PDF(Pubmed)
Abstract：
(1) Background: Acute pulmonary embolism (PE) is a significant public health concern that requires efficient risk estimation to optimize patient care and resource allocation. The purpose of this retrospective study was to show the correlation of NLR (neutrophil-to-lymphocyte ratio) and PESI (pulmonary embolism severity index)/sPESI (simplified PESI) in determining the risk of in-hospital mortality in patients with pulmonary thromboembolism. (2) Methods: A total of 160 patients admitted at the County Clinical Emergency Hospital of Sibiu from 2019 to 2022 were included and their hospital records were analyzed. (3) Results: Elevated NLR values were significantly correlated with increased in-hospital mortality. Furthermore, elevated NLR was associated with PESI and sPESI scores and their categories, as well as the individual components of these parameters, namely increasing age, hypotension, hypoxemia, and altered mental status. We leveraged the advantages of machine learning algorithms to integrate elevated NLR into PE risk stratification. Utilizing two-step cluster analysis and CART (classification and regression trees), several distinct patient subgroups emerged with varying in-hospital mortality rates based on combinations of previously validated score categories or their defining elements and elevated NLR, WBC (white blood cell) count, or the presence COVID-19 infection. (4) Conclusion: The findings suggest that integrating these parameters in risk stratification can aid in improving predictive accuracy of estimating the in-hospital mortality of PE patients.
摘要：
（1）背景：急性肺栓塞（PE）是一个重要的公共卫生问题，需要有效的风险评估以优化患者护理和资源分配。这项回顾性研究的目的是显示NLR（中性粒细胞与淋巴细胞之比）和PESI（肺栓塞严重程度指数）/sPESI（简化的PESI）在确定患者院内死亡风险中的相关性肺血栓栓塞症。(2)方法:纳入锡比乌县临床急诊医院2019-2022年收治的160例患者,对其住院记录进行分析。（3）结果：NLR值升高与住院死亡率升高显著相关。此外，NLR升高与PESI和sPESI分数及其类别相关，以及这些参数的各个组成部分，即年龄增长，低血压,低氧血症,和改变精神状态。我们利用机器学习算法的优势将提高的NLR集成到PE风险分层中。利用两步聚类分析和CART(分类和回归树)，根据先前验证的评分类别或其定义要素和NLR升高的组合，出现了几个不同的患者亚组，其院内死亡率不同。白细胞计数,或存在COVID-19感染。（4）结论：研究结果表明，在风险分层中整合这些参数可以帮助提高评估PE患者住院死亡率的预测准确性。
