High In-Hospital Mortality in SARS-CoV-2-Infected Patients with Active Cancer Disease during Omicron Phase of the Pandemic: Insights from the CORONA Germany Study.

INTRODUCTION: SARS-CoV-2 infected patients with cancer have a worse outcome including a significant higher mortality, compared to non-cancer patients. However, limited data are available regarding in-hospital mortality during the Omicron phase of the pandemic. Therefore, the aim of the study was the comparison of mortality in patients with history of cancer and patients with active cancer disease during the different phases of the COVID-19 pandemic, focusing on the current Omicron variant of concern. METHODS: We conducted a multicenter, observational, epidemiological cohort study at 45 hospitals in Germany. Until July 20, 2022, all adult hospitalized SARS-CoV-2 positive patients were included. The primary endpoint was in-hospital mortality regarding cancer status (history of cancer and active cancer disease) and SARS-CoV-2 virus type. RESULTS: From March 11, 2020, to July 20, 2022, a total of 27,490 adult SARS-CoV-2 positive patients were included in the study. 2,578 patients (9.4%) had diagnosis of cancer, of whom 1,065 (41.3%) had history of cancer, whereas 1,513 (58.7%) had active cancer disease. Overall 3,749 out of the total of 27,490 patients (13.6%) died during the hospital stay. Patients with active cancer disease had a significantly higher mortality compared to patients without cancer diagnosis, in both phases of the pandemic (wild-type to Delta: OR 1.940 [1.646-2.285]); Omicron: 2.864 [2.354-3.486]). After adjustment to co-variables, SARS-CoV-2 infected patients with active cancer disease had the highest risk for in-hospital mortality compared to the other groups, in both phases of the pandemic. CONCLUSION: The CORONA Germany study indicates that hospitalized patients with active cancer disease are at high risk of death during a SARS-CoV-2 infection. Mortality of patients with history of cancer improved to nearly the level of non-cancer patients during Omicron phase.

Is Dementia Associated with COVID-19 Mortality? A Multicenter Retrospective Cohort Study Conducted in 50 Hospitals in Germany.

BACKGROUND: Dementia has been identified as a major predictor of mortality associated with COVID-19. OBJECTIVE: The objective of this study was to investigate the association between dementia and mortality in COVID-19 inpatients in Germany across a longer interval during the pandemic. METHODS: This retrospective study was based on anonymized data from 50 hospitals in Germany and included patients with a confirmed COVID-19 diagnosis hospitalized between March 11, 2020 and July, 20, 2022. The main outcome of the study was the association of mortality during inpatient stays with dementia diagnosis, which was studied using multivariable logistic regression adjusted for age, sex, and comorbidities as well as univariate logistic regression for matched pairs. RESULTS: Of 28,311 patients diagnosed with COVID-19, 11.3% had a diagnosis of dementia. Prior to matching, 26.5% of dementia patients and 11.5% of non-dementia patients died; the difference decreased to 26.5% of dementia versus 21.7% of non-dementia patients within the matched pairs (n = 3,317). This corresponded to an increase in the risk of death associated with dementia (OR = 1.33; 95% CI: 1.16-1.46) in the univariate regression conducted for matched pairs. CONCLUSION: Although dementia was associated with COVID-19 mortality, the association was weaker than in previously published studies. Further studies are needed to better understand whether and how pre-existing neuropsychiatric conditions such as dementia may impact the course and outcome of COVID-19.

Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission.

BACKGROUND: Machine learning (ML) algorithms have been trained to early predict critical in-hospital events from COVID-19 using patient data at admission, but little is known on how their performance compares with each other and/or with statistical logistic regression (LR). This prospective multicentre cohort study compares the performance of a LR and five ML models on the contribution of influencing predictors and predictor-to-event relationships on prediction model´s performance. METHODS: We used 25 baseline variables of 490 COVID-19 patients admitted to 8 hospitals in Germany (March-November 2020) to develop and validate (75/25 random-split) 3 linear (L1 and L2 penalty, elastic net [EN]) and 2 non-linear (support vector machine [SVM] with radial kernel, random forest [RF]) ML approaches for predicting critical events defined by intensive care unit transfer, invasive ventilation and/or death (composite end-point: 181 patients). Models were compared for performance (area-under-the-receiver-operating characteristic-curve [AUC], Brier score) and predictor importance (performance-loss metrics, partial-dependence profiles). RESULTS: Models performed close with a small benefit for LR (utilizing restricted cubic splines for non-linearity) and RF (AUC means: 0.763-0.731 [RF-L1]); Brier scores: 0.184-0.197 [LR-L1]). Top ranked predictor variables (consistently highest importance: C-reactive protein) were largely identical across models, except creatinine, which exhibited marginal (L1, L2, EN, SVM) or high/non-linear effects (LR, RF) on events. CONCLUSIONS: Although the LR and ML models analysed showed no strong differences in performance and the most influencing predictors for COVID-19-related event prediction, our results indicate a predictive benefit from taking account for non-linear predictor-to-event relationships and effects. Future efforts should focus on leveraging data-driven ML technologies from static towards dynamic modelling solutions that continuously learn and adapt to changes in data environments during the evolving pandemic. TRIAL REGISTRATION NUMBER: NCT04659187.

Retinal Vascular Occlusion after COVID-19 Vaccination: More Coincidence than Causal Relationship? Data from a Retrospective Multicentre Study.

BACKGROUND: To investigate whether vaccination against SARS-CoV-2 is associated with the onset of retinal vascular occlusive disease (RVOD). METHODS: In this multicentre study, data from patients with central and branch retinal vein occlusion (CRVO and BRVO), central and branch retinal artery occlusion (CRAO and BRAO), and anterior ischaemic optic neuropathy (AION) were retrospectively collected during a 2-month index period (1 June-31 July 2021) according to a defined protocol. The relation to any previous vaccination was documented for the consecutive case series. Numbers of RVOD and COVID-19 vaccination were investigated in a case-by-case analysis. A case-control study using age- and sex-matched controls from the general population (study participants from the Gutenberg Health Study) and an adjusted conditional logistic regression analysis was conducted. RESULTS: Four hundred and twenty-one subjects presenting during the index period (61 days) were enrolled: one hundred and twenty-one patients with CRVO, seventy-five with BRVO, fifty-six with CRAO, sixty-five with BRAO, and one hundred and four with AION. Three hundred and thirty-two (78.9%) patients had been vaccinated before the onset of RVOD. The vaccines given were BNT162b2/BioNTech/Pfizer (n = 221), followed by ChadOx1/AstraZeneca (n = 57), mRNA-1273/Moderna (n = 21), and Ad26.COV2.S/Johnson & Johnson (n = 11; unknown n = 22). Our case-control analysis integrating population-based data from the GHS yielded no evidence of an increased risk after COVID-19 vaccination (OR = 0.93; 95% CI: 0.60-1.45, p = 0.75) in connection with a vaccination within a 4-week window. CONCLUSIONS: To date, there has been no evidence of any association between SARS-CoV-2 vaccination and a higher RVOD risk.

Prognostic Impact of Acute Cardiovascular Events in COVID-19 Hospitalized Patients-Results from the CORONA Germany Study.

BACKGROUND: Acute myocardial injury (AMJ), assessed by elevated levels of cardiac troponin, is associated with fatal outcome in coronavirus disease 2019 (COVID-19). However, the role of acute cardiovascular (CV) events defined by clinical manifestation rather than sole elevations of biomarkers is unclear in hospitalized COVID-19 patients. OBJECTIVE: The aim of this study was to investigate acute clinically manifest CV events in hospitalized COVID-19 patients. METHODS: From 1 March 2020 to 5 January 2021, we conducted a multicenter, prospective, epidemiological cohort study at six hospitals from Hamburg, Germany (a portion of the state-wide 45-center CORONA Germany cohort study) enrolling all hospitalized COVID-19 patients. Primary endpoint was occurrence of a clinically manifest CV-event. RESULTS: In total, 132 CV-events occurred in 92 of 414 (22.2%) patients in the Hamburg-cohort: cardiogenic shock in 10 (2.4%), cardiopulmonary resuscitation in 12 (2.9%), acute coronary syndrome in 11 (2.7%), de-novo arrhythmia in 31 (7.5%), acute heart-failure in 43 (10.3%), myocarditis in 2 (0.5%), pulmonary-embolism in 11 (2.7%), thrombosis in 9 (2.2%) and stroke in 3 (0.7%). In the Hamburg-cohort, mortality was 46% (42/92) for patients with a CV-event and 33% (27/83) for patients with only AMJ without CV-event (OR 1.7, CI: (0.94-3.2), p = 0.077). Mortality was higher in patients with CV-events (Odds ratio(OR): 4.8, 95%-confidence-interval(CI): [2.9-8]). Age (OR 1.1, CI: (0.66-1.86)), atrial fibrillation (AF) on baseline-ECG (OR 3.4, CI: (1.74-6.8)), systolic blood-pressure (OR 0.7, CI: (0.53-0.96)), potassium (OR 1.3, CI: (0.99-1.73)) and C-reactive-protein (1.4, CI (1.04-1.76)) were associated with CV-events. CONCLUSION: Hospitalized COVID-19 patients with clinical manifestation of acute cardiovascular events show an almost five-fold increased mortality. In this regard, the emergence of arrhythmias is a major determinant.

Clinical outcome, risk assessment, and seasonal variation in hospitalized COVID-19 patients-Results from the CORONA Germany study.

BACKGROUND: After one year of the pandemic and hints of seasonal patterns, temporal variations of in-hospital mortality in COVID-19 are widely unknown. Additionally, heterogeneous data regarding clinical indicators predicting disease severity has been published. However, there is a need for a risk stratification model integrating the effects on disease severity and mortality to support clinical decision-making. METHODS: We conducted a multicenter, observational, prospective, epidemiological cohort study at 45 hospitals in Germany. Until 1 January 2021, all hospitalized SARS CoV-2 positive patients were included. A comprehensive data set was collected in a cohort of seven hospitals. The primary objective was disease severity and prediction of mild, severe, and fatal cases. Ancillary analyses included a temporal analysis of all hospitalized COVID-19 patients for the entire year 2020. FINDINGS: A total of 4704 COVID-19 patients were hospitalized with a mortality rate of 19% (890/4704). Rates of mortality, need for ventilation, pneumonia, and respiratory insufficiency showed temporal variations, whereas age had a strong influence on the course of mortality. In cohort conducting analyses, prognostic factors for fatal/severe disease were: age (odds ratio (OR) 1.704, CI:[1.221-2.377]), respiratory rate (OR 1.688, CI:[1.222-2.333]), lactate dehydrogenase (LDH) (OR 1.312, CI:[1.015-1.695]), C-reactive protein (CRP) (OR 2.132, CI:[1.533-2.965]), and creatinine values (OR 2.573, CI:[1.593-4.154]. CONCLUSIONS: Age, respiratory rate, LDH, CRP, and creatinine at baseline are associated with all cause death, and need for ventilation/ICU treatment in a nationwide series of COVID 19 hospitalized patients. Especially age plays an important prognostic role. In-hospital mortality showed temporal variation during the year 2020, influenced by age. TRIAL REGISTRATION NUMBER: NCT04659187.