Prevalence and risks of severe events for cancer patients with COVID-19 infection: a systematic review and meta-analysis
Bibliographic record
Abstract
Summary Background The corona virus disease 2019 (COVID-19) pandemic poses a severe challenge to public health, especially to those patients with underlying diseases. In this meta-analysis, we studied the prevalence of cancer among patients with COVID-19 infection and their risks of severe events. Methods We searched the Pubmed, Embase and MedRxiv databases for studies between December 2019 and May 3, 2020 using the following key words and terms: sars-cov-2, covid-19, 2019-ncov, 2019 novel coronavirus, corona virus disease-2019, clinical, clinical characteristics, clinical course, epidemiologic features, epidemiology, and epidemiological characteristics. We extracted data following PICO (patient, intervention, comparison and outcome) chart. Statistical analyses were performed with R Studio (version 3.5.1) on the group-level data. We assessed the studies’ risk of bias in accordance to the adjusted Joanna Briggs Institute. We estimated the prevalence or risks for severe events including admission into intensive care unit or death using meta-analysis with random effects. Findings Out of the 2,551 studies identified, 32 studies comprising 21,248 participants have confirmed COVID-19. The total prevalence of cancer in COVID-19 patients was 3.97% (95% CI, 3.08% to 5.12%), higher than that of the total cancer rate (0.29%) in China. Stratification analysis showed that the overall cancer prevalence of COVID-19 patients in China was 2.59% (95% CI, 1.72% to 3.90%), and the prevalence reached 3.79% in Wuhan (95% CI, 2.51% to 5.70%) and 2.31% (95% CI, 1.16% to 4.57%) in other areas outside Wuhan in China. The incidence of ICU admission in cancer patients with COVID-19 was 26.80% (95% CI, 21.65% to 32.67%) and the mortality was 24.32% (95% CI, 13.95% to 38.91%), much higher than the overall rates of COVID-19 patients in China. The fatality in COVID patients with cancer was lower than those with cardiovascular disease (OR 0.49; 95% CI, 0.34 to 0.71; p=0.39), but comparable with other comorbidities such as diabetes (OR 1.32; 95% CI, 0.42 to 4.11; p=0.19), hypertension (OR 1.27; 95% CI, 0.35 to 4.62; p=0.13), and respiratory diseases (OR 0.79; 95% CI, 0.47 to 1.33; p=0.45). Interpretation This comprehensive meta-analysis on the largest number of patients to date provides solid evidence that COVID-19 infection significantly and negatively affected the disease course and prognosis of cancer patients. Awareness of this could help guide clinicians and health policy makers in combating cancer in the context of COVID-19 pandemic. Funding Beijing Natural Science Foundation Program and Scientific Research Key Program of Beijing Municipal Commission of Education (KZ202010025047).
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.047 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".