Red Blood Cell Transfusion and Chemotherapy Administration: A Study of Resource Utilization at the London Regional Cancer Program (LRCP).
Bibliographic record
Abstract
Abstract We conducted previously a prospective evaluation of the relative chair-time consumption between various clinical activities (chemotherapy administration, RBC transfusion, other treatment related events) at the LRCP. Transfusions were reported to occupy at least twice as much chair-time relative to an average chemotherapy session. We report here a retrospective analysis of the LRCP electronic database over a 6-month period to further evaluate clinic resource utilization. A total of 11016 clinic visits were documented and analyzed, of which 83% involved an active treatment session (chemotherapy/transfusion). The most prevalent tumours (>60%) were breast, gastrointestinal (GI), genitourinary (GU) and gynecological (GYN) cancers. Hematologic malignancies accounted for <20% of all tumour types, where myeloma and lymphoma constituted >50% of this cohort. One hundred seventy-one patients received a total of 337 transfusions (656 RBC units), of which the majority were solid (58.5%) and hematologic (40.9%) tumour patients. The transfusion trigger was observed at Hb <80g/L (mean 76.5 +/− 12.3g/L) and on average 1.9 +/− 0.3 units of blood were administered on each occasion. The incidence of transfusion (Mean 7.5 & 6.0 per transfused patient) and units of blood administered (Mean 15 & 12 units per transfused patient) were highest among chronic myeloproliferative disorder and non-malignant hematology patients. Based on our previous reported transfusion chair-time (i.e. Chair-time occupied by a patient while receiving 1 RBC unit, independent of time consumed by pre-/post-transfusion related nursing and laboratory activities) at 109 +/- 19 minutes, an estimated 71504 minutes were consumed by all RBC transfusions, constituting 5.4% of all LRCP out-patient clinic chair-time events. Only 11% of all transfused patients were treated with an erythropoietin receptor agonist (ERA), corroborating with published literature that ERAs are highly effective in reducing the transfusion burden of oncology patients. This study also identified a major discrepancy between the blood bank’s record of distributed blood and LRCP’s record of transfused blood. A total of 129 transfusions (37%) were not documented on the LRCP database but were administered to patients, of which 20% took place on stretcher beds. As hospital billings rely solely on this database, about 26923 min of clinic time and hospital resources that were utilized was not funded. A very conservative estimate of unbilled nursing time ($30/hr) alone translates into a minimum of $13461 over the study period. In conclusion, RBC transfusion consumes a significant proportion of chair-time at the LRCP, which could otherwise be more efficiently utilized by chemotherapy administration. Chemotherapy protocols commonly administered to GYN (platinum based) and lymphoma (combination regimens containing rituximab) patients are previously reported to be the most chair-time consuming (Median 3–4 hr, up to 7–8hrs). Since these tumour types accounted for a significant proportion of the patient population who receive care at the LRCP, an intense competition for chair-time and nursing resources that could lead to clinic cancellations and delayed treatment had likely occurred. A greater application of transfusion alternatives (e.g. ERAs) could reserve such facilities for their intended purposes, thus allowing the timely administration of chemotherapy and optimization of clinic resource utilization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".