Franco-Moreno A I, Bustamante-Fermosel A, Ruiz-Giardin J M, Muñoz-Rivas N, Torres-Macho J, Brown-Lavalle D
Servicio de Medicina Interna, Hospital Universitario Infanta Leonor - Hospital Virgen de la Torre, Madrid, España.
Servicio de Medicina Interna, Hospital Universitario de Fuenlabrada, Fuenlabrada, Madrid, España.
Rev Clin Esp. 2023 Jan;223(1):40-49. doi: 10.1016/j.rce.2022.07.004. Epub 2022 Aug 5.
Clinical prediction models determine the pre-test probability of pulmonary embolism (PE) and assess the need for tests for these patients. Coronavirus infection is associated with a greater risk of PE, increasing its severity and conferring a worse prognosis. The pathogenesis of PE appears to be different in patients with and without SARS-CoV-2 infection. This systematic review aims to discover the utility of probability models developed for PE in patients with COVID-19 by reviewing the available literature.
A literature search on the PubMed, Scopus, and EMBASE databases was carried out. All studies that reported data on the use of clinical prediction models for PE in patients with COVID-19 were included. Study quality was assessed using the Newcastle-Ottawa scale for non-randomized studies.
Thirteen studies that evaluated five prediction models (Wells score, Geneva score, YEARS algorithm, and PERC and PEGeD clinical decision rules) were included. The different scales were used in 1,187 patients with COVID-19. Overall, the models showed limited predictive ability. The two-level Wells score with low (or unlikely) clinical probability in combination with a D-dimer level < 3000 ng/mL or a normal bedside lung ultrasound showed an adequate correlation for ruling out PE.
Our systematic review suggests that the clinical prediction models available for PE that were developed in the general population are not applicable to patients with COVID-19. Therefore, their use is in clinical practice as the only diagnostic screening tool is not recommended. New clinical probability models for PE that are validated in these patients are needed.
临床预测模型可确定肺栓塞（PE）的检测前概率，并评估这些患者的检测需求。冠状病毒感染与PE风险增加、病情加重及预后较差相关。PE的发病机制在感染和未感染SARS-CoV-2的患者中似乎有所不同。本系统评价旨在通过回顾现有文献，探讨为COVID-19患者开发的PE概率模型的效用。
在PubMed、Scopus和EMBASE数据库中进行文献检索。纳入所有报告了COVID-19患者使用PE临床预测模型数据的研究。采用纽卡斯尔-渥太华量表对非随机研究的质量进行评估。
纳入了13项评估5种预测模型（Wells评分、Geneva评分、YEARS算法以及PERC和PEGeD临床决策规则）的研究。1187例COVID-19患者使用了不同的量表。总体而言，这些模型的预测能力有限。两级Wells评分结合低（或不太可能）临床概率以及D-二聚体水平<3000 ng/mL或床边肺部超声正常，在排除PE方面具有充分的相关性。
我们的系统评价表明，在普通人群中开发的现有的PE临床预测模型不适用于COVID-19患者。因此，不建议在临床实践中将其作为唯一的诊断筛查工具使用。需要在这些患者中验证新的PE临床概率模型。
