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Background and Aim: We aimed to develop a clinical prediction model for pulmonary thrombosis (PT) diagnosis in hospitalized COVID-19 patients. Methods: Non-intensive care unit hospitalized COVID-19 patients who underwent a computed tomography pulmonary angiogram (CTPA) for suspected PT were included in the study. Demographic, clinical, analytical, and radiological variables as potential factors associated with the presence of PT were selected. Multivariable Cox regression analysis to develop a score for estimating the pre-test probability of PT was performed. The score was internally validated by bootstrap analysis. Results: Among the 271 patients who underwent a CTPA, 132 patients (48.7%) had PT. Heart rate >100 bpm (OR = 4.63 [95% CI: 2.30-9.34]; P < 0.001), respiratory rate >22 bpm (OR = 5.21 [95% CI: 2.00-13.54]; P < 0.001), RALE score ≥4 (OR = 3.24 [95% CI: 1.66-6.32]; P < 0.001), C-reactive protein (CRP) >100 mg/L (OR = 2.10 [95% CI: 0.95-4.63]; P = 0.067), and D-dimer >3.000 ng/mL (OR = 6.86 [95% CI: 3.54-13.28]; P < 0.001) at the time of suspected PT were independent predictors of thrombosis. Using these variables, we constructed a nomogram (CRP, Heart rate, D-dimer, RALE score, and respiratory rate [CHEDDAR score]) for estimating the pre-test probability of PT. The score showed a high predictive accuracy (area under the receiver-operating characteristics curve = 0.877; 95% CI: 0.83-0.92). A score lower than 182 points on the nomogram confers a low probability for PT with a negative predictive value of 92%. Conclusions: CHEDDAR score can be used to estimate the pre-test probability of PT in hospitalized COVID-19 patients outside the intensive care unit. Relevance for Patients: Developing a new clinical prediction model for PT diagnosis in COVID-19 may help in the triage of patients, and limit unnecessary exposure to radiation and the risk of nephrotoxicity due to iodinated contrast.
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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 <3000 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.
RESUMEN
Antecedentes y objetivo: Las escalas de predicción clínica para embolia de pulmón (EP) determinan la probabilidad pretest y valoran la necesidad de las pruebas para estos pacientes. La infección por coronavirus se asocia a un mayor riesgo de EP aumentando su gravedad y confiriendo un peor pronóstico. La patogénesis de la EP parece ser diferente en pacientes con y sin infección por SARS-CoV-2. Esta revisión sistemática pretende conocer, revisando la bibliografía disponible, la utilidad de los modelos predictivos desarrollados para EP en pacientes con COVID-19. Métodos: Se realizó una búsqueda bibliográfica en las bases de datos de PubMed, Scopus y EMBASE, incluyendo todos los estudios que comunican datos relacionados con la aplicación de escalas de predicción clínica para EP en pacientes con COVID-19. La calidad de los estudios se evaluó con la escala Newcastle-Ottawa para estudios no aleatorizados. Resultados: Se incluyeron 13 estudios de cohortes que evaluaron cinco modelos predictivos (escala de Wells, puntuación de Ginebra, algoritmo YEARS y las reglas de decisión clínica PERC y PEGeD). Las diversas escalas se aplicaron en 1.187 pacientes con COVID-19. En general, los modelos tuvieron una capacidad predictiva limitada. La escala de Wells de dos categorías con probabilidad clínica baja (o improbable) en combinación con un dímero D <3000 ng/mL o con una ecografía pulmonar a pie de cama normal mostraron una adecuada correlación para excluir la EP. Conclusión: Nuestra revisión sistemática sugiere que las escalas de predicción disponibles para EP desarrolladas en población general no son aplicables a los pacientes con COVID-19 por lo que, de momento, no se recomienda su uso en la práctica clínica como única herramienta de cribado diagnóstico. Se necesitan nuevas escalas de probabilidad clínica para EP validadas en estos pacientes.