Derivation and Validation of a Novel Prediction Model to Identify Low-Risk Patients With Acute Pulmonary Embolism

Abstract Accurate identification of low-risk patients with acute pulmonary embolism(PE) who may be eligible for outpatient treatment or early discharge can have substantial cost saving benefit. The purpose of this study was to derive and validate a prediction model to effectively identify PE patients at low risk of short term mortality, right ventricular (RV) dysfunction, and other non-fatal outcomes. This study analysed data from 400 consecutive patients with acute PE. We derived and internally validated our prediction rule based on clinically significant variables that are routinely available at initial examination, that were categorized and weighted using coefficients in the multivariate logistic regression. The model was externally validated in an independent cohort of 82 patients. The final model (HOPPE score) consisted of 5 categorized patient variables (1,2,or 3 points, respectively):systolic blood p ressure(>120,100-119,80,65-79,80,60-79, 11) patients across the derivation and validation cohorts. In comparison to the previously validated PESI score, the HOPPE score had a higher discriminatory power(AUC 0.74 vs 0.85, p=0.033) and significantly improved both the discrimination(integrated discrimination improvement,p=0.002) and reclassification(net reclassification improvement,p=.003) of the model for short term mortality. In conclusion, the HOPPE score accurately identifies acute PE patients at low risk of short term mortality, RV dysfunction, and other non fatal outcomes. Prospective validation of the prediction model is necessary before implementation in clinical practice.