Coronavirus disease 2019 (COVID-19) is expected to significantly affect cancer patients due to adverse outcomes with COVID-19 and disruptions in cancer care. Another important point is the stress and anxiety burden of COVID-19, which could affect quality of life. Patient education is vital due to the vulnerability of the topic to disinformation. To determine the areas needing improvements in patient education, and coping with stress, the burden of the problem should be pictured. From this point, we aimed to assess the perspectives and fears of cancer patients about COVID-19 with resources of COVID-19 knowledge with a questionnaire. A total of 250 adult cancer patients applied to the outpatient chemotherapy unit of Hacettepe University Cancer Center between May 27, 2020, and June 9, 2020, invited to answer a questionnaire of 13 multiple-choice questions with a return rate of 78% (195/250). Most patients acquired their knowledge about COVID-19 from television (91.9%). Social media were the second most common source of knowledge (43.8%) with a predilection in younger patients, nonsmokers, targeted therapy-or immunotherapy-treated patients, and breast cancer patients (>65 vs. <65 years of age, p = 0.057; nonsmoker vs. ever-smoker, p = 0.036; targeted therapy and immunotherapy vs. chemotherapy, p = 0.004; breast cancer vs. other cancers, p = 0.019). The percentage of patients seeing the information about COVID-19 as adequate (38.9%) or inadequate (35.1%) was similar. More than 90% of the patients had a moderate to severe degree of COVID-19 fear. In addition, 27.6% of patients had false knowledge of glove using as a protective measure for COVID-19. More than half of the patients had another wrong knowledge as the need for the supplements for COVID-19 protection. A significant percentage of patients (84.7%) expected some level of disruption in oncological care with the expectation of a moderate-to-severe disruption was more common in the advanced-stage patients (p = 0.026). In our experience, most cancer patients had a significant degree of fear about both infecting COVID-19 and the disruption of cancer care by COVID-19. A significant amount of our patients had wrong information about the protection necessities, which denotes the need for better patient education about COVID-19.

Background The 30-day readmission rate is an important indicator of patient safety and hospital’s quality performance. In this study, we aimed to find out the 30-day readmission rate of mild and moderate severity COVID-19 patients discharged from a tertiary care university hospital and to demonstrate the possible factors associated with readmission. Methods This is an observational, single-center study. Epidemiological and clinical data of patients who were hospitalized with a diagnosis of COVID-19 were retrieved from a research database where patient information was recorded prospectively. Readmission data was sought from the hospital information management system and National Health Record System to detect if the patients were readmitted to any hospital within 30 days of discharge. Adult patients (≥18 years-old) hospitalized in COVID-19 wards with a diagnosis of mild or moderate COVID-19 between March 20, 2020 (when the first case was admitted to our hospital), and April 26, 2020 were included. Results From March 26 to May 1, there were 154 mild or moderate severity (non-critical) COVID-19 patients discharged from COVID-19 wards, of which 11 (7.1%) were readmitted The median time of readmission was 8.1 days (IQR=5.2). Two patients (18.1%) were categorized to have mild disease and the remaining 9 (81.9%) as moderate disease. Two patients who were over 65 years of age and had metastatic cancers and hypertension developed sepsis and died in the hospital during the readmission episode. Malignancy (18.7% vs 2.1%, P = 0.04) and hypertension (45.5% vs 14%, P = 0.02) were more common in those who were readmitted. Conclusions This is one of the first studies to report on 30-day readmission rate of COVID-19 in the literature. More comprehensive studies are needed to reveal the causes and predictors of COVID-19 readmissions.

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BackgroundWe compared the new outpatient clinic referrals during the first 10 months of the COVID-19 pandemic with the year before.MethodsWe compared baseline characteristics of the 2208 new referrals in 2020 (n=922) and 2019 (n=1286) with Χ2 and Mann-Whitney U tests and calculated ORs with binary logistic regression. To evaluate the expected changes in the cancer survival secondary to stage migration, we used the 5-year survival data of Survival, Epidemiology and End Results (SEER) Program 2010–2016.ResultsThe percentage of patients with inoperable or metastatic disease was significantly increased during the pandemic (49.8% vs 39%, OR: 1.553, 95% CI: 1.309 to 1.843, p<0.001). We observed a significant decrease in the percentage of patients diagnosed via the screening methods (18.8% vs 28.7%, OR: 1.698, 95% CI: 1.240 to 2.325, p=0.001). The 90-day mortality after the cancer diagnosis was significantly higher during the pandemic (10.5% vs 6.6%, OR: 1.661, 95% CI: 1.225 to 2.252, p=0.001). Due to the increased advanced-stage disease rate at first referral, significant decreases in 5-year survival rates were expected for breast cancer (−8.9%), colorectal cancer (−11.1%), cervix cancer (−10.3%) and melanoma (−7%).ConclusionWe think that collaborative efforts are paramount to prevent the pandemic of late cancer diagnoses and ensure patient safety during the pandemic.

BackgroundCOVID-19 pandemic could create a collateral damage to cancer care denoting disruptions in care due to a significant burden on healthcare and resource allocations. Herein, we evaluate the early changes in the inpatient and outpatient oncology clinics to take a snapshot of this collateral damage at Hacettepe University Cancer Institute.MethodsPatients applying the outpatient clinic and outpatient palliative care (OPC) clinic for the first time and patients admitted to inpatient wards in the first 30 days after the first case of COVID-19 in Turkey were evaluated. These data were compared with data from the same time frame in the previous 3 years.ResultsThe mean number of daily new patient applications to the outpatient clinic (9.87±3.87 vs 6.43±4.03, p<0.001) and OPC clinic (3.87±1.49 vs 1.13±1.46, p<0.001) was significantly reduced compared with the previous years. While the number of inpatient admissions was similar for a month frame, the median duration of hospitalisation was significantly reduced. The frequency of hospitalisations for chemotherapy was higher than in previous years (p<0.001). By comparison, the rate of hospitalisations for palliative care (p=0.028) or elective interventional procedures (p=0.001) was significantly reduced.ConclusionIn our experience, almost all domains of care were affected during the pandemic other than patients’ systemic treatments. There were significant drops in the numbers of newly diagnosed patients, patients having interventional procedures and palliative care services, and these problems should be the focus points for the risk mitigation efforts for prevention of care disruptions.

IntroductionAfter the results of phase III vaccine studies became available, the leading oncology societies recommended two doses of COVID-19 vaccination to all patients with cancer with no specific recommendation for tumor type and active treatments. However, the data on the COVID-19 vaccine efficacy in cancer patients is limited due to exclusion of cancer patients from most vaccine clinical trials. Therefore, we systemically reviewed the available evidence evaluating the antibody responses in cancer patients.MethodsWe conducted a systematic search from the Pubmed database and calculated risk differences (RD) and 95% confidence intervals (CI) to compare seroconversion rates between cancer patients and controls using the Review Manager software, version 5.3.ResultsOur systematic search retrieved a total 27 studies and we included 17 studies with control arms in the analyses. Cancer patients had significantly lower seroconversion rates (37.3%) than controls (74.1%) (RD: -0.44, 95% CI: -0.52, -0.35, p&lt;0.001) with first vaccine dose. After two doses, the seroconversion rates were 99.6% in control arm and 78.3% in cancer patients (RD: -0.19, 95% CI: -0.28, -0.10, p&lt;0.001). The difference in seroconversion rates was more pronounced patients with hematologic malignancies (72.6%) (RD: -0.25, 95% CI: -0.27, -0.22, p&lt;0.001) than patients with solid tumors (91.6%) (RD: -0.09, 95% CI: -0.13, -0.04, p&lt;0.003) and patients in remission (RD: -0.10, 95% CI: -0.14, -0.06, p&lt;0.001).ConclusionIn conclusion, COVID-19 vaccine seroconversion rates were significantly lower in patients with hematological malignancies and patients under active treatment. Further research focusing on the approaches to improve vaccine efficacy and exploration of novel treatment options is urgently needed for these patients.

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Background: Prognostic scores derived from the blood count have garnered significant interest as an indirect measure of the inflammatory pressure in cancer. The recently developed pan-immune-inflammation value (PIV), an equation including the neutrophil, platelet, monocyte, and lymphocyte levels, has been evaluated in several cohorts, although with variations in the tumor types, disease stages, cut-offs, and treatments. Therefore, we evaluated the association between survival and PIV in cancer, performing a systematic review and meta-analysis. Methods: We conducted a systematic review from the Pubmed, Medline, and Embase databases to filter the published studies until 17 May 2022. The meta-analyses were performed with the generic inverse-variance method with a random-effects model. Results: Fifteen studies encompassing 4942 patients were included. In the pooled analysis of fifteen studies, the patients with higher PIV levels had significantly increased risk of death than those with lower PIV levels (HR: 2.00, 95% CI: 1.51–2.64, p < 0.001) and increased risk of progression or death (HR: 1.80, 95% CI: 1.39–2.32, p < 0.001). Analyses were consistent across several clinical scenarios, including non-metastatic or metastatic disease, different cut-offs (500, 400, and 300), and treatment with targeted therapy or immunotherapy (p < 0.001 for each). Conclusion: The available evidence demonstrates that PIV could be a prognostic biomarker in cancer. However, further research is needed to explore the promise of PIV as a prognostic biomarker in patients with non-metastatic disease or patients treated without immunotherapy or targeted therapy.