Predicting Risk of Febrile Neutropenia after CHOP Chemotherapy.
Bibliographic record
Abstract
Abstract Introduction: Febrile neutropenia (FN) is a major cause of morbidity following chemotherapy for lymphoma and results in increased hospitalization, antibiotic use and cost. Predicting who is at highest risk of FN allows preventive measures such as GCSF to be used for those who will benefit the most. This study was performed to determine the incidence of FN and to identify which prognostic factors may predict for increased risk for FN Methods: Retrospective chart review of patients who received CHOP or CHOP plus Rituximab chemotherapy between 2001– 2003 at our centre. The following prognostic factors were used- IPI score, LDH, age, stage, number of extranodal sites, performance status, B symptoms, bone marrow involvement, albumin (ALB), hemoglobin (HGB), platelet count and neutrophil count prior to first chemotherapy. Outcome measured was FN after any cycle of chemotherapy. Results: Seventy patients receiving a total of 232 cycles (median 6, 1–8) were included. Twenty-one patients were male and 39 were female. Median age was 60 (29 – 85). LDH was elevated in 56%, 21% had bone marrow involvement, 25% had ECOG > 1, 21% had >1 extranodal site involved and 32% had B symptoms. 30% of patients had IPI score 0–1, 64% IPI 2–3, and 6% IPI 4–5. CHOP alone was given to 51(73%) of patients and 19 (27%) patients received CHOP plus Rituximab. FN occurred in 50 out of 232 cycles (22%). 42% of patients developed FN, 21% with their 1st cycle. Thirteen (19%) patients had more than 1 episode of FN. Two patients received primary prophylaxis with G-CSF prior to 1st cycle of chemotherapy and neither developed febrile neutropenia. 29 patients started secondary prophylaxis with G-CSF after developing FN, or for other reasons, and 13 of these (45%) had least one more episode of FN. These 29 patients received a total of 95 cycles of chemotherapy after starting secondary prophylaxis, and FN developed in 20 of these cycles (21%). By univariate analysis, only hemoglobin (p=.044) and albumin (p=0.01) were statistically significant predictors of FN. Using logistic regression analysis neither was an independent predictor due to high correlation between the two (r= 0.49, p<0.01). Using the mean values for HGB (110g/L) and ALB (30g/L), we found that rate of FN was 61% (19/31) if ALB, HGB, or both were below these thresholds, compared to 33% (13/39) if neither was low. Conclusion: The incidence of FN was almost twice as high in patients with low hemoglobin or albumin. Other factors were not statistically significant. These two factors may be indicative of poor underlying health and diminished bone marrow reserve, both of which could predispose to FN. Interestingly, disease specific factors such as IPI, LDH, etc were not predictive. On multivariate analysis, none of these prognostic factors appeared to be an independent predictor of febrile neutropenia. This was mainly due to the high correlation seen between the two, but may also be affected by the small sample size studied, and the overall unexpectedly high rate of FN in this group.
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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) | 0.000 | 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) | 0.001 | 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".