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Bhatt AS, Kosiborod MN, Claggett BL, Miao ZM, Vaduganathan M, Lam CSP, Hernandez AF, Martinez FA, Inzucchi SE, Shah SJ, de Boer RA, Jhund PS, Desai AS, Fang JC, Han Y, Comin-Colet J, Drożdż J, Vardeny O, Merkely B, Lindholm D, Peterson M, Langkilde AM, McMurray JJV, Solomon SD. Impact of COVID-19 in patients with heart failure with mildly reduced or preserved ejection fraction enrolled in the DELIVER trial. Eur J Heart Fail 2023; 25:2177-2188. [PMID: 37771274 DOI: 10.1002/ejhf.3043] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 09/15/2023] [Accepted: 09/25/2023] [Indexed: 09/30/2023] Open
Abstract
AIM COVID-19 may affect clinical risk in patients with heart failure. DELIVER began before and was conducted during the COVID-19 pandemic. This study aimed to evaluate the association between COVID-19 and clinical outcomes among DELIVER participants. METHODS AND RESULTS Participants with chronic heart failure with mildly reduced or preserved ejection fraction (HFmrEF/HFpEF) were randomized to dapagliflozin or placebo across 350 sites in 20 countries. COVID-19 was investigator-reported and the contribution of COVID-19 to death was centrally adjudicated. We assessed (i) the incidence of COVID-19, (ii) event rates before/during the pandemic, and (iii) risks of death after COVID-19 diagnosis compared to risks of death in participants without COVID-19. Further, we performed a sensitivity analysis assessing treatment effects of dapagliflozin vs. placebo censored at pandemic onset. Of 6263 participants, 589 (9.4%) developed COVID-19, of whom 307 (52%) required/prolonged hospitalization. A total of 155 deaths (15% of all deaths) were adjudicated as definitely/possibly COVID-19-related. COVID-19 cases and deaths did not differ by randomized assignment. Death rate in the 12 months following diagnosis was 56.1 (95% confidence interval [CI] 48.0-65.6) versus 6.4 (95% CI 6.0-6.8)/100 participant-years among trial participants with versus without COVID-19 (adjusted hazard ratio [aHR] 8.60, 95% CI 7.18-10.30). Risk was highest 0-3 months following diagnosis (153.5, 95% CI 130.3-180.8) and remained elevated at 3-6 months (12.6, 95% CI 6.6-24.3/100 participant-years). After excluding investigator-reported fatal COVID-19 events, all-cause death rates in the 12 months following diagnosis among COVID-19 survivors (n = 458) remained higher (aHR 2.46, 95% CI 1.83-3.33) than rates for all trial participants from randomization, with censoring of participants who developed COVID-19 at the time of diagnosis. Dapagliflozin reduced cardiovascular death/worsening HF events when censoring participants at COVID-19 diagnosis (HR 0.81, 95% CI 0.72-0.91) and pandemic onset (HR 0.72, 95% CI 0.58-0.89). There were no diabetic ketoacidosis or major hypoglycaemic events within 30 days of COVID-19. CONCLUSION DELIVER is one of the most extensive experiences with COVID-19 of any cardiovascular trial, with >75% of follow-up time occurring during the pandemic. COVID-19 was common, with >50% of cases leading to hospitalization or death. Treatment benefits of dapagliflozin persisted when censoring at COVID-19 diagnosis and pandemic onset. Patients surviving COVID-19 had a high early residual risk. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov Identifier NCT03619213.
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Affiliation(s)
- Ankeet S Bhatt
- Kaiser Permanente San Francisco Medical Center and Division of Research, San Francisco, CA, USA
| | - Mikhail N Kosiborod
- Saint Luke's Mid America Heart Institute and University of Missouri-Kansas City, Kansas City, MO, USA
| | - Brian L Claggett
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zi Michael Miao
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Muthiah Vaduganathan
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Carolyn S P Lam
- National Heart Centre Singapore & Duke-National University of Singapore, Singapore
| | | | | | | | - Sanjiv J Shah
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Rudolf A de Boer
- Erasmus Medical Center Department of Cardiology, Rotterdam, The Netherlands
| | - Pardeep S Jhund
- BHF Glasgow Cardiovascular Research Center, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Akshay S Desai
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - James C Fang
- University of Utah Health Sciences Center, Salt Lake City, UT, USA
| | - Yaling Han
- Department of Cardiology, General Hospital of Shenyang Military Region, Shenyang, China
| | - Josep Comin-Colet
- Department of Clinical Sciences, School of Medicine, University of Barcelona, Barcelona, Spain
| | | | - Orly Vardeny
- Minneapolis VA Center for Care Delivery and Outcomes Research, University of Minnesota, Minneapolis, MN, USA
| | | | - Daniel Lindholm
- Late-Stage Development, Cardiovascular, Renal, and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - Magnus Peterson
- Late-Stage Development, Cardiovascular, Renal, and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - Anna Maria Langkilde
- Late-Stage Development, Cardiovascular, Renal, and Metabolism, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - John J V McMurray
- BHF Glasgow Cardiovascular Research Center, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Scott D Solomon
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Saputra J, Lawrencya C, Saini JM, Suharjito S. Hyperparameter optimization for cardiovascular disease data-driven prognostic system. Vis Comput Ind Biomed Art 2023; 6:16. [PMID: 37524951 PMCID: PMC10390457 DOI: 10.1186/s42492-023-00143-6] [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: 03/15/2023] [Accepted: 07/04/2023] [Indexed: 08/02/2023] Open
Abstract
Prediction and diagnosis of cardiovascular diseases (CVDs) based, among other things, on medical examinations and patient symptoms are the biggest challenges in medicine. About 17.9 million people die from CVDs annually, accounting for 31% of all deaths worldwide. With a timely prognosis and thorough consideration of the patient's medical history and lifestyle, it is possible to predict CVDs and take preventive measures to eliminate or control this life-threatening disease. In this study, we used various patient datasets from a major hospital in the United States as prognostic factors for CVD. The data was obtained by monitoring a total of 918 patients whose criteria for adults were 28-77 years old. In this study, we present a data mining modeling approach to analyze the performance, classification accuracy and number of clusters on Cardiovascular Disease Prognostic datasets in unsupervised machine learning (ML) using the Orange data mining software. Various techniques are then used to classify the model parameters, such as k-nearest neighbors, support vector machine, random forest, artificial neural network (ANN), naïve bayes, logistic regression, stochastic gradient descent (SGD), and AdaBoost. To determine the number of clusters, various unsupervised ML clustering methods were used, such as k-means, hierarchical, and density-based spatial clustering of applications with noise clustering. The results showed that the best model performance analysis and classification accuracy were SGD and ANN, both of which had a high score of 0.900 on Cardiovascular Disease Prognostic datasets. Based on the results of most clustering methods, such as k-means and hierarchical clustering, Cardiovascular Disease Prognostic datasets can be divided into two clusters. The prognostic accuracy of CVD depends on the accuracy of the proposed model in determining the diagnostic model. The more accurate the model, the better it can predict which patients are at risk for CVD.
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Affiliation(s)
- Jayson Saputra
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia.
| | - Cindy Lawrencya
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
| | - Jecky Mitra Saini
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
| | - Suharjito Suharjito
- Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University, Jakarta 11480, Indonesia
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Aragona CO, Bagnato G, Tomeo S, Rosa DL, Chiappalone M, Tringali MC, Singh EB, Versace AG. Echocardiography in Coronavirus Disease 2019 Era: A Single Tool for Diagnosis and Prognosis. J Cardiovasc Echogr 2023; 33:10-16. [PMID: 37426709 PMCID: PMC10328134 DOI: 10.4103/jcecho.jcecho_11_23] [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: 02/04/2023] [Revised: 03/07/2023] [Accepted: 03/18/2023] [Indexed: 07/11/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is characterized by multi-organ involvement, including respiratory and cardiac events. Echocardiography is widely considered the first-choice tool for the evaluation of cardiac structures and function because of its reproducibility, feasibility, easy to use at bedside, and for good cost-effectiveness. The aim of our literature review is to define the utility of echocardiography in the prediction of prognosis and mortality in COVID-19 patients with mild to critical respiratory illness, with or without known cardiovascular disease. Moreover, we focused our attention on classical echocardiographic parameters and the use of speckle tracking to predict the evolution of respiratory involvement. Finally, we tried to explore the possible relationship between pulmonary disease and cardiac manifestations.
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Affiliation(s)
- Caterina Oriana Aragona
- Department of Emergency, Unit of Emergency Mecicine, AOU Policlinico “G.Martino”, University of Messina, Messina, Italy
| | - Gianluca Bagnato
- Department of Emergency, Unit of Emergency Mecicine, AOU Policlinico “G.Martino”, University of Messina, Messina, Italy
| | - Simona Tomeo
- Department of Emergency, Unit of Emergency Mecicine, AOU Policlinico “G.Martino”, University of Messina, Messina, Italy
| | - Daniela La Rosa
- Department of Emergency, Unit of Emergency Mecicine, AOU Policlinico “G.Martino”, University of Messina, Messina, Italy
| | - Marianna Chiappalone
- Department of Emergency, Unit of Emergency Mecicine, AOU Policlinico “G.Martino”, University of Messina, Messina, Italy
| | - Maria Concetta Tringali
- Department of Emergency, Unit of Emergency Mecicine, AOU Policlinico “G.Martino”, University of Messina, Messina, Italy
| | - Emanuele Balwinder Singh
- Department of Emergency, Unit of Emergency Mecicine, AOU Policlinico “G.Martino”, University of Messina, Messina, Italy
| | - Antonio Giovanni Versace
- Department of Emergency, Unit of Emergency Mecicine, AOU Policlinico “G.Martino”, University of Messina, Messina, Italy
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