Electronic cardiac arrest triage score best predicts mortality after intervention in patients with massive and submassive pulmonary embolism

Abstract

Compared to PESI score, CART and eCART scores better predict mortality in massive or submassive PE patients undergoing PMT.

Catheter-Directed Treatment for Pulmonary Embolism in Light of Current Evidence

“…Recent literature has suggested that more specific models geared to assess the risk for cardiac arrest using vital signs, laboratory values, and age may be more accurate than PESI for predicting 30-day mortality. [25] These scores may therefore be more beneficial in choosing patients that are more likely to benefit from CDT due to higher risk for mortality.…”

“…Current prognostic models developed to assess PE mortality such as the Pulmonary Embolism Severity Index (PESI) were derived from low-risk patients and therefore may not be applicable to the submassive patient population and selecting those at risk for decompensation. [25] Additional metrics such as RV/LV ratio and PA pressures are indirect measures that cannot reliably predict progression to RV failure. Recent literature has suggested that more specific models geared to assess the risk for cardiac arrest using vital signs, laboratory values, and age may be more accurate than PESI for predicting 30-day mortality.…”

Submassive pulmonary embolus: The challenge of thrombolytic decision‐making in a heterogenous cohort

“…The authors of the present study (Hirai et al) should be congratulated for their investigation which advances clinical assessment tools for PE risk stratification. This single institution retrospective analysis of patients with massive or submassive PE undergoing pharmaco‐mechanical therapy assessed prediction of 30‐day mortality comparing two scoring systems: (1) the widely utilized but somewhat complex Pulmonary Embolism Severity Index (PESI) ; versus (2) the CART score, a clinical severity scoring system designed to predict in‐hospital cardiac arrest employing four simple parameters (respiratory rate, heart rate, diastolic pressure, and age), which in the present study was expanded to enhance predictive power via a computerized data mining approach (“eCART”) by including additional variables (length of ICU stay, renal and liver function and blood count).…”