Point of care hemoglobin measurement to streamline clinical decision-making: A single center experience
Bibliographic record
Abstract
Abstract Introduction: Achieving appropriate transfusion and phlebotomy target is essential to the health and wellbeing of patients with congenital blood and iron metabolism disorders (including sickle cell disease and transfusion-dependent thalassemia) on regular red blood cell (RBC) transfusion, partial manual RBC exchange transfusion (PMX), and therapeutic phlebotomy (TP). Maintenance of such targets is dependent on the hemoglobin result from complete blood count via automated hematology analyzer (AHA) on venipuncture-collected EDTA samples. Long turnaround time forms the rate-limiting step for completion of the treatment plan. Point-of-care hemoglobin measurement device (PoCHb) that relies on capillary blood obtained from the finger-prick method provides results within approximately 60 seconds. The purpose of this study was to determine whether PoCHb can produce equivalent treatment decisions to AHA while reducing time of treatment. Hypothesis: In patients with congenital blood disorders, PoCHb device reduces time to treatment plan initiation but provide comparable treatment decision when compared to AHA. Methods: Single center (University Health Network, Toronto, Canada) prospective observational study of patients with sickle cell disease, transfusion-dependent thalassemia, and other congenital blood and iron metabolism disordersaged ≥18 years on regular RBC simple transfusion, exchange transfusion, or phlebotomy between May 1–June 30, 2023. Two samples, a venous sample for AHA and a capillary blood sample for the PoCHb device Hemocue 201, were collected following informed consent. Treatment decisions (i.e. amount of RBC to be transfused or phlebotomized) were made based on the AHA result by the pre-determined treatment plan and the treating physician. These treatment decisions were then compared with the decisions that would have been made according to the pre-determined treatment plan if the result from the PoCHb device had been utilized. Time from sample collection to treatment initiation was recorded for both actual (AHA-based) and simulated (PoCHb device-based) work flows. Agreement in treatment decisions was assessed with Cohen's kappa. Results: A total of 157 participants were included, of which 26 were participants with sickle cell disease, 128 were participants with thalassemia and 3 participants had other diagnoses. The female to male ratio was approximately 3:2. The cohort included 127 simple RBC transfusions, 23 PMX, and 7 TP. Mean (SD) number of 500 cc units transfused for simple transfusions was 3.2 (SD = 0.9) and for PMX was 2.0 (SD =0.5). Mean (SD) number of 500 cc units phlebotomized for PMX was 1.8 (SD = 0.7) and for TP was 1.0 (SD = 0.0). Simulated PoCHb device-based workflows would have resulted in RBC transfusion initiation 58 minutes earlier(p<0.001, Cohen’s d = 1.54) and phlebotomy initiation 55 minutes earlier(p<0.001, Cohen’s d = 1.30) compared to actual AHA-based workflows. Agreement in treatment decisions between PoCHb device and AHA was excellent for transfusion (κ = 0.842, p<0.001) and perfect for phlebotomy (κ = 1.000, p<0.001). Disagreements, when present, were generally by only one transfusion unit. Diagnosis did not affect the difference in time saved in clinical decision (p=0.72, cohen’s d = 0.085). Utilizing the PoCHb device result would have led to a saving of 55 minutes and 58 minutes in RBC exchange transfusion and in RBC transfusion respectively. Conclusion: Hemoglobin measurement using point-of-care device showed excellent agreement with automated hematology analyzer results for guiding transfusion and phlebotomy decisions, while offering the potential to reduce treatment initiation times by nearly one hour. Implementation of point-of-care device could streamline workflows and improve patient satisfaction and accommodate larger patient volumes without compromising safety. Further evaluation in larger and more diverse patient populations is warranted.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".