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.

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.

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.

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.

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.

Hospitalisations for emergency-sensitive conditions in Germany during the COVID-19 pandemic: insights from the German-wide Helios hospital network.

BACKGROUND: While there are numerous reports that describe emergency care during the early COVID-19 pandemic, there is scarcity of data for later stages. This study analyses hospitalisation rates for 37 emergency-sensitive conditions in the largest German-wide hospital network during different pandemic phases. METHODS: Using claims data of 80 hospitals, consecutive cases between 1 January and 17 November 2020 were analysed and compared with a corresponding period in 2019. Incidence rate ratios (IRRs) comparing the two periods were calculated using Poisson regression to model the number of hospitalisations per day. RESULTS: There was a reduction in hospitalisations between 12 March and 13 June 2020 (coinciding with the first pandemic wave) with 32 807 hospitalisations (349.0/day) as opposed to 39 379 (419.0/day) in 2019 (IRR 0.83, 95% CI 0.82 to 0.85, p<0.01). During the following period (14 June-17 November 2020, including the start of second wave), hospitalisations were reduced from 63 799 (406.4/day) in 2019 to 59 910 (381.6/day) in 2020, but this reduction was not as pronounced (IRR 0.94, 95% CI 0.93 to 0.95, p<0.01). During the first wave hospitalisations for acute myocardial infarction, aortic aneurysm/dissection, pneumonitis, paralytic ileus/intestinal obstruction and pulmonary embolism declined but subsequently increased compared with the corresponding periods in 2019. In contrast, hospitalisations for sepsis, pneumonia, obstructive pulmonary disease and intracranial injuries were reduced during the entire observation period. CONCLUSIONS: There was an overall reduction of absolute hospitalisations for emergency-sensitive conditions in Germany during the first 10 months of the COVID-19 pandemic with heterogeneous effects on different disease categories. The increase in hospitalisations for acute myocardial infarction, aortic aneurysm/dissection and pulmonary embolism requires attention and further studies.

Potential Contributors to Increased Pulmonary Embolism Hospitalizations During the COVID-19 Pandemic: Insights From the German-Wide Helios Hospital Network.

Background: After the first COVID-19 infection wave, a constant increase of pulmonary embolism (PE) hospitalizations not linked with active PCR-confirmed COVID-19 was observed, but potential contributors to this observation are unclear. Therefore, we analyzed associations between changes in PE hospitalizations and (1) the incidence of non-COVID-19 pneumonia, (2) the use of computed tomography pulmonary angiography (CTPA), (3) volume depletion, and (4) preceding COVID-19 infection numbers in Germany. Methods: Claims data of Helios hospitals in Germany were used, and consecutive cases with a hospital admission between May 6 and December 15, 2020 (PE surplus period), were analyzed and compared to corresponding periods covering the same weeks in 2016-2019 (control period). We analyzed the number of PE cases in the target period with multivariable Poisson general linear mixed models (GLMM) including (a) cohorts of 2020 versus 2016-2019, (b) the number of cases with pneumonia, (c) CTPA, and (d) volume depletion and adjusted for age and sex. In order to associate the daily number of PE cases in 2020 with the number of preceding SARS-CoV-2 infections in Germany, we calculated the average number of daily infections (divided by 10,000) occurring between 14 up to 90 days with increasing window sizes before PE cases and modeled the data with Poisson regression. Results: There were 2,404 PE hospitalizations between May 6 and December 15, 2020, as opposed to 2,112-2,236 (total 8,717) in the corresponding 2016-2019 control periods (crude rate ratio [CRR] 1.10, 95% CI 1.05-1.15, P < 0.01). With the use of multivariable Poisson GLMM adjusted for age, sex, and volume depletion, PE cases were significantly associated with the number of cases with pneumonia (CRR 1.09, 95% CI 1.07-1.10, P < 0.01) and with CTPA (CRR 1.10, 95% CI 1.09-1.10, P < 0.01). The increase of PE cases in 2020 compared with the control period remained significant (CRR 1.07, 95% CI 1.02-1.12, P < 0.01) when controlling for those factors. In the 2020 cohort, the number of preceding average daily COVID-19 infections was associated with increased PE case incidence in all investigated windows, i.e., including preceding infections from 14 to 90 days. The best model (log likelihood -576) was with a window size of 4 days, i.e., average COVID-19 infections 14-17 days before PE hospitalization had a risk of 1.20 (95% CI 1.12-1.29, P < 0.01). Conclusions: There is an increase in PE cases since early May 2020 compared to corresponding periods in 2016-2019. This surplus was significant even when controlling for changes in potential modulators such as demographics, volume depletion, non-COVID-19 pneumonia, CTPA use, and preceding COVID-19 infections. Future studies are needed (1) to investigate a potential causal link for increased risk of delayed PE with preceding SARS-CoV-2 infection and (2) to define optimal screening for SARS-CoV-2 in patients presenting with pneumonia and PE.

Utilization of in- and outpatient hospital care in Germany during the Covid-19 pandemic insights from the German-wide Helios hospital network.

BACKGROUND: During the early phase of the Covid-19 pandemic, reductions of hospital admissions with a focus on emergencies have been observed for several medical and surgical conditions, while trend data during later stages of the pandemic are scarce. Consequently, this study aims to provide up-to-date hospitalization trends for several conditions including cardiovascular, psychiatry, oncology and surgery cases in both the in- and outpatient setting. METHODS AND FINDINGS: Using claims data of 86 Helios hospitals in Germany, consecutive cases with an in- or outpatient hospital admission between March 13, 2020 (the begin of the "protection" stage of the German pandemic plan) and December 10, 2020 (end of study period) were analyzed and compared to a corresponding period covering the same weeks in 2019. Cause-specific hospitalizations were defined based on the primary discharge diagnosis according to International Statistical Classification of Diseases and Related Health Problems (ICD-10) or German procedure classification codes for cardiovascular, oncology, psychiatry and surgery cases. Cumulative hospitalization deficit was computed as the difference between the expected and observed cumulative admission number for every week in the study period, expressed as a percentage of the cumulative expected number. The expected admission number was defined as the weekly average during the control period. A total of 1,493,915 hospital admissions (723,364 during the study and 770,551 during the control period) were included. At the end of the study period, total cumulative hospitalization deficit was -10% [95% confidence interval -10; -10] for cardiovascular and -9% [-10; -9] for surgical cases, higher than -4% [-4; -3] in psychiatry and 4% [4; 4] in oncology cases. The utilization of inpatient care and subsequent hospitalization deficit was similar in trend with some variation in magnitude between cardiovascular (-12% [-13; -12]), psychiatry (-18% [-19; -17]), oncology (-7% [-8; -7]) and surgery cases (-11% [-11; -11]). Similarly, cardiovascular and surgical outpatient cases had a deficit of -5% [-6; -5] and -3% [-4; -3], respectively. This was in contrast to psychiatry (2% [1; 2]) and oncology cases (21% [20; 21]) that had a surplus in the outpatient sector. While in-hospital mortality, was higher during the Covid-19 pandemic in cardiovascular (3.9 vs. 3.5%, OR 1.10 [95% CI 1.06-1.15], P<0.01) and in oncology cases (4.5 vs. 4.3%, OR 1.06 [95% CI 1.01-1.11], P<0.01), it was similar in surgical (0.9 vs. 0.8%, OR 1.06 [95% CI 1.00-1.13], P = 0.07) and in psychiatry cases (0.4 vs. 0.5%, OR 1.01 [95% CI 0.78-1.31], P<0.95). CONCLUSIONS: There have been varying changes in care pathways and in-hospital mortality in different disciplines during the Covid-19 pandemic in Germany. Despite all the inherent and well-known limitations of claims data use, this data may be used for health care surveillance as the pandemic continues worldwide. While this study provides an up-to-date analysis of utilization of hospital care in the largest German hospital network, short- and long-term consequences are unknown and deserve further studies.

Admission rates and care pathways in patients with atrial fibrillation during the COVID-19 pandemic-insights from the German-wide Helios hospital network.

AIMS: Several reports indicate lower rates of emergency admissions in the cardiovascular sector and reduced admissions of patients with chronic diseases during the Coronavirus SARS-CoV-2 (COVID-19) pandemic. The aim of this study was therefore to evaluate numbers of admissions in incident and prevalent atrial fibrillation and flutter (AF) and to analyse care pathways in comparison to 2019. METHODS: A retrospective analysis of claims data of 74 German Helios hospitals was performed to identify consecutive patients hospitalized with a main discharge diagnosis of AF. A study period including the start of the German national protection phase (13 March 2020 to 16 July 2020) was compared to a previous year control cohort (15 March 2019 to 18 July 2019), with further sub-division into early and late phase. Incidence rate ratios (IRRs) were calculated. Numbers of admission per day (A/day) for incident and prevalent AF and care pathways including readmissions, numbers of transesophageal echocardiogram (TEE), electrical cardioversion (CV), and catheter ablation (CA) were analysed. RESULTS: During the COVID-19 pandemic, there was a significant decrease in total AF admissions both in the early (44.4 vs. 77.5 A/day, IRR 0.57 [95% confidence interval (CI) 0.54-0.61], P < 0.01) and late (59.1 vs. 63.5 A/day, IRR 0.93 [95% CI 0.90-0.96], P < 0.01) phases, length of stay was significantly shorter (3.3 ± 3.1 nights vs. 3.5 ± 3.6 nights, P < 0.01), admissions were more frequently in high-volume centres (77.0% vs. 75.4%, P = 0.02), and frequency of readmissions was reduced (21.7% vs. 23.6%, P < 0.01) compared to the previous year. Incident AF admission rates were significantly lower both in the early (21.9 admission per day vs. 41.1 A/day, IRR 0.53 [95% CI 0.48-0.58]) and late (35.5 vs. 39.3 A/day, IRR 0.90 [95% CI 0.86-0.95]) phases, whereas prevalent admissions were only lower in the early phase (22.5 vs. 36.4 A/day IRR 0.62 [95% CI 0.56-0.68]), but not in the late phase (23.6 vs. 24.2 A/day IRR 0.97 [95% CI 0.92-1.03]). Analysis of care pathways showed reduced numbers of TEE during the early phase [34.7% vs. 41.4%, odds ratio (OR) 0.74 [95% CI 0.64-0.86], P < 0.01], but not during the late phase (39.9% vs. 40.2%, OR 0.96 [95% CI 0.88-1.03], P = 0.26). Numbers of CV were comparable during early (40.6% vs. 39.7%, OR 1.08 [95% CI 0.94-1.25], P = 0.27) and late (38.6% vs. 37.5%, OR 1.06 [95% CI 0.98-1.14], P = 0.17) phases, compared to the previous year, respectively. Numbers of CA were comparable during the early phase (21.6% vs. 21.1%, OR 0.98 [95% CI 0.82-1.17], P = 0.82) with a distinct increase during the late phase (22.9% vs. 21.5%, OR 1.05 [95% CI 0.96-1.16], P = 0.28). CONCLUSION: During the COVID-19 pandemic, AF admission rates declined significantly, with a more pronounced reduction in incident than in prevalent AF. Overall AF care was maintained during early and late pandemic phases with only minor changes, namely less frequent use of TEE. Confirmation of these findings in other study populations and identification of underlying causes are required to ensure optimal therapy in patients with AF during the COVID-19 pandemic.

Hospitalization deficit of in- and outpatient cases with cardiovascular diseases and utilization of cardiological interventions during the COVID-19 pandemic: Insights from the German-wide helios hospital network.

BACKGROUND: Treatment numbers of various cardiovascular diseases were reduced throughout the early phase of the ongoing COVID-19 pandemic. Aim of this study was to (a) expand previous study periods to examine the long-term course of hospital admission numbers, (b) provide data for in- and outpatient care pathways, and (c) illustrate changes of numbers of cardiovascular procedures. METHODS AND RESULTS: Administrative data of patients with ICD-10-encoded primary diagnoses of cardiovascular diseases (heart failure, cardiac arrhythmias, ischemic heart disease, valvular heart disease, hypertension, peripheral vascular disease) and in- or outpatient treatment between March, 13th 2020 and September, 10th 2020 were analyzed and compared with 2019 data. Numbers of cardiovascular procedures were calculated using OPS-codes. The cumulative hospital admission deficit (CumAD) was computed as the difference between expected and observed admissions for every week in 2020. In total, 80 hospitals contributed 294 361 patient cases to the database without relevant differences in baseline characteristics between the studied periods. There was a CumAD of -10% to -16% at the end of the study interval in 2020 for all disease groups driven to varying degrees by both reductions of in- and outpatient case numbers. The number of performed interventions was significantly reduced for all examined procedures (catheter ablations: -10%; cardiac electronic device implantations: -7%; percutaneous cardiovascular interventions: -9%; cardiovascular surgery: -15%). CONCLUSIONS: This study provides data on the long-term development of cardiovascular patient care during the COVID-19 pandemic demonstrating a significant CumAD for several cardiovascular diseases and a concomitant performance deficit of cardiovascular interventions.