Clime: CLL Immune Micro-Environment and Risk of Infections Pre- and Post-Diagnosis: A Population-Based Study
Bibliographic record
Abstract
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".