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The choice between a simplified or an elaborate mortality risk prediction tool for patients with acute pulmonary embolism
IJCA-24162; No of Page 1 International Journal of Cardiology xxx (2016) xxx
Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Correspondence
The choice between a simplified or an elaborate mortality risk prediction tool for patients with acute pulmonary embolism Mihaela Opriș, Dan Nistor ⁎, Voichița Sîrbu University of Medicine and Pharmacy Targu Mures, Department of Internal Medicine V, Romania
a r t i c l e
i n f o
Article history: Received 16 November 2016 Accepted 23 November 2016 Available online xxxx Keywords: Acute pulmonary embolism Mortality risk
We read with great interest the article “The prognostic impact of chest pain in 1306 patients presenting with confirmed acute pulmonary embolism” by Wong et al. [1] in which they identify chest pain as a predictor of survival in patients with acute pulmonary embolism. Mortality risk scores are an important tool in the management of patients with acute pulmonary embolism and much of the focus of research has been split into finding either positive or negative predictive risk factors for these patients. The most frequently used risk calculator is the Pulmonary Embolism Severity Index (PESI) [2] which uses 11 clinical characteristics to divide patients into five risk categories, and which has been more recently simplified to a version that uses six clinical characteristics to divide patients into two risk categories, the sPESI [3]. The main use of the sPESI is to identify low-risk patients, which can benefit from an outpatient management strategy, and interestingly, the authors developed a multivariable model that adds chest pain and abnormal haemoglobin or sodium levels to the sPESI, with a better cumulative prognostic value. The presence of chest pain is a novel risk predictor, but abnormal haemoglobin and sodium levels have been also identified
as risk predictors in the original PESI study [2] which developed and alternate 17 variable risk prediction model that included laboratory values, but was considered to add more complexity than benefit over the 11 variable one. Since then, other studies have identified novel prognostic risk factors such as laboratory biomarkers (BNP, troponins or H-FABP) [4] or echocardiographic signs of right ventricular dysfunction [5]. Perhaps a more complex risk assessment calculator, that included all these variables, could provide an improved prognostic tool and a better management support for these patients. Conflict of interest The authors report no relationships that could be construed as a conflict of interest References [1] C.C.Y. Wong, A.C.C. Ng, J.K. Lau, V. Chow, A.P. Sindone, L. Kritharides, The prognostic impact of chest pain in 1306 patients presenting with confirmed acute pulmonary embolism, Int. J. Cardiol. 221 (2016) 794–799. [2] D. Aujesky, D. Obrosky, R. Stone, et al., Derivation and validation of a prognostic model for pulmonary embolism, Am. J. Respir. Crit. Care Med. 172 (2005) 1041–1046. [3] D. Jimenez, D. Aujesky, L. Moores, et al., Simplification of the pulmonary embolism severity index for prognostication in patients with acute symptomatic pulmonary embolism, Arch. Intern. Med. 170 (2010) 1383–1389. [4] D. Jimenez, D. Kopecna, V. Tapson, et al., Derivation and validation of multimarker prognostication for normotensive patients with acute symptomatic pulmonary embolism, Am. J. Crit. Care Med. 189 (2014) 718–726. [5] D. Khemasuwan, T. Yingchoncharoen, P. Tunsupon, et al., Right ventricular echocardiographic parameters are associated with mortality after acute pulmonary embolism, J. Am. Soc. Echocardiogr. 28 (2015) 355–362.
⁎ Corresponding author at: Gh. Marinescu street, no. 50, room 11079, Targu Mures,540139, Mures County, Romania. E-mail addresses: [email protected] (M. Opriș), [email protected] (D. Nistor), [email protected] (V.ț Sîrbu).
http://dx.doi.org/10.1016/j.ijcard.2016.11.300 0167-5273/© 2016 Elsevier Ireland Ltd. All rights reserved.
Please cite this article as: M. Opriș, et al., , Int J Cardiol (2016), http://dx.doi.org/10.1016/j.ijcard.2016.11.300
Contents lists available at ScienceDirect
International Journal of Cardiology journal homepage: www.elsevier.com/locate/ijcard
Correspondence
The choice between a simplified or an elaborate mortality risk prediction tool for patients with acute pulmonary embolism Mihaela Opriș, Dan Nistor ⁎, Voichița Sîrbu University of Medicine and Pharmacy Targu Mures, Department of Internal Medicine V, Romania
a r t i c l e
i n f o
Article history: Received 16 November 2016 Accepted 23 November 2016 Available online xxxx Keywords: Acute pulmonary embolism Mortality risk
We read with great interest the article “The prognostic impact of chest pain in 1306 patients presenting with confirmed acute pulmonary embolism” by Wong et al. [1] in which they identify chest pain as a predictor of survival in patients with acute pulmonary embolism. Mortality risk scores are an important tool in the management of patients with acute pulmonary embolism and much of the focus of research has been split into finding either positive or negative predictive risk factors for these patients. The most frequently used risk calculator is the Pulmonary Embolism Severity Index (PESI) [2] which uses 11 clinical characteristics to divide patients into five risk categories, and which has been more recently simplified to a version that uses six clinical characteristics to divide patients into two risk categories, the sPESI [3]. The main use of the sPESI is to identify low-risk patients, which can benefit from an outpatient management strategy, and interestingly, the authors developed a multivariable model that adds chest pain and abnormal haemoglobin or sodium levels to the sPESI, with a better cumulative prognostic value. The presence of chest pain is a novel risk predictor, but abnormal haemoglobin and sodium levels have been also identified
as risk predictors in the original PESI study [2] which developed and alternate 17 variable risk prediction model that included laboratory values, but was considered to add more complexity than benefit over the 11 variable one. Since then, other studies have identified novel prognostic risk factors such as laboratory biomarkers (BNP, troponins or H-FABP) [4] or echocardiographic signs of right ventricular dysfunction [5]. Perhaps a more complex risk assessment calculator, that included all these variables, could provide an improved prognostic tool and a better management support for these patients. Conflict of interest The authors report no relationships that could be construed as a conflict of interest References [1] C.C.Y. Wong, A.C.C. Ng, J.K. Lau, V. Chow, A.P. Sindone, L. Kritharides, The prognostic impact of chest pain in 1306 patients presenting with confirmed acute pulmonary embolism, Int. J. Cardiol. 221 (2016) 794–799. [2] D. Aujesky, D. Obrosky, R. Stone, et al., Derivation and validation of a prognostic model for pulmonary embolism, Am. J. Respir. Crit. Care Med. 172 (2005) 1041–1046. [3] D. Jimenez, D. Aujesky, L. Moores, et al., Simplification of the pulmonary embolism severity index for prognostication in patients with acute symptomatic pulmonary embolism, Arch. Intern. Med. 170 (2010) 1383–1389. [4] D. Jimenez, D. Kopecna, V. Tapson, et al., Derivation and validation of multimarker prognostication for normotensive patients with acute symptomatic pulmonary embolism, Am. J. Crit. Care Med. 189 (2014) 718–726. [5] D. Khemasuwan, T. Yingchoncharoen, P. Tunsupon, et al., Right ventricular echocardiographic parameters are associated with mortality after acute pulmonary embolism, J. Am. Soc. Echocardiogr. 28 (2015) 355–362.
⁎ Corresponding author at: Gh. Marinescu street, no. 50, room 11079, Targu Mures,540139, Mures County, Romania. E-mail addresses: [email protected] (M. Opriș), [email protected] (D. Nistor), [email protected] (V.ț Sîrbu).
http://dx.doi.org/10.1016/j.ijcard.2016.11.300 0167-5273/© 2016 Elsevier Ireland Ltd. All rights reserved.
Please cite this article as: M. Opriș, et al., , Int J Cardiol (2016), http://dx.doi.org/10.1016/j.ijcard.2016.11.300