Incidence of hypoxic hepatitis in patients with cardiogenic shock and association with mortality.

AIMS: Shock of any cause leads to end-organ damage due to ischaemia, especially in perfusion-sensitive organs such as the liver. In septic shock, hypoxic hepatitis (S-HH) is defined as the 20-fold increase of the upper normal limit of aspartate aminotransferase (ASAT) and alanine aminotransferase (ALAT) and is associated with a mortality of up to 60%. However, as pathophysiology, dynamics, and treatment differ between septic and cardiogenic shock (CS), the S-HH definition may not be suitable for CS. Therefore, we aim to evaluate if the S-HH definition is applicable in CS patients. METHODS AND RESULTS: This analysis was based on a registry of all-comer CS patients treated between 2009 and 2019 at a tertiary care centre with exclusion of minors and patients without all necessary ASAT and ALAT values. N = 698. During in-hospital follow-up, 386 (55.3%) patients died. The S-HH was not significantly associated with in-hospital mortality in CS patients. To define HH among patients with CS (C-HH), optimal cut-off values were found to be ≥1.34-fold increase for ASAT and ≥1.51-fold increase for ALAT in serial measurements. The incidence of C-HH was 254/698 patients (36%) and C-HH showed a strong association with in-hospital mortality (odds ratio 2.36, 95% confidence interval: 1.61, 3.49). CONCLUSION: The C-HH is a frequent and relevant comorbidity in patients with CS, although its definition varies from the established definition of HH in patients with septic shock. As C-HH contributed to excess mortality risk, these findings emphasize the need for further investigation of therapies reducing the occurrence of C-HH and also improving the associated outcome.

Pragmatic screening for heart failure in the general population using an electrocardiogram-based neural network.

AIMS: We aim to develop a pragmatic screening tool for heart failure at the general population level. METHODS AND RESULTS: This study was conducted within the Hamburg-City-Health-Study, an ongoing, prospective, observational study enrolling randomly selected inhabitants of the city of Hamburg aged 45-75 years. Heart failure was diagnosed per current guidelines. Using only digital electrocardiograms (ECGs), a convolutional neural network (CNN) was built to discriminate participants with and without heart failure. As comparisons, known risk variables for heart failure were fitted into a logistic regression model and a random forest classifier. Of the 5299 individuals included into this study, 318 individuals (6.0%) had heart failure. Using only the digital ECGs instead of several risk variables as an input, the CNN provided a comparable predictive accuracy for heart failure versus the logistic regression model and the random forest classifier [area under the curve (AUC) of 0.75, a sensitivity of 0.67 and a specificity of 0.69 for the CNN; AUC 0.77, a sensitivity of 0.63 and a specificity of 0.76 for the logistic regression; AUC 0.79, a sensitivity of 0.67 and a specificity of 0.72 for the random forest classifier]. CONCLUSIONS: Using a CNN build on digital ECGs only and requiring no additional input, we derived a screening tool for heart failure in the general population. This could be perfectly embedded into clinical routine of general practitioners, as it builds on an already established diagnostic tool and does not require additional, time-consuming input. This could help to alleviate the underdiagnosis of heart failure.