Factors affecting COVID-19 outcomes in cancer patients: A first report from Guy’s Cancer Centre in London
Bibliographic record
Abstract
Abstract Background There is insufficient evidence to support clinical decision-making for cancer patients diagnosed with COVID-19 due to the lack of large studies. Methods We used data from a single large UK Cancer Centre to assess demographic/clinical characteristics of 156 cancer patients with a confirmed COVID-19 diagnosis between 29 February-12 May 2020. Logistic/Cox proportional hazards models were used to identify which demographic and/or clinical characteristics were associated with COVID-19 severity/death. Results 128 (82%) presented with mild/moderate COVID-19 and 28 (18%) with severe disease. Initial diagnosis of cancer >24m before COVID-19 (OR:1.74 (95%CI: 0.71-4.26)), presenting with fever (6.21 (1.76-21.99)), dyspnoea (2.60 (1.00-6.76)), gastro-intestinal symptoms (7.38 (2.71-20.16)), or higher levels of CRP (9.43 (0.73-121.12)) were linked with greater COVID-19 severity. During median follow-up of 47d, 34 patients had died of COVID-19 (22%). Asian ethnicity (3.73 (1.28-10.91), palliative treatment (5.74 (1.15-28.79), initial diagnosis of cancer >24m before (2.14 (1.04-4.44), dyspnoea (4.94 (1.99-12.25), and increased CRP levels (10.35 (1.0552.21)) were positively associated with COVID-19 death. An inverse association was observed with increased levels of albumin (0.04 (0.01-0.04). Conclusions A longer-established diagnosis of cancer was associated with increasing severity of infection as well as COVID-19 death, possibly reflecting effects of more advanced malignant disease impact on this infection. Asian ethnicity and palliative treatment were also associated with COVID-19 death in cancer patients. Contribution to the field In the context of cancer, the COVID-19 pandemic has led to challenging decision-making. These are supported by limited evidence with small case studies being reported from China, Italy, New York and a recent consortium of 900 patients from over 85 hospitals in the USA, Canada, and Spain. As a result of their limited sample sizes, most studies were not able to distinguish between the effects of age, cancer, and other comorbidities on COVID-19 outcomes. Moreover, the case series from New York analysed which patient characteristics are associated with COVID-19 death, but only made a comparison with non-cancer patients. The first results of the COVID-19 and Cancer Consortium provide insights from a large cohort in terms of COVID-19 mortality, though a wide variety of institutions with different COVID-19 testing procedures were included. Given the current lack of (inter)national guidance for cancer patients in the context of COVID-19, we believe that our large cancer centre can provide an important contribution to the urgent need for further insight into the intersection between COVID-19 and cancer. With comprehensive in-house patient details, consistent inclusion criteria and up-to-date cancer and COVID-19 outcomes, we are in position to provide rapid analytical information to the oncological community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".