Febrile Neutropenia in Lymphoma Patients Is Associated with Substantial Resource Use and Healthcare Costs in Clinical Practice in Spain.
Bibliographic record
Abstract
Abstract Background Despite the significant impact of chemotherapy-induced febrile neutropenia (FN) on patients (pts) with cancer and its consequences for health care costs, there have been no studies in common clinical practice in Spain assessing the burden and economic impact of this complication. Methods This is a sub-analysis of lymphoma pts included in a multicentre, retrospective, chart review of adult pts from 16 Spanish hospitals who suffered from at least one FN episode related to cytotoxic chemotherapy (CT). Resource use and subsequent costs including days of hospitalization, number of transfusions, number and type of complementary tests, use of colony-stimulating factors (CSFs), and use of antibiotics and other drugs to manage FN were assessed for each episode. The impact of FN on planned CT was also analysed in terms of dose delays (DD) and/or reductions (DR). Results Medical charts from 194 pts were reviewed, 67 (34.5%) of whom had lymphoma, which accounted for 87 documented FN episodes included in this analysis. The median (range) age of patients was 62 (19–85) years, 31.7% had aggressive NHL, and 58.2% were treated with CHOP-like CT. FN appeared during first CT cycle in 61.2% of the pts. Hospitalization was required in 100% of the pts and the median length of hospital stay due to FN was 8 days (p25:6–p75:11). During an FN episode, 42% of pts required ≥1 transfusion, 100% needed a blood test and 98.9% a blood culture. Microbiologically documented infection appeared in 33% of FN episodes. All pts were treated with antibiotics (69.3% with cephalosporins) and CSFs were used in 64.8% of pts. In 40.9% of episodes, FN impacted on planned CT dose and/or schedule: DR was observed in 16.7% of pts, DD in 24.0% and CT withdrawal in 15.2%. Conclusions FN has a substantial impact on resource use and associated costs in pts with lymphoma. Hospitalization and antibiotic treatment were the main drivers of the cost associated with the management of FN in current clinical practice. Furthermore, FN has a meaningful effect on planned CT dose and/or schedule, with potential consequences for treatment outcome. Mean (SD) healthcare costs per FN episode (All cost data expressed as €) Hospitalization Transfusions Complementary Tests CSFs Antibiotics and Other Drugs Total 3,557.17 (3,050.44) 43.24 (58.59) 162.77 (135.36) 223.39(231.40) 527.67 (448.56) 4,514.24 (3,392.20)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".