A Scoring System Based on Clinical, Blood Count and Immunophenotypic Data Can Predict the Risk of Disease Progression in Patients with Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
Abstract There is limited access to advanced molecular and immunophenotypic techniques such as immunoglobulin heavy chain variable mutational status and ZAP-70 expression analysis in many parts of the world. To facilitate the development of a clinical model based on readily available clinical and well-standardized pathological data, we analyzed the well known prognostic factors in chronic lymphocytic leukemia (CLL) and proposed a scoring system. We searched the CLL database and among 465 patients (pts) we included 128 pts where the staging, blood counts, immunophenotypic and follow up data are complete. The baseline characteristics of this group and the significance of the proposed factors on the progression-free survival on univariate analysis are summarized in the table. Progression–free survival (PFS) was defined as the time from diagnosis to the time where treatment for CLL is indicated and if no treatment id indicated, the time of last follow up. In this 5 point scoring system we added one point for the presence of each one of the following factors: male gender, Rai stage 1 or more, lymphocyte count of 20 ×109/l or higher, lymphocyte doubling time less than 12 m, CD 38 expression of 20% or more. We further lumped these groups into 3 groups: no risk factors, one risk factor and tow or more risk factors. Thirty eight patients (30%) were found to have no risk factors and their 5 and 10 year PFS was 100% and 100% respectively. Forty nine pts (38%) have at least one risk factor and their 5 and 10 y PFS was 92% and 81% respectively. Forty pts (31%) have two or more risk factors and their 5 and 10 y PFS was 48% and 20% respectively. Those differences in PFS were significant (p<0.00001). We conclude that readily available clinical and laboratory information in patients with CLL can be utilized to predict the risk of disease progression. Pts with no risk factors can be reliably reassured and do not require regular monitoring. Correlation of these findings with other molecular prognostic factors is needed. Table: Risk factors for disease progression Parameter N (%) 5-y PFS 10-y PFS p value (UVA)* *Univariate analysis Sex: Male 71 (56) 67% 43% 0.01 Female 56 (44) 91% 73% Rai stage: 0 103 (80) 87% 77% <0.0001 1,2,3,4 25 (20) 49% 18% Lymphocyte count: < 20×109/l 108 (84) 85% 60% <0.0001 ≥20×109/l 20 (16) 40% 40% Lymphocyte doubling time ≥ 12m 111 (87) 84% 68% 0.001 <12 m 17 (13) 50% 22% CD38 <20% 99 (77) 83% 67% 0.006 ≥20% 29 (23) 62% 36% Fig: Progression free survival based on the number of risk factors for disease progression (p<0.00001) Fig:. Progression free survival based on the number of risk factors for disease progression (p<0.00001)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".