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.

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.

Autopsy Findings and Venous Thromboembolism in Patients With COVID-19: A Prospective Cohort Study.

BACKGROUND: The new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused more than 210 000 deaths worldwide. However, little is known about the causes of death and the virus's pathologic features. OBJECTIVE: To validate and compare clinical findings with data from medical autopsy, virtual autopsy, and virologic tests. DESIGN: Prospective cohort study. SETTING: Autopsies performed at a single academic medical center, as mandated by the German federal state of Hamburg for patients dying with a polymerase chain reaction-confirmed diagnosis of COVID-19. PATIENTS: The first 12 consecutive COVID-19-positive deaths. MEASUREMENTS: Complete autopsy, including postmortem computed tomography and histopathologic and virologic analysis, was performed. Clinical data and medical course were evaluated. RESULTS: Median patient age was 73 years (range, 52 to 87 years), 75% of patients were male, and death occurred in the hospital (n = 10) or outpatient sector (n = 2). Coronary heart disease and asthma or chronic obstructive pulmonary disease were the most common comorbid conditions (50% and 25%, respectively). Autopsy revealed deep venous thrombosis in 7 of 12 patients (58%) in whom venous thromboembolism was not suspected before death; pulmonary embolism was the direct cause of death in 4 patients. Postmortem computed tomography revealed reticular infiltration of the lungs with severe bilateral, dense consolidation, whereas histomorphologically diffuse alveolar damage was seen in 8 patients. In all patients, SARS-CoV-2 RNA was detected in the lung at high concentrations; viremia in 6 of 10 and 5 of 12 patients demonstrated high viral RNA titers in the liver, kidney, or heart. LIMITATION: Limited sample size. CONCLUSION: The high incidence of thromboembolic events suggests an important role of COVID-19-induced coagulopathy. Further studies are needed to investigate the molecular mechanism and overall clinical incidence of COVID-19-related death, as well as possible therapeutic interventions to reduce it. PRIMARY FUNDING SOURCE: University Medical Center Hamburg-Eppendorf.

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.