Perspectives on Collector Collaboration

In 2019, we launched the Northern Arizona Paleoindian Project to expand on findings from the Rock Art Ranch (RAR) Research Experiences for Undergraduates (REU;NSF#1262184). The REU recovered 24 Paleoindian artifacts in association with drainages. Expansion of the research required mitigation of the patchwork landownership in the area, which encouraged a collector-collaboration model following Pitblado (2014) and Douglass et alia (2017). We held public events in collaboration with a network of agencies, avocational groups, collectors, and landowners to assess potential for Paleoindian archaeology in the area. In March 2020, however, the COVID-19 pandemic halted our efforts, allowing us to evaluate our project and practice. We find that tapping into existing local networks of responsible resource stewards (RRS) can greatly accelerate project development. We also find that private collections are endangered, and preserving this portion of the archaeological record requires documentation and long-term curation. Most importantly, we find that archaeologists working with collectors are uniquely positioned to build bridges between Indigenous communities, RRS, and professional archaeologists to help stabilize legacy collections and that this focus should drive collector-collaboration research design. Ultimately, the project must move toward a community-based participatory research design to seek equitable and culturally appropriate curation plans for local legacy collections. Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of Society for American Archaeology.

Longitudinal characterisation of haematological and biochemical parameters in cancer patients prior to and during COVID-19 reveals features associated with outcome.

BACKGROUND: Cancer patients are at increased risk of death from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Cancer and its treatment affect many haematological and biochemical parameters, therefore we analysed these prior to and during coronavirus disease 2019 (COVID-19) and correlated them with outcome. PATIENTS AND METHODS: Consecutive patients with cancer testing positive for SARS-CoV-2 in centres throughout the United Kingdom were identified and entered into a database following local governance approval. Clinical and longitudinal laboratory data were extracted from patient records. Data were analysed using Mann-Whitney U test, Fisher's exact test, Wilcoxon signed rank test, logistic regression, or linear regression for outcomes. Hierarchical clustering of heatmaps was performed using Ward's method. RESULTS: In total, 302 patients were included in three cohorts: Manchester (n = 67), Liverpool (n = 62), and UK (n = 173). In the entire cohort (N = 302), median age was 69 (range 19-93 years), including 163 males and 139 females; of these, 216 were diagnosed with a solid tumour and 86 with a haematological cancer. Preinfection lymphopaenia, neutropaenia and lactate dehydrogenase (LDH) were not associated with oxygen requirement (O2) or death. Lymphocyte count (P < 0.001), platelet count (P = 0.03), LDH (P < 0.0001) and albumin (P < 0.0001) significantly changed from preinfection to during infection. High rather than low neutrophils at day 0 (P = 0.007), higher maximal neutrophils during COVID-19 (P = 0.026) and higher neutrophil-to-lymphocyte ratio (NLR; P = 0.01) were associated with death. In multivariable analysis, age (P = 0.002), haematological cancer (P = 0.034), C-reactive protein (P = 0.004), NLR (P = 0.036) and albumin (P = 0.02) at day 0 were significant predictors of death. In the Manchester/Liverpool cohort 30 patients have restarted therapy following COVID-19, with no additional complications requiring readmission. CONCLUSION: Preinfection biochemical/haematological parameters were not associated with worse outcome in cancer patients. Restarting treatment following COVID-19 was not associated with additional complications. Neutropaenia due to cancer/treatment is not associated with COVID-19 mortality. Cancer therapy, particularly in patients with solid tumours, need not be delayed or omitted due to concerns that treatment itself increases COVID-19 severity.

Development of a model to predict hospital admission and severe outcome in cancer patients with COVID-19

Background: Patients (pts) with cancer are at increased risk of severe COVID-19 infection and death. Due to the heterogeneity of manifestations of COVID-19, accurate assessment of patients presenting to hospital is crucial. Early identification of pts who are likely to deteriorate allows timely discussions regarding escalation of care. It is equally important to identify pts who could be safely managed at home. To aid clinical decision making, we developed a model to determine which pts should be admitted vs. discharged at presentation to hospital. Methods: Consecutive pts with solid or haematological malignancies presenting with symptoms who tested positive for SARS-CoV-2 at 10 UK hospitals from March-May 2020 were identified following institutional board approval. Clinical and laboratory data were extracted from pt records. Clinical outcome measures were discharge within 24 hours, requirement for oxygen at any stage during admission and death. The associations between clinical features and outcomes were examined using ANOVA or Chi-squared tests. A logistic model was developed using clinical features with p<0.05 to predict patients who need hospital admission. Results: 52 pts were included (27 male, 25 female;median age 63). 80.5% pts had solid cancers, 19.5% haematological. Association analysis indicated that smoking status, prior cancer therapy and comorbidities had no significant association with outcomes. A number of other factors presented in the table had significant associations. A multivariate logistic regression model was generated to predict need for admission to hospital. Of note, age and male sex lost significance in the multivariate model (p>0.8). Using haematological cancer, NEWS2 score, dyspnoea, CRP and albumin, the model predicted requirement for admission with an area under the curve of 0.88. [Formula presented] Conclusions: We have developed a model to predict which pts require hospital admission. Further refinement and validation in larger cohorts of pts will be presented. Legal entity responsible for the study: The Christie NHS Foundation Trust. Funding: Has not received any funding. Disclosure: R. Lee: Honoraria (self): Bristol Myers Squibb;Honoraria (self): Astra Zeneca;Research grant/Funding (institution): Bristol Myers Squibb. M.P. Rowe: Travel/Accommodation/Expenses: Astellas Pharma. L. Horsley: Travel/Accommodation/Expenses: Lilly. C. Wilson: Honoraria (self), Advisory/Consultancy, Speaker Bureau/Expert testimony: Pfizer;Amgen;Novartis. T. Cooksley: Speaker Bureau/Expert testimony: Bristol Myers Squibb. A. Armstrong: Shareholder/Stockholder/Stock options, husband had shares now sold: Astra Zeneca. All other authors have declared no conflicts of interest.