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Record W2021107760 · doi:10.14740/jocmr2123w

A Prospective Study on Red Blood Cell Transfusion Related Hyperkalemia in Critically Ill Patients

2015· article· en· W2021107760 on OpenAlexvenueno aff
Shahzad Raza, Mahadi Baig, Christopher Chang, Ridhima Dabas, Mallika Akhtar, Areej Khan, Krishna Nemani, Rahima Alani, Omran L. Majumder, Natalya Gazizova, Shaluk Biswas, Priyeshkumar Patel, Jaffar A. Al-Hilli, Yasar Shad, Barbara J. Berger, Mohammad Zaman

Bibliographic record

VenueJournal of Clinical Medicine Research · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood transfusion and management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHyperkalemiaPacked red blood cellsAcute kidney injuryMetabolic acidosisAcidosisBlood transfusionMyocardial infarctionProspective cohort studyAnesthesiaInternal medicineIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Transfusion-associated hyperkalemic cardiac arrest is a serious complication in patients receiving packed red blood cell (PRBC) transfusions. Mortality from hyperkalemia increases with large volumes of PRBC transfusion, increased rate of transfusion, and the use of stored PRBCs. Theoretically, hyperkalemia may be complicated by low cardiac output, acidosis, hyperglycemia, hypocalcemia, and hypothermia. In this study, we focus on transfusion-related hyperkalemia involving only medical intensive care unit (MICU) patients. METHOD: This prospective observational study focuses on PRBC transfusions among MICU patients greater than 18 years of age. Factors considered during each transfusion included patient's diagnosis, indication for transfusion, medical co-morbidities, acid-base disorders, K(+) levels before and after each PRBC transfusion, age of stored blood, volume and rate of transfusion, and other adverse events. We used Pearson correlation and multivariate analysis for each factor listed above and performed a logistic regression analysis. RESULTS: Between June 2011 and December 2011, 125 patients received a total of 160 units of PRBCs. Median age was 63 years (22 - 92 years). Seventy-one (57%) were females. Sixty-three patients (50%) had metabolic acidosis, 75 (60%) had acute renal failure (ARF), and 12 (10%) had end-stage renal disease (ESRD). Indications for transfusion included septic shock (n = 65, 52%), acute blood loss (n = 25, 20%), non-ST elevation myocardial infarction (NSTEMI) (n = 25, 20%) and preparation for procedures (n = 14, 11%). Baseline K(+) value was 3.9 ± 1.1 mEq/L compared to 4.3 ± 1.2 mEq/L post-transfusion respectively (P = 0.9). During this study period, 4% of patients developed hyperkalemia (K(+) 5.5 mEq/L or above). The mean change of serum potassium in patients receiving transfusion ≥ 12 days old blood was 4.1 ± 0.4 mEq/L compared to 4.8 ± 0.3 mEq/L (mean ± SD) in patients receiving blood 12 days or less old. Sixty-two patients (77.5%) that were transfused stored blood (for more than 12 days) had increased serum K(+); eight (17.7%) patients received blood that was stored for less than 12 days. In both univariate (P = 0.02) and multivariate (P = 0.04) analysis, findings showed that among all factors, transfusion of stored blood was the only factor that affected serum potassium levels (95% CI: 0.32 - 0.91). No difference was found between central and peripheral intravenous access (P = 0.12), acidosis (P = 0.12), ARF (P = 0.6), ESRD (P = 0.5), and multiple transfusions (P = 0.09). One subject developed a sustained cardiac arrest after developing severe hyperkalemia (K(+) = 9.0) following transfusion of seven units of PRBCs. Multivariate logistic regression showed linear correlation between duration of stored blood and serum K(+) (R(2) = 0.889). CONCLUSION: This study assesses factors that affect K(+) in patients admitted to MICU. Results from the study show that rise in serum K(+) level is more pronounced in patients who receive stored blood (> 12 days). Future studies should focus on the use of altered storage solution, inclusion of potassium absorption filters during transfusion and cautious use of blood warmer in patients requiring massive blood transfusions.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.177
GPT teacher head0.497
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations39
Published2015
Admission routes1
Has abstractyes

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