Diagnostic Patterns of Care and Outcomes for Patients with Chronic Lymphocytic Leukemia in the Surveillance, Epidemiology, and End-Results (SEER)-Medicare Database
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".