Derivation and Validation of a Prognostic Model for Pulmonary Embolism

Abstract

Rationale: An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment. Objectives: To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes. Methods: We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our prediction rule using logistic regression with 30-day mortality as the primary outcome, and patient demographic and clinical data routinely available at presentation as potential predictor variables. We externally validated the rule in 221 inpatients with pulmonary embolism from Switzerland and France. Measurements: We compared mortality and nonfatal adverse medical outcomes across the derivation and two validation samples. Main Results: The prediction rule is based on 11 simple patient characteristics that were independently associated with mortality and stratifies patients with pulmonary embolism into five severity classes, with 30-day mortality rates of 0-1.6% in class I, 1.7-3.5% in class II, 3.2-7.1% in class III, 4.0-11.4% in class IV, and 10.0-24.5% in class V across the derivation and validation samples. Inpatient death and nonfatal complications were р 1.1% among patients in class I and р 1.9% among patients in class II. Conclusions: Our rule accurately classifies patients with pulmonary embolism into classes of increasing risk of mortality and other adverse medical outcomes. Further validation of the rule is important before its implementation as a decision aid to guide the initial management of patients with pulmonary embolism.

A new simple risk score in patients with acute chest pain without existing known coronary disease

“…We tested for homogeneity the prediction rule in the validation cohort by comparing the slope of the increase of event rate with the increase in the score of the Florence prediction rule using least squares linear regression analysis. To asses the model's discrimination power to predict the composite end point, we compared the area under the receiver operating characteristic curve between the validation and the test cohort [29,30]. Fisher exact test was used when expected frequencies are less than 5.…”

Early Use of Echocardiography in Patients With Acute Pulmonary Embolism: Findings From the RIETE Registry

“…For this study, we included patients with acute symptomatic PE from March 2001 to July 2017. We compared the demographics, comorbidities (such as underlying coronary disease, heart failure, and atrial fibrillation) as well as the baseline simplified PE severity index (sPESI)8 and PESI9 in PE patients with versus those without early TTE. We used a generalized linear mixed model, with random effect for enrolling centers, to determine significant predictors of early TTE.…”

Pulmonary embolism and deep vein thrombosis

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