Predictors Of Delay In Diagnosis and Treatment In Diffuse Large B-Cell Lymphoma and Impact On Survival
Notice bibliographique
Résumé
Abstract Background Although diagnostic and treatment delays in solid tumors are known to negatively impact on outcomes, little is known with respect to hematological malignancies. Diffuse Large B-Cell Lymphoma may present with a wide array of symptoms, thus rendering initial diagnosis challenging and time consuming. We evaluated disease-specific, patient-related and socioeconomic factors leading to delays in DLBCL diagnosis and treatment and the respective impact on overall and progression-free survival. Methods A comprehensive clinical database of patients with a new diagnosis or new presentation of transformed DLBCL treated at our center between 2002 and 2010 was utilized. A total 278 patients were included. All patients received at least one cycle of Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone (R-CHOP) immuno-chemotherapy. We defined various time intervals based on Cancer Care Ontario guidelines as follows: patient associated delay – time from symptoms onset to first known contact with a primary care physician (PCP); diagnostic delay – >6 weeks from first PCP contact to initial hematology consultation; and treatment delay – >4 weeks from first hematology consultation to chemotherapy initiation. Results In the population studied (n=278), the median age was 63 and 46% were female. Patients waited a median of 4 weeks (IQR 2-13) before seeking medical attention. A further median of 8 weeks (IQR 4-17) was required for the PCP to diagnose DLBCL or at least to achieve enough clinical suspicion for referral to hematology. From initial hematology consult, a median of 3 weeks (IQR 1-4) elapsed until chemotherapy initiation. In univariate analyses, patients who lacked bone marrow involvement (p=.005), had lower IPI scores (p=.031), higher Charlson comorbidity index (p=.048) and who had initiation of treatment in the outpatient setting (vs. inpatient; p=.021), were more likely to experience diagnostic delays >6 weeks. In multivariable logistic regression analysis, bone marrow involvement (OR=0.41, p=.018), Charlson comorbidity index (OR=1.42, p=.017) and requirement for urgent inpatient chemotherapy administration (OR=0.40, p=.012) remained associated with diagnostic delays. With respect to treatment delays, in univariate analyses, patients who did not have a pathology diagnosis at the time of initial hematology consultation (p<.0001) and those with B symptoms (p=.039) were more likely to experience treatment delays >4 weeks. On multivariable analysis, lack of pathological diagnosis at the time of hematology referral was the only factor that remained associated with treatment delays (OR=8.25, p<0.001). No socioeconomic factors (low income, level of education, and cohabiting alone) predicted for either diagnostic or treatment delays. On Cox multivariable regression analyses, diagnostic (Fig 1) or treatment delays (Fig 2) did not impact on survival or progression-free survival; only IPI score and number of R-CHOP cycles significantly impacted overall survival (HR=1.82, p<.001; HR=0.70 p<.001) and progression-free survival (HR=1.56, p<.001; HR=0.82, p=.004). Conclusion Selected disease and patient-related factors may be associated with delays in management of DLBCL. However, unlike in solid tumor presentations, we can reassure patients that waiting a reasonable time frame to complete diagnostic and staging milestones should not affect their disease course, as long as appropriate chemotherapy dosing is administered. Disclosures: No relevant conflicts of interest to declare.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».