Subscribe to RSS
DOI: 10.1055/s-0037-1613830
Derivation of a Simple Clinical Model to Categorize Patients Probability of Pulmonary Embolism: Increasing the Models Utility with the SimpliRED D-dimer
Funding for this study was provided by the National Health Research and Development Program of Canada (project #6606-5283-403). Dr. Philip Wells, David Anderson, Clive Kearon and Jeffrey Ginsberg are the recipients of Research Scholarships from the Heart and Stroke Foundation of Canada. Dr.Weitz is a recipient of a Career Investigator Award from the Heart and Stroke Foundation of Ontario, and Dr. Hirsh is a Distinguished professor of the Heart and Stroke Foundation of Canada.Publication History
Received
23 September 1999
Accepted after revision
05 November 1999
Publication Date:
14 December 2017 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035024/200003/lookinside/thumbnails/10-1055s-0037-1613830-1.jpg)
Summary
We have previously demonstrated that a clinical model can be safely used in a management strategy in patients with suspected pulmonary embolism (PE). We sought to simplify the clinical model and determine a scoring system, that when combined with D-dimer results, would safely exclude PE without the need for other tests, in a large proportion of patients. We used a randomly selected sample of 80% of the patients that participated in a prospective cohort study of patients with suspected PE to perform a logistic regression analysis on 40 clinical variables to create a simple clinical prediction rule. Cut points on the new rule were determined to create two scoring systems. In the first scoring system patients were classified as having low, moderate and high probability of PE with the proportions being similar to those determined in our original study. The second system was designed to create two categories, PE likely and unlikely. The goal in the latter was that PE unlikely patients with a negative D-dimer result would have PE in less than 2% of cases. The proportion of patients with PE in each category was determined overall and according to a positive or negative SimpliRED D-dimer result. After these determinations we applied the models to the remaining 20% of patients as a validation of the results. The following seven variables and assigned scores (in brackets) were included in the clinical prediction rule: Clinical symptoms of DVT (3.0), no alternative diagnosis (3.0), heart rate >100 (1.5), immobilization or surgery in the previous four weeks (1.5), previous DVT/PE (1.5), hemoptysis (1.0) and malignancy (1.0). Patients were considered low probability if the score was <2.0, moderate of the score was 2.0 to 6.0 and high if the score was over 6.0. Pulmonary embolism unlikely was assigned to patients with scores <4.0 and PE likely if the score was >4.0. 7.8% of patients with scores of less than or equal to 4 had PE but if the D-dimer was negative in these patients the rate of PE was only 2.2% (95% CI = 1.0% to 4.0%) in the derivation set and 1.7% in the validation set.
Importantly this combination occurred in 46% of our study patients. A score of <2.0 and a negative D-dimer results in a PE rate of 1.5% (95% CI = 0.4% to 3.7%) in the derivation set and 2.7% (95% CI = 0.3% to 9.0%) in the validation set and only occurred in 29% of patients. The combination of a score <4.0 by our simple clinical prediction rule and a negative SimpliRED D-Dimer result may safely exclude PE in a large proportion of patients with suspected PE.
-
References
- 1 Anderson Jr FA, Wheeler HB, Goldberg RJ. et al. A population-based perspective of the hospital incidence and case-fatality rates of deep vein thrombosis and pulmonary embolism. The Worcester study. Arch Intern Med 1991; 151: 933-8.
- 2 PIOPED Investigators. The Value of the ventilation/perfusion scan in acute pulmonary embolism: Results of the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED). JAMA 1990; 263: 2753-9.
- 3 Carson JL, Kelley MA, Duff A. et al. Clinical course of pulmonary embolism. N Engl J Med 1992; 326: 1240-5.
- 4 Wells PS, Ginsberg JS, Anderson DR. et al. Use of a Clinical Model for Safe Management of Patients with Suspected Pulmonary Embolism. Ann Intern Med 1998; 129: 997-1005.
- 5 Ginsberg JS, Wells PS, Brill-Edwards P. et al. Application of a novel and rapid whole blood assay for D-Dimer in patients with clinically suspected pulmonary embolism. Thromb Haemost 1995; 73: 35-8.
- 6 Neter J, Wasserman W, Kutner MH. Applied linear statistical methods, regression, analysis of variance and experimental designs. 2nd ed. Homewood Irwin; 1985
- 7 Stein PD, Athanasoulis C, Alavi A. et al. Complications and validity of pulmonary angiography in acute pulmonary embolism. Circulation 1992; 85: 462-8.
- 8 Perrier A, Desmarais S, Miron MJ. et al. Noninvasive diagnosis of venous thromboembolism in outpatients. Lancet 1999; 353: 190-5.
- 9 Hull RD, Hirsh J, Carter CJ. et al. Pulmonary angiography, ventilation lung scanning, and venography for clinically suspected pulmonary embolism with abnormal perfusion lung scan. Ann Intern Med 1983; 98: 891-9.
- 10 Schluger N, Henschke C. et al. Diagnosis of pulmonary embolism at a large teaching hospital. J Thor Imaging 1994; 09: 180-4.
- 11 Kember PG, Euinton HA, Morcos SK. Clinicians’ interpretation of the indeterminate ventilation-perfusion scan report. Br J Rad 1997; 70: 1109-11.
- 12 Frankel N, Coleman RE. Utilization of lung scans by clinicians. J Nucl Med 1986; 27: 366-9.
- 13 Gupta A, Frazer CK, Ferguson JM. et al. Acute pulmonary embolism: diagnosis with MR angiography. Radiology 1999; 210: 353-9.
- 14 Remy-Jardin M, Remy J, Deschildre F. et al. Diagnosis of Pulmonary Embolism with Spriral CT: Comparison with Pulmonary Angiography and scintigraphy. Radiology 1996; 200: 699-706.
- 15 Ferretti GR, Bosson JL, Buffax PD, Ayanian D, Pison C, Blanc F. et al. Acute pulmonary embolism: role of helical CT in 164 patients with intermediate probability at ventilation-perfusion scintigraphy and normal results at duplex US of the legs. Radiology 1997; 205: 453-8.
- 16 Charlson ME, Ale KL, Simon R, MacKenzie R. Why predictive indexes perform less well in validation studies. Is it Magic or Methods? Arch Intern Med 1987; 147: 2155-61.