Abstract S02-01: Clinical characteristics and outcomes of coronavirus 2019 disease (COVID-19) in cancer patients treated with immune checkpoint inhibitors (ICI)
Bibliographic record
Abstract
Abstract Background: ICI are widely used in the treatment of various cancer types. It has been hypothesized that ICI could confer an increased risk of severe acute lung injury or other complications associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: We analyzed data from 113 patients with laboratory-confirmed COVID-19 while on treatment with ICI without chemotherapy in 19 hospitals in North America, Europe, and Australia. Data collected included details on symptoms, comorbidities, medications, treatments and investigations for COVID-19, and outcomes (hospital admission, ICU admission, and mortality). Results: The median age was 63 years (range 27–86); 40 (35%) patients were female. Most common malignancies were melanoma (n=64, 57%), non-small cell lung cancer (n=19, 17%), and renal cell carcinoma (n=11, 10%); 30 (27%) patients were treated for early (neoadjuvant/adjuvant) and 83 (73%) for advanced cancer. Most patients received anti-PD-1 (n=85, 75%), combination anti-PD-1 and anti-CTLA-4 (n=15, 13%), or anti-PD-L1 (n=8, 7%) ICI. Comorbidities included cardiovascular disease (n=31, 27%), diabetes (n=17, 15%), and pulmonary disease (n=14, 12%). Symptoms were present in 68 (60%) patients; 46 (68%) had fever, 40 (59%) cough, and 23 (34%) dyspnea. Overall, ICI was interrupted in 58 (51%) patients. At data cutoff, 33 (29%) patients were admitted to hospital, 6 (5%) to ICU, and 9 (8%) patients died. COVID-19 was the primary cause of death in 7 patients, 3 of whom were admitted to ICU. Cancer types in patients who died were melanoma (2), non-small cell lung cancer (2), renal cell carcinoma (2), and others (3); all (9) patients had advanced cancer. Administered treatments were oxygen therapy (8), mechanical ventilation (2), vasopression (2), antibiotics (7), antiviral drugs (4), glucocorticoids (2), and anti-IL-6 (2). Of all hospitalized patients, 20 (61%) had been discharged and 4 (12%) were still in hospital at data cutoff. Conclusion: The mortality rate of COVID-19 in patients on ICI is higher than rates reported for the general population without comorbidities but may not be higher than rates reported for the cancer population. Despite these preliminary findings, COVID-19 patients on ICI may not have symptoms and a proportion may continue ICI. Correlative analyses are ongoing and will be presented. Citation Format: Aljosja Rogiers, Carlo Tondini, Joe M. Grimes, Megan H. Trager, Sharon Nahm, Leyre Zubiri, Neha Papneja, Arielle Elkrief, Jessica Borgers, April Rose, Johanna Mangana, Michael Erdmann, Ines Pires da Silva, Christian Posch, Axel Hauschild, Lisa Zimmer, Paola Queirolo, Caroline Robert, Karijn Suijkerbuijk, Paolo A. Ascierto, Paul Lorigan, Richard Carvajal, Osama E Rahma, Mario Mandala, Georgina V. Long. Clinical characteristics and outcomes of coronavirus 2019 disease (COVID-19) in cancer patients treated with immune checkpoint inhibitors (ICI) [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr S02-01.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".