1690P Development of a model to predict hospital admission and severe outcome in cancer patients with COVID-19

R. Lee, C. Zhou, R. Shotton, A. Tivey, E. Dickens, P. Huddar, H. Mckenzie, H. Boyce, A. Maynard, M. P. Rowe, S. Khan, L. Eastlake, A. Angelakas, M. Baxter, E. Copson, L. Horsley, A. Thomas, C. Wilson, T. Cooksley, A. Armstrong

Research output: Contribution to journalMeeting abstractpeer-review


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.
Original languageEnglish
Pages (from-to)S999
Number of pages1
JournalAnnals of Oncology
Issue numberSupplement 4
Early online date22 Sept 2020
Publication statusPublished - Sept 2020

ASJC Scopus subject areas

  • Hematology
  • Oncology


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