A New Model for Risk Stratification of Patients With Acute Pulmonary Embolism.

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A New Model for Risk Stratification of Patients With Acute Pulmonary Embolism.

Clin Appl Thromb Hemost. 2018 Oct 28;:1076029618808922

Authors: Jen WY, Jeon YS, Kojodjojo P, Lee EHE, Lee YH, Ren YP, Tan TJS, Song Y, Zhang T, Teo L, Feng M, Chee YL

Pulmonary embolism (PE) is associated with mortality. There are many clinical prediction tools to predict early mortality in acute PE but little consensus on which is best. Our study aims to validate existing prediction tools and derive a predictive model that can be applied to all patients with acute PE in both inpatient and outpatient settings. This is a retrospective cohort study of patients with acute PE. For each patient, the Pulmonary Embolism Severity Index (PESI), simplified PESI (sPESI), European Society of Cardiology (ESC), and Angriman scores were calculated. Scores were assessed by the area under the receive-operating curve (AUC) for 30-day, all-cause mortality. To develop a new prognostic model, elastic logistic regression was used on the derivation cohort to estimate β-coefficients of 8 different variables; these were normalized to weigh them. A total of 321 patients (mean age 60±17 years) were included. Overall 30-day mortality was 10.3%. None of the scores performed well; the AUCs for the PESI, sPESI, ESC, and Angriman scores were 0.67 (95% confidence interval [CI], 0.57-0.77), 0.58 (0.48-0.69), 0.65 (0.55-0.75), and 0.67 (0.57-0.76), respectively. Our new prediction model outperformed PESI, with an AUC of 0.82 (95% CI, 0.76-0.88). At a cutoff score of 100, 195 (60.1%) patients were classified as low risk. Thirty-day mortality was 2.1% (95% CI, 0.8%-5.2%) and 23.0% (16.5%-31.1%) for low- and high-risk groups, respectively ( P < .001). In conclusion, we have developed a new model that outperforms existing prediction tools in all comers with PE. However, further validation on external cohorts is required before application.

PMID: 30370786 [PubMed - as supplied by publisher]

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