Abstract：
Extensive research has been devoted to predicting ICU mortality, to assist clinical teams managing critical patients. Electronic health records (EHR) contain both static and dynamic medical data, with the latter accumulating during ICU stays. Existing models often rely on a fixed time window (e.g., first 24 h) for prediction, potentially missing vital post-24-hour data. The present study aims to improve mortality prediction for ICU patients following Cardiac Arrest (CA) using a dynamic sliding window approach that accommodates evolving data characteristics. Our cohort included 2331 CA patients, of whom 684 died in the ICU and 1647 survived. Applying the sliding window technique, we created six different time windows and used each separately for model training and validation. We compared our results to a baseline accumulative window. The different time windows created by the sliding window technique differed in their prediction performance and outperformed the baseline 24-hour window significantly. The XGBoost model outperformed all other models, with the 30-42 h time window achieving the best results (AUC = 0.8, accuracy = 0.77). Our work shows that the sliding window technique is effective in improving mortality prediction. We demonstrated how important time-window selection is and showed that enhancing it can save time and thus improve mortality prediction. These findings promise to improve the clinical team\'s efficiency in prioritizing patients and giving greater attention to higher-risk patients. To conclude, mortality prediction in the ICU can be improved if we consider alternative time windows instead of the 24-hour window, which is currently the most widely accepted among scoring systems today.
摘要：
广泛的研究致力于预测ICU死亡率，协助临床团队管理危重患者。电子健康记录（EHR）包含静态和动态医疗数据，后者在ICU停留期间积累。现有模型通常依赖于固定的时间窗口(例如，前24小时)用于预测，可能会丢失重要的24小时后数据。本研究旨在使用适应不断发展的数据特征的动态滑动窗口方法来改善对心脏骤停（CA）后ICU患者的死亡率预测。我们的队列包括2331例CA患者，其中684人在ICU死亡，1647人幸存。应用滑动窗口技术，我们创建了六个不同的时间窗口，并分别用于模型训练和验证。我们将我们的结果与基线累积窗口进行了比较。通过滑动窗口技术创建的不同时间窗口在其预测性能上有所不同，并且显着优于基线24小时窗口。XGBoost模型优于所有其他模型，30-42h时间窗口达到最佳结果(AUC=0.8，准确度=0.77)。我们的工作表明，滑动窗口技术在改善死亡率预测方面是有效的。我们证明了时间窗口选择的重要性，并表明增强时间窗口可以节省时间，从而改善死亡率预测。这些发现有望提高临床团队在优先考虑患者和更多关注高风险患者方面的效率。最后，如果我们考虑替代时间窗口而不是24小时窗口，可以改善ICU的死亡率预测，这是目前最广泛接受的评分系统今天。
