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Background: Post-COVID Syndrome (PCS) is an important sequela of COVID-19, characterised by symptom persistence >3 months, subacute symptom onset, and worsening of pre-existing comorbidities. The causes and public health impact of PCS are still unclear, not least for the lack of efficient means to assess the presence and severity of PCS. Method(s): COVIDOM is a population-based cohort study of PCR-confirmed cases of SARS-CoV-2 infection, recruited through local public health authorities in three German regions. Standardised interviews and in-depth onsite examinations were scheduled 6-12 months post infection. Based upon 12 long-term symptom complexes, we developed a comprehensive PCS severity score in a training cohort and validated the score in two independent subcohorts. Result(s): In the training sub-cohort (n=667, 56% female), 90% of participants were treated as outpatients for acute COVID-19. Neurological ailments (61.5%) and fatigue (57.1%) persisted most frequently. Across all sub-cohorts, higher PCS scores were associated with lower health-related quality of life (EQ-5D-5L-VAS/-index, all p<0.001). Similarly, participants with a higher PCS score consistently showed increased blood inflammatory markers and Ddimer as well as lower diffusing capacity in lung function (all p<0.01). Significant early predictors of the PCS score included the number and intensity of acute symptoms, resilience, and general anxiousness. Conclusion(s): PCS severity can be quantified by an easy-to-use score summarising individual disease burden and reflecting pathological processes. The PCS score promises to facilitate diagnosis of PCS, studies of its natural course, and of therapeutic interventions.
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Introduction: Since the emergence of the novel coronavirus SARS-CoV-2 in December 2019 in Wuhan, cases of the associated disease COVID-19 are seen worldwide. To collect clinical data of the pandemic the international, multicenter Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) registry was established. Here, we present a first description of cancer patients with COVID-19 from LEOSS. Patients and Methods: We retrospectively analyzed a cohort of 283 patients (pts) with cancer and COVID-19 from a total of 1808 pts enrolled between March 6th, 2020, and June 26th, 2020. Baseline characteristics include socio-demographics, comorbidity according to Charlson Comor-bidity Index (CCI), ECOG and outcome of COVID-19. Clinical manifestation of COVID-19 was described in four phases: uncomplicated (asymptomatic/mild symptoms), complicated (need for oxygen supplementation), critical (need for life supporting therapy) and recovery (clinical improvement/discharge). Result(s): Median observational period was 11 (range 0-48) days, median inpatients stay 12.5 (range 0-72) days. Most patients were aged 66 years or older (75.5%), 112 (39.5%) pts were female. Median CCI was 4 (0-15), 46/119 (16.5%) pts had an ECOG >2. Solid tumors were seen in 61%, lymphoma and leukemia in 14.5% and 10.5% respectively. One hundred and seven pts (38%) had an active malignant disease and 76 (27%) had received anti-cancer treatment within the last 3 months. In 181 (64%) pts COVID-19 remained in the uncomplicated phase whereas 93 (33%) pts developed a complicated or critical phase. Sixty-three (22.5%) pts required intensive care, 35 out of 63 needed mechanical ventilation. A total of 79 (28%) pts died, 67 (23.5%) from COVID-19. Median survival was 33 days and worse compared to non-cancer pts (non-cancer pts: med. survival not reached, p-value < 0.001). Conclusion(s): As expected, cancer patients hospitalized for COVID-19 frequently have severe disease and an adverse outcome. To confrm these results, age-and comorbidity adjusted analysis are needed. An update of the analysis will be presented at the DGHO Annual Meeting.
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Background: Community acquired respiratory viruses (CARVs) may cause severe respiratory infections in patients (pts) with cancer. To collect epidemiological and clinical data of CARV-infections the multicentric registry OncoReVir was established. Here, we present a preliminary analysis of pts with cancer infected with CARVs. Method(s): A total of 1,142 pts with cancer and CARV-infection were enrolled between Nov2018 and Jan2022. Most cases were documented for season 17/18 and 18/19. Data on demographics, comorbidities, cancer, CARVs and infection course were collected. Pre-defined endpoints were pneumonia, admission to ICU and mortality. The relationship between cancer-specific factors and outcome was evaluated by bivariate logistic regression. Result(s): The median age was 60 (IQR 50-67) years, 42% of pts were female. Solid tumors were present in < 10%, leukemia, lymphoma and multiple myeloma in 36.5%, 27% and 23%, respectively. 50% had active cancer, 40% had received chemotherapy within the last 3 months. Targeted therapy was reported in 11.5%, high-dose steroids in 16% of pts, 56% were SCTrecipients. Commonly detected CARVs were influenza (39.5%), parainfluenza (18%), respiratory syncytial virus (15%), rhinovirus (14.5%), human metapneumovirus (hMPV, 5.5%), endemic coronavirus (5.5%) and SARSCoV- 2 (2%). Among all CARVs, frequent symptoms were cough, fever, dyspnea and rhinitis. Rates of pneumonia were highest in hMPV (33%) and SARS-CoV-2 (32%), lowest in endemic coronavirus (16%, p=0.334). 8.5% required intensive care, most of them due to COVID-19 (p=0.084). Infection-associated mortality but not rate of pneumonia showed significant differences comparing CARVs. In regression analysis, active cancer was associated with all endpoints: infection-related mortality (4.02 [1.63- 9.88], p=0.002), ICU admission (1.75 [1.07-2.88], p= 0.027) and pneumonia (1.47 [1.1-1.96], 0.009). Conclusion(s): In our cohort, all CARVs could potentially lead to severe disease. Active cancer was an independent risk factor for adverse outcome in pts with cancer and CARV-infection.
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Background: Active cancer has been identified as an independent risk factor for severity and mortality in COVID-19. However, direct comparisons of SARS-CoV-2 infected patients (pts) with active and non-active cancers remain scarce. Method(s): We retrospectively analyzed a cohort of pts with cancer with confirmed SARS-CoV-2 infection, enrolled 03/16/2020 - 07/31/2021. Data on demographics, cancer and laboratory findings were collected. Descriptive and subsequent regression analysis was performed. Endpoints were progression to severe COVID-19 and infection-associated mortality. Result(s): In total, 987 pts with cancer (510 active vs 477 non-active) were included in our analysis. Majority was male and > 55 years, with a higher number of elderly pts with non-active cancer. CCI was 4.75 vs 3.85 in pts with active and non-active cancer (p<0.001). Localized solid tumors were reported in 38 vs 79% (p<0.001), metastasized in 37.5 vs 5.5% (p<0.001) and hematological diseases in 37.5 vs 19.5% (p<0.001) pts with active and non-active cancer, respectively. At virus detection, majority of pts showed mild to moderate symptoms, while deterioration to severe COVID-19 was slightly more common in pts with active cancer (19% vs 16%;p=0.284). COVID-19 related mortality was significantly higher in pts with active cancer (24% vs 17.5%, p<0.001). In line, severe cytopenia and an increase of inflammatory markers were common findings in pts with active cancer at baseline, particularly in those who developed severe infection or died. Multivariate analysis revealed that ferritin (14.24 [2.1-96], p=0.006) and CRP (2.85 [1.02-8.02], p=0.046) were associated with severe COVID-19 and infection-related mortality. In pts with non-active cancer, association was seen for ferritin only (4.1 [1.51-11.17], p=0.006). Conclusion(s): Comparing pts with active and non-active cancer, mortality rate was significantly higher in pts with active cancer. Also inflammatory markers were significantly increased assuming higher levels of inflammation may play a role in adverse outcome of COVID-19 in pts with active cancer.
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Background. Numerous predictive clinical scores with varying discriminatory performance have been developed in the context of the current coronavirus disease 2019 (COVID-19) pandemic. To support clinical application, we test the transferability of the frequently applied 4C mortality score (4C score) to the German prospective Cross-Sectoral Platform (SUEP) of the National Pandemic Cohort Network (NAPKON) compared to the non COVID-19 specific quick sequential organ failure assessment score (qSOFA). Our project aims to externally validate these two scores, stratified for the most prevalent variants of concerns (VOCs) of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) in Germany. Methods. A total of 685 adults with polymerase chain reaction (PCR)-detected SARS-CoV-2 infection were included from NAPKON-SUEP. Patients were recruited from 11/2020 to 03/2022 at 34 university and non-university hospitals across Germany. Missing values were complemented using multiple imputation. Predictive performance for in-hospital mortality at day of baseline visit was determined by area under the curve (AUC) with 95%-confidence interval (CI) stratified by VOCs of SARS-CoV-2 (alpha, delta, omicron) (Figure 1). Figure 1: Study flow chart with inclusion criteria and methodological workflow. Results. Preliminary results suggest a high predictive performance of the 4C score for in-hospital mortality (Table 1). This applies for the overall cohort (AUC 0.813 (95%CI 0.738-0.888)) as well as the VOC-strata (alpha: AUC 0.859 (95%CI 0.748-0.970);delta: AUC 0.769 (95%CI 0.657-0.882);omicron: AUC 0.866 (95%CI 0.724-1.000)). The overall mortality rates across the defined 4C score risk groups are 0.3% (low), 3.2% (intermediate), 11.6% (high), and 49.5% (very high). The 4C score performs significantly better than the qSOFA (Chi2-test: p=0.001) and the qSOFA does not seem to be a suitable tool in this context. Table 1: Discriminatory performance of the 4C Mortality Score and the qSOFA score within the validation cohort NAPKON-SUEP stratified by the Variant of Concerns of SARS-CoV- 2. Conclusion. Despite its development in the early phase of the pandemic and improved treatment, external validation of the 4C score in NAPKON-SUEP indicates a high predictive performance for in-hospital mortality across all VOCs. However, since the qSOFA was not specifically designed for this predictive issue, it shows low discriminatory performance, as in other validation studies. Any interpretations regarding the omicron stratum are limited due to the sample size.
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INTRODUCTION: Since the early SARS-CoV-2 pandemic, cancer patients have been assumed to be at higher risk for severe COVID-19. Here, we present an analysis of cancer patients from the LEOSS (Lean European Open Survey on SARS-CoV-2 Infected Patients) registry to determine whether cancer patients are at higher risk. PATIENTS AND METHODS: We retrospectively analyzed a cohort of 435 cancer patients and 2636 non-cancer patients with confirmed SARS-CoV-2 infection, enrolled between March 16 and August 31, 2020. Data on socio-demographics, comorbidities, cancer-related features and infection course were collected. Age-, sex- and comorbidity-adjusted analysis was performed. Primary endpoint was COVID-19-related mortality. RESULTS: In total, 435 cancer patients were included in our analysis. Commonest age category was 76-85 years (36.5%), and 40.5% were female. Solid tumors were seen in 59% and lymphoma and leukemia in 17.5% and 11% of patients. Of these, 54% had an active malignancy, and 22% had recently received anti-cancer treatments. At detection of SARS-CoV-2, the majority (62.5%) presented with mild symptoms. Progression to severe COVID-19 was seen in 55% and ICU admission in 27.5%. COVID-19-related mortality rate was 22.5%. Male sex, advanced age, and active malignancy were associated with higher death rates. Comparing cancer and non-cancer patients, age distribution and comorbidity differed significantly, as did mortality (14% vs 22.5%, p value < 0.001). After adjustments for other risk factors, mortality was comparable. CONCLUSION: Comparing cancer and non-cancer patients, outcome of COVID-19 was comparable after adjusting for age, sex, and comorbidity. However, our results emphasize that cancer patients as a group are at higher risk due to advanced age and pre-existing conditions.