Blood Loss Due to Laboratory Testing in Critical Care Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Background: Anemia and red blood cell (RBC) transfusions occur frequently in intensive care unit (ICU) patients and are associated with morbidity and mortality. Observational data suggest that anemia (nadir hemoglobin ≤ 9 g/dL) in ICU patients is associated with an increased risk of death within 30 days (OR 1.54, 95% confidence interval [CI] 1.12-2.12). Although anemia in this population is likely multifactorial, phlebotomy for laboratory testing is substantial (up to 41 mL/day) and may be a modifiable contributor as only 10% of the blood collected is required for analysis. This study aims to (i) characterize the blood volume taken for laboratory testing in ICU patients, and (ii) explore the effect of blood taken for testing on rates of RBC transfusion and anemia in a contemporary cohort of ICU patients. Methods: We conducted a retrospective cohort study of consecutive adult patients, age ≥18 years, admitted to a medical-surgical ICU for ≥48 hours in Hamilton, Ontario between November 1, 2012 to October 1, 2015. We extracted data from previously validated administrative databases. The primary outcome was estimated volume of blood drawn for laboratory testing during index ICU admission calculated using the number of unique blood specimens drawn and known sampling tube volumes. Secondary outcomes included: incidence of severe anemia (hemoglobin ≤9 g/dL), discharge hemoglobin adjusted for RBC transfusion (1.0 g/dL subtracted for each RBC transfusion administered), proportion of patients receiving RBC transfusion, number of RBC units transfused per patient, duration of ICU admission and ICU mortality. Categorical data are reported as counts and proportions. Continuous data are reported as means with standard deviations (SD) or medians with interquartile range (IQR). Predictors of RBC transfusion were examined using Cox regression analysis. P-values <0.05 were considered statistically significant. Results: We included 7,273 ICU patients. The median age was 66 years (IQR 54-76 years) and 40.8% of patients were female. Most responsible diagnosis was circulatory (33.3%), followed by injury/poisoning (15.9%), respiratory (12.8%), and gastrointestinal (9.2%). The ICU mortality rate was 13.8% and the median ICU length of stay was 5 days (IQR 3-10 days). On ICU admission, median hemoglobin value was 9.7 g/dL (IQR 8.2-11.6 g/dL) and 39.6% of patients had hemoglobin ≤ 9.0 g/dL. Median blood loss due to laboratory testing was 25 mL per patient per day (IQR 14-43 mL) and 213 mL per patient for the ICU admission (IQR 133-382 mL). On ICU discharge, median hemoglobin was 8.4 g/dL (IQR 5.8-10.5 g/dL), and 67.0% of patients had hemoglobin ≤ 9.0 g/dL while in ICU. After adjusting for RBC transfusions, the median decrease in hemoglobin from ICU admission to discharge was 1.2 g/dL (IQR 0-2.9 g/dL). RBC transfusions were administered to 47.5% of patients while in the ICU with 37.3% of these patients receiving 5 or more units. The median number of RBC units received in transfused patients was 3 (IQR 2-7). Additional blood drawn for laboratory testing of 150 mL from day 2 to day 7 of ICU admission (hazard ratio [HR] 2.3, 95%CI 2.1-2.5), hematologic diagnosis vs. other (HR 1.5, 95%CI 1.2-2.0), and RBC transfusion prior to ICU admission vs. none (HR 1.5, 95%CI 1.2-1.9) were associated with a higher risk of RBC transfusion in the ICU. Conclusions: Anemia and RBC transfusions are frequent in ICU patients with two-thirds of patients developing severe anemia (hemoglobin <9.0 g/dL) and 47.5% receiving RBC transfusion, respectively. Our results suggest that blood loss for laboratory testing remains substantial in contemporary medical-surgical ICU patients and is associated with the need for RBC transfusion. Strategies to reduce blood loss from laboratory testing represent an area for further prospective investigation. Disclosures Siegal: BMS-Pfizer: Honoraria; Bayer: Honoraria; Portola Pharmaceuticals: Honoraria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".