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J Am Heart Assoc ; 10(16): e021204, 2021 08 17.
Article in English | MEDLINE | ID: covidwho-1352600

ABSTRACT

Background Limited information is available regarding in-hospital cardiac arrest (IHCA) in patients with COVID-19. Methods and Results We leveraged the American Heart Association COVID-19 Cardiovascular Disease (AHA COVID-19 CVD) Registry to conduct a cohort study of adults hospitalized for COVID-19. IHCA was defined as those with documentation of cardiac arrest requiring medication or electrical shock for resuscitation. Mixed effects models with random intercepts were used to identify independent predictors of IHCA and mortality while accounting for clustering at the hospital level. The study cohort included 8518 patients (6080 not in the intensive care unit [ICU]) with mean age of 61.5 years (SD 17.5). IHCA occurred in 509 (5.9%) patients overall with 375 (73.7%) in the ICU and 134 (26.3%) patients not in the ICU. The majority of patients at the time of ICHA were not in a shockable rhythm (76.5%). Independent predictors of IHCA included older age, Hispanic ethnicity (odds ratio [OR], 1.9; CI, 1.4-2.4; P<0.001), and non-Hispanic Black race (OR, 1.5; CI, 1.1-1.9; P=0.004). Other predictors included oxygen use on admission, quick Sequential Organ Failure Assessment score on admission, and hypertension. Overall, 35 (6.9%) patients with IHCA survived to discharge, with 9.1% for ICU and 0.7% for non-ICU patients. Conclusions Older age, Black race, and Hispanic ethnicity are independent predictors of IHCA in patients with COVID-19. Although the incidence is much lower than in ICU patients, approximately one-quarter of IHCA events in patients with COVID-19 occur in non-ICU settings, with the latter having a substantially lower survival to discharge rate.


Subject(s)
African Americans , COVID-19 , Heart Arrest/ethnology , Inpatients , Intensive Care Units , Patient Admission , Age Factors , Aged , Aged, 80 and over , Death, Sudden, Cardiac/ethnology , Death, Sudden, Cardiac/prevention & control , Female , Heart Arrest/diagnosis , Heart Arrest/mortality , Heart Arrest/therapy , Hospital Mortality/ethnology , Humans , Incidence , Male , Middle Aged , Prognosis , Race Factors , Registries , Risk Assessment , Risk Factors , Time Factors , United States/epidemiology
2.
Mayo Clin Proc ; 96(8): 2081-2094, 2021 08.
Article in English | MEDLINE | ID: covidwho-1336718

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.


Subject(s)
Artificial Intelligence , COVID-19/diagnosis , Electrocardiography , Case-Control Studies , Humans , Predictive Value of Tests , Sensitivity and Specificity
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