Predicting Risk of Febrile Neutropenia after CHOP Chemotherapy.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».