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

Abstract

Abstract Background: We aimed to develop a clinical prediction model for pulmonary embolism (PE) diagnosis in hospitalized COVID-19 patients. Methods: Hospitalized non-intensive care unit COVID-19 patients who underwent a computed tomography pulmonary angiogram for suspected PE were included. Demographic, clinical, laboratory and radiological variables were selected as potential factors associated with the presence of PE. Multivariable Cox regression analysis to develop a score for estimating the pretest probability of PE was used. The score was internally validated by bootstrap analysis.Results: Among the 271 patients who underwent a computed tomography pulmonary angiogram, 132 patients (48.70%) had PE. 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 pulmonary thrombosis were independent predictors of PE. 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 PE in an individual patient. The score showed a high predictive ability (AUC 0.877; 95% CI: 0.83−0.92). A score lower than 182 points on the nomogram confers low probability of PE with a negative predictive value of 92%. Conclusions: CHEDDAR score can be used to estimate the pretest probability of PE in hospitalized COVID-19 patients outside intensive care unit.