Abujaber Ahmad A, Albalkhi Ibrahem, Imam Yahia, Nashwan Abdulqadir, Akhtar Naveed, Alkhawaldeh Ibraheem M
Nursing Department, Hamad Medical Corporation, Doha, Qatar.
College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Heliyon. 2024 Apr 3;10(7):e28869. doi: 10.1016/j.heliyon.2024.e28869. eCollection 2024 Apr 15.
Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke.
Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors.
The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes.
This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments.
预测卒中死亡率对于个性化医疗至关重要。本研究旨在设计并评估一种机器学习模型，以预测卒中后的一年死亡率。
使用了来自国家多民族卒中登记处的数据。使用各种指标对八个机器学习（ML）模型进行了训练和评估。采用SHapley加性解释（SHAP）分析来识别有影响力的预测因素。
最终分析纳入了9840例诊断为卒中的患者。XGBoost算法表现出最佳性能，准确率高（94.5%），AUC为（87.3%）。核心预测因素包括入院时的美国国立卫生研究院卒中量表（NIHSS）、年龄、住院时间、到达方式、心率和血压。NIHSS升高、年龄增加和住院时间延长与更高的死亡率相关。救护车送达以及较低的舒张压和较低的体重指数预示着预后较差。
该模型的预测能力强调了NIHSS、年龄、住院时间、到达方式、心率、血压和BMI在卒中死亡率预测中的重要性。具体发现为提高数据质量、扩大登记范围和进行真实世界验证提供了途径。该研究强调了机器学习在早期死亡率预测、改善风险评估和个性化医疗方面的潜力。通过强大的ML预测工具对卒中结局进行护理交付的潜在变革可能会彻底改变患者护理，为卒中患者制定个性化计划并改进预防策略。然而，必须进行前瞻性验证，以评估其实际临床效果，并确保其在各种医疗环境中成功应用。
