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Record W4414740845 · doi:10.1093/clinchem/hvaf086.536

B-142 Determining Hemolysis Index Cutoffs for Potassium Measurement in Pediatric Population on the VITROS XT 7600

2025· article· en· W4414740845 on OpenAlexaff
Urun Erbas Oz, Cynthia Balion

Bibliographic record

VenueClinical Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal function and acid-base balance
Canadian institutionsMcMaster University Medical CentreMcMaster University
Fundersnot available
KeywordsHemolysisPotassiumAnalytePopulationSignificant difference

Abstract

fetched live from OpenAlex

Abstract Background Hemolysis, the breakdown of erythrocytes with release of intracellular components, is the most common preanalytical error, accounting for the majority of unsuitable specimens. Modern chemistry analyzers quantify hemolysis and assign a hemolysis index (HI) to samples. While manufacturers provide suggestive HI cutoffs for analytes susceptible to hemolytic interference, the evidence supporting these thresholds is often unclear. Potassium is one of the most affected analytes by hemolysis due to the 30-40-fold higher intracellular concentration, thereby resulting in pseudohyperkalemia and pseudoeukalemia. Few studies have investigated HI cutoffs for potassium measurement, and none have accounted for age-related differences. Given that hemolysis is more prevalent in the pediatric population, we aimed to establish appropriate HI cutoffs in the pediatric population. Methods Plasma potassium test results (N = 9,227) and their corresponding HI were extracted from two VITROS XT 7600 analyzers (QuidelOrtho) at McMaster Children’s Hospital. Data were explored based on sample type (capillary, venous, arterial, and central line) and age. Data were divided into two age groups: 2,788 infants (<1 year) and 6,418 older children (1 year up to 18 years). Analyses were done separately for each group. Each age group was further divided into bins based on the HI value. The bin with the lowest level of hemolysis (HI <15) was used as the base group for comparison. The relative difference in mean potassium (deltaK) between a bin and the base group was calculated. The maximum thresholds examined were: (1) 5% (allowable performance limit, Institute of Quality Management in Healthcare), and (2) 13.8%, the reference change value (RCV) calculated using the within-subject biological variation of 4.6% for a pediatric population (Clin Chem. 2014 Mar 1;60(3):518-529) and an analytical coefficient of variation of 0.8% from our instruments. Results Capillary samples had a higher median potassium value (5.37 mmol/L) and HI (78) compared to other sample types (3.84-4.02 mmol/L, HI: 0). Over 90% of capillary samples were drawn from infants (<1 year), who have a higher reference interval than older children. Regression analysis showed positive correlation between deltaK and HI in both age groups with best-fit equations deltaK=0.0112xHI for infants and deltaK=0.0051xHI for older children (HI =150), suggesting hemolytic interference is more severe in infants. The HI cutoffs corresponding to a 5% and 13.8% increase in potassium were 25 and 70 for infants and 50 and 140 for older children, respectively. Applying these thresholds would reduce the number of falsely elevated potassium results by 30% and 60% for infants, and 16% and 6% for older children. Conclusion We have shown that establishing HI cutoffs for the pediatric population requires separate thresholds for infants and older children. Implementing validated HI cutoffs is important for minimizing falsely elevated potassium results while reducing unnecessary specimen rejections. Although capillary sampling is widely used in infants, our findings highlight the need for improved blood collection techniques to ensure accurate potassium measurement. Venous sampling had a lower prevalence of hemolysis in infants and is therefore preferred method for accurate potassium measurement.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.056
GPT teacher head0.357
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2025
Admission routes1
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