PB1935 REAL LIFE EXPERIENCE IN CML PATIENTS IN CANARY ISLAND
Bibliographic record
Abstract
Background: In February of 2009 the Canarian registry of chronic myeloid leukemia (CML) was created in order get a better insight of treatment response, behavior and outcome in our region. For that purpose data from patients (pts) treated in the 7 hospitals of the Canary Islands was collected. Aims: Analyze Overall survival and responses to the tyrosine kinase inhibitor (TKI) treatment on real life CP‐CML pts. Methods: Our series consisted of 262 pts with CP‐CML diagnosed between 2000 and 2019 and treated with TKIs, 138 (52.7%) were males and 124 (47.3%) females with a mean age at diagnosis of 52.66 ± 17.36. The median follow‐up for the whole series is 6.26 years (0.02‐19.21). We analyzed overall survival (OS), cumulative incidence (CI) of major molecular response (MMR) and deep molecular response (DMR) on 2 first lines with Kaplan‐Meier test (KM) and compared the molecular response of different treatments on first and second line of treatment using the Jonckheere‐Terpstra test (JT). For the statistics we used SPSS V.23 and the p‐value was 0.05. Results: We have an OS at 8 years of 88.8%. Totally 39 pts died from which 3 (7.7%) were clinically related to CML. 183 (69.85%) pts received only 1 line of treatment, 55 (20.99%) 2 lines of treatment, 16 (6.1%) 3 lines of treatment and 8 (3.05%) 4 or more lines of treatment. At this moment 87 (39.01%) pts are on Imatinib (IM), 63 (28.25%) on Nilotinib (NI), 34 (15.25%) on Dasatinib (DA), 3 (1.35%) on Bosutinib, 2 (0.89%) on Ponatinib and 30 (13.45%) on Treatment free remission (TFR). All pts on TFR are on DMR with a median follow up of 6 months (0.5‐64). 172 (77.13%) pts are on DMR, 30 (13.45%) pts are on MMR, 9 (4.03%) pts are on cytogenetic response, 10 (4.45%) pts are on complete hematologic response and only 2 (0.89%) did not achieve any response. In 1st line 193 (86.54%) pts were treated with IM, 51 (22.87%) with NI and 18 (8.07%) with DA. By the JT test we found that there is a statistically significant difference between the medians of BCR/ABL levels at 3 (IM = 1.00, NI = 0.06, DA = 0.14), 6 (IM = 0.06, NI = 0.003, DA = 0.021) and 12 (IM = 0.014, NI = 0.0006, DA = 0.022) months between the different drugs (p < 0.001). We also found statistically significant difference for the CI of MMR between NI and IM (p < 0.001) and between NI and DA (p = 0.029) with a median of time till MMR of 3.27 months NI, 9.53 for IM and 7.9 for DA (Fig1a). We found a statistically significant difference for the CI of DMR between IM and NI (p = 0.004) with a median of time till DMR of 23.33 months for DA, 19.8 for IM and 11.2 for NI. We treated 79 pts with a TKI in 2nd line with a median follow‐up of 4.34 years (0‐11.37). 3 (73.66%) pts were treated with IM, 52 (19.47%) with NI and 24 (6.87%) with DA. By the JT test we found no statistically significant difference between the medians of BCR/ABL levels at 3 (IM = 0.14, NI = 0.02, DA = 0.10), 6 (IM = 1.00, NI = 0.06, DA = 0.00) and 12 (IM = 0.04, NI = 0.01, DA = 0.00) months between the different drugs (p = 0.979, p = 0.31 and p = 0.54 respectively). There is no statistically significant difference for the CI of MMR between the three treatment groups with a median of time till MMR of 5.13 months NI, 4.3 for IM and 6.7 for DA (Fig1b). Summary/Conclusion: Our OS is comparable to the one presented on the IRIS study 8‐year follow‐up (85%). When we analyzed the responses on 1st line we found that NI reaches faster the MMR than IM and DA. We also found differences reaching DMR between NI and IM but no with DA and we think the reason for not finding it is due to the small sample of pts with DA. The differences between 2nd generation TKIs are not present on 2nd line. image
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".