Incidence of Mental Health Events and Its Association with Survival Among Patients with Diffuse Large B-Cell Lymphoma: A Population-Based Cohort Study
Notice bibliographique
Résumé
Abstract Introduction While prior studies suggest that mental health complications are underdiagnosed and undertreated in patients with cancer, a paucity of data exists for patients with diffuse large B-cell lymphoma (DLBCL). Indeed, mental illness can impact the success of potentially curative treatment for DLBCL including delays in treatment initiation, poor chemotherapy compliance, and suboptimal rates of completion. Accordingly, we aimed to examine the risk of incident mental health events following DLBCL diagnosis, and the association of mental health conditions with overall survival (OS). Methods We conducted a population-based observational study using linked administrative healthcare databases from Ontario, Canada. All Ontario residents aged ≥18 years with DLBCL treated with rituximab-based chemotherapy for curative intent between January 2005 and December 2017 were identified and followed from the date of first rituximab until March 1, 2020. The primary outcome was any incident mental health event (emergency department visit, hospitalization, or outpatient visit for mood disturbance including depression and anxiety, psychotic disorder, or substance-related disorder). Patients with a DLBCL diagnosis without pre-existing mental health comorbidity in the 2-years prior to start of rituximab were matched to mental health condition- and cancer-free controls in a 1:4 ratio based on birth year and sex. The cumulative incidence function was used to estimate incidence of mental health events while accounting for the competing risk of death, and differences were compared using Gray's K-sample test. A cause-specific Cox regression model was used to estimate mental health events up to two-years following rituximab initiation, while controlling for relevant covariates (sex, age modeled in 10-year interval increments, rural vs. urban residence, income quintile, and quartile of sum of aggregated diagnosis groups (ADGs) as a measure of comorbid disease burden (mental health and cancer diagnoses excluded). The secondary outcome was the association of mental health conditions on OS for all identified DLBCL patients, evaluated using Cox regression (with mental health event as time-varying variable). Results We identified 10,299 patients diagnosed with DLBCL and treated with a rituximab-containing regimen in Ontario, with median age 67 years (IQR 56-76), 45.9% female, median ADG score of 9 (IQR 6-11), and median of 6 cycles of rituximab received (IQR 4-6). For patients with available stage data (49.2% of cohort), 34.6% had stage IV at diagnosis. When compared to birth year- and sex-matched controls (n=29,620), DLBCL cases (n=7,405) had a greater comorbidity burden (p<0.001). During a median follow-up of 5.16 years (SD+4.13), 28.8% of DLBCL patients without pre-existing mental health conditions developed an incident mental health event. With adjustment of potential confounders, patients with DLBCL still had significantly higher risk of an incident mental health event compared to controls (adjusted hazard ratio [aHR] 1.29, 95% confidence interval [CI] 1.21-1.39, p<0.0001) (Figure 1, Table 1). Younger age, female sex and higher comorbidity burden were associated with an increased risk of an incident mental health event (Table 1). In addition, having a mental health condition (either pre-existing or after start of rituximab) was associated with a significantly worse one-year OS (aHR 1.11, 95% CI 1.01-1.22, p<0.0001) and at end of follow-up (aHR 1.24, 95% CI 1.16-1.32, p<0.0001; Table 2). Additional factors independently associated with worse overall OS included older age, increased comorbidity, and male sex (Table 2). Conclusions In this large population-based study, patients with DLBCL were found to have a significantly higher risk of incident mental health events compared to controls. Moreover, the presence of a mental health condition was associated with worse survival outcomes. These data suggest that patients with DLBCL, particularly those with pre-existing mental health condition(s), would benefit from routine mental health assessment and management during follow-up, not only for mental health itself but also potentially to improve survival. Figure 1 Figure 1. Disclosures Prica: Astra-Zeneca: Honoraria; Kite Gilead: Honoraria.
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Étiquettes directes de modèles (non validées)
Étiquettes de catégorie et de devis d'étude par modèle, issues des rondes d'étiquetage. C'est une sortie machine, non validée, et le désaccord entre modèles est livré comme donnée. Aucun devis ici n'est encore validé contre MEDLINE.
| Bras | Catégories | Devis d'étude | Confiance |
|---|---|---|---|
| gemma | aucune catégorie Domaine: non disponible · Genre: Empirique Porte sur le système de recherche canadien: non · Porte sur un sujet canadien: non | Observationnel | low |
| gpt | aucune catégorie Domaine: non disponible · Genre: Empirique Porte sur le système de recherche canadien: non · Porte sur un sujet canadien: non | Observationnel | low |
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 validéeÉtiqueté directement par 2 modèles lisant le dossier complet.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».