Society for Acute Medicine Benchmarking Audit 2021 (SAMBA21): assessing national performance of acute medicine services.

INTRODUCTION: The Society for Acute Medicine Benchmarking Audit 2021 (SAMBA21) took place on 17th June 2021, providing the first assessment of performance against the Society for Acute Medicine's Clinical Quality Indicators (CQIs) within acute medical units since the start of the COVID-19 pandemic. METHODS: All acute hospitals in the UK were invited to participate. Data were collected on unit structure, and for patients admitted to acute medicine services over a 24-hour period, with follow-up at 7 days. RESULTS: 158 units participated in SAMBA21, from 156 hospitals. 8973 patients were included. The number of admissions per unit had increased compared to SAMBA19 (Sign test p<0.005). An early warning score was recorded within 30 minutes of hospital arrival in 77.4% of patients. 87.4% of unplanned admissions were seen by a tier 1 clinician within 4 hours of arrival. Overall, the medical team performed the initial clinician assessment for 36.4% of unplanned medical admissions. More than a third of medical admissions had their initial assessment in Same Day Emergency Care (SDEC) in 25.4% of hospitals. 62.1% of unplanned admissions were seen by two other clinical decision makers prior to consultant review. Of those unplanned admissions requiring consultant review, 67.8% were seen within the target time. More than a third of unplanned admissions were discharged the same day in 41.8% of units. CONCLUSION: Performance against the CQIs for acute medicine was maintained in comparison to previous rounds of SAMBA, despite increased admissions. There remains considerable variation in unit structure and performance within acute medical services.

CORONET;COVID-19 in Oncology evaluatiON Tool: Use of machine learning to inform management of COVID-19 in patients with cancer

Background: Patients (pts) with cancer are at increased risk of severe COVID-19 infection and death. Due to COVID-19 outcome heterogeneity, accurate assessment of pts is crucial. Early identification of pts who are likely to deteriorate allows timely discussions regarding escalation of care. Likewise, safe home management will reduce risk of nosocomial infection. To aid clinical decisionmaking, we developed a model to help determine which pts should be admitted vs. managed as an outpatient and which pts are likely to have severe COVID-19. Methods: Pts with active solid or haematological cancer presenting with symptoms/asymptomatic and testing positive for SARS-CoV-2 in Europe and USA 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. Random Forest (RF) algorithm was used for model derivation as it compared favourably vs. lasso regression. Relevant clinical features were identified using recursive feature elimination based on SHAP. Internal validation (bootstrapping) with multiple imputations for missing data (maximum ≤2) were used for performance evaluation. Cost function determined cut-offs were defined for admission/death. The final CORONET model was trained on the entire cohort. Results: Model derivation set comprised 672 pts (393 male, 279 female, median age 71). 83% had solid cancers, 17% haematological. Predictive features were selected based on clinical relevance and data availability, supported by recursive feature elimination based on SHAP. RF model using haematological cancer, solid cancer stage, no of comorbidities, National Early Warning Score 2 (NEWS2), neutrophil:lymphocyte ratio, platelets, CRP and albumin achieved AUROC for admission 0.79 (+/-0.03) and death 0.75 (+/-0.02). RF explanation using SHAP revealed NEWS2 and C-reactive protein as the most important features predicting COVID-19 severity. In the entire cohort, CORONET recommended admission of 96% of patients requiring oxygen and 99% of patients who died. We then built a decision support tool using the model, which aids clinical decisions by presenting model predictions and explaining key contributing features. Conclusions: We have developed a model and tool available athttps://coronet.manchester.ac.uk/ to predict which pts with cancer and COVID-19 require hospital admission and are likely to have a severe disease course. CORONET is being continuously refined and validated over time.

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.

Longitudinal analysis of biochemical and haematological features of cancer patients with COVID-19

Background: Cancer patients (pts) are at increased risk of severe COVID-19 infection and death. Older pts, men and those with haematological malignancies and receiving anti-tumour therapy within 14 days appear to be at highest risk for poor outcomes. In general populations, severe COVID-19 infection has been associated with neutrophilia, raised lactate dehydrogenase (LDH) and C-reactive protein (CRP). Cancer and its treatment affect many haematological and biochemical parameters. We examined whether COVID-19 infection affected these compared to pts’ baseline parameters by longitudinal tracking. We also investigated whether changes were associated with poor outcome. Methods: Consecutive pts with solid or haematological malignancies presenting with index symptoms and testing positive for SARS-CoV-2 at a tertiary oncology centre were identified following institutional board approval. Clinical and laboratory data were extracted from the pt record. Paired T-tests were used for longitudinal sampling and ANOVA/Chi squared for outcomes. Results: 52 pts tested positive (27 male, 25 female;median age 63). 80.5% had solid cancers, and 19.5% haematological. 31/52 pts were lymphopenic prior to infection. Comparing mean pre-infection counts (6 months-14 days=PRE) with mean counts from the 5 days following positive test (DURING) lymphocyte counts significantly decreased during infection (p<0.0001). Platelets were significantly reduced DURING vs. PRE COVID-19 (p=0.0028). 17/52 pts developed transient (median 2 days) neutropenia (<2x109/L) DURING infection (6 pts <1x109/L, 2 pts <0.5x109/L), 8/17 attributed to cancer/cancer therapy, the rest had no underlying cause. 8/17 pts received growth factor support. Reduced lymphocytes/neutrophils/platelets at diagnosis were not associated with oxygen requirement (O2) or death. Different CRP trajectories were observed when comparing pts grouped by discharge/ O2/death. Higher CRP and LDH at diagnosis were associated with admission (p=0.02 CRP/0.2 LDH), O2 (p=0.0002 CRP/p<0.01 LDH) and death (p=0.069 CRP/p=0.04 LDH). Updated analysis will be presented. Conclusions: Infection with SARS-CoV-2 commonly affects haematological parameters in cancer pts. High CRP and LDH are associated with poor outcomes. Legal entity responsible for the study: The Christie NHS Foundation Trust. Funding: Has not received any funding. Disclosure: R. Lee: Speaker Bureau/Expert testimony, Research grant/Funding (self): Bristol Myers Squibb;Speaker Bureau/Expert testimony: Astra Zeneca. A. Armstrong: Shareholder/Stockholder/Stock options, Husband has shares: Astra Zeneca. T. Cooksley: Speaker Bureau/Expert testimony: Bristol Myers Squibb. All other authors have declared no conflicts of interest.

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.

Supportive Care: An Indispensable Component of Modern Oncology.

The advent of new cancer therapies, alongside expected growth and ageing of the population, better survival rates and associated costs of care, is uncovering a need to more clearly define and integrate supportive care services across the whole spectrum of the disease. The current focus of cancer care is on initial diagnosis and treatment, and end of life care. The Multinational Association of Supportive Care in Cancer defines supportive care as 'the prevention and management of the adverse effects of cancer and its treatment'. This encompasses the entire cancer journey, and necessitates involvement and integration of most clinical specialties. Optimal supportive care can assist in accurate diagnosis and management, and ultimately improve outcomes. A national strategy to implement supportive care is needed to acknowledge evolving oncology practice, changing disease patterns and the changing patient demographic.