Kuo Chao-Yang, Su Emily Chia-Yu, Yeh Hsu-Ling, Yeh Jiann-Horng, Chiu Hou-Chang, Chung Chen-Chih
Graduate Institute of Artificial Intelligence and Big Data in Healthcare, Smart Healthcare Interdisciplinary College, National Taipei University of Nursing and Health Sciences, Taipei, 112, Taiwan.
Institute of Biomedical Informatics, College of Medicine, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan.
Heliyon. 2024 Dec 9;10(24):e41084. doi: 10.1016/j.heliyon.2024.e41084. eCollection 2024 Dec 30.
Myasthenia gravis (MG), a low-prevalence autoimmune disorder characterized by clinical heterogeneity and unpredictable disease fluctuations, presents significant risks of acute exacerbations requiring intensive care. These crises contribute substantially to patient morbidity and mortality. This study aimed to develop and validate machine-learning models for predicting intensive care unit (ICU) admission risk among patients with MG-related disease instability.
In this retrospective analysis of 314 MG patients hospitalized between 2015 and 2018, we implemented four machine learning algorithms, including logistic regression, support vector machine, extreme gradient boosting (XGBoost), and random forest, to predict ICU admission risk. The models incorporated fourteen clinical parameters as predictive features. The SHapley Additive exPlanations method was utilized to assess the importance of factors associated with ICU admission.
Through 10-fold cross-validation, the XGBoost model demonstrated superior predictive performance (area under the receiver operating characteristic curve: 0.8943, accuracy: 0.8603, sensitivity: 0.7222, and specificity: 0.9125). Among the analyzed features, MG severity, as classified by the Myasthenia Gravis Foundation of America clinical classification, was identified as the most significant factor influencing ICU admission. Additionally, disease duration, a key continuous variable, was inversely correlated with the risk of ICU admission.
MG severity is the primary determinant of ICU admission, with shorter disease duration increasing the risk, possibly due to greater susceptibility to exacerbations. The XGBoost model exhibited excellent performance and accuracy, effectively identifying critical clinical factors for predicting ICU admission risk in MG patients. This novel, personalized approach to risk stratification elucidates crucial risk factors and has the potential to enhance clinical decision-making, optimize resource allocation, and ultimately improve patient outcomes.
重症肌无力（MG）是一种低患病率的自身免疫性疾病，具有临床异质性和不可预测的疾病波动，存在急性加重需重症监护的重大风险。这些危象对患者的发病率和死亡率有很大影响。本研究旨在开发并验证用于预测MG相关疾病不稳定患者入住重症监护病房（ICU）风险的机器学习模型。
在这项对2015年至2018年期间住院的314例MG患者的回顾性分析中，我们实施了四种机器学习算法，包括逻辑回归、支持向量机、极端梯度提升（XGBoost）和随机森林，以预测ICU入住风险。模型纳入了14个临床参数作为预测特征。采用SHapley加法解释方法评估与ICU入住相关因素的重要性。
通过10倍交叉验证，XGBoost模型表现出卓越的预测性能（受试者操作特征曲线下面积：0.8943，准确率：0.8603，灵敏度：0.7222，特异性：0.9125）。在分析的特征中，美国重症肌无力基金会临床分类所划分的MG严重程度被确定为影响ICU入住的最主要因素。此外，疾病持续时间这一关键连续变量与ICU入住风险呈负相关。
MG严重程度是ICU入住的主要决定因素，疾病持续时间越短风险越高，这可能是由于对病情加重的易感性更高。XGBoost模型表现出优异的性能和准确性，有效识别了预测MG患者ICU入住风险的关键临床因素。这种新颖的个性化风险分层方法阐明了关键风险因素，有可能加强临床决策、优化资源分配并最终改善患者结局。
