Variation in the risk of death due to COVID-19: An international multicenter cohort study of hospitalized adults.

BACKGROUND: There is wide variation in mortality among patients hospitalized with COVID-19. Whether this is related to patient or hospital factors is unknown. OBJECTIVE: To compare the risk of mortality for patients hospitalized with COVID-19 and to determine whether the majority of that variation was explained by differences in patient characteristics across sites. DESIGN, SETTING, AND PARTICIPANTS: An international multicenter cohort study of hospitalized adults with laboratory-confirmed COVID-19 enrolled from 10 hospitals in Ontario, Canada and 8 hospitals in Copenhagen, Denmark between January 1, 2020 and November 11, 2020. MAIN OUTCOMES AND MEASURES: Inpatient mortality. We used a multivariable multilevel regression model to compare the in-hospital mortality risk across hospitals and quantify the variation attributable to patient-level factors. RESULTS: There were 1364 adults hospitalized with COVID-19 in Ontario (n = 1149) and in Denmark (n = 215). In Ontario, the absolute risk of in-hospital mortality ranged from 12.0% to 39.8% across hospitals. Ninety-eight percent of the variation in mortality in Ontario was explained by differences in the characteristics of the patients. In Denmark, the absolute risk of inpatients ranged from 13.8% to 20.6%. One hundred percent of the variation in mortality in Denmark was explained by differences in the characteristics of the inpatients. CONCLUSION: There was wide variation in inpatient COVID-19 mortality across hospitals, which was largely explained by patient-level factors, such as age and severity of presenting illness. However, hospital-level factors that could have affected care, including resource availability and capacity, were not taken into account. These findings highlight potential limitations in comparing crude mortality rates across hospitals for the purposes of reporting on the quality of care.

Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence-powered analysis of 12-lead intake electrocardiogram.

BACKGROUND: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. OBJECTIVE: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). METHODS: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. RESULTS: A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. CONCLUSION: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.

Heart failure in COVID-19: the multicentre, multinational PCHF-COVICAV registry.

AIMS: We assessed the outcome of hospitalized coronavirus disease 2019 (COVID-19) patients with heart failure (HF) compared with patients with other cardiovascular disease and/or risk factors (arterial hypertension, diabetes, or dyslipidaemia). We further wanted to determine the incidence of HF events and its consequences in these patient populations. METHODS AND RESULTS: International retrospective Postgraduate Course in Heart Failure registry for patients hospitalized with COVID-19 and CArdioVascular disease and/or risk factors (arterial hypertension, diabetes, or dyslipidaemia) was performed in 28 centres from 15 countries (PCHF-COVICAV). The primary endpoint was in-hospital mortality. Of 1974 patients hospitalized with COVID-19, 1282 had cardiovascular disease and/or risk factors (median age: 72 [interquartile range: 62-81] years, 58% male), with HF being present in 256 [20%] patients. Overall in-hospital mortality was 25% (n = 323/1282 deaths). In-hospital mortality was higher in patients with a history of HF (36%, n = 92) compared with non-HF patients (23%, n = 231, odds ratio [OR] 1.93 [95% confidence interval: 1.44-2.59], P < 0.001). After adjusting, HF remained associated with in-hospital mortality (OR 1.45 [95% confidence interval: 1.01-2.06], P = 0.041). Importantly, 186 of 1282 [15%] patients had an acute HF event during hospitalization (76 [40%] with de novo HF), which was associated with higher in-hospital mortality (89 [48%] vs. 220 [23%]) than in patients without HF event (OR 3.10 [2.24-4.29], P < 0.001). CONCLUSIONS: Hospitalized COVID-19 patients with HF are at increased risk for in-hospital death. In-hospital worsening of HF or acute HF de novo are common and associated with a further increase in in-hospital mortality.

Ratio of Transmitral Early Filling Velocity to Early Diastolic Strain Rate as a Predictor of Cardiovascular Morbidity and Mortality Following Acute Coronary Syndrome.

The ratio of early mitral inflow velocity (E) to early diastolic strain rate (E/e'sr) is a significant predictor of cardiac outcomes in various patient populations. This study aims to evaluate the predictive value of E/e'sr for heart failure, acute myocardial infarction, and death due to cardiovascular disease following acute coronary syndrome (ACS). In total, 432 ACS patients underwent echocardiography following percutaneous coronary intervention. The end point was the composite of heart failure, acute myocardial infarction, and death due to cardiovascular disease. Median follow-up was 4.4 (interquartile range 0.2 to 6.3) years. During the follow-up period, 199 (46.1%) met the composite outcome. Mean value of E/e'sr in patients was 0.70 ± 0.37 m. In univariable Cox regression, E/e'sr was a predictor of the composite outcome (hazard ratio [HR] 1.05 95% confidence interval [CI] 1.03 to 1.07, p <0.001, per 0.10 m increase). After multivariable adjustment for demographic and clinical parameters, E/e'sr remained an independent predictor (HR 1.03; 95% CI 1.01 to 1.06; p = 0.013, per 0.10 m increase). Global longitudinal strain (GLS) modified the relation between E/e'sr and outcome (p value for interaction = 0.011). In ACS patients with a relatively preserved systolic function assessed by GLS (GLS ≥ 13.2%), E/e'sr showed to be a significant predictor (HR 1.20; 95% CI 1.06 to 1.36; p = 0.005, per 0.10 m increase). In contrast, E/e'sr was not a significant predictor in ACS patients with impaired systolic function (GLS < 13.2%; HR 1.02; 95% CI 0.99 to 1.04; p = 0.28). In conclusion, E/e'sr provides important prognostic information regarding cardiovascular morbidity and mortality in ACS patients. However, E/e'sr was not an independent predictor over that of echocardiographic parameters. Furthermore, E/e'sr is a stronger prognosticator in patients with relatively preserved systolic function as opposed to patients with impaired systolic function.