The Effect of Neutropenia and Filgrastim (G-CSF) in Cancer Patients With COVID-19 Infection
Bibliographic record
Abstract
BACKGROUND: Neutropenia is commonly encountered in cancer patients, and recombinant human granulocyte colony-stimulating factor (G-CSF, filgrastim) is widely given to oncology patients to counteract neutropenia and prevent infection. G-CSF is both a growth factor and cytokine that initiates proliferation and differentiation of mature granulocytes. However, the clinical impact of neutropenia and G-CSF use in cancer patients, who are also afflicted with coronavirus disease 2019 (COVID-19), remains unknown. METHODS: An observational cohort of 304 hospitalized patients with COVID-19 at Memorial Sloan Kettering Cancer Center was assembled to investigate links between concurrent neutropenia (N=55) and G-CSF administration (N=16) on COVID-19-associated respiratory failure and death. These factors were assessed as time-dependent predictors using an extended Cox model, controlling for age and underlying cancer diagnosis. To determine whether the degree of granulocyte response to G-CSF affected outcomes, a similar model was constructed with patients that received G-CSF, categorized into high- and low-response, based on the level of absolute neutrophil count (ANC) rise 24 hours after growth factor administration. RESULTS: Neutropenia (ANC < 1 K/mcL) during COVID-19 course was not independently associated with severe respiratory failure or death (HR: 0.71, 95% Cl: 0.34-1.50, P value: 0.367) in hospitalized COVID-19 patients. When controlling for neutropenia, G-CSF administration was associated with increased need for high oxygen supplementation and death (HR: 2.97, 95% CI: 1.06-8.28, P value: 0.038). This effect was predominantly seen in patients that exhibited a high response to G-CSF based on their ANC increase post-G-CSF administration (HR: 5.18, 95% CI: 1.61-16.64, P value: 0.006). CONCLUSION: Possible risks versus benefits of G-CSF administration should be weighed in neutropenic cancer patients with COVID-19 infection, as G-CSF may lead to worsening clinical and respiratory status in this setting.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".