Gimema Seifem Real-Life Study VS Randomized CPX-351 Registrative Trial for Older Patients with Secondary ACUTE Myeloid Leukemia: An Unanchored Matching-Adjusted Indirect Comparison of Infection Rates and Survival Outcomes
Bibliographic record
Abstract
Background: Unanchored MAIC (Matching-Adjusted Indirect Comparison) is an ITC (Indirect Treatment Comparison) method adjusting for cross-trial heterogeneity in patient demographic or disease that are believed to be either prognostic or treatment effect modifiers. In this analysis, two trials for adults with secondary acute myeloid leukemia (AML) were compared by an unanchored MAIC. Aims: The GIMEMA (Gruppo Italiano Malattie EMatologiche dell'Adulto)-SEIFEM (Sorveglianza Epidemiologica Infezioni nelle Emopatie) real-life study on the use of CPX-351 (Fianchi et al- Cancers 2023) was weighted for the aggregated patients characteristics from the standard arm(“7+3”) of the CPX-351 trial (cytarabine and daunorubicin Liposome for Injection Versus Conventional Cytarabine Plus Daunorubicin in Older Patients With Newly Diagnosed Secondary Acute Myeloid Leukemia, Lancet et al - JCO 2018). This analysis aimed to test the feasibility to compare individual patients' data with aggregated published results and evaluate the rate of infections of CPX-351 in real life vs the “3+7” regimen and their impact on the survival outcomes. Methods: Patients-level data from GIMEMA-SEIFEM on the use of CPX-351 (n=202) and aggregated data from CPX-351 (“3+7” arm, n=156) trials were used to conduct an unanchored MAIC. GIMEMA-SEIFEM study included included all consecutive patients with AMLfrom 30 Italian hematologic centers who received at least 1 course of CPX-351 from July 2018 to June 2021 according to clinical practice. Patients from the GIMEMA-SEIFEM study were weighted to balance with baseline characteristics from the USA and Canada cohoort. Accordingly, weighted Overall and Event-free survival (w-OS, w-EFS) estimates, as well as rates of febrile neutropenia, pneumonia, CR, and the interval of PMN recovery, were computed. Results: Four potential effect modifiers were identified and used for adjustment: age, sex, AML subtype (tAML, sAML, MRC), and prior HMA exposure. Median w-OS and w-EFS were 14.2 (95%CI: 11.6-18.7) and 7.4 (95%CI: 3.0-10.6) months, respectively. These estimates were slightly lower than those documented in the most recent report of the GIMEMA-SEIFEM trial (median OS 17.7 months and median EFS 9.8 months) and higher than the results obtained by the standard arm of the CPX-351 trial (median OS 5.9 months, median EFS 1.3 months). Weighted rates of febrile neutropenia, pneumonia, CR, and interval of PMN recovery were comparable to the observed values and better than observed in the standard arm of the CPX-351 trial for all considered variables, except for febrile neutropenia (Table 1). Conclusions: The MAIC method allowed a robust comparison of two clinical trials for the treatment of AML patients. After adjustment, survival outcomes of the real-life cohort were slightly lower than the observed estimates and higher than the observed in the standard arm of the CPX-351 trial. Pneumonia risk was confirmed lower in GIMEMA-SEIFEM CPX-351 matched group than in “3+7” arm. This pilot analysis underlined the potentiality of this statistical method. Indeed, it could be useful to compare with high accuracy studies with strong differences in the selection of patients.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".