Intravenous Iron Infusion Administration Schedule and Hematologic Response; A Single-Center Retrospective Cohort Study
Bibliographic record
Abstract
Background: Clinical trials studying the efficacy of intravenous (IV) iron replacement have used a variety of dosing schedules, even for the same iron formulations and there isn't a clear standard for how IV iron replacement should be scheduled. Differences in iron absorption efficiency have been observed with different oral iron supplementation schedules but it is not known if the same is true for IV iron. The objective of this study was to evaluate the association between iron administration schedule and hemoglobin response in patients treated at a tertiary care center. Methods: Data was collected retrospectively using The Ottawa Hospital Data Warehouse (OHDW) capturing all iron infusions given at 3 local institutions between January 2007 and December 2018. Patients that received at least 2 intravenous iron infusions within 180 days of each other were included. A 'course' of iron replacement was defined as consecutive infusions with ≤180 days between doses. Patients transfused red blood cells within 90 days of the last iron infusion in each course were excluded. Patient age, sex, dose and formulation of administered, date of each iron infusion, and laboratory parameters from the period starting 3 months before the first infusion to 6 months after the last infusion were extracted for analysis. Patients were categorized into 4 groups based on the mean time between iron infusions in each course: 1-10 days, 11-20 days, 21-30 days, and >30 days. Achieving a maximum hemoglobin (Hb) 10 g/L or higher than the pre-infusion Hb was defined as a 'good' response. A logistic regression model was used to evaluate the association between interval between infusions and achieving a 'good' Hb response. The model was adjusted for sex, age, presence of chronic kidney disease (CKD), dose of iron per course, number of infusions per course, and number of courses. CKD was defined as having a serum creatinine >177 µmol/L. Iron sucrose, iron gluconate, and iron dextran were available for administration during the study period. Results: A total of 4350 patients were included in the analysis. These patients received a total of 6409 courses of iron replacement, with a median of 2 courses (interquartile range [IQR] 1-3) per patient, and 4 infusions (IQR 3-6) per replacement course. Infusions were given a median of 21.9 days (IQR 12-34) apart in each course, with a range of 1-179 days. Iron sucrose was given in most courses (81.6%), followed by iron gluconate (18.2%) and iron dextran (0.1%). Patient characteristics are summarized in Table 1 and laboratory values prior to the first infusion of each course are summarized in Table 2. The interval between iron infusions was associated with Hb response. Results of the logistic regression are summarized Table 3. Compared with patients receiving infusions every 10-20 days, patients receiving infusions more frequently or less frequently were less likely to achieve a good Hb response. Male sex was associated with increased odds of response, while increasing age, having CKD, and receiving more courses of iron were associated with decreased odds of response. Conclusions: In this single-center retrospective cohort analysis, an association between interval of iron infusions and hematologic response was observed. Patients given iron every 10-20 days more likely to achieve a good response compared with more, or less frequent dosing intervals. Strengths of this study include the large sample size, adjustment for sex, age, presence of CKD, and number of iron infusions and courses given. Transfused patients were excluded, so the hemoglobin response is attributable to iron replacement. The study has several important limitations. The clinical decision-making for the infusion schedule was not known and factors that went into this decision are a possible source of confounding (for example, ongoing bleeding). The suspected cause of iron deficiency was not known. Additionally, concomitant oral iron supplementation and iron infusions or transfusions occurring outside the study institution are unknowns and may influence to outcome. The hypothesis-generating findings suggest that the schedule of iron administration may play an important role in hematologic response to iron infusions. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".