Identifying patients at high risk for neutropenic complications during chemotherapy for metastatic breast cancer (MBC) with doxorubicin or pegylated liposomal doxorubicin: Development of a prediction model
Bibliographic record
Abstract
6598 Background: Despite the effectiveness of anthracycline (ACH) therapy in the adjuvant and MBC settings, neutropenic complications (NC) remain a common and often unpredictable problem. Consequences may include dose reductions or delays in chemotherapy, or hospitalization for fever or infection. This study describes the development of a cycle-based risk prediction model for NC during chemotherapy with traditional doxorubicin (DOX) or a pegylated liposomal formulation (PLD) for MBC. Methods: Data analyzed was from a randomized clinical trial of MBC patients (n=509), who received chemotherapy with DOX (60 mg/m2 every 3 wks) or PLD (50 mg/m2 every 4 wks) [O'Brien, 2004]. NC were defined as an absolute neutrophil count (ANC) = 1.5 x106 cells/L, febrile neutropenia or neutropenia with infection. Patient, treatment and hematological factors potentially associated with NC were evaluated. Factors with a p-value of ≤ 0.25 within a cycle were included in a generalized estimating equations (GEE) regression model. Using backward elimination, we derived a risk scoring algorithm (range 0–63) from the final reduced model. Results: Risk factors retained in the model included poor performance status, ANC = 2.0 × 106 cells/L at some point in the previous cycle, the first cycle of chemotherapy, DOX vs. PLD and older age. A precycle risk score from = 25 to < 40 for a given patient was identified as being the optimal threshold for sensitivity (58.0%) and specificity (78.7%). Patients with a score at or beyond this threshold would be considered at high risk for developing NC in later cycles. Risk scores below, within, or above this threshold predict a 0.3%–2%, 3%–8% and a 9%–45% probability risk of NC, respectively. Conclusion: This risk prediction tool demonstrated acceptable internal validity and can be readily applied by the clinician prior to a given cycle of chemotherapy. The application of this prediction tool may allow for identification and targeted intervention (such as growth factor support or the use of PLD) for those most likely to experience NC during anthracycline-based chemotherapy for MBC. No significant financial relationships to disclose.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".