Real-world demographic and clinical characteristics of patients diagnosed with diffuse large B-cell lymphoma (DLBCL) in the United States (US).
Bibliographic record
Abstract
e18347 Background: DLBCL, the most common type of non-Hodgkin lymphoma in the US, is associated with significant morbidity and mortality. In October 2015, DLBCL was differentiated from other related lymphoma entities with the advent of ICD-10-CM DLBCL-specific codes. With limited real-world data on patients (pts) with DLBCL in the modern treatment era, this study was conducted to characterize these pts. Methods: A retrospective study was conducted using the Optum Clinformatics Data Mart database (01/2013–03/2018). Pts ≥ 18 years of age with ≥ 1 hospitalization or ≥ 2 outpatient visits with an ICD-10-CM diagnosis code for DLBCL (or an antecedent diagnosis of other lymphoma, which may have been assigned before confirmation of DLBCL) after October 1st, 2015 (index date) and no prior ICD-9-CM code for unspecified DLBCL were identified as incident. Pts with an ICD-9-CM code for unspecified DLBCL before October 2015 (index date) were classified as prevalent. At least 12 months of continuous enrollment pre-index date (baseline period) was required. Pts with ICD-10-CM code for primary mediastinal B-cell lymphoma (PMBCL), baseline diagnoses of other malignancies such as Hodgkin lymphoma and multiple myeloma were excluded. Characteristics, including baseline comorbidities, healthcare resource utilization, and costs were assessed. Results: Among 4,074 DLBCL pts (3,201 incident; 873 prevalent), mean age ± standard deviation (SD) was 71 ± 12 years; 46% were female. Incident and prevalent pts had mean Charlson comorbidity index scores of 2.7 and 2.3, respectively. Most common baseline Elixhauser comorbidities were hypertension (68.4%), diabetes (31.1%), and cardiac arrhythmia (25.3%) in incident pts and hypertension (62.5%), diabetes (28.3%), and chronic pulmonary disease (20.6%) in prevalent pts. Mean ± SD number of baseline hospitalizations was 0.32 ± 0.83 and 0.21 ± 0.49 in incident and prevalent pts, respectively. Total mean ± SD baseline healthcare costs (before diagnosis) were $24,621 ± 45,628 for incident pts and $19,137 ± 29,307 for prevalent pts. Conclusions: This study documents substantial co-morbid and economic burden of incident as well as prevalent pts with DLBCL.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".