Diagnostic Patterns of Care and Outcomes for Patients with Chronic Lymphocytic Leukemia in the Surveillance, Epidemiology, and End-Results (SEER)-Medicare Database
Notice bibliographique
Résumé
Abstract Background: Little is known about the patterns of care related to the diagnosis of chronic lymphocytic leukemia (CLL), including the use of modern diagnostic techniques such as flow cytometry. A population-based analysis of the diagnostic process for CLL patients, including predictors and consequences of diagnostic delay, could fuel quality improvement efforts. Methods: The SEER-Medicare linked database for the years 1991–2003 was used to identify traditional Medicare enrollees diagnosed with CLL. Both inpatient and outpatient claims were analyzed from one year before, through six months following, the SEER diagnosis date. Signs, symptoms, and diagnostic studies commonly encountered in CLL diagnoses were identified by ICD-9 diagnosis and CPT procedure codes. Using the dates on claims, we calculated the time between the first visit for a sign or symptom and the SEER diagnosis date. Diagnostic delay was considered present if this time period met or exceeded the median number of days for the sample. Logistic regression models were used to estimate the likelihood of receipt of flow cytometry and of diagnostic delay, using clinical and sociodemographic predictor variables. Overall survival was examined using a Cox proportional hazards model. Analyses were adjusted to account for a known lag time in SEER cancer diagnosis dates. Results: We studied 5,086 patients who met eligibility criteria. Of those, 2,282 (48.9%) had a claim for flow cytometry during the study period, and 1,965 (38.6%) were performed within 30 days of the SEER diagnosis date. The most frequent signs and symptoms prior to diagnosis were infection (32.2%), lymphocytosis, (28.7%), and anemia (23.9%). The median survival time was 9.9 years. The median time between sign or symptom and CLL diagnosis date in SEER (defined as diagnostic delay) was 63 days (interquartile range = 251). Significant predictors of diagnostic delay included age of 75 or higher (OR=1.45, 95% CI = 1.27 to 1.65), female gender (OR = 1.22, 95% CI = 1.07 to 1.39), urban resident (OR = 1.46, 95% CI = 1.19 to 1.79), one or more comorbidities, as measured by the Charlson Comorbidity Index (OR = 2.83, 95% CI = 2.45 to 3.28), and care in a teaching hospital in the year preceding diagnosis (OR = 1.20, 95% CI = 1.05 to 1.38). Significant predictors of receipt of flow cytometry were age below 75 (OR = 1.46, 95% CI = 1.30 to 1.66), urban residence (OR = 1.27, 95% CI = 1.05 to 1.53), northeast residence (OR = 2.01, 95% CI = 1.69 to 2.39), southern residence (OR = 1.51, 95% CI = 1.22 to 1.89) and increasing number of pre-diagnosis signs or symptoms (OR = 1.15, 95% CI = 1.08 to 1.22). In multivariate models, diagnostic delay was not a significant predictor of overall survival (HR = 1.10, 95% CI = 0.98–1.25). Conclusions: In this large national cohort of older adults, age and gender both significantly impact diagnostic delay for CLL, raising a concern for sociodemographic differences in clinicians’ responses to signs and symptoms of hematologic malignancy. In addition, our analysis suggests that the presence of comorbidities may lead clinicians to overlook malignancy as an explanation for hematologic anomalies. Finally, initial use of flow cytometry varies significantly by geography and population density, which may reflect knowledge gaps in recommended diagnostic studies or lack of access to hematopathology services.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| 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 ».