Epoetin Alfa Resistance: Valuation of a Management Algorithm
Bibliographic record
Abstract
ABSTRACT Background: Patients who require large doses of epoetin alfa to achieve and maintain a target hemoglobin of 110 to 120 g/L are usually considered epoetin alfa–resistant. The Kidney Disease Outcomes Quality Initiative guidelines suggest that epoetin alfa resistance be considered when subcutaneous epoetin alfa doses exceed 300 IU kg-1 week-1. Objective: The objective of this project was to develop and validate an algorithm to guide the identification and management of patients on chronic dialysis with suspected epoetin alfa resistance. Methods: The algorithm developed was used for 3 consecutive months to identify patients who did not respond to epoetin alfa; to identify the causes of nonresponse, including epoetin alfa resistance; and to guide the management of their cases. Patients were excluded from the final analysis if they did not complete the 3-month follow-up. Results: Of the 212 patients screened, the algorithm identified 21 who were resistant to epoetin alfa. Of the 16 evaluable patients, 11 achieved their target hemoglobin during the followup period. Mean hemoglobin concentrations improved from 92.8 ± 11.0 to 106.9 ± 14.7 g/L (p = 0.0009). The most common causes of epoetin alfa resistance were iron deficiency, chronic infection, inflammation, and dialysis inadequacy. Many patients had more than one cause of epoetin alfa resistance. Conclusion: The algorithm used in this project can be successfully used to identify epoetin alfa resistance and to manage epoetin alfa therapy for patients on hemodialysis. RESUME Historique : Les patients qui ont besoin de fortes doses d’epoetine alfa pour atteindre et maintenir des taux d’hemoglobine cibles de 110 a 120 g/L sont habituellement reputes etre resistants a l’epoetine alfa. Les lignes directrices de l’Initiative sur la qualite des resultats pour la maladie renale (Kidney Disease Outcomes Quality Initiative) suggerent d’envisager une resistance a l’epoetine alfa lorsque les doses necessaires d’epoetine alfa sous-cutanee chez un patient depassent 300 UI kg-1 semaine-1. Objectif : L’objectif de ce projet etait d’elaborer et de valider un algorithme permettant de depister et de prendre en charge les patients sous dialyse a repetition, chez qui l’on soupconne une resistance a l’epoetine alfa. Methodes : L’algorithme mis au point a ete utilise pendant trois mois consecutifs pour depister les patients qui n’ont pas repondu a l’administration d’epoetine alfa, identifier les causes de l’absence de reponse, y compris la resistance a l’epoetine alfa, et aiguiller la prise en charge de ces cas. Les patients etaient exclus de l’analyse finale s’ils n’achevaient pas la periode de suivi de trois mois. Resultats : Parmi les 212 patients selectionnes, l’algorithme a permis de depister 21 patients resistants a l’epoetine alfa. Des 16 patients evaluables, 11 ont atteint leur taux d’hemoglobine cible au cours de la periode de suivi. Les concentrations moyennes d’hemoglobine ont augmente, passant de 92,8 ± 11,0 a 106,9 ± 14,7 g/L (p = 0,0009). Les causes les plus frequentes de resistance a l’epoetine alfa etaient les suivantes : carence en fer, infection chronique, inflammation et dialyse insuffisante. Chez de nombreux patients, la resistance a l’epoetine alfa etait due a plus d’une cause. Conclusion : L’algorithme auquel on a eu recours dans le cadre de ce projet peut etre utilise avec succes pour depister la resistance a l’epoetine alfa et prendre en charge le traitement par l’epoetine alfa chez les patients hemodialyses.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".