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- Attia, Zachi I., et al.
(författare)
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Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram
- 2021
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Ingår i: Mayo Clinic proceedings. - : ELSEVIER SCIENCE INC. - 0025-6196 .- 1942-5546. ; 96:8, s. 2081-2094
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). Methods: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. Results: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. Conclusion: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control. (C) 2021 Mayo Foundation Medical Education and Research
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- Loncar, Goran, et al.
(författare)
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Effect of beta blockade on natriuretic peptides and copeptin in elderly patients with heart failure and preserved or reduced ejection fraction: Results from the CIBIS-ELD trial
- 2012
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Ingår i: Clinical Biochemistry. - : Elsevier BV. - 0009-9120 .- 1873-2933. ; 45, s. 117-122
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Tidskriftsartikel (refereegranskat)abstract
- Background: We sought to investigate the effect of beta-blocker (BB) up-titration on serum levels of NT-proBNP and copeptin in patients with heart failure (HF) with reduced (HFREF) or preserved ejection fraction (HFPEF). Methods: Serial measurements of NT-proBNP and copeptin were obtained after initiation of BB up-titration in 219 elderly patients with HFREF or HFPEF. Results: After initial increasing trend of NT-proBNP at 6. weeks in HFREF patients, there was a subsequent decrease at 12. weeks of BB treatment up-titration (p = 0.003), while no difference was found compared to baseline levels. In contrast to NT-proBNP, there was a continuous decreasing trend of copeptin in HFREF patients (at 12. weeks: p = 0.026). In HFPEF patients, NT-proBNP significantly decreased (p = 0.043) compared to copeptin after 12. weeks of BB up-titration. Conclusions: After 12. weeks of BB optimization copeptin might reflect successful up-titration faster than NT-proBNP in HFREF, while the opposite was found in patients with HFPEF. © 2011 Elsevier B.V..
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- Sokolski, Mateusz, et al.
(författare)
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Phenotype clustering of hospitalized high-risk patients with COVID-19-a machine learning approach within the multicentre, multinational PCHF-COVICAV registry
- 2024
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Ingår i: CARDIOLOGY JOURNAL. - 1897-5593 .- 1898-018X.
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: The high-risk population of patients with cardiovascular (CV) disease or risk factors (RF) suffering from COVID-19 is heterogeneous. Several predictors for impaired prognosis have been identified. However, with machine learning (ML) approaches, certain phenotypes may be confined to classify the affected population and to predict outcome. This study aimed to phenotype patients using unsupervised ML technique within the International Postgraduate Course Heart Failure Registry for patients hospitalized with COVID-19 and Cardiovascular disease and/or RF (PCHF-COVICAV). Material and methods: Patients from the eight centres with follow-up data available from the PCHF-COVICAV registry were included in this ML analysis (K-medoids algorithm). Results: Out of 617 patients included into the prospective part of the registry, 458 [median age: 76 (IQR:65-84) years, 55% male] were analyzed and 46 baseline variables, including demographics, clinical status, comorbidities and biochemical characteristics were incorporated into the ML. Three clusters were extracted by this ML method. Cluster 1 (n = 181) represents mainly women with the least number of overall comorbidities and cardiovascular RF. Cluster 2 (n = 227) is characterized mainly by men with non-CV conditions and less severe symptoms of infection. Cluster 3 (n = 50) mainly represents men with the highest prevalence of cardiac comorbidities and RF, more extensive inflammation and organ dysfunction with the highest 6-month all-cause mortality risk. Conclusions: The ML process has identified three important clinical clusters from hospitalized COVID-19 CV and/or RF patients. The cluster of males with severe CV disease, particularly HF, and multiple RF presenting with increased inflammation had a particularly poor outcome.
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