[The Usefulness of ERAS Concepts for Colorectal Resections - an Economic Analysis under DRG Conditions]. / Sinnhaftigkeit von ERAS-Konzepten bei kolorektalen Resektionen ­ eine ökonomische Analyse unter DRG-Bedingungen.

BACKGROUND: ERAS (Enhanced Recovery After Surgery) describes a multimodal, interdisciplinary and interprofessional treatment concept that optimizes the postoperative convalescence of the patient through the use of evidence-based measures. GOAL OF THE WORK: The aim of this article is to examine the economic feasibility of the concept in the German DRG system. MATERIAL AND METHODS: Since August 2019, patients have been treated in our clinic according to the later certified ERAS concept. The last 20 patients before ERAS implementation are compared below with 20 patients after ERAS implementation, who were identified using a matched pair analysis. In addition to the comparison of costs and revenues, the clinical outcome of the patients is also presented. RESULTS: The cases of the patients in the pre-ERAS cohort caused median costs of € 7432.83. BWR of 3.38 were billable. The resulting DRG revenue for the patients in this group amounted to € 11325.78. The proceeds generated in the end amounted to € 4575.14. The cases of patients in the ERAS cohort resulted in costs of € 5582.96. BWR of 2.84 could be billed. The DRG proceeds for the patients in this group therefore amounted to € 10014.18. The profit generated was thus € 4993.84. DISCUSSION: The cost reduction generated by ERAS was comparable to the "loss" caused by the BWR decrease. ERAS is therefore also possible to cover costs in the German DRG system.

Mortality in cancer patients with SARS-CoV-2 or seasonal influenza: an observational cohort study from a German-wide hospital network.

PURPOSE: At the beginning of the COVID-19 pandemic, SARS-CoV-2 was often compared to seasonal influenza. We aimed to compare the outcome of hospitalized patients with cancer infected by SARS-CoV-2 or seasonal influenza including intensive care unit admission, mechanical ventilation and in-hospital mortality. METHODS: We analyzed claims data of patients with a lab-confirmed SARS-CoV-2 or seasonal influenza infection admitted to one of 85 hospitals of a German-wide hospital network between January 2016 and August 2021. RESULTS: 29,284 patients with COVID-19 and 7442 patients with seasonal influenza were included. Of these, 360 patients with seasonal influenza and 1625 patients with COVID-19 had any kind of cancer. Cancer patients with COVID-19 were more likely to be admitted to the intensive care unit than cancer patients with seasonal influenza (29.4% vs 24.7%; OR 1.31, 95% CI 1.00-1.73 p < .05). No statistical significance was observed in the mechanical ventilation rate for cancer patients with COVID-19 compared to those with seasonal influenza (17.2% vs 13.6% OR 1.34, 95% CI 0.96-1.86 p = .09). 34.9% of cancer patients with COVID-19 and 17.9% with seasonal influenza died (OR 2.45, 95% CI 1.81-3.32 p < .01). Risk factors among cancer patients with COVID-19 or seasonal influenza for in-hospital mortality included the male gender, age, a higher Elixhauser comorbidity index and metastatic cancer. CONCLUSION: Among cancer patients, SARS-CoV-2 was associated with a higher risk for in-hospital mortality than seasonal influenza. These findings underline the need of protective measurements to prevent an infection with either COVID-19 or seasonal influenza, especially in this high-risk population.

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.

The usefulness of Enhanced Recovery After Surgery concepts for colorectal resections: an economic analysis under DRG conditions.

PURPOSE: ERAS® (Enhanced Recovery After Surgery) describes a multimodal, interdisciplinary, and interprofessional treatment concept that optimizes the postoperative convalescence of the patient through the use of evidence-based measures. Goal of the work. The aim of this article is to examine the economic feasibility of the ERAS® concept in the German DRG (diagnosis-related groups) system. MATERIAL AND METHODS: Since August 2019, patients have been treated in our clinic according to the later certified ERAS® concept. The last 50 patients before ERAS® implementation are compared below with 50 patients after ERAS® implementation, who were identified using a matched pair analysis. In addition to the comparison of costs and revenues, the clinical outcome of the patients is also presented. RESULTS: The cases of the patients in the pre-ERAS® cohort caused median costs of € 7432.83. BWR (valuation ratio) of 3.38 were billable. The resulting DRG revenue for the patients in this group amounted to € 11,325.78. The proceeds generated in the end amounted to € 4575.14. The cases of patients in the ERAS® cohort resulted in costs of € 5582.96. BWR of 2.84 could be billed. The DRG proceeds for the patients in this group therefore amounted to € 10,014.18. The profit generated was thus € 4993.84. CONCLUSION: The cost reduction generated by ERAS® was more pronounced than the "loss" due to the decrease in BWR. ERAS® is therefore also possible in the German DRG system at absolutely cost-covering levels.

Changes in nationwide in-hospital stroke care during the first four waves of COVID-19 in Germany.

Introduction: In the early stages of the global COVID-19 pandemic hospital admissions for acute ischemic stroke (AIS) decreased substantially. As health systems have become more experienced in dealing with the pandemic, and as the proportion of the population vaccinated rises, it is of interest to determine whether the prevalence of AIS hospitalization and outcomes from hospitalization have returned to normal. Patients and methods: In this observational, retrospective cohort study, we compared the prevalence and outcomes of AIS during the first four waves of the pandemic to corresponding pre-pandemic periods in 2019 using administrative data collected from a nationwide network of 76 hospitals that manages 7% of all in-hospital cases in Germany. Results: We included 25,821 AIS cases in the study period (2020/2021) and used 26,295 AIS cases as controls (2019). Compared to pre-pandemic numbers, mean daily AIS admissions decreased only during wave 1 (from 39.6 to 34.1; p < 0.01) and wave 2 (from 39.9 to 38.3; p = 0.03) and returned to normal levels during waves 3 and 4. AIS case fatality increased in wave 1 only (from 6.0% to 7.6%; p = 0.03). We observed a consistent decrease in the prevalences of arterial hypertension, diabetes, and obesity among AIS cases throughout the pandemic and no changes in rates of systemic thrombolysis, mechanical thrombectomy, or decompressive craniectomy. The rate of transfer to stroke units increased only during waves 2 (by 4.6%; p < 0.01) and 3 (by 3.0%; p < 0.01). The proportion of patients with coinciding SARS-CoV-2 and AIS was low, peaking at 3.4% in wave 2 and subsequently decreasing to 0.4% in wave 4. Conclusion: In Germany, the COVID-19 pandemic seems to have had a larger effect on nationwide in-hospital AIS care during the early pandemic stages, in which AIS case numbers decreased and case fatality rose. This may reflect a nationwide "learning curve" within health care systems in providing AIS care in times of a pandemic.

Effect of gender, age and vaccine on reactogenicity and incapacity to work after COVID-19 vaccination: a survey among health care workers.

BACKGROUND: The aim of our study was to assess the impact the impact of gender and age on reactogenicity to three COVID-19 vaccine products: Biontech/Pfizer (BNT162b2), Moderna (mRNA-1273) and AstraZeneca (ChAdOx). Additional analyses focused on the reduction in working capacity after vaccination and the influence of the time of day when vaccines were administered. METHODS: We conducted a survey on COVID-19 vaccinations and eventual reactions among 73,000 employees of 89 hospitals of the Helios Group. On May 19th, 2021 all employees received an email, inviting all employees who received at least 1 dose of a COVID-19 to participate using an attached link. Additionally, the invitation was posted in the group's intranet page. Participation was voluntary and non-traceable. The survey was closed on June 21st, 2021. RESULTS: 8375 participants reported on 16,727 vaccinations. Reactogenicity was reported after 74.6% of COVID-19 vaccinations. After 23.0% vaccinations the capacity to work was affected. ChAdOx induced impairing reactogenicity mainly after the prime vaccination (70.5%), while mRNA-1273 led to more pronounced reactions after the second dose (71.6%). Heterologous prime-booster vaccinations with ChAdOx followed by either mRNA-1273 or BNT162b2 were associated with the highest risk for impairment (81.4%). Multivariable analyses identified the factors older age, male gender and vaccine BNT162b as independently associated with lower odds ratio for both, impairing reactogenicity and incapacity to work. In the comparison of vaccine schedules, the heterologous combination ChAdOx + BNT162b or mRNA-1273 was associated with the highest and the homologue prime-booster vaccination with BNT162b with the lowest odds ratios. The time of vaccination had no significant influence. CONCLUSIONS: Around 75% of the COVID-19 vaccinations led to reactogenicity and nearly 25% of them led to one or more days of work loss. Major risk factors were female gender, younger age and the administration of a vaccine other than BNT162b2. When vaccinating a large part of a workforce against COVID-19, especially in professions with a higher proportion of young and women such as health care, employers and employees must be prepared for a noticeable amount of absenteeism. Assuming vaccine effectiveness to be equivalent across the vaccine combinations, to minimize reactogenicity, employees at risk should receive a homologous prime-booster immunisation with BNT162b2. TRIAL REGISTRATION: The study was approved by the Ethic Committee of the Aerztekammer Berlin on May 27th, 2021 (Eth-37/21) and registered in the German Clinical Trials Register (DRKS 00025745). The study was supported by the Helios research grant HCRI-ID 2021-0272.

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.

[Psychiatric Emergency Admissions and Inpatient Length of Stay Before and During the COVID-19 Pandemic in Germany]. / Psychiatrische Notfallaufnahmen und stationäre Aufenthaltsdauer vor und während der COVID-19-Pandemie in Deutschland.

OBJECTIVE: The impact of the COVID-19 year on the number of daily psychiatric emergency admissions and length of stay was compared with previous years. METHODS: In a retrospective study, the four quarters of 2020 of several psychiatric hospitals in Germany were statistically compared with the respective quarters of 2018 and 2019. RESULTS: A total of 73,412 cases was analyzed. In the 2nd quarter of 2020, the number of daily admissions was significantly lower as compared to the control period (59.1 vs. 70.7; incidence rate ratio [95 % confidence interval] 0.81 [0.69; 0.96]; p = 0.012). Length of stay was numerically but not significantly different as compared to the control periods. CONCLUSION: The COVID-19 pandemic had a strong impact on inpatient psychiatric care. In the future, multimodal care structures must ensure the care of severely mentally ill people in crisis situations.

Cholecystectomies in the COVID-19 Pandemic During and After the First Lockdown in Germany: an Analysis of 8561 Patients.

PURPOSE: The COVID-19 pandemic has led to global changes in healthcare systems. The purpose of this study was to investigate the effects on surgical care of patients. METHODS: We performed a retrospective analysis of routine data from the largest hospital group in Germany (68 acute hospitals). Included were inpatients who underwent cholecystectomy between March 19, 2020 (beginning of the first lockdown in Germany) and September 22, 2020. These patients were compared with those treated in the same interval in 2019. RESULTS: In the 2020 study period, 4035 patients met the inclusion criteria (2019: 4526 patients). During the first lockdown, there was a significant reduction in the number of cholecystectomies performed (51.1% decrease). More patients with a higher risk profile underwent urgent operations, which were accompanied by a significant increase in conversion from laparoscopic to open cholecystectomy. The patients were treated as inpatients for a longer duration than 2019, and the mortality rate increased significantly to 1.3% (2019: 0.1%). The complication rate also showed a significant increase. After the end of the first lockdown, daily admission rates normalized very quickly. However, it was not possible to fully address the backlog of operations. CONCLUSION: There is still a "patient stagnation" 6 months after the first German lockdown. Extrapolated to the national level, this corresponds to almost 21,000 fewer cholecystectomies performed in Germany in 2020. It remains to be seen whether surgical rates will return to pre-pandemic levels and whether complications will arise in the future due to the lack of operations.

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.

Sex Differences in Clinical Course and Intensive Care Unit Admission in a National Cohort of Hospitalized Patients with COVID-19.

Males have a higher risk for an adverse outcome of COVID-19. The aim of the study was to analyze sex differences in the clinical course with focus on patients who received intensive care. Research was conducted as an observational retrospective cohort study. A group of 23,235 patients from 83 hospitals with PCR-confirmed infection with SARS-CoV-2 between 4 February 2020 and 22 March 2021 were included. Data on symptoms were retrieved from a separate registry, which served as a routine infection control system. Males accounted for 51.4% of all included patients. Males received more intensive care (ratio OR = 1.61, 95% CI = 1.51-1.71) and mechanical ventilation (invasive or noninvasive, OR = 1.87, 95% CI = 1.73-2.01). A model for the prediction of mortality showed that until the age 60 y, mortality increased with age with no substantial difference between sexes. After 60 y, the risk of death increased more in males than in females. At 90 y, females had a predicted mortality risk of 31%, corresponding to males of 84 y. In the intensive care unit (ICU) cohort, females of 90 y had a mortality risk of 46%, equivalent to males of 72 y. Seventy-five percent of males over 90 died, but only 46% of females of the same age. In conclusion, the sex gap was most evident among the oldest in the ICU. Understanding sex-determined differences in COVID-19 can be useful to facilitate individualized treatments.

Outcomes and proportions of pregnant women during the first and consecutive waves of coronavirus disease 2019: observational cohort study.

OBJECTIVES: It has been suggested that pregnant women were affected more severely during the late wave, as opposed to the early wave of the coronavirus disease 2019 (COVID-19) pandemic. The aim of our study was to compare the proportion of pregnant women among hospitalized women of childbearing age, their rate of intensive care (ICU) admission, need for mechanical ventilation and mortality during the waves. METHODS: The study is a retrospective analysis of claims data on women of childbearing age (16-49 years) admitted to 76 hospitals with a laboratory-confirmed severe acute respiratory syndrome coronavirus 2 infection. The observation period was divided into first wave (7 March 2020 to 30 September 2020) and second wave (1 October to 17 April 2021). Co-morbidities derived from claims data were summarized in the Elixhauser Co-morbidity Index (ECI). RESULTS: A total of 1879 women were included, 532 of whom were pregnant. During the second wave, the proportion of pregnant women was higher (29.3% (484/1650) versus 21.0% (48/229), p < 0.01). They were older (mean ± SD 29.1 ± 5.9 years versus 27 ± 6.3 years, p 0.02 in the first wave) and had comparable co-morbidities (ECI mean ± SD 0.3 ± 3.5 versus -0.2 ± 2.0, p 0.30). Of the pregnant women, 6.2% (3/48) were admitted to ICU during the first wave versus 3.3% (16/484) during the second wave (OR 0.51, 95% CI 0.14-1.83, p 0.30), 2.1% (1/48) were ventilated versus 1.2% (6/484, OR 0.60, 95% CI 0.07-5.23, p 0.64). No deaths were observed among the hospitalized pregnant women in either wave. CONCLUSIONS: Proportionally more pregnant women with COVID-19 were hospitalized in the second wave compared with the first wave but no more severe outcomes were registered.

[Has the COVID19 Pandemic Changed the Emergency Situation in German Clinics? A Nationwide Analysis of Routine Data from 73 Acute Hospitals]. / Hat die COVID-19-Pandemie das Notfallgeschehen in deutschen Kliniken verändert? Eine bundesweite Analyse von Routinedaten aus 73 Akutkrankenhäusern.

COVID-19 has led to profound changes in the world as we have known it. Due to the sharp increase in intensive care, COVID patients, elective admissions and interventions have been postponed. But emergencies such as myocardial infarction have also decreased. The present study deals with the effects of the COVID pandemic on visceral surgical emergencies on the basis of 5 indicator operations. Routine data from 73 acute hospitals of the Helios Group were evaluated for this purpose. The interventions that were carried out between March 13, 2020 and March 12, 2021 were included. The data was compared with the period from March 13, 2019 to March 12, 2020. The number of interventions in serious emergencies (ileus, mesenteric ischemia and ulcer perforation) has remained constant. However, the length of stay in hospital in the pandemic year 2020 was significantly shorter than in the reference year 2019. The number of cholecystectomies and appendectomies in the pandemic year was significantly lower than in the reference year 2019. The outcome parameters intensive care, invasive ventilation and hospital mortality were comparable for the two periods for these interventions.

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.