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Alwan L, Benz DC, Cuddy SAM, Dobner S, Shiri I, Caobelli F, Bernhard B, Stämpfli SF, Eberli F, Reyes M, Kwong RY, Falk RH, Dorbala S, Gräni C. Current and Evolving Multimodality Cardiac Imaging in Managing Transthyretin Amyloid Cardiomyopathy. JACC Cardiovasc Imaging 2024; 17:195-211. [PMID: 38099914 DOI: 10.1016/j.jcmg.2023.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/06/2023] [Accepted: 10/18/2023] [Indexed: 01/29/2024]
Abstract
Amyloid transthyretin (ATTR) amyloidosis is a protein-misfolding disease characterized by fibril accumulation in the extracellular space that can result in local tissue disruption and organ dysfunction. Cardiac involvement drives morbidity and mortality, and the heart is the major organ affected by ATTR amyloidosis. Multimodality cardiac imaging (ie, echocardiography, scintigraphy, and cardiac magnetic resonance) allows accurate diagnosis of ATTR cardiomyopathy (ATTR-CM), and this is of particular importance because ATTR-targeting therapies have become available and probably exert their greatest benefit at earlier disease stages. Apart from establishing the diagnosis, multimodality cardiac imaging may help to better understand pathogenesis, predict prognosis, and monitor treatment response. The aim of this review is to give an update on contemporary and evolving cardiac imaging methods and their role in diagnosing and managing ATTR-CM. Further, an outlook is presented on how artificial intelligence in cardiac imaging may improve future clinical decision making and patient management in the setting of ATTR-CM.
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Affiliation(s)
- Louhai Alwan
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Dominik C Benz
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Division of Nuclear Medicine, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Cardiac Imaging, Department of Cardiology and Nuclear Medicine, Zurich University Hospital, Zurich, Switzerland
| | - Sarah A M Cuddy
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Division of Nuclear Medicine, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Stephan Dobner
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Isaac Shiri
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Federico Caobelli
- University Clinic of Nuclear Medicine, Inselspital, Bern University Hospital, Switzerland
| | - Benedikt Bernhard
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Simon F Stämpfli
- Department of Cardiology, Heart Centre Lucerne, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Franz Eberli
- Department of Cardiology, Triemli Hospital (Triemlispital), Zurich, Switzerland
| | - Mauricio Reyes
- Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland; Artificial Intelligence in Medical Imaging, ARTORG Center for Biomedical Research, University of Bern, Bern, Switzerland
| | - Raymond Y Kwong
- CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Rodney H Falk
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Sharmila Dorbala
- Amyloidosis Program, Division of Cardiology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; Division of Nuclear Medicine, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA; CV Imaging Program, Cardiovascular Division, Department of Medicine and Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Christoph Gräni
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
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Wang Q, Ouyang H, Lv L, Gui L, Yang S, Hua P. Left main coronary artery morphological phenotypes and its hemodynamic properties. Biomed Eng Online 2024; 23:9. [PMID: 38254133 PMCID: PMC10804578 DOI: 10.1186/s12938-024-01205-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Atherosclerosis may be linked to morphological defects that lead to variances in coronary artery hemodynamics. Few objective strategies exit at present for generalizing morphological phenotypes of coronary arteries in terms of hemodynamics. We used unsupervised clustering (UC) to classify the morphology of the left main coronary artery (LM) and looked at how hemodynamic distribution differed between phenotypes. METHODS In this study, 76 LMs were obtained from 76 patients. After LMs were reconstructed with coronary computed tomography angiography, centerlines were used to extract the geometric characteristics. Unsupervised clustering was carried out using these characteristics to identify distinct morphological phenotypes of LMs. The time-averaged wall shear stress (TAWSS) for each phenotype was investigated by means of computational fluid dynamics (CFD) analysis of the left coronary artery. RESULTS We identified four clusters (i.e., four phenotypes): Cluster 1 had a shorter stem and thinner branches (n = 26); Cluster 2 had a larger bifurcation angle (n = 10); Cluster 3 had an ostium at an angulation to the coronary sinus and a more curved stem, and thick branches (n = 10); and Cluster 4 had an ostium at an angulation to the coronary sinus and a flatter stem (n = 14). TAWSS features varied widely across phenotypes. Nodes with low TAWSS (L-TAWSS) were typically found around the branching points of the left anterior descending artery (LAD), particularly in Cluster 2. CONCLUSION Our findings demonstrated that UC is a powerful technique for morphologically classifying LMs. Different LM phenotypes exhibited distinct hemodynamic characteristics in certain regions. This morphological clustering method could aid in identifying people at high risk for developing coronary atherosclerosis, hence facilitating early intervention.
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Affiliation(s)
- Qi Wang
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China
- Department of Cardiovascular Surgery, Qilu Hospital of Shandong University, Shandong University, Jinan, China
| | - Hua Ouyang
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China
| | - Lei Lv
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China
- Department of Cardiac and Vascular Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming Medical University, Kunming, China
| | - Long Gui
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China
| | - Songran Yang
- Department of Biobank and Bioinformatics, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China.
| | - Ping Hua
- Department of Cardio-Vascular Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yan Jiang West Road, Guangzhou, 510120, China.
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Williams MC, Bednarski BP, Pieszko K, Miller RJH, Kwiecinski J, Shanbhag A, Liang JX, Huang C, Sharir T, Dorbala S, Di Carli MF, Einstein AJ, Sinusas AJ, Miller EJ, Bateman TM, Fish MB, Ruddy TD, Acampa W, Hauser MT, Kaufmann PA, Dey D, Berman DS, Slomka PJ. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging. Eur J Nucl Med Mol Imaging 2023; 50:2656-2668. [PMID: 37067586 PMCID: PMC10317876 DOI: 10.1007/s00259-023-06218-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 03/29/2023] [Indexed: 04/18/2023]
Abstract
PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.
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Affiliation(s)
- Michelle C Williams
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Bryan P Bednarski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Konrad Pieszko
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Robert J H Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada
| | - Jacek Kwiecinski
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
- Department of Interventional Cardiology and Angiology, Institute of Cardiology, Warsaw, Poland
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Joanna X Liang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Cathleen Huang
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Tali Sharir
- Department of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, and Ben Gurion University of the Negev, Beer Sheba, Israel
| | - Sharmila Dorbala
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA
| | - Marcelo F Di Carli
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Boston, MA, USA
| | - Andrew J Einstein
- Division of Cardiology, Department of Medicine, and Department of Radiology, Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA
| | - Albert J Sinusas
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Edward J Miller
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
| | | | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, OR, USA
| | - Terrence D Ruddy
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, ON, Canada
| | - Wanda Acampa
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples, Italy
| | - M Timothy Hauser
- Department of Nuclear Cardiology, Oklahoma Heart Hospital, Oklahoma City, OK, USA
| | - Philipp A Kaufmann
- Department of Nuclear Medicine, Cardiac Imaging, University Hospital Zurich, Zurich, Switzerland
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA
| | - Piotr J Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences, and Imaging, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA.
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Mazzucato S, Bandini A, Micera S, Vergaro G, Dalmiani S, Emdin M, Passino C, Moccia S. Classification of patients with cardiac amyloidosis using machine learning models on Italian electronic clinical health records. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083260 DOI: 10.1109/embc40787.2023.10340074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Amyloidosis refers to a range of medical conditions in which misshapen proteins accumulate in various organs and tissues, forming insoluble fibrils. Cardiac amyloidosis is frequently linked to the buildup of misfolded transthyretin (TTR) or immunoglobulin light chains (AL). Delayed diagnosis, due to lack of disease awareness, results in a poor prognosis, especially in patients with AL amyloidosis. Early identification is therefore a key factor to improve patient outcomes. This study investigates the use of supervised machine-learning algorithms to support clinicians in classifying amyloidosis and control subjects. The aim of this work is to foster model interpretability reporting the most important risk factors in predicting the presence of cardiac amyloidosis. We analyzed electronic health records (EHRs) of 418 participants acquired in a time window of 12 years as part of a case-control study conducted in Fondazione Toscana Gabriele Monasterio (Italy) clinical practice. This work paves the way for the creation of digital health solutions that can aid in amyloidosis screening. The effective handling, analysis, and interpretation of these solutions can have a transformative effect on modern healthcare, offering new opportunities for improved patient care.
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Berthelot E, Broussier A, Hittinger L, Donadio C, Rovani X, Salengro E, Megbemado R, Godreuil C, Belmin J, David JP, Genet B, Damy T. Patients with cardiac amyloidosis are at a greater risk of mortality and hospital readmission after acute heart failure. ESC Heart Fail 2023; 10:2042-2050. [PMID: 37051755 DOI: 10.1002/ehf2.14337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 02/13/2023] [Accepted: 02/16/2023] [Indexed: 04/14/2023] Open
Abstract
AIMS Cardiac amyloidosis (CA) is an under-diagnosed cause of heart failure (HF) and has a worse prognosis than other forms of HF. The frequency of death or rehospitalization following discharge for acute heart failure (AHF) in CA (relative to other causes) has not been documented. The study aims to compare hospital readmission and death rates 90 days after discharge for AHF in patients with vs. without CA and to identify risk factors associated with these events in each group. METHODS AND RESULTS Patients with HF and CA (HF + CA+) were recruited from the ICREX cohort, after screening of their medical records. The cases were matched 1:5 by sex and age with control HF patients without CA (HF + CA-). There were 27 HF + CA + and 135 HF + CA- patients from the ICREX cohort included in the study. Relative to the HF + CA- group, HF + CA+ patients had a higher heart rate (P = 0.002) and N-terminal prohormone of brain natriuretic peptide levels (P < 0.001) and lower blood pressure (P < 0.001), weight, and body mass index values (P < 0.001) on discharge. Ninety days after discharge, the HF + CA+ group displayed a higher death rate, a higher all-cause hospital readmission rate, and a higher hospital readmission rate for AHF. Death and hospital readmissions occurred sooner after discharge in the HF + CA+ group than in the HF + CA- group. CONCLUSIONS The presence of CA in patients with HF was associated with a three-fold greater risk of death and a two-fold greater risk of all-cause hospital readmission 90 days after discharge. These findings emphasize the importance of close, active management of patients with CA and AHF.
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Affiliation(s)
- Emmanuelle Berthelot
- Université Paris Sud, Paris, France
- Department of Cardiology, AP-HP, Hopital Bicêtre, 78, rue du général Leclerc, Le Kremlin Bicêtre, 94043, Paris, France
| | - Amaury Broussier
- Department of Geriatrics, AP-HP, Henri-Mondor/Emile-Roux Hospitals, Paris, France
- Univsité Paris Est Créteil, INSERM, IMRB, Paris, France
| | - Luc Hittinger
- Department of Cardiology, Heart Failure and Amyloidosis Unit, Referral Center For Cardiac Amyloidosis, Université Paris Est, AP-HP, Henri-Mondor/Albert-Chenevier Hospitals, Paris, France
| | - Cristiano Donadio
- Department of Geriatrics, AP-HP, Hôpital Charles Foix and Sorbonne Université, Paris, France
| | | | | | | | | | - Joel Belmin
- Department of Geriatrics, AP-HP, Hôpital Charles Foix and Sorbonne Université, Paris, France
| | - Jean Philippe David
- Department of Geriatrics, AP-HP, Henri-Mondor/Emile-Roux Hospitals, Paris, France
- Univsité Paris Est Créteil, INSERM, IMRB, Paris, France
| | | | - Thibaud Damy
- Department of Cardiology, Heart Failure and Amyloidosis Unit, Referral Center For Cardiac Amyloidosis, Université Paris Est, AP-HP, Henri-Mondor/Albert-Chenevier Hospitals, Paris, France
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Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis. Int J Mol Sci 2023; 24:ijms24065680. [PMID: 36982754 PMCID: PMC10051237 DOI: 10.3390/ijms24065680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/12/2023] [Accepted: 03/14/2023] [Indexed: 03/18/2023] Open
Abstract
Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from “raw data” without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.
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Girerd N, Kobayashi M. The new era of evidence-based echocardiographic algorithms using artificial intelligence. Int J Cardiol 2023; 380:35-36. [PMID: 36924948 DOI: 10.1016/j.ijcard.2023.03.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/13/2023] [Indexed: 03/18/2023]
Affiliation(s)
- Nicolas Girerd
- Université de Lorraine, Inserm, Centre d'Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, Nancy, France; Département de cardiologie, CHRU Nancy, Nancy, France..
| | - Masatake Kobayashi
- Université de Lorraine, Inserm, Centre d'Investigations Cliniques- 1433, and Inserm U1116, CHRU Nancy, F-CRIN INI-CRCT, Nancy, France; Department of cardiology, Tokyo Medical University, Tokyo, Japan
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Wu Y, Bai J, Zhang M, Shao F, Yi H, You D, Zhao Y. Heterogeneity of Treatment Effects for Intensive Blood Pressure Therapy by Individual Components of FRS: An Unsupervised Data-Driven Subgroup Analysis in SPRINT and ACCORD. Front Cardiovasc Med 2022; 9:778756. [PMID: 35187120 PMCID: PMC8850629 DOI: 10.3389/fcvm.2022.778756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 01/04/2022] [Indexed: 11/21/2022] Open
Abstract
Background Few studies have answered the guiding significance of individual components of the Framingham risk score (FRS) to the risk of cardiovascular disease (CVD) after antihypertensive treatment. This study on the systolic blood pressure intervention trial (SPRINT) and the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD-BP) aimed to reveal previously undetected association patterns between individual components of the FRS and heterogeneity of treatment effects (HTEs) of intensive blood pressure control. Methods A self-organizing map (SOM) methodology was applied to identify CVD-risk-specific subgroups in the SPRINT (n = 8,773), and the trained SOM was utilized directly in 4,495 patients from the ACCORD. The primary endpoints were myocardial infarction (MI), non-myocardial infarction acute coronary syndrome (non-MI ACS), stroke, heart failure (HF), death from CVD causes, and a primary composite cardiovascular outcome. Cox proportional hazards models were then used to explore the potential heterogeneous response to intensive SBP control. Results We identified four SOM-based subgroups with distinct individual components of FRS profiles and the CVD risk. For individuals with type 2 diabetes mellitus (T2DM) in the ACCORD or without diabetes in the SPRINT, subgroup I characterized by male with the lowest concentrations for total cholesterol (TC) and high-density lipoprotein (HDL) cholesterol measures, experienced the highest risk for major CVD. Conversely, subgroup III characterized by a female with the highest values for these measures represented as the lowest CVD risk. Furthermore, subgroup II, with the highest systolic blood pressure (SBP) and no antihypertensive agent use at baseline, had a significantly greater frequency of non-MI ACS under intensive BP control, the number needed to harm (NNH) was 84.24 to cause 1 non-MI ACS [absolute risk reduction (ARR) = −1.19%; 95% CI: −2.08, −0.29%] in the SPRINT [hazard ratio (HR) = 3.62; 95% CI: 1.33, 9.81; P = 0.012], and the NNH of was 43.19 to cause 1 non-MI ACS (ARR = −2.32%; 95% CI: −4.63, 0.00%) in the ACCORD (HR = 1.81; 95% CI: 1.01–3.25; P = 0.046). Finally, subgroup IV characterized by mostly younger patients with antihypertensive medication use and smoking history represented the lowest risk for stroke, HF, and relatively low risk for death from CVD causes and primary composite CVD outcome in SPRINT, however, except stroke, a low risk for others were not observed in ACCORD. Conclusion Similar findings in patients with hypertensive with T2DM or without diabetes by multivariate subgrouping suggested that the individual components of the FRS could enrich or improve CVD risk assessment. Further research was required to clarify the potential mechanism.
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Affiliation(s)
- Yaqian Wu
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
| | - Jianling Bai
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
- Key Laboratory of Medical Big Data Research and Application, Nanjing Medical University, Nanjing, China
| | - Mingzhi Zhang
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
| | - Fang Shao
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
| | - Honggang Yi
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
| | - Dongfang You
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
- Dongfang You
| | - Yang Zhao
- Department of Biostatistics, Nanjing Medical University, Nanjing, China
- Key Laboratory of Medical Big Data Research and Application, Nanjing Medical University, Nanjing, China
- Jiangsu Provincial Key Laboratory of Biomarkers of Cancer Prevention and Control, Nanjing Medical University, Nanjing, China
- Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- Key Laboratory of Modern Toxicology, Nanjing Medical University, Nanjing, China
- *Correspondence: Yang Zhao
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Ruberg FL. Phenotype Mapping in Cardiac Amyloidosis. J Am Coll Cardiol 2021; 78:2193-2195. [PMID: 34823662 DOI: 10.1016/j.jacc.2021.09.857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 09/12/2021] [Indexed: 01/07/2023]
Affiliation(s)
- Frederick L Ruberg
- Section of Cardiovascular Medicine, Department of Medicine, and Amyloidosis Center, Boston University School of Medicine, Boston Medical Center, Boston, Massachusetts, USA.
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