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Clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with SARS-CoV-2 infection

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...

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Autores principales: Franco-Moreno, Anabel, Brown-Lavalle, David, Rodríguez-Ramírez, Nicolás, Muñoz-Roldán, Candela, Rubio-Aguilera, Ana Ignes, Campos-Arenas, Maria, Muñoz-Rivas, Nuria, Moya-Mateo, Eva, Ruiz-Giardín, José Manuel, Pardo-Guimerá, Virginia, Ulla-Anes, Mariano, Pedrero-Tomé, Roberto, Torres-Macho, Juan, Bustamante-Fermosel, Ana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Whioce Publishing Pte. Ltd. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10075091/
https://www.ncbi.nlm.nih.gov/pubmed/37034002
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author Franco-Moreno, Anabel
Brown-Lavalle, David
Rodríguez-Ramírez, Nicolás
Muñoz-Roldán, Candela
Rubio-Aguilera, Ana Ignes
Campos-Arenas, Maria
Muñoz-Rivas, Nuria
Moya-Mateo, Eva
Ruiz-Giardín, José Manuel
Pardo-Guimerá, Virginia
Ulla-Anes, Mariano
Pedrero-Tomé, Roberto
Torres-Macho, Juan
Bustamante-Fermosel, Ana
author_facet Franco-Moreno, Anabel
Brown-Lavalle, David
Rodríguez-Ramírez, Nicolás
Muñoz-Roldán, Candela
Rubio-Aguilera, Ana Ignes
Campos-Arenas, Maria
Muñoz-Rivas, Nuria
Moya-Mateo, Eva
Ruiz-Giardín, José Manuel
Pardo-Guimerá, Virginia
Ulla-Anes, Mariano
Pedrero-Tomé, Roberto
Torres-Macho, Juan
Bustamante-Fermosel, Ana
author_sort Franco-Moreno, Anabel
collection PubMed
description 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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spelling pubmed-100750912023-04-06 Clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with SARS-CoV-2 infection Franco-Moreno, Anabel Brown-Lavalle, David Rodríguez-Ramírez, Nicolás Muñoz-Roldán, Candela Rubio-Aguilera, Ana Ignes Campos-Arenas, Maria Muñoz-Rivas, Nuria Moya-Mateo, Eva Ruiz-Giardín, José Manuel Pardo-Guimerá, Virginia Ulla-Anes, Mariano Pedrero-Tomé, Roberto Torres-Macho, Juan Bustamante-Fermosel, Ana J Clin Transl Res Original Article 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. Whioce Publishing Pte. Ltd. 2023-02-06 /pmc/articles/PMC10075091/ /pubmed/37034002 Text en Copyright: © 2023 Author(s). https://creativecommons.org/licenses/by-nc/4.0/This is an Open-Access article distributed under the terms of the Creative Commons Attribution-Noncommercial License, permitting all noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Franco-Moreno, Anabel
Brown-Lavalle, David
Rodríguez-Ramírez, Nicolás
Muñoz-Roldán, Candela
Rubio-Aguilera, Ana Ignes
Campos-Arenas, Maria
Muñoz-Rivas, Nuria
Moya-Mateo, Eva
Ruiz-Giardín, José Manuel
Pardo-Guimerá, Virginia
Ulla-Anes, Mariano
Pedrero-Tomé, Roberto
Torres-Macho, Juan
Bustamante-Fermosel, Ana
Clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with SARS-CoV-2 infection
title Clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with SARS-CoV-2 infection
title_full Clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with SARS-CoV-2 infection
title_fullStr Clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with SARS-CoV-2 infection
title_full_unstemmed Clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with SARS-CoV-2 infection
title_short Clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with SARS-CoV-2 infection
title_sort clinical prediction model for pulmonary thrombosis diagnosis in hospitalized patients with sars-cov-2 infection
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10075091/
https://www.ncbi.nlm.nih.gov/pubmed/37034002
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