Characteristics and outcome of hospitalized patients with heart failure stratified for chronic kidney disease.

AIMS: Heart failure (HF) and chronic kidney disease (CKD) place significant challenges on the healthcare system, and their co-existence is associated with shared adverse outcomes. The multinational CaReMe project was initiated to provide contemporary, real-world epidemiological data on cardiovascular and reno-metabolic diseases. Utilizing data from the German CaReMe cohort, we characterize a multicentric HF population and describe in-hospital outcomes stratified for co-morbid CKD. METHODS AND RESULTS: This retrospective, observational study analysed administrative data from inpatient cases hospitalized in 87 German Helios hospitals between 1 January 2016 and 31 August 2022. The first hospitalization of patients aged ≥18 years with a primary discharge diagnosis of HF, based on ICD-10 codes, were considered the index cases, and subsequent hospitalizations were considered as readmissions. Baseline characteristics and outcomes were stratified for co-morbid CKD using ICD-10-encoding from the index cases. Cox regression was utilized for readmission endpoints and in-hospital mortality. In total, 174 829 index cases (mean age 79 ± 15 years, 49.9% female) were included; of these, 55.0% had coexisting CKD. Patients with CKD were older, suffered from worse HF-related symptoms, had a higher co-morbidity burden, and in-hospital mortality was increased at index and during follow-up. Prevalent CKD was associated with higher rehospitalization rates and was an independent predictor for in-hospital death. CONCLUSIONS: Within this HF inpatient cohort from a multicentric German database, CKD was diagnosed in more than half of the patients and was associated with increased in-hospital mortality at baseline and during follow-up. Rehospitalizations were observed earlier and more frequently in patients with HF and co-morbid CKD.

Retrospective, Observational Analysis on the Impact of SARS-CoV-2 Variant Omicron in Hospitalized Immunocompromised Patients in a German Hospital Network-The VISAGE Study.

AIMS: Endemic SARS-CoV-2 infections still burden the healthcare system and represent a considerable threat to vulnerable patient cohorts, in particular immunocompromised (IC) patients. This study aimed to analyze the in-hospital outcome of IC patients with severe SARS-CoV-2 infection in Germany. METHODS: This retrospective, observational study, analyzed administrative data from inpatient cases (n = 146,324) in 84 German Helios hospitals between 1 January 2022 and 31 December 2022 with regard to in-hospital outcome and health care burden in IC patients during the first 12 months of Omicron dominance. As the primary objective, in-hospital outcomes of patients with COVID-19-related severe acute respiratory infection (SARI) were analyzed by comparing patients with (n = 2037) and without IC diagnoses (n = 14,772). Secondary analyses were conducted on IC patients with (n = 2037) and without COVID-19-related SARI (n = 129,515). A severe in-hospital outcome as a composite endpoint was defined per the WHO definition if one of the following criteria were met: intensive care unit (ICU) treatment, mechanical ventilation (MV), or in-hospital death. RESULTS: In total, 12% of COVID-related SARI cases were IC patients, accounting for 15% of ICU admissions, 15% of MV use, and 16% of deaths, resulting in a higher prevalence of severe in-hospital courses in IC patients developing COVID-19-related SARI compared to non-IC patients (Odds Ratio, OR = 1.4, p < 0.001), based on higher in-hospital mortality (OR = 1.4, p < 0.001), increased need for ICU treatment (OR = 1.3, p < 0.001) and mechanical ventilation (OR = 1.2, p < 0.001). Among IC patients, COVID-19-related SARI profoundly increased the risk for severe courses (OR = 4.0, p < 0.001). CONCLUSIONS: Our findings highlight the vulnerability of IC patients to severe COVID-19. The persistently high prevalence of severe outcomes in these patients in the Omicron era emphasizes the necessity for continuous in-hospital risk assessment and monitoring of IC patients.

Artificial intelligence-based identification of left ventricular systolic dysfunction from 12-lead electrocardiograms: external validation and advanced application of an existing model.

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.

Patient perspectives on same-day discharge following catheter ablation for atrial fibrillation: results from a patient survey as part of the monocentric FAST AFA trial.

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.

COVID-19 and Severe Acute Respiratory Infections: Monitoring Trends in 421 German Hospitals During the First Four Pandemic Waves.

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.

A Digital Infrastructure for Cardiovascular Patient Care Based on Mobile Health Data and Patient-Reported Outcomes: Concept Details of the Helios TeleWear Project Including Preliminary Experiences.

BACKGROUND: Mobile health (mHealth) approaches are already having a fundamental impact on clinical practice in cardiovascular medicine. A variety of different health apps and wearable devices for capturing health data such as electrocardiograms (ECGs) exist. However, most mHealth technologies focus on distinct variables without integrating patients' quality of life, and the impact on clinical outcome measures of implementing those digital solutions into cardiovascular health care is still to be determined. OBJECTIVE: Within this document, we describe the TeleWear project, which was recently initiated as an approach for contemporary patient management integrating mobile-collected health data and the standardized mHealth-guided measurement of patient-reported outcomes (PROs) in patients with cardiovascular disease. METHODS: The specifically designed mobile app and clinical frontend form the central elements of our TeleWear infrastructure. Because of its flexible framework, the platform allows far-reaching customization with the possibility to add different mHealth data sources and respective questionnaires (patient-reported outcome measures). RESULTS: With initial focus on patients with cardiac arrhythmias, a feasibility study is currently carried out to assess wearable-recorded ECG and PRO transmission and its evaluation by physicians using the TeleWear app and clinical frontend. First experiences made during the feasibility study yielded positive results and confirmed the platform's functionality and usability. CONCLUSIONS: TeleWear represents a unique mHealth approach comprising PRO and mHealth data capturing. With the currently running TeleWear feasibility study, we aim to test and further develop the platform in a real-world setting. A randomized controlled trial including patients with atrial fibrillation that investigates PRO- and ECG-based clinical management based on the established TeleWear infrastructure will evaluate its clinical benefits. Widening the spectrum of health data collection and interpretation beyond the ECG and use of the TeleWear infrastructure in different patient subcohorts with focus on cardiovascular diseases are further milestones of the project with the ultimate goal to establish a comprehensive telemedical center entrenched by mHealth.

Changing trends of patient characteristics and treatment pathways during the COVID-19 pandemic: A cross-sectional analysis of 72,459 inpatient cases from the German Helios database.

Background: This study compared patient profiles and clinical courses of SARS-CoV-2 infected inpatients over different pandemic periods. Methods: In a retrospective cross-sectional analysis, we examined administrative data of German Helios hospitals using ICD-10-codes at discharge. Inpatient cases with SARS-CoV-2 infection admitted between 03/04/2020 and 07/19/2022 were included irrespective of the reason for hospitalization. All endpoints were timely assigned to admission date for trend analysis. The first pandemic wave was defined by change points in time-series of incident daily infections and compared with different later pandemic phases according to virus type predominance. Results: We included 72,459 inpatient cases. Patients hospitalized during the first pandemic wave (03/04/2020-05/05/2020; n = 1,803) were older (68.5 ± 17.2 vs. 64.4 ± 22.6 years, p < 0.01) and severe acute respiratory infections were more prevalent (85.2 vs. 53.3%, p < 0.01). No differences were observed with respect to distribution of sex, but comorbidity burden was higher in the first pandemic wave. The risk of receiving intensive care therapy was reduced in all later pandemic phases as was in-hospital mortality when compared to the first pandemic wave. Trend analysis revealed declines of mean age and Elixhauser comorbidity index over time as well as a decline of the utilization of intensive care therapy, mechanical ventilation and in-hospital mortality. Conclusion: Characteristics and outcomes of inpatients with SARS-CoV-2 infection changed throughout the observational period. An ongoing evaluation of trends and care pathways will allow for the assessment of future demands.

Characteristics and outcomes of COVID-19 patients during B.1.1.529 (Omicron) dominance compared to B.1.617.2 (Delta) in 89 German hospitals.

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.

Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network.

BACKGROUND: Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk stratification and quality of care assessment is scarce. We aimed to develop reliable models for mortality prediction in SARI patients utilizing ML algorithms and compare its performances with a classic regression analysis approach. METHODS: Administrative data (dataset randomly split 75%/25% for model training/testing) from years 2016-2019 of 86 German Helios hospitals was retrospectively analyzed. Inpatient SARI cases were defined by ICD-codes J09-J22. Three ML algorithms were evaluated and its performance compared to generalized linear models (GLM) by computing receiver operating characteristic area under the curve (AUC) and area under the precision-recall curve (AUPRC). RESULTS: The dataset contained 241,988 inpatient SARI cases (75 years or older: 49%; male 56.2%). In-hospital mortality was 11.6%. AUC and AUPRC in the testing dataset were 0.83 and 0.372 for GLM, 0.831 and 0.384 for random forest (RF), 0.834 and 0.382 for single layer neural network (NNET) and 0.834 and 0.389 for extreme gradient boosting (XGBoost). Statistical comparison of ROC AUCs revealed a better performance of NNET and XGBoost as compared to GLM. CONCLUSION: ML algorithms for predicting in-hospital mortality were trained and tested on a large real-world administrative dataset of SARI patients and showed good discriminatory performances. Broad application of our models in clinical routine practice can contribute to patients' risk assessment and quality management.

Catheter Ablation of Atrial Fibrillation in Patients With Heart Failure and Preserved Ejection Fraction.

BACKGROUND: Coexistence of atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF) is common, affecting morbidity and prognosis. This study evaluates outcome after cryoballoon ablation for AF in HFpEF compared with patients without heart failure. METHODS: A total of 102 AF patients with left ventricular ejection fraction ≥50% undergoing cryoballoon ablation were prospectively enrolled. Baseline evaluation included echocardiography, stress echocardiography, 6-minute walk test, biomarkers, and quality of life assessment (Short-Form-36). Procedural parameters and clinical, functional and echocardiographic end points at follow-up ≥12 months after AF ablation were compared between patients with and without HFpEF. RESULTS: Patients with HFpEF (n=24) were older (median, 74 years versus 65 years; P=0.001) more often female (83% versus 28%; P<0.001) and characterized by more pronounced AF-related symptoms (median European Heart Rhythm Association score 3 versus 2; P<0.001), higher left atrial pressures (median, 14 mm Hg versus 10 mm Hg; P=0.008), reduced left atrial-appendage velocity (median, 36 cm/s versus 59 cm/s; P<0.001), and reduced distance in the 6-minute walk test (median, 488 m versus 539 m; P<0.001). Patients with HFpEF more often experienced AF recurrence (57% versus 23%; P=0.003), repeat AF ablation (39% versus 14%; P=0.01) and AF-related rehospitalization (26% versus 7%; P=0.016). Heart failure symptoms and elevated cardiac biomarkers persisted, even in patients with HFpEF with successful rhythm control at follow-up. Echocardiographic follow-up showed progression of adverse left atrial remodeling and no relevant improvement in diastolic function in HFpEF. Quality of life improved in patients without HFpEF, whereas patients with HFpEF still exhibited a lower physical component summary score (median, 41.5 versus 53.4; P<0.004). CONCLUSIONS: Patients with HFpEF constitute a distinct subgroup with elevated risk for AF recurrence after cryoballon ablation. Functional hallmarks of HFpEF persist, irrespective of rhythm status at follow-up. Future research is needed to optimize treatment strategies in patients with HFpEF. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04317911.

A Comparative Analysis of In-Hospital Mortality per Disease Groups in Germany Before and During the COVID-19 Pandemic From 2016 to 2020.

Importance: Throughout the ongoing SARS-CoV-2 pandemic, it has been critical to understand not only the viral disease itself but also its implications for the overall health care system. Reports about excess mortality in this regard have mostly focused on overall death counts during specific pandemic phases. Objective: To investigate hospitalization rates and compare in-hospital mortality rates with absolute mortality incidences across a broad spectrum of diseases, comparing 2020 data with those of prepandemic years. Design, Setting, and Participants: Retrospective, cross-sectional, multicentric analysis of administrative data from 5 821 757 inpatients admitted from January 1, 2016, to December 31, 2020, to 87 German Helios primary to tertiary care hospitals. Exposures: Exposure to SARS-CoV-2. Main Outcomes and Measures: Administrative data were analyzed from January 1, 2016, to March 31, 2021, as a consecutive sample for all inpatients. Disease groups were defined according to International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10; German modification) encoded main discharge diagnoses. Incidence rate ratios (IRRs) for hospital admissions and hospital mortality counts, as well as relative mortality risks (RMRs) comparing 2016-2019 with 2020 (exposure to the SARS-CoV-2 pandemic), were calculated with Poisson regression with log-link function. Results: Data were examined for 5 821 757 inpatients (mean [SD] age, 56.4 [25.3] years; 51.5% women), including 125 807 in-hospital deaths. Incidence rate ratios for hospital admissions were associated with a significant reduction for all investigated disease groups (IRR, 0.82; 95% CI, 0.79-0.86; P < .001). After adjusting for age, sex, the Elixhauser Comorbidity Index score, and SARS-CoV-2 infections, RMRs were associated with an increase in infectious diseases (RMR, 1.28; 95% CI, 1.21-1.34; P < .001), musculoskeletal diseases (RMR, 1.19; 95% CI, 1.04-1.36; P = .009), and respiratory diseases (RMR, 1.09; 95% CI, 1.05-1.14; P < .001) but not for the total cohort (RMR, 1.00; 95% CI, 0.99-1.02; P = .66). Regarding in-hospital mortality, IRR was associated with an increase within the ICD-10 chapter of respiratory diseases (IRR, 1.28; 95% CI, 1.13-1.46; P < .001) in comparing 2020 with 2016-2019, in contrast to being associated with a reduction in IRRs for the overall cohort and several other subgroups. After exclusion of patients with SARS-CoV-2 infections, IRRs were associated with a reduction in absolute in-hospital mortality for the overall cohort (IRR, 0.78; 95% CI, 0.72-0.84; P < .001) and the subgroup of respiratory diseases (IRR, 0.83; 95% CI, 0.74-0.92; P < .001). Conclusions and Relevance: This cross-sectional study of inpatients from a multicentric German database suggests that absolute in-hospital mortality for 2020 across disease groups was not higher compared with previous years. Higher IRRs of in-hospital deaths observed in patients with respiratory diseases were likely associated with individuals with SARS-CoV-2 infections.

Expected and observed in-hospital mortality in heart failure patients before and during the COVID-19 pandemic: Introduction of the machine learning-based standardized mortality ratio at Helios hospitals.

BACKGROUND: Reduced hospital admission rates for heart failure (HF) and evidence of increased in-hospital mortality were reported during the COVID-19 pandemic. The aim of this study was to apply a machine learning (ML)-based mortality prediction model to examine whether the latter is attributable to differing case mixes and exceeds expected mortality rates. METHODS AND RESULTS: Inpatient cases with a primary discharge diagnosis of HF non-electively admitted to 86 German Helios hospitals between 01/01/2016 and 08/31/2020 were identified. Patients with proven or suspected SARS-CoV-2 infection were excluded. ML-based models were developed, tuned, and tested using cases of 2016-2018 (n = 64,440; randomly split 75%/25%). Extreme gradient boosting showed the best model performance indicated by a receiver operating characteristic area under the curve of 0.882 (95% confidence interval [CI]: 0.872-0.893). The model was applied on data sets of 2019 and 2020 (n = 28,556 cases) and the hospital standardized mortality ratio (HSMR) was computed as the observed to expected death ratio. Observed mortality rates were 5.84% (2019) and 6.21% (2020), HSMRs based on an individual case-based mortality probability were 100.0 (95% CI: 93.3-107.2; p = 1.000) for 2019 and 99.3 (95% CI: 92.5-106.4; p = .850) for 2020. Within subgroups of age or hospital volume, there were no significant differences between observed and expected deaths. When stratified for pandemic phases, no excess death during the COVID-19 pandemic was observed. CONCLUSION: Applying an ML algorithm to calculate expected inpatient mortality based on administrative data, there was no excess death above expected event rates in HF patients during the COVID-19 pandemic.

From population- to patient-based prediction of in-hospital mortality in heart failure using machine learning.

Aims: Utilizing administrative data may facilitate risk prediction in heart failure inpatients. In this short report, we present different machine learning models that predict in-hospital mortality on an individual basis utilizing this widely available data source. Methods and results: Inpatient cases with a main discharge diagnosis of heart failure hospitalized between 1 January 2016 and 31 December 2018 in one of 86 German Helios hospitals were examined. Comorbidities were defined by ICD-10 codes from administrative data. The data set was randomly split into 75/25% portions for model development and testing. Five algorithms were evaluated: logistic regression [generalized linear models (GLMs)], random forest (RF), gradient boosting machine (GBM), single-layer neural network (NNET), and extreme gradient boosting (XGBoost). After model tuning, the receiver operating characteristics area under the curves (ROC AUCs) were calculated and compared with DeLong's test. A total of 59 074 inpatient cases (mean age 77.6 ± 11.1 years, 51.9% female, 89.4% NYHA Class III/IV) were included and in-hospital mortality was 6.2%. In the test data set, calculated ROC AUCs were 0.853 [95% confidence interval (CI) 0.842-0.863] for GLM, 0.851 (95% CI 0.840-0.862) for RF, 0.855 (95% CI 0.844-0.865) for GBM, 0.836 (95% CI 0.823-0.849) for NNET, and 0.856 (95% CI 9.846-0.867) for XGBoost. XGBoost outperformed all models except GBM. Conclusion: Machine learning-based processing of administrative data enables the creation of well-performing prediction models for in-hospital mortality in heart failure patients.

SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results.

OBJECTIVES: SARS-CoV-2 rapid antigen tests (RAT) provide fast identification of infectious patients when RT-PCR results are not immediately available. We aimed to develop a prediction model for identification of false negative (FN) RAT results. METHODS: In this multicenter trial, patients with documented paired results of RAT and RT-PCR between October 1st 2020 and January 31st 2021 were retrospectively analyzed regarding clinical findings. Variables included demographics, laboratory values and specific symptoms. Three different models were evaluated using Bayesian logistic regression. RESULTS: The initial dataset contained 4,076 patients. Overall sensitivity and specificity of RAT was 62.3% and 97.6%. 2,997 cases with negative RAT results (FN: 120; true negative: 2,877; reference: RT-PCR) underwent further evaluation after removal of cases with missing data. The best-performing model for predicting FN RAT results containing 10 variables yielded an area under the curve of 0.971. Sensitivity, specificity, PPV and NPV for 0.09 as cut-off value (probability for FN RAT) were 0.85, 0.99, 0.7 and 0.99. CONCLUSION: FN RAT results can be accurately identified through ten routinely available variables. Implementation of a prediction model in addition to RAT testing in clinical care can provide decision guidance for initiating appropriate hygiene measures and therefore helps avoiding nosocomial infections.

Predictors and Prognostic Implications of Cardiac Arrhythmias in Patients Hospitalized for COVID-19.

BACKGROUND: Cardiac manifestation of COVID-19 has been reported during the COVID pandemic. The role of cardiac arrhythmias in COVID-19 is insufficiently understood. This study assesses the incidence of cardiac arrhythmias and their prognostic implications in hospitalized COVID-19-patients. METHODS: A total of 166 patients from eight centers who were hospitalized for COVID-19 from 03/2020-06/2020 were included. Medical records were systematically analyzed for baseline characteristics, biomarkers, cardiac arrhythmias and clinical outcome parameters related to the index hospitalization. Predisposing risk factors for arrhythmias were identified. Furthermore, the influence of arrhythmia on the course of disease and related outcomes was assessed using univariate and multiple regression analyses. RESULTS: Arrhythmias were detected in 20.5% of patients. Atrial fibrillation was the most common arrhythmia. Age and cardiovascular disease were predictors for new-onset arrhythmia. Arrhythmia was associated with a pronounced increase in cardiac biomarkers, prolonged hospitalization, and admission to intensive- or intermediate-care-units, mechanical ventilation and in-hospital mortality. In multiple regression analyses, incident arrhythmia was strongly associated with duration of hospitalization and mechanical ventilation. Cardiovascular disease was associated with increased mortality. CONCLUSIONS: Arrhythmia was the most common cardiac event in association with hospitalization for COVID-19. Older age and cardiovascular disease predisposed for arrhythmia during hospitalization. Whereas in-hospital mortality is affected by underlying cardiovascular conditions, arrhythmia during hospitalization for COVID-19 is independently associated with prolonged hospitalization and mechanical ventilation. Thus, incident arrhythmia may indicate a patient subgroup at risk for a severe course of disease.

Patients' preferences regarding the digital capturing of patient-reported outcomes: planning the future follow-up in a prospective heart failure registry.

Aims: Digital health technologies have the potential to improve patient care sustainably. A digital capturing of patient-reported outcome measures (PROMs) could facilitate patients' surveillance and endpoint assessment within clinical trials especially in heart failure (HF) patients. However, data regarding the availability of digital infrastructure and patients' willingness to use digital health solutions are scarce. Therefore, we conducted a survey as part of a digital-based HF registry. Methods and results: The Helios Heart registry (H2-registry) has been introduced as a prospective registry being based on digitally augmented processes throughout the whole trial conduction from patients' selection to data collection and follow-up (FU). Patient-reported outcome measures are captured paper-based at recruitment, but patients are offered two digital solutions for FU. Overall, 125 patients (mean age 67.8 years, 34.4% female) were included in the single-centre run-in phase of 16 weeks. Of them, 52.0% were not interested in any digital contact as part of the FU. If digital PROM capturing was conceivable, a web-based solution (70.0%) was preferred to an application-based approach (30.0%). Discrepancies occurred regarding the availability of email accounts and smartphones. Patients in the non-digital group were older (72.0 years vs. 63.2 years, P < 0.01) and more frequently female (female sex, non-digital vs. digital group: 47.7% vs. 20.0%, P < 0.01). Conclusions: Our survey illustrated difficulties of implementing a digital FU to record PROMs in a contemporary HF cohort in particular among older patients. Further research is required to specify reasons in case of patients' unwillingness and to better tailor digital health solutions to patients' specific needs.