Megan Cummins Lancaster1, Alaa Mabrouk Salem Omar2, Sukrit Narula1, Hemant Kulkarni3, Jagat Narula1, Partho P Sengupta4. 1. Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York. 2. Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Internal Medicine, Medical Division, National Research Centre, Cairo, Egypt; Department of Internal Medicine, Bronx Lebanon Hospital Center, Bronx, New York. 3. M&H Research, LLC, San Antonio, Texas. 4. Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York; WVU Heart & Vascular Institute, West Virginia University, Morgantown, West Virginia. Electronic address: Partho.sengupta@wvumedicine.org.
Abstract
OBJECTIVES: This study sought to explore the natural clustering of echocardiographic variables used for assessing left ventricular (LV) diastolic dysfunction (DD) in order to isolate high-risk phenotypic patterns and assess their prognostic significance. BACKGROUND: Assessment of LV DD is important in the management and prognosis of cardiovascular diseases. Data-driven approaches such as cluster analysis may be useful in segregating similar cases without the constraint of an a priori algorithm for risk stratification. METHODS: The study included a convenience sample of 866 consecutive patients referred for myocardial function assessment (age 65 ± 17 years; 55.3% women; ejection fraction 60 ± 9%) for whom echocardiographic parameters of DD assessment were obtained per conventional guideline recommendations. Unsupervised, hierarchical cluster analysis of these parameters was conducted using the Ward linkage method. Major adverse cardiovascular events, hospitalization, and mortality were compared between conventional and cluster-based classifications. RESULTS: Clustering algorithms for screening the presence of DD in 559 of 866 patients identified 2 distinct groups and revealed modest agreement with conventional classification (kappa = 0.41, p < 0.001). Further cluster analysis in 387 patients with DD helped to classify the severity of DD into 2 groups, with good agreement with conventional classification (kappa = 0.619, p < 0.001). Survival analyses of patients assessed by both clustering algorithms for screening and grading DD showed improved prediction of event-free survival by clusters over conventional classification for all-cause mortality and cardiac mortality, even after accounting for a multivariable, balanced propensity score. CONCLUSIONS: An unsupervised assessment of echocardiographic variables for assessing LV DD revealed unique patterns of grouping. These natural patterns of clustering may better identify patient groups who have similar risk, and their incorporation into clinical practice may help eliminate indeterminate results and improve clinical outcome prediction.
OBJECTIVES: This study sought to explore the natural clustering of echocardiographic variables used for assessing left ventricular (LV) diastolic dysfunction (DD) in order to isolate high-risk phenotypic patterns and assess their prognostic significance. BACKGROUND: Assessment of LV DD is important in the management and prognosis of cardiovascular diseases. Data-driven approaches such as cluster analysis may be useful in segregating similar cases without the constraint of an a priori algorithm for risk stratification. METHODS: The study included a convenience sample of 866 consecutive patients referred for myocardial function assessment (age 65 ± 17 years; 55.3% women; ejection fraction 60 ± 9%) for whom echocardiographic parameters of DD assessment were obtained per conventional guideline recommendations. Unsupervised, hierarchical cluster analysis of these parameters was conducted using the Ward linkage method. Major adverse cardiovascular events, hospitalization, and mortality were compared between conventional and cluster-based classifications. RESULTS: Clustering algorithms for screening the presence of DD in 559 of 866 patients identified 2 distinct groups and revealed modest agreement with conventional classification (kappa = 0.41, p < 0.001). Further cluster analysis in 387 patients with DD helped to classify the severity of DD into 2 groups, with good agreement with conventional classification (kappa = 0.619, p < 0.001). Survival analyses of patients assessed by both clustering algorithms for screening and grading DD showed improved prediction of event-free survival by clusters over conventional classification for all-cause mortality and cardiac mortality, even after accounting for a multivariable, balanced propensity score. CONCLUSIONS: An unsupervised assessment of echocardiographic variables for assessing LV DD revealed unique patterns of grouping. These natural patterns of clustering may better identify patient groups who have similar risk, and their incorporation into clinical practice may help eliminate indeterminate results and improve clinical outcome prediction.
Authors: Damini Dey; Piotr J Slomka; Paul Leeson; Dorin Comaniciu; Sirish Shrestha; Partho P Sengupta; Thomas H Marwick Journal: J Am Coll Cardiol Date: 2019-03-26 Impact factor: 24.094
Authors: Rakesh K Mishra; Geoffrey H Tison; Qizhi Fang; Rebecca Scherzer; Mary A Whooley; Nelson B Schiller Journal: J Am Soc Echocardiogr Date: 2020-01-14 Impact factor: 5.251
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