Clime: CLL Immune Micro-Environment and Risk of Infections Pre- and Post-Diagnosis: A Population-Based Study
Notice bibliographique
Résumé
Background: Immune dysfunction precedes a diagnosis of chronic lymphocytic leukemia (CLL) in some patients. More information is needed about which patients experience immune dysfunction and how it impacts disease progression, as well as the development of post-diagnosis infections and second malignancies. The objectives of this study were to 1) examine the rate of pre-diagnosis infection for individuals diagnosed with CLL compared to population controls, 2) evaluate the impact of pre-diagnosis infection on time to first treatment, 3) compare the rate of immune dysfunction before the start of treatment by stage, and 4) compare the rate of immune dysfunction before the start of treatment for each year post-treatment. Methods: We used a retrospective matched cohort study design that included all individuals in Manitoba diagnosed with CLL from 2006 to 2019. Individuals were identified from the Manitoba CLL database and the Manitoba Cancer Registry (MCR). Individuals with CLL were matched by age, sex, and area of residence (urban versus rural) to population controls identified from the Manitoba Health Population Registry. Provincial pharmacy, hospital admissions, medical claims, and MCR data were used to determine infection, second malignancies, and IgR therapy. Immune dysfunction was defined as two or more infections, a diagnosis with a secondary malignancy, or receipt of IgR therapy. The pre-diagnosis time period was defined as three to 15 months prior to diagnosis. The pre-treatment time period was defined as 12 months prior to treatment. The following analyses were used: conditional logistic regression for objective 1, competing risk for objective 2, logistic regression for objective 3, and logistic GEE models for objective 4. Results: A total of 1,776 CLL patients were matched to 17,760 non-CLL controls. Sixty-two percent were male, the mean age at diagnosis was 71 years, and 62% lived in an urban area. Fifty-one percent of CLL patients had a pre-diagnosis infection compared to 46% of controls. Compared to individuals with no prior infection, those who had a prior infection were more likely to be diagnosed with CLL (Odds Ratio (OR) 1.17, 95% confidence Interval (CI) 1.03, 1.33 for one infection, OR 1.29, 95% CI 1.15, 1.45 for two or more infections). After adjusting for the number of outpatient visits and comorbidity, individuals with two or more infections were 14% more likely to be diagnosed with CLL (OR 1.14, 95% CI 1.00-1.30). Conclusions: We found that infections that occurred in the three to 15 months before diagnosis increased the odds of being diagnosed with CLL. Work is ongoing examining time to treatment, immune dysfunction by stage, as well comparing immune dysfunction before and after treatment.
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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 | 0,001 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».