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.

Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital.

PURPOSE: Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET). METHODS: Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort. RESULTS: The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation. CONCLUSION: CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.

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.

Immune checkpoint inhibitor therapy and outcomes from SARS-CoV-2 infection in patients with cancer: a joint analysis of OnCovid and ESMO-CoCARE registries.

BACKGROUND: As management and prevention strategies against COVID-19 evolve, it is still uncertain whether prior exposure to immune checkpoint inhibitors (ICIs) affects COVID-19 severity in patients with cancer. METHODS: In a joint analysis of ICI recipients from OnCovid (NCT04393974) and European Society for Medical Oncology (ESMO) CoCARE registries, we assessed severity and mortality from SARS-CoV-2 in vaccinated and unvaccinated patients with cancer and explored whether prior immune-related adverse events (irAEs) influenced outcome from COVID-19. FINDINGS: The study population consisted of 240 patients diagnosed with COVID-19 between January 2020 and February 2022 exposed to ICI within 3 months prior to COVID-19 diagnosis, with a 30-day case fatality rate (CFR30) of 23.6% (95% CI 17.8 to 30.7%). Overall, 42 (17.5%) were fully vaccinated prior to COVID-19 and experienced decreased CFR30 (4.8% vs 28.1%, p=0.0009), hospitalization rate (27.5% vs 63.2%, p<0.0001), requirement of oxygen therapy (15.8% vs 41.5%, p=0.0030), COVID-19 complication rate (11.9% vs 34.6%, p=0.0040), with a reduced need for COVID-19-specific therapy (26.3% vs 57.9%, p=0.0004) compared with unvaccinated patients. Inverse probability of treatment weighting (IPTW)-fitted multivariable analysis, following a clustered-robust correction for the data source (OnCovid vs ESMO CoCARE), confirmed that vaccinated patients experienced a decreased risk of death at 30 days (adjusted OR, aOR 0.08, 95% CI 0.01 to 0.69).Overall, 38 patients (15.8%) experienced at least one irAE of any grade at any time prior to COVID-19, at a median time of 3.2 months (range 0.13-48.7) from COVID-19 diagnosis. IrAEs occurred independently of baseline characteristics except for primary tumor (p=0.0373) and were associated with a significantly decreased CFR30 (10.8% vs 26.0%, p=0.0462) additionally confirmed by the IPTW-fitted multivariable analysis (aOR 0.47, 95% CI 0.33 to 0.67). Patients who experienced irAEs also presented a higher median absolute lymphocyte count at COVID-19 (1.4 vs 0.8 109 cells/L, p=0.0098). CONCLUSION: Anti-SARS-CoV-2 vaccination reduces morbidity and mortality from COVID-19 in ICI recipients. History of irAEs might identify patients with pre-existing protection from COVID-19, warranting further investigation of adaptive immune determinants of protection from SARS-CoV-2.

Is there an association between depression, anxiety disorders and COVID-19 severity and mortality? A multicenter retrospective cohort study conducted in 50 hospitals in Germany.

BACKGROUND: The aim of this retrospective cohort study was to investigate associations between depression and anxiety disorder and the risk of COVID-19 severity and mortality in patients treated in large hospitals in Germany. METHODS: This retrospective study was based on anonymized electronic medical data from 50 public healthcare service hospitals across Germany. Multivariable logistic regression models were used to study associations between depression, anxiety and mechanical ventilation and mortality due to COVID adjusted for age, sex, time of COVID-19 diagnosis, and pre-defined co-diagnoses. RESULTS: Of 28,311 patients diagnosed with COVID-19, 1970 (6.9%) had a diagnosis of depression and 369 (1.3%) had a diagnosis of anxiety disorder prior to contracting COVID-19. While multivariable logistic regression models did not indicate any association between depression diagnosis and the risk of mechanical ventilation, depression was associated with a decreased risk of mortality (OR: 0.71; 95% CI: 0.53-0.94). There was no association between anxiety disorders and risk of mortality, but there was a strong positive association between anxiety disorders and the risk of mechanical ventilation (OR: 2.04; 95% CI: 1.35-3.10). CONCLUSION: In the present study, depression and anxiety disorder diagnoses were not associated with increased COVID-19 mortality. Anxiety disorder was strongly associated with an increased risk of mechanical ventilation. Further studies are needed to clarify how depression and anxiety disorders may influence COVID-19 severity and mortality.

An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves.

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.

Highlights from the 2022 ASCO gastrointestinal cancer symposium: An overview by the EORTC gastrointestinal tract cancer group.

Recently, we have witnessed impressive diagnostic and therapeutic changes for gastrointestinal cancer patients. New challenges brought by the COVID-19 pandemic have led us to re-evaluate our work priorities. Thanks to the commendable resilience of both investigators and patients, however, clinical research never stopped. In addition to conducting cutting-edge research and serving patients' needs, as EORTC Gastrointestinal Tract Cancer Group, we are committed to pursuing educational initiatives beneficial to the entire European oncology community and beyond. In this regard, we have been providing critical discussions of new data from major international meetings. In this article, we discuss results of important selected studies presented at the 2022 ASCO Gastrointestinal Cancer Symposium, putting them in perspectives and highlighting potential implications for routine practice. With the number of in-person attendees and practice-changing/informing trials presented, this meeting represented a milestone in the return to normality as well as in the fight against cancer.

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.