Published online Sep 20, 2026. doi: 10.5662/wjm.v16.i3.117845
Revised: January 20, 2026
Accepted: February 11, 2026
Published online: September 20, 2026
Processing time: 205 Days and 13.7 Hours
The surge of severe acute respiratory syndrome coronavirus 2 [coronavirus di
To evaluate the prognostic predictors and the adherence to appropriate use cri
We performed a retrospective cohort study analyzing records from patients with confirmed COVID-19 who underwent TTE. Socio-demographic, biochemical, and echocardiographic parameters were collected. Mortality was the primary outcome. We assessed inter-observer agreement (Kappa statistic) for TTE indications (based on American College of Cardiology Foundation 2011 and American Society of Echocardiography 2020 guidelines) and the determination of clinical impact (a subsequent change in patient management).
Total 149 patients were analyzed. Median age was 66 years [interquartile range (IQR): 56-73], median hospital stay was 13 days (IQR: 6-23). Overall and intensive care unit mortality rates were 39.6% and 60%, respectively. Elevated biochemical markers (leukocytes, neutrophils, lactate dehydrogenase, and C-reactive protein) were associated with mortality. Crucially, right ventricular (RV) dilatation and/or strain (P = 0.008) was identified as the sole echocardiographic finding significantly predictive of mortality. Inter-observer agreement for classifying AUC was high (κ ≥ 0.798). Furthermore, TTE prompted a change in clinical management in 79.7% of the cases.
RV pathology is a potent, quantifiable prognostic indicator. While AUC demonstrated high reliability, the sig
Core Tip: This study highlights right ventricular dilatation as the primary echocardiographic predictor of mortality in hospitalized coronavirus disease 2019 patients. While adherence to appropriate use criteria for transthoracic echocardiography (TTE) was exceptionally high (98%), the immediate clinical impact on management remained modest (12.7%). These findings suggest that during pandemics, TTE is a robust prognostic tool; however, its systematic use should be further refined to maximize therapeutic yield. This research provides a data-driven foundation for optimizing cardiac imaging resources and developing future artificial intelligence models for risk stratification in acute viral infections.