Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics
Background: Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster analysis of cardiovascular magnetic resonance (CMR)-...
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Elsevier
2024-01-01
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| Series: | Journal of Cardiovascular Magnetic Resonance |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1097664724010871 |
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| author | Addison Gearhart Sunakshi Bassi Rahul H. Rathod Rebecca S. Beroukhim Stuart Lipsitz Maxwell P. Gold David M. Harrild Audrey Dionne Sunil J. Ghelani |
| author_facet | Addison Gearhart Sunakshi Bassi Rahul H. Rathod Rebecca S. Beroukhim Stuart Lipsitz Maxwell P. Gold David M. Harrild Audrey Dionne Sunil J. Ghelani |
| author_sort | Addison Gearhart |
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| description | Background: Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster analysis of cardiovascular magnetic resonance (CMR)-derived dyssynchrony metrics can separate ventricles in the Fontan circulation from normal control left ventricles and identify prognostically distinct subgroups within the Fontan cohort. Methods: This single-center, retrospective study used 503 CMR studies from Fontan patients (median age 15 y) and 42 from age-matched controls from January 2005 to May 2011. Feature tracking on short-axis cine stacks assessed radial and circumferential strain, strain rate, and displacement. Unsupervised K-means clustering was applied to 24 mechanical dyssynchrony metrics derived from these deformation measurements. Clusters were compared for demographic, anatomical, and composite outcomes of death, or heart transplantation. Results: Four distinct phenotypic clusters were identified. Over a median follow-up of 4.2 y (interquartile ranges 1.7–8.8 y), 58 (11.5%) patients met the composite outcome. The highest-risk cluster (largely comprised of right or mixed ventricular morphology and dilated, dyssynchronous ventricles) exhibited a higher hazard for the composite outcome compared to the lowest-risk cluster while controlling for ventricular morphology (hazard ratio [HR] 6.4; 95% confidence interval [CI] 2.1–19.3; P value 0.001) and higher indexed end-diastolic volume (HR 3.2; 95% CI 1.04–10.0; P value 0.043) per 10 mL/m2. Conclusion: Unsupervised machine learning using CMR-derived dyssynchrony metrics identified four distinct clusters of patients with Fontan circulation and healthy controls with varying clinical characteristics and risk profiles. This technique can be used to guide future studies and identify more homogeneous subsets of patients from an overall heterogeneous population. |
| format | Article |
| id | doaj-art-59249554e8554ed3b57ca3aa9cbb6e61 |
| institution | Kabale University |
| issn | 1097-6647 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cardiovascular Magnetic Resonance |
| spelling | doaj-art-59249554e8554ed3b57ca3aa9cbb6e612024-12-16T05:34:41ZengElsevierJournal of Cardiovascular Magnetic Resonance1097-66472024-01-01262101060Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metricsAddison Gearhart0Sunakshi Bassi1Rahul H. Rathod2Rebecca S. Beroukhim3Stuart Lipsitz4Maxwell P. Gold5David M. Harrild6Audrey Dionne7Sunil J. Ghelani8Department of Cardiology, Boston Children’s Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA; Corresponding author.Department of Cardiology, Children's Hospital of Philadelphia, Phildelphia, Pennsylvannia, USADepartment of Cardiology, Boston Children’s Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USADepartment of Cardiology, Boston Children’s Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USADepartment of Cardiology, Boston Children’s Hospital, Boston, Massachusetts, USA; Division of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USAMassachusetts Institute of Technology, Boston, Massachusetts, USADepartment of Cardiology, Boston Children’s Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USADepartment of Cardiology, Boston Children’s Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USADepartment of Cardiology, Boston Children’s Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USABackground: Individuals with a Fontan circulation encompass a heterogeneous group with adverse outcomes linked to ventricular dilation, dysfunction, and dyssynchrony. The purpose of this study was to assess if unsupervised machine learning cluster analysis of cardiovascular magnetic resonance (CMR)-derived dyssynchrony metrics can separate ventricles in the Fontan circulation from normal control left ventricles and identify prognostically distinct subgroups within the Fontan cohort. Methods: This single-center, retrospective study used 503 CMR studies from Fontan patients (median age 15 y) and 42 from age-matched controls from January 2005 to May 2011. Feature tracking on short-axis cine stacks assessed radial and circumferential strain, strain rate, and displacement. Unsupervised K-means clustering was applied to 24 mechanical dyssynchrony metrics derived from these deformation measurements. Clusters were compared for demographic, anatomical, and composite outcomes of death, or heart transplantation. Results: Four distinct phenotypic clusters were identified. Over a median follow-up of 4.2 y (interquartile ranges 1.7–8.8 y), 58 (11.5%) patients met the composite outcome. The highest-risk cluster (largely comprised of right or mixed ventricular morphology and dilated, dyssynchronous ventricles) exhibited a higher hazard for the composite outcome compared to the lowest-risk cluster while controlling for ventricular morphology (hazard ratio [HR] 6.4; 95% confidence interval [CI] 2.1–19.3; P value 0.001) and higher indexed end-diastolic volume (HR 3.2; 95% CI 1.04–10.0; P value 0.043) per 10 mL/m2. Conclusion: Unsupervised machine learning using CMR-derived dyssynchrony metrics identified four distinct clusters of patients with Fontan circulation and healthy controls with varying clinical characteristics and risk profiles. This technique can be used to guide future studies and identify more homogeneous subsets of patients from an overall heterogeneous population.http://www.sciencedirect.com/science/article/pii/S1097664724010871Unsupervised machine learningPediatricsFontanCardiovascular magnetic resonance imagingDyssynchrony |
| spellingShingle | Addison Gearhart Sunakshi Bassi Rahul H. Rathod Rebecca S. Beroukhim Stuart Lipsitz Maxwell P. Gold David M. Harrild Audrey Dionne Sunil J. Ghelani Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics Journal of Cardiovascular Magnetic Resonance Unsupervised machine learning Pediatrics Fontan Cardiovascular magnetic resonance imaging Dyssynchrony |
| title | Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics |
| title_full | Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics |
| title_fullStr | Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics |
| title_full_unstemmed | Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics |
| title_short | Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics |
| title_sort | identifying high risk fontan phenotypes using k means clustering of cardiac magnetic resonance based dyssynchrony metrics |
| topic | Unsupervised machine learning Pediatrics Fontan Cardiovascular magnetic resonance imaging Dyssynchrony |
| url | http://www.sciencedirect.com/science/article/pii/S1097664724010871 |
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