Jawad Baker Nawfal, Shaker Shakir Maytham, Altintas Izzet, Eugen-Olsen Jesper, Nehlin Jan O, Andersen Ove, Kallemose Thomas
Department of Clinical Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark.
Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
Sci Rep. 2024 Mar 11;14(1):5942. doi: 10.1038/s41598-024-56638-6.
Several scores predicting mortality at the emergency department have been developed. However, all with shortcomings either simple and applicable in a clinical setting, with poor performance, or advanced, with high performance, but clinically difficult to implement. This study aimed to explore if machine learning algorithms could predict all-cause short- and long-term mortality based on the routine blood test collected at admission.
We analyzed data from a retrospective cohort study, including patients > 18 years admitted to the Emergency Department (ED) of Copenhagen University Hospital Hvidovre, Denmark between November 2013 and March 2017. The primary outcomes were 3-, 10-, 30-, and 365-day mortality after admission. PyCaret, an automated machine learning library, was used to evaluate the predictive performance of fifteen machine learning algorithms using the area under the receiver operating characteristic curve (AUC).
Data from 48,841 admissions were analyzed, of these 34,190 (70%) were randomly divided into training data, and 14,651 (30%) were in test data. Eight machine learning algorithms achieved very good to excellent results of AUC on test data in a of range 0.85-0.93. In prediction of short-term mortality, lactate dehydrogenase (LDH), leukocyte counts and differentials, Blood urea nitrogen (BUN) and mean corpuscular hemoglobin concentration (MCHC) were the best predictors, whereas prediction of long-term mortality was favored by age, LDH, soluble urokinase plasminogen activator receptor (suPAR), albumin, and blood urea nitrogen (BUN).
The findings suggest that measures of biomarkers taken from one blood sample during admission to the ED can identify patients at high risk of short-and long-term mortality following emergency admissions.
已经开发了几种预测急诊科死亡率的评分系统。然而，它们都存在缺点，要么简单且适用于临床环境，但性能较差，要么先进且性能良好，但在临床上难以实施。本研究旨在探讨机器学习算法是否能够根据入院时采集的常规血液检测结果预测全因短期和长期死亡率。
我们分析了一项回顾性队列研究的数据，包括2013年11月至2017年3月期间入住丹麦哥本哈根大学医院霍夫勒急诊科的18岁以上患者。主要结局是入院后3天、10天、30天和365天的死亡率。使用自动化机器学习库PyCaret，通过受试者操作特征曲线下面积（AUC）评估15种机器学习算法的预测性能。
分析了48841例入院数据，其中34190例（70%）被随机分为训练数据，14651例（30%）为测试数据。八种机器学习算法在测试数据上的AUC结果在0.85 - 0.93范围内，达到了非常好到优秀的水平。在预测短期死亡率方面，乳酸脱氢酶（LDH）、白细胞计数及分类、血尿素氮（BUN）和平均红细胞血红蛋白浓度（MCHC）是最佳预测指标，而在预测长期死亡率方面，年龄、LDH、可溶性尿激酶型纤溶酶原激活剂受体（suPAR）、白蛋白和血尿素氮（BUN）更具优势。
研究结果表明，在急诊科入院时从一份血液样本中获取的生物标志物测量值可以识别急诊入院后短期和长期死亡风险高的患者。
