cision support described in our article.1 The article by Wells et al2 cited in their letter ... decision support advice appropriately states âBased on ... Boston, Mass.
The Reply We very much appreciate Nguyen and Kuschner’s thoughtful questions regarding the methodology of the decision support described in our article.1 The article by Wells et al2 cited in their letter did, indeed, have 2 distinct stratiﬁcation schemes for calculating pretest probability—a 3-part categorization (low, intermediate, and high) and a 2-part categorization (unlikely and likely). It should be noted, however, that both schemes can be used independently and have proved equally valid and safe in 2 large metaanalyses.3,4 We chose to use the 3-part categorization in our decision support. In addition, while Wells’ initial data demonstrated that a serum D-dimer could be used only to rule out pulmonary embolism in low-probability patients, subsequent studies have demonstrated that a highsensitivity D-dimer, such as the one used at our institution, can be used to do so in both low- and intermediateprobability patients.5,6 Review of our own institutional data has conﬁrmed this (not yet published), and so our decision support advice appropriately states “Based on current evidence, as well as our experience at Brigham and Women’s Hospital, diagnosing an acute pulmonary embolism by CT pulmonary angiography in low- or intermediate-risk patients with a normal D-dimer level is extremely unlikely.”
Funding: The original study was funded in part by Grant 1UC4EB012952-01 from the National Institute of Biomedical Imaging and Bioengineering. Conﬂict of Interest: RK: Consultant to Medicalis Corporation. RK is named on US Patent 6,029,138 held by Brigham and Women’s Hospital on Clinical Decision Support-related software licensed to Medicalis Corporation in the year 2000. As a result of licensing, Brigham and Women’s Hospital and its parent organization, Partners Healthcare Inc., have equity and royalty interests in Medicalis. Others: Nothing to disclose. Authorship: All authors had access to the data and a role in writing the manuscript.
0002-9343/$ -see front matter Ó 2014 Elsevier Inc. All rights reserved.
Ali S. Raja, MD, MBA, MPHa,b Luciano M. Prevedello, MD, MPHa Ivan K. Ip, MD, MPHa,c Aaron Sodickson, MD, PhDa Ramin Khorasani, MD, MPHa a
Center for Evidence-Based Imaging Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston, Mass b Department of Emergency Medicine Brigham and Women’s Hospital Harvard Medical School Boston, Mass c Department of Medicine Brigham and Women’s Hospital Harvard Medical School Boston, Mass
References 1. Prevedello LM, Raja AS, Ip IK, et al. Does clinical decision support reduce unwarranted variation in yield of CT pulmonary angiogram? Am J Med. 2013;126(11):975-981. 2. Wells PS, Anderson DR, Rodger M, et al. Derivation of a simple clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thromb Haemost. 2000;83(3):416-420. 3. Lucassen W, Geersing G-J, Erkens PMG, et al. Clinical decision rules for excluding pulmonary embolism: a meta-analysis. Ann Intern Med. 2011;155(7):448-460. 4. Ceriani E, Combescure C, Le Gal G, et al. Clinical prediction rules for pulmonary embolism: a systematic review and meta-analysis. J Thromb Haemost. 2010;8(5):957-970. 5. Warren DJ, Matthews S. Pulmonary embolism: investigation of the clinically assessed intermediate risk subgroup. Br J Radiol. 2012; 85(1009):37-43. 6. Gupta RT, Kakarla RK, Kirshenbaum KJ, Tapson VF. D-dimers and efﬁcacy of clinical risk estimation algorithms: sensitivity in evaluation of acute pulmonary embolism. AJR Am J Roentgenol. 2009;193(2): 425-430.