Background: The SARS-CoV-2 variant B.1.1.529 (Omicron) was first described in November 2021 and became the dominant variant worldwide. Existing data suggests a reduced disease severity with Omicron infections in comparison to B.1.617.2 (Delta). Differences in characteristics and in-hospital outcomes of COVID-19 patients in Germany during the Omicron period compared to Delta are not thoroughly studied. ICD-10-code-based severe acute respiratory infections (SARI) surveillance represents an integral part of infectious disease control in Germany. Methods: Administrative data from 89 German Helios hospitals was retrospectively analysed. Laboratory-confirmed SARS-CoV-2 infections were identified by ICD-10-code U07.1 and SARI cases by ICD-10-codes J09-J22. COVID-19 cases were stratified by concomitant SARI. A nine-week observational period between December 6, 2021 and February 6, 2022 was defined and divided into three phases with respect to the dominating virus variant (Delta, Delta to Omicron transition, Omicron). Regression analyses adjusted for age, gender and Elixhauser comorbidities were applied to assess in-hospital patient outcomes. Results: A total cohort of 4,494 inpatients was analysed. Patients in the Omicron dominance period were younger (mean age 47.8 vs. 61.6; p < 0.01), more likely to be female (54.7% vs. 47.5%; p < 0.01) and characterized by a lower comorbidity burden (mean Elixhauser comorbidity index 5.4 vs. 8.2; p < 0.01). Comparing Delta and Omicron periods, patients were at significantly lower risk for intensive care treatment (adjusted odds ratio 0.72 [0.57–0.91]; p = 0.005), mechanical ventilation (adjusted odds ratio 0.42 [0.31–0.57]; p < 0.001), and in-hospital mortality (adjusted odds ratio 0.42 [0.32–0.56]; p < 0.001). This also applied mostly to the separate COVID-SARI group. During the Delta to Omicron transition, case numbers of COVID-19 without SARI exceeded COVID-SARI for the first time in the pandemic’s course. Conclusion: Patient characteristics and outcomes differ during the Omicron dominance period as compared to Delta suggesting a reduced disease severity with Omicron infections. SARI surveillance might play a crucial role in assessing disease severity of future SARS-CoV-2 variants.

Aims: The diagnostic application of artificial intelligence (AI)-based models to detect cardiovascular diseases from electrocardiograms (ECGs) evolves, and promising results were reported. However, external validation is not available for all published algorithms. The aim of this study was to validate an existing algorithm for the detection of left ventricular systolic dysfunction (LVSD) from 12-lead ECGs. Methods and results: Patients with digitalized data pairs of 12-lead ECGs and echocardiography (at intervals of ≤7 days) were retrospectively selected from the Heart Center Leipzig ECG and electronic medical records databases. A previously developed AI-based model was applied to ECGs and calculated probabilities for LVSD. The area under the receiver operating characteristic curve (AUROC) was computed overall and in cohorts stratified for baseline and ECG characteristics. Repeated echocardiography studies recorded ≥3 months after index diagnostics were used for follow-up (FU) analysis. At baseline, 42 291 ECG-echocardiography pairs were analysed, and AUROC for LVSD detection was 0.88. Sensitivity and specificity were 82% and 77% for the optimal LVSD probability cut-off based on Youden’s J. AUROCs were lower in ECG subgroups with tachycardia, atrial fibrillation, and wide QRS complexes. In patients without LVSD at baseline and available FU, model-generated high probability for LVSD was associated with a four-fold increased risk of developing LVSD during FU. Conclusion: We provide the external validation of an existing AI-based ECG-analysing model for the detection of LVSD with robust performance metrics. The association of false positive LVSD screenings at baseline with a deterioration of ventricular function during FU deserves a further evaluation in prospective trials.

The COVID-19 pandemic and subsequent consequences led to profound changes in health care worldwide in 2020. Excess all-cause mortality was reported during incidence peaks of infections for several countries.^1-7 Moreover, significant reductions in inpatient hospital admission rates were observed in parallel to the pandemic waves for cardiovascular, respiratory, and intestinal diseases.^8-11 Especially in studies investigating patient treatment pathways for cardiac diseases, those hospitalization deficits were accompanied by an increased relative in-hospital mortality rate in some cohorts.^12-14 Whether numbers of absolute deaths were also increased in those or other disease groups is unclear because of lacking data but must be considered as 1 step toward adequately interpreting the implications of the pandemic on the health care system. Therefore, the aim of this study was to investigate hospitalization rates and compare relative in-hospital mortality rates with absolute mortality incidences across a broad spectrum of disease groups, comparing data from 2020 with those of the prepandemic years of 2016-2019 to better understand inpatient care pathways.

Aims: Same-day discharge (SDD) following catheter ablation (CA) of atrial fibrillation (AF) was already introduced in selected facilities in Europe, but a widespread implementation has not yet succeeded. Data on patients’ perspectives are lacking. Therefore, we conducted a survey to address patients’ beliefs towards SDD and identify variables that are associated with their evaluation. Methods and results: As part of the prospective, monocentric FAST AFA trial, patients aged ≥20 years undergoing left atrial CA for AF were asked to participate in the survey consisting of a study-specific questionnaire, the AF knowledge scale, and pre-defined patient-reported outcome measures. The study cohort was stratified based on SDD willingness, and a logistic regression analysis was used to identify predictors for patients’ valuation. Between 26 July 2021 and 01 July 2022, 256 of 376 screened patients consented to study participation of whom 248 (mean age 61.8 years, 33.9% female) completed the SDD survey. Of them, 50.0% were willing to have SDD concepts integrated into their clinical course with increased patient comfort (27.5%), shorter waiting times (14.6%), and a cost-efficient treatment (14.0%) being imaginable benefits. In contrast, expressed concerns included uncertainties with occurring complaints (50.6%), the insufficient recognition (47.8%), and treatment (48.9%) of complications. European Heart Rhythm Association class at baseline and inpatient treatments within the preceding year were predictors for SDD willingness whereas comorbidity burden or AF knowledge were not. Conclusion: We provide a detailed survey expressing patients’ beliefs towards SDD following left atrial CA. Our findings may facilitate adequate patient selection to improve the future implementation of SDD programs in suitable cohorts.

Introduction: Reliable surveillance systems to monitor trends of COVID-19 case numbers and the associated healthcare burden play a central role in efficient pandemic management. In Germany, the federal government agency Robert-Koch-Institute uses an ICD-code-based inpatient surveillance system, ICOSARI, to assess temporal trends of severe acute respiratory infection (SARI) and COVID-19 hospitalization numbers. In a similar approach, we present a large-scale analysis covering four pandemic waves derived from the Initiative of Quality Medicine (IQM), a German-wide network of acute care hospitals. Methods: Routine data from 421 hospitals for the years 2019– 2021 with a “pre-pandemic” period (01– 01-2019 to 03– 03-2020) and a “pandemic” period (04– 03-2020 to 31– 12-2021) was analysed. SARI cases were defined by ICD-codes J09-J22 and COVID-19 by ICD-codes U07.1 and U07.2. The following outcomes were analysed: intensive care treatment, mechanical ventilation, in-hospital mortality. Results: Over 1.1 million cases of SARI and COVID-19 were identified. Patients with COVID-19 and additional codes for SARI were at higher risk for adverse outcomes when compared to non-COVID SARI and COVID-19 without any coding for SARI. During the pandemic period, non-COVID SARI cases were associated with 28%, 23% and 27% higher odds for intensive care treatment, mechanical ventilation and in-hospital mortality, respectively, compared to pre-pandemic SARI. Conclusion: The nationwide IQM network could serve as an excellent data source to enhance COVID-19 and SARI surveillance in view of the ongoing pandemic. Future developments of COVID-19/SARI case numbers and associated outcomes should be closely monitored to identify specific trends, especially considering novel virus variants. Graphical Abstract: