定的 aSAH 风险因素进行了调整。在 501,847 名参与者中，确定了 893 例 aSAH 病例。ML 确定了 214 个 SHAP 值不为零的变量。对前 25 个变量进行逻辑回归后，发现了四个潜在的新型 aSAH 风险因素。aSAH 风险的增加与球形细胞平均体积（OR 1.02，95% CI 1.00-1.03）和茶摄入量（OR 1.03，95% CI 1.01-1.05）有关。ASAH风险的降低与呼气峰值流量（OR 0.80，95% CI 0.66-0.96）和血细胞比容百分比（OR 0.97，95% CI 0.95-1.00）有关。未来的研究应验证这些发现，并探索 ML 模型所显示的潜在非线性关系和相互作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

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