Comparing three clinical prediction rules for primarily predicting the 30-day mortality of patients with pulmonary embolism: The “Simplified Revised Geneva Score,” the “Original PESI,” and the “Simplified PESI”

Tamizifar, Babak; Fereyduni, Farid; Esfahani, Morteza Abdar; Kheyri, Saeed

Comparing three clinical prediction rules for primarily predicting the 30-day mortality of patients with pulmonary embolism: The “Simplified Revised Geneva Score,” the “Original PESI,” and the “Simplified PESI”

Authors

Department of Internal medicine, Al-Zahra Hospital, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: Patients with suspected pulmonary embolism (PE) should be evaluated for the clinical probability of PE using an applicable risk score. The Geneva prognostic score, the PE Severity Index (PESI), and its simplified version (sPESI) are well-known clinical prognostic scores for PE. The purpose of this study was to analyze these clinical scores as prognostic tools.
Materials and Methods: A historical cohort study was conducted on patients with acute PE in Al-Zahra Teaching Hospital, Isfahan, Iran, from June 2013 to August 2014. To compare survival in the 1-month follow-up and factor-analyze mortality from the survival graph, Kaplan–Meier, and log-rank logistic regression were applied.
Results: Two hundred and twenty four patients were assigned to two “low risk” and “high risk” groups using the three versions of “Simplified PESI, Original PESI, and Simplified Geneva.” They were followed for a period of 1 month after admission. The overall mortality rate within 1 month from diagnosis was about 24% (95% confidence interval, 21.4–27.2). The mortality rate of low risk PE patients was about 4% in the PESI, 17% in the Geneva, and <1% in the simplified PESI scales (P < 0.005). The mortality rate among high risk patients was 33%, 33.5%, and 27.5%, respectively.
Conclusions: Among patients with acute PE, the simplified PESI model was able to accurately predict mortality rate for low risk patients. Among high risk patients, however, the difference between the three models in predicting prognosis was not significant.

Keywords

References

1.	Stein PD, Sostman HD, Hull RD, Goodman LR, Leeper KV Jr, Gottschalk A, et al. Diagnosis of pulmonary embolism in the coronary care unit. Am J Cardiol 2009;103:881-6.
2.	Schaefer-Prokop C, Prokop M. MDCT for the diagnosis of acute pulmonary embolism. Eur Radiol 2005;15 Suppl 4:D37-41.
3.	Torbicki A, Perrier A, Konstantinides S, Agnelli G, Galiè N, Pruszczyk P, et al. Guidelines on the diagnosis and management of acute pulmonary embolism: The Task Force for the Diagnosis and Management of Acute Pulmonary Embolism of the European Society of Cardiology (ESC). Eur Heart J 2008;29:2276-315.
4.	Wells PS, Anderson DR, Rodger M, Ginsberg JS, Kearon C, Gent 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;83:416-20.
5.	Wells PS. Integrated strategies for the diagnosis of venous thromboembolism. J Thromb Haemost 2007;5 Suppl 1:41-50.
6.	Le Gal G, Righini M, Roy PM, Sanchez O, Aujesky D, Bounameaux H, et al. Prediction of pulmonary embolism in the emergency department: The revised Geneva score. Ann Intern Med 2006;144:165-71.
7.	Klok FA, Mos IC, Nijkeuter M, Righini M, Perrier A, Le Gal G, et al. Simplification of the revised Geneva score for assessing clinical probability of pulmonary embolism. Arch Intern Med 2008;168:2131-6.
8.	Aujesky D, Obrosky DS, Stone RA, Auble TE, Perrier A, Cornuz J, et al. Derivation and validation of a prognostic model for pulmonary embolism. Am J Respir Crit Care Med 2005;172:1041-6.
9.	Aujesky D, Roy PM, Le Manach CP, Verschuren F, Meyer G, Obrosky DS, et al. Validation of a model to predict adverse outcomes in patients with pulmonary embolism. Eur Heart J 2006;27:476-81.
10.	Donzé J, Le Gal G, Fine MJ, Roy PM, Sanchez O, Verschuren F, et al. Prospective validation of the Pulmonary Embolism Severity Index. A clinical prognostic model for pulmonary embolism. Thromb Haemost 2008;100:943-8.
11.	Jiménez D, Aujesky D, Moores L, Gómez V, Lobo JL, Uresandi F, et al. Simplification of the pulmonary embolism severity index for prognostication in patients with acute symptomatic pulmonary embolism. Arch Intern Med 2010;170:1383-9.
12.	Barra SN, Paiva L, Providência R, Fernandes A, Marques AL. A review on state-of-the-art data regarding safe early discharge following admission for pulmonary embolism: What do we know? Clin Cardiol 2013;36:507-15.
13.	Moores L, Zamarro C, Gómez V, Aujesky D, García L, Nieto R, et al. Changes in PESI scores predict mortality in intermediate-risk patients with acute pulmonary embolism. Eur Respir J 2013;41:354-9.
14.	Klok FA, Kruisman E, Spaan J, Nijkeuter M, Righini M, Aujesky D, et al. Comparison of the revised Geneva score with the Wells rule for assessing clinical probability of pulmonary embolism. J Thromb Haemost 2008;6:40-4.
15.	Ouatu A, Tanase DM, Ionescu SD, Rezus C, Ambarus V, Arsenescu-Georgescu C. The importance of clinical prediction models in non-fatal pulmonary embolism: An analysis of the best known clinical scores. Rev Med Chir Soc Med Nat Iasi 2014;118:932-41.
16.	Zwierzina D, Limacher A, Méan M, Righini M, Jaeger K, Beer HJ, et al. Prospective comparison of clinical prognostic scores in elder patients with a pulmonary embolism. J Thromb Haemost 2012;10:2270-6.
17.	Van der Schouw YT, Verbeek AL, Ruijs JH. ROC curves for the initial assessment of new diagnostic tests. Fam Pract 1992;9:506-11.

Advanced Biomedical Research