Extended D-dimer cut-offs and machine learning for ruling out pulmonary embolism in individuals undergoing computed tomography pulmonary angiography. Issue 5 (26th May 2022)

• Record Type: Journal Article
• Authors: Franciosi, Alessandro N.; McCarthy, Nicholas; Gaffney, Brian; Duignan, John; Sweeney, Eamon; O'Connell, Niall; Murphy, Karen; Ní Áinle, Fionnuala; Butler, Marcus W.; Dodd, Jonathan D.; Keane, Michael P.; Murphy, David J.; Curran, Kathleen M.; McCarthy, Cormac
• Is Part Of: European respiratory journal. Volume 59:Issue 5(2022)
• Publication Date: 2022-05-26
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiration -- Periodicals; 616.2
• DOI: 10.1183/13993003.00075-2022
• Languages: English
• ISSNs: 0903-1936

Abstract

Pulmonary embolism (PE) is a major cause of morbidity and mortality [1]. Computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosing PE [2] and a common investigation which contributes to potentially avoidable radiation exposure. CTPA use has quadrupled in the past two decades [3], and this has been associated with lower rates of PE detection [4] and possible overdiagnosis [5]. Combining novel machine learning algorithms with extended D-dimer cut-offs may improve pulmonary embolism prediction and reduce patient radiation exposure resulting from avoidable scans https://bit.ly/3oUKd7X