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10.1016/j.mayocp.2021.05.027 http://scihub22266oqcxt.onion/10.1016/j.mayocp.2021.05.027 34353468!8327278!34353468     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Mayo+Clin+Proc 2021 ; 96 (8): 2081-2094 Nephropedia Template TP {{tp|p=34353468|t=2021. Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram |pdf=|usr=}}{{34353468}} gab.com Text Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram JO: Mayo Clin Proc http://www.kidney.de/pget.php?&tw=1&pmid=34353468 #FOAvip 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. Twit Text FOAVip Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram ????Mayo Clin Proc http://www.kidney.de/pget.php?&tw=1&pmid=34353468 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34353468 #FOAvip English Wikipedia <ref>{{Cite pmid|34353468}}</ref> Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram #MMPMID34353468 Attia ZI; Kapa S; Dugan J; Pereira N; Noseworthy PA; Jimenez FL; Cruz J; Carter RE; DeSimone DC; Signorino J; Halamka J; Chennaiah Gari NR; Madathala RS; Platonov PG; Gul F; Janssens SP; Narayan S; Upadhyay GA; Alenghat FJ; Lahiri MK; Dujardin K; Hermel M; Dominic P; Turk-Adawi K; Asaad N; Svensson A; Fernandez-Aviles F; Esakof DD; Bartunek J; Noheria A; Sridhar AR; Lanza GA; Cohoon K; Padmanabhan D; Pardo Gutierrez JA; Sinagra G; Merlo M; Zagari D; Rodriguez Escenaro BD; Pahlajani DB; Loncar G; Vukomanovic V; Jensen HK; Farkouh ME; Luescher TF; Su Ping CL; Peters NS; Friedman PA Mayo Clin Proc 2021[Aug]; 96 (8): 2081-2094 PMID34353468show ga 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. |*Artificial Intelligence[MESH] |*Electrocardiography[MESH] |COVID-19/*diagnosis[MESH] |Case-Control Studies[MESH] |Humans[MESH] |Predictive Value of Tests[MESH]Rapid Exclusion of COVID Infection With the Artificial Intelligence E(epmc).pdf DeepDyve Pubget Overpricing