ECG Abnormalities and Biomarkers Enable Rapid Risk Stratification in Normotensive Patients With Acute Pulmonary Embolism

Background

The patients with suspected pulmonary embolism (PE) were usually screened using electrocardiogram (ECG) and blood panel of D-dimer, troponin, and blood gas analysis in the emergency.

Objectives

This study aimed to explore a rapid risk model to predict in-hospital adverse events for normotensive PE patients.

Methods

Patients with acute PE having normal blood pressure on appearance were retrospectively enrolled at China-Japan Friendship Hospital from January 2017 to February 2020. The in-hospital adverse events were defined as death and clinical deterioration during hospitalization. The risk model for in-hospital adverse events was generated by multivariate regression analysis. The discrimination ability of the model was compared with PESI, Bova, and FAST risk score, and evaluated by the receiver operating characteristic curve (ROC), net reclassification improvement (NRI), and integrated discrimination improvement index (IDI).

Results

Of the 213 patients, 35 (16.4%) experienced in-hospital adverse events,y including 15 deaths. The average age was 69 ± 19 years, and 118 (44.6%) were females. Multiple logistic regression analysis showed that independent risk factors associated with in-hospital adverse events were low QRS voltage in ECG (OR: 5.321; 95% CI: 1.608–7.310), positive age-adjusted D-dimer (OR: 2.061; 95% CI: 0.622–6.836), positive troponin (OR: 3.504; 95% CI: 1.744–8.259), and PaO₂/FiO₂ < 300 (OR: 3.268; 95% CI: 0.978–5.260). The ROC analysis showed that the AUC of the new model (0.847, 95% CI: 0.786–0.901) was better than the PESI score (0.628, 95% CI: 0.509–0.769), the Bova score (0.701, 95% CI: 0.594–0.808), and the FAST score (0.775 95% CI: 0.690–0.859).

Conclusion

ECG abnormalities and biomarkers on admission may provide a rapid and effective approach to identify patients with poor prognoses during hospitalization.