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

Anabel Franco-Moreno^{1 *}, David Brown-Lavalle^{1 *}, Nicolás Rodríguez-Ramírez², Candela Muñoz-Roldán², Ana Ignes Rubio-Aguilera², Maria Campos-Arenas², Nuria Muñoz-Rivas¹, Eva Moya-Mateo¹, José Manuel Ruiz-Giardín³, Virginia Pardo-Guimerá¹, Mariano Ulla-Anes¹, Roberto Pedrero-Tomé^4,5, Juan Torres-Macho^{1 *}, Ana Bustamante-Fermosel¹

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¹ Department of Internal Medicine, Hospital Universitario Infanta Leonor–Virgen de la Torre, Madrid, Spain

² Department of Radiology, Hospital Universitario Infanta Leonor–Virgen de la Torre, Madrid, Spain

³ Department of Internal Medicine, Hospital Universitario de Fuenlabrada, Madrid, Spain

⁴ EPINUT-UCM (Ref. 920325) Investigation Group, Universidad Complutense de Madrid, Madrid, Spain

⁵ Fundación para la Investigación e Innovación Biomédica de los Hospitales Universitarios Infanta Leonor y del Sureste, Madrid, Spain

JCTR 2023, 9(2), 59–68; https://doi.org/10.18053/jctres.09.202302.002

Submitted: 19 November 2022 | Revised: 14 December 2022 | Accepted: 12 January 2023 | Published: 6 February 2023

© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )

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Abstract

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. 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 pretest 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 >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 (CHEDDAR score [C-reactive protein, HEart rate, D-Dimer, RALE score, and Respiratory rate]) for estimating the pretest probability of PT. The score showed a high predictive accuracy (AUC 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 pretest 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.

Keywords

COVID-19

Clinical prediction model

Computed tomography pulmonary angiogram

Diagnosis

Pretest probability

Pulmonary thrombosis

Thromboinflammation

Conflict of interest

The authors declared no conflicts of interest.

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