Jakobsson C, Jiménez D, Gómez V, Zamarro C, Méan M, Aujesky D. Validation of a clinical algorithm to identify low-risk patients with pulmonary embolism.
J Thromb Haemost 2010;
8:1242-7. [PMID:
20230422 DOI:
10.1111/j.1538-7836.2010.03836.x]
[Citation(s) in RCA: 33] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
BACKGROUND
We previously derived a clinical prognostic algorithm to identify patients with pulmonary embolism (PE) who are at low risk of short-term mortality and who could be safely discharged early or treated entirely in an outpatient setting.
OBJECTIVES
To externally validate the clinical prognostic algorithm in an independent patient sample.
METHODS
We validated the algorithm in 983 consecutive patients prospectively diagnosed with PE at an emergency department of a university hospital. Patients with none of the algorithm's 10 prognostic variables (age > or = 70 years, cancer, heart failure, chronic lung disease, chronic renal disease, cerebrovascular disease, pulse > or = 110 min(-1), systolic blood pressure < 100 mmHg, oxygen saturation < 90%, and altered mental status) at baseline were defined as being at low risk. We compared 30-day overall mortality among low-risk patients, on the basis of the algorithm, between the validation sample and the original derivation sample. We also assessed the rate of PE-related and bleeding-related mortality among low-risk patients.
RESULTS
Overall, the algorithm classified 16.3% of patients with PE as being at low risk. Mortality at 30 days was 1.9% among low-risk patients, and did not differ between the validation sample and the original derivation sample. Among low-risk patients, only 0.6% died from definite or possible PE, and 0% died from bleeding.
CONCLUSIONS
This study validates an easy-to-use, clinical prognostic algorithm for PE that accurately identifies patients with PE who are at low risk of short-term mortality. Patients who are at low risk according to our algorithm are potential candidates for less costly outpatient treatment.
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