ABSTRACT
BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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 <3000â¯ng/mL or a normal bedside lung ultrasound showed an adequate correlation for ruling out PE. CONCLUSIONS: 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.