Key result
ML-based 3D echo RVEF predicts ~58% higher risk of adverse clinical outcomes in CTEPH.
Why the study?
Right ventricular function is vital for prognosis in chronic thromboembolic pulmonary hypertension, but rapid 3D quantification methods were needed to predict adverse outcomes.
Does machine learning-based 3D echocardiographic quantification of right ventricular ejection fraction predict adverse clinical outcomes in patients with chronic thromboembolic pulmonary hypertension?
Cohort (n=151)
No
Does machine learning-based 3D echocardiographic quantification of right ventricular ejection fraction predict adverse clinical outcomes in patients with chronic thromboembolic pulmonary hypertension?
Hazard Ratio: 1.576 (95% CI 1.046–2.372)
p-value: p=0.030
Machine learning-based 3D echocardiographic quantification of right ventricular ejection fraction is a significant predictor of adverse clinical outcomes in patients with chronic thromboembolic pulmonary hypertension.
Automated 3DE RV quantification may aid CTEPH risk stratification; leaves open whether it improves outcomes over standard metrics.
Background: Right ventricular (RV) function plays a vital role in the prognosis of patients with chronic thromboembolic pulmonary hypertension (CTEPH). We used new machine learning (ML)-based fully automated software to quantify RV function using three-dimensional echocardiography (3DE) to predict adverse clinical outcomes in CTEPH patients. Methods: A total of 151 consecutive CTEPH patients were registered in this prospective study between April 2015 and July 2019. New ML-based methods were used for data management, and quantitative analysis of RV volume and ejection fraction (RVEF) was performed offline. RV structural and functional parameters were recorded using 3DE. CTEPH was diagnosed using right heart catheterization, and 62 patients underwent cardiac magnetic resonance to assess right heart function. Adverse clinical outcomes were defined as PH-related hospitalization with hemoptysis or increased RV failure, including conditions requiring balloon pulmonary angioplasty or pulmonary endarterectomy, as well as death. Results: The median follow-up time was 19.7 months (interquartile range, 0.5–54 months). Among the 151 CTEPH patients, 72 experienced adverse clinical outcomes. Multivariate Cox proportional-hazard analysis showed that ML-based 3DE analysis of RVEF was a predictor of adverse clinical outcomes (hazard ratio, 1.576; 95% confidence interval (CI), 1.046~2.372; P = 0.030). Conclusions: The new ML-based 3DE algorithm is a promising technique for rapid 3D quantification of RV function in CTEPH patients.
No takes yet. Share an insight, caveat, or question.
Li et al. (2021) conducted a cohort in Chronic thromboembolic pulmonary hypertension (n=151). Machine learning-based 3D echocardiographic quantification of right ventricular ejection fraction was evaluated on Adverse clinical outcomes (PH-related hospitalization with hemoptysis or increased RV failure, including conditions requiring balloon pulmonary angioplasty or pulmonary endarterectomy, as well as death) (HR 1.576, 95% CI 1.046-2.372, p=0.030). Machine learning-based three-dimensional echocardiographic analysis of right ventricular ejection fraction significantly predicted adverse clinical outcomes in patients with chronic thromboembolic pulmonary hypertension (HR 1.576).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: