| 2. |
- Attia, Zachi I., et al.
(author)
-
Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram
- 2021
-
In: Mayo Clinic proceedings. - : ELSEVIER SCIENCE INC. - 0025-6196 .- 1942-5546. ; 96:8, s. 2081-2094
-
Journal article (peer-reviewed)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
|
|
| 3. |
- Loncar, Goran, et al.
(author)
-
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
-
In: Clinical Biochemistry. - : Elsevier BV. - 0009-9120 .- 1873-2933. ; 45, s. 117-122
-
Journal article (peer-reviewed)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..
|
|
