Author + information
- Received January 2, 2018
- Revision received February 5, 2018
- Accepted February 6, 2018
- Published online November 14, 2018.
- Megan Cummins Lancaster, MD, PhDa,
- Alaa Mabrouk Salem Omar, MD, PhDa,b,c,
- Sukrit Narula, BSa,
- Hemant Kulkarni, MDd,
- Jagat Narula, MD, PhDa and
- Partho P. Sengupta, MD, DMa,e,∗ ()
- aDepartment of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York
- bDepartment of Internal Medicine, Medical Division, National Research Centre, Cairo, Egypt
- cDepartment of Internal Medicine, Bronx Lebanon Hospital Center, Bronx, New York
- dM&H Research, LLC, San Antonio, Texas
- eWVU Heart & Vascular Institute, West Virginia University, Morgantown, West Virginia
- ↵∗Address for correspondence:
Dr. Partho P. Sengupta, Heart & Vascular Institute, West Virginia University, 1 Medical Center Drive, Morgantown, West Virginia 26506-8059.
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.
Dr. Sengupta has served as a consultant for HeartSciences and Hitachi Aloka Ltd. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Drs. Lancaster and Omar contributed equally to this work and are joint first authors.
- Received January 2, 2018.
- Revision received February 5, 2018.
- Accepted February 6, 2018.
- 2018 American College of Cardiology Foundation
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