Zhou Zhou, Chen Bo, Mei Zhao-Jun, Chen Wei, Cao Wei, Xu En-Xi, Wang Jun, Ye Lei, Cheng Hong-Wei
Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Neurosurgery, Affiliated People's Hospital of Jiangsu University, Jiangsu, China.
Front Neurol. 2025 Mar 18;16:1534961. doi: 10.3389/fneur.2025.1534961. eCollection 2025.
Stroke is a leading cause of mortality and disability globally. Among ischemic stroke patients, those with moderate to severe consciousness disorders constitute a particularly high-risk subgroup. Accurate predictive models are essential for guiding clinical decisions in this population. This study aimed to develop and validate an automated scoring system using machine learning algorithms for predicting short-term (3- and 7-day) and relatively long-term (30- and 90-day) mortality in this population.
This retrospective observational study utilized data from the MIMIC-IV database, including 648 ischemic stroke patients with Glasgow Coma Scale (GCS) scores ≤12, admitted to the ICU between 2008 and 2019. Patients with GCS scores indicating speech dysfunction but clear consciousness were excluded. A total of 47 candidate variables were evaluated, and the top six predictors for each mortality model were identified using the AutoScore framework. Model performance was assessed using the area under the curve (AUC) from receiver operating characteristic (ROC) analyses.
The median age of the cohort was 76.8 years (IQR, 64.97-86.34), with mortality rates of 8.02% at 3 days, 18.67% at 7 days, 33.49% at 30 days, and 38.89% at 90 days. The AUCs for the test cohort's 3-, 7-, 30-, and 90-day mortality prediction models were 0.698, 0.678, 0.724, and 0.730, respectively.
We developed and validated a novel machine learning-based scoring tool that effectively predicts both short-term and relatively long-term mortality in ischemic stroke patients with moderate to severe consciousness disorders. This tool has the potential to enhance clinical decision-making and resource allocation for these patients in the ICU.
中风是全球死亡和残疾的主要原因。在缺血性中风患者中，中度至重度意识障碍患者构成了一个特别高危的亚组。准确的预测模型对于指导该人群的临床决策至关重要。本研究旨在开发并验证一种使用机器学习算法的自动评分系统，以预测该人群的短期（3天和7天）和相对长期（30天和90天）死亡率。
这项回顾性观察研究利用了MIMIC-IV数据库的数据，包括2008年至2019年间入住重症监护病房（ICU）的648例格拉斯哥昏迷量表（GCS）评分≤12的缺血性中风患者。排除GCS评分表明存在言语功能障碍但意识清醒的患者。共评估了47个候选变量，并使用AutoScore框架确定了每个死亡率模型的前六个预测因素。使用来自受试者工作特征（ROC）分析的曲线下面积（AUC）评估模型性能。
该队列的中位年龄为76.8岁（四分位间距，64.97 - 86.34），3天死亡率为8.02%，7天死亡率为18.67%，30天死亡率为33.49%，90天死亡率为38.89%。测试队列的3天、7天、30天和90天死亡率预测模型的AUC分别为0.698、0.678、0.724和0.730。
我们开发并验证了一种基于机器学习的新型评分工具，该工具可有效预测中度至重度意识障碍的缺血性中风患者的短期和相对长期死亡率。该工具有可能改善ICU中这些患者的临床决策和资源分配。
