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) - 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.; et al.
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