Benjamin Planquette1,2,3,4, Lina Khider2,5,6, Alice Le Berre7, Simon Soudet8, Gilles Pernod9, Raphaël Le Mao10, Matthieu Besutti11, Nicolas Gendron1,2,4,12, Alexandra Yanoutsos13,14, David M Smadja15, Guillaume Goudot2,5,6, Salma Al Kahf1,2,3,4, Nassim Mohamedi2,5,6, Antoine Al Hamoud1,2,3,4, Aurélien Philippe1,2,4,12, Laure Fournier15, Bastien Rance16, Jean-Luc Diehl1,2,17, Tristan Mirault5,18, Emmanuel Messas5,7,19, Joseph Emmerich13,14, Richard Chocron18, Francis Couturaud10, Gilbert Ferretti20, Marie Antoinette Sevestre8, Nicolas Meneveau11, Gilles Chatellier21, Olivier Sanchez1,2,3,4. 1. Innovative Therapies in Haemostasis, Université de Paris, INSERM, Paris, France. 2. Biosurgical Research Lab (Carpentier Foundation), Université de Paris, Paris, France. 3. Department of Respiratory Medicine, Assistance Publique Hôpitaux de Paris, Centre-Université de Paris (APHP-CUP), Paris, France. 4. F-CRIN INNOVTE, Saint-Étienne, France. 5. Department of Vascular Medicine, Assistance Publique Hôpitaux de Paris, Centre-Université de Paris (APHP-CUP), Paris, France. 6. Physics for Medicine Paris, INSERM U1273, ESPCI Paris, Paris, France. 7. Department of Radiology, Groupe Hospitalier Paris Saint-Joseph, Paris, France. 8. EA7516 CHIMERE and Service de Médecine Vasculaire, Université Picardie Jules Verne, CHU Amiens-Picardie, Amiens, France. 9. Service Universitaire de Médecine Vasculaire, CHU de Grenoble-Alpes, Université Grenoble-Alpes, CNRS/TIMC-IMAG UMR 5525/Thèmas 38043 Grenoble, F-CRIN INNOVTE, Saint-Étienne, France. 10. Département de Médecine Interne et Pneumologie, Centre Hospitalo-Universitaire de Brest, Université de Bretagne Occidentale, EA 3878, CIC INSERM 1412, Brest, F-CRIN INNOVTE, Saint-Étienne, France. 11. Department of Cardiology, University Hospital, Besançon, EA3920, University of Burgundy Franche Comté, Besançon, France. 12. Department of Haematology, Assistance Publique Hôpitaux de Paris.Centre-Université de Paris (APHP-CUP), Paris, France. 13. Department of vascular medicine, Groupe hospitalier Paris Saint Joseph, Paris, France. 14. Department of Vascular Medicine, INSERM CRESS UMR 1153, Paris, France. 15. Department of Radiology, Université de Paris, Assistance Publique Hôpitaux de Paris, Centre-Université de Paris (APHP-CUP), Paris, France. 16. Department of Medical Informatics, Université de Paris, Assistance Publique Hôpitaux de Paris, Centre-Université de Paris (APHP-CUP), Paris, France. 17. Intensive Care Unit, Assistance Publique Hôpitaux de Paris, Centre-Université de Paris (APHP-CUP), Paris, France. 18. Department of Emergency, Université de Paris, PARCC, INSERM, Assistance Publique Hôpitaux de Paris, Centre-Université de Paris (APHP-CUP), Paris, France. 19. Department of Vascular Medicine, Paris Research Cardiovascular Center, PARCC, INSERM UMR-S 970, Paris, France. 20. Department of Radiology, CHU, Université Grenoble-Alpes, Saint-Étienne, France. 21. Department of Statistics, Bioinformatics and Public Health, INSERM CIC 14-18, Paris, France.
Abstract
OBJECTIVE: D-dimer measurement is a safe tool to exclude pulmonary embolism (PE), but its specificity decreases in coronavirus disease 2019 (COVID-19) patients. Our aim was to derive a new algorithm with a specific D-dimer threshold for COVID-19 patients. METHODS: We conducted a French multicenter, retrospective cohort study among 774 COVID-19 patients with suspected PE. D-dimer threshold adjusted to extent of lung damage found on computed tomography (CT) was derived in a patient set (n = 337), and its safety assessed in an independent validation set (n = 337). RESULTS: According to receiver operating characteristic curves, in the derivation set, D-dimer safely excluded PE, with one false negative, when using a 900 ng/mL threshold when lung damage extent was <50% and 1,700 ng/mL when lung damage extent was ≥50%. In the derivation set, the algorithm sensitivity was 98.2% (95% confidence interval [CI]: 94.7-100.0) and its specificity 28.4% (95% CI: 24.1-32.3). The negative likelihood ratio (NLR) was 0.06 (95% CI: 0.01-0.44) and the area under the curve (AUC) was 0.63 (95% CI: 0.60-0.67). In the validation set, sensitivity and specificity were 96.7% (95% CI: 88.7-99.6) and 39.2% (95% CI: 32.2-46.1), respectively. The NLR was 0.08 (95% CI; 0.02-0.33), and the AUC did not differ from that of the derivation set (0.68, 95% CI: 0.64-0.72, p = 0.097). Using the Co-LEAD algorithm, 76 among 250 (30.4%) COVID-19 patients with suspected PE could have been managed without CT pulmonary angiography (CTPA) and 88 patients would have required two CTs. CONCLUSION: The Co-LEAD algorithm could safely exclude PE, and could reduce the use of CTPA in COVID-19 patients. Further prospective studies need to validate this strategy. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
OBJECTIVE: D-dimer measurement is a safe tool to exclude pulmonary embolism (PE), but its specificity decreases in coronavirus disease 2019 (COVID-19) patients. Our aim was to derive a new algorithm with a specific D-dimer threshold for COVID-19 patients. METHODS: We conducted a French multicenter, retrospective cohort study among 774 COVID-19 patients with suspected PE. D-dimer threshold adjusted to extent of lung damage found on computed tomography (CT) was derived in a patient set (n = 337), and its safety assessed in an independent validation set (n = 337). RESULTS: According to receiver operating characteristic curves, in the derivation set, D-dimer safely excluded PE, with one false negative, when using a 900 ng/mL threshold when lung damage extent was <50% and 1,700 ng/mL when lung damage extent was ≥50%. In the derivation set, the algorithm sensitivity was 98.2% (95% confidence interval [CI]: 94.7-100.0) and its specificity 28.4% (95% CI: 24.1-32.3). The negative likelihood ratio (NLR) was 0.06 (95% CI: 0.01-0.44) and the area under the curve (AUC) was 0.63 (95% CI: 0.60-0.67). In the validation set, sensitivity and specificity were 96.7% (95% CI: 88.7-99.6) and 39.2% (95% CI: 32.2-46.1), respectively. The NLR was 0.08 (95% CI; 0.02-0.33), and the AUC did not differ from that of the derivation set (0.68, 95% CI: 0.64-0.72, p = 0.097). Using the Co-LEAD algorithm, 76 among 250 (30.4%) COVID-19 patients with suspected PE could have been managed without CT pulmonary angiography (CTPA) and 88 patients would have required two CTs. CONCLUSION: The Co-LEAD algorithm could safely exclude PE, and could reduce the use of CTPA in COVID-19 patients. Further prospective studies need to validate this strategy. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Authors: Michał Machowski; Anna Polańska; Magdalena Gałecka-Nowak; Aleksandra Mamzer; Marta Skowrońska; Katarzyna Perzanowska-Brzeszkiewicz; Barbara Zając; Aisha Ou-Pokrzewińska; Piotr Pruszczyk; Jarosław D Kasprzak Journal: J Clin Med Date: 2022-06-09 Impact factor: 4.964
Authors: Claire Auditeau; Lina Khider; Benjamin Planquette; Olivier Sanchez; David M Smadja; Nicolas Gendron Journal: Res Pract Thromb Haemost Date: 2022-05-25