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Record W4387080148 · doi:10.1093/clinchem/hvad097.487

B-154 Characterizing Ability of the Serum Potassium (K) to Flag Hypokalemia or Hyperkalemia as Observed in Plasma: A Simulation Study

2023· article· en· W4387080148 on OpenAlexaff
Douglas F. Stickle, K. Rebecca Koob, Christopher R. McCudden

Bibliographic record

VenueClinical Chemistry · 2023
Typearticle
Languageen
FieldMedicine
TopicPotassium and Related Disorders
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHyperkalemiaHypokalemiaInternal medicineConfidence intervalChemistryPotassiumPopulationEconomic shortageMedicineEndocrinology

Abstract

fetched live from OpenAlex

Abstract Background Plasma has long been the recommended specimen type for K measurement. However, serum usage persists, including use due to recent plasma tube shortages. In comparison to plasma K, serum K is generally right-shifted (higher). This shift varies across individual samples according to a normal distribution. Consequentially, K results outside of the population reference interval (RI) for plasma (hypokalemia or hyperkalemia) are unlikely to be strictly concordant with classification in serum according to the serum RI. Serum may thus have a lower sensitivity to detect either hypokalemia or hyperkalemia relative to plasma. We examined this premise from a theoretical standpoint by simulation. Methods We used longstanding and widely implemented textbook K reference intervals (Tietz, 4th Ed.) for plasma (PRI = 3.4–4.5 mmol/L) and serum (SRI = 3.5–5.1 mmol/L). The difference between plasma K and serum K is characterized by a normally distributed function where serum = plasma + 0.35 ± 0.3 mmol/L. This transformation was applied to an at-large patient data distribution from a large academic medical center to generate a theoretical serum K distribution from real world plasma patient K results. The simulated serum K data were characterized as to whether hypokalemic and hyperkalemic plasma K specimens as defined by PRI were also classified in serum as being either below or above lower and upper limits of SRI, respectively. Results Primary data were a plasma K patient distribution for a three month interval (n = 59 570; median = 4.1 mmol/L; hypokalemia = 6.5%; hyperkalemia = 16.7%). Simulated serum K data from the plasma transformation (n = 100 000) yielded a right-shifted distribution (median = 4.4 mmol/L) with 4.1% of results below the lower limit of SRI, and 11.4% of results above the upper limit of SRI. For samples originating as hypokalemic according to the PRI, sensitivity for detection by being flagged as below the lower limit of the SRI was 44.1% (specificity = 98.7%). For samples originating as hyperkalemic according to the PRI, sensitivity for detection by being flagged as above the upper limit of the SRI was 58.7% (specificity = 97.6%). Conclusions Simulation results indicate that serum K should best be thought of as an inferior substitute marker for plasma K. Depending on reference intervals employed, sensitivity of serum RI for detection either of plasma hypokalemia or hyperkalemia may be significantly less than 100%. This can result in a significant degree of misidentification of hyperkalemia or hyperkalemia as being within SRI. In our simulation, 55.9% of plasma hypokalemia results were in serum classified as being within the SRI; 41.3% of plasma hyperkalemia results were in serum classified as being within the SRI. Additionally, note that a switch from serum to plasma will likely precipitate a significant increase in flag rates for plasma K outside of the PRI compared to flag rates observed for serum K outside of the SRI. These results follow simply from the fact that serum K includes a random component not present in plasma.

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)0.004
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 score0.052
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.092
GPT teacher head0.388
Teacher spread0.296 · 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
Published2023
Admission routes1
Has abstractyes

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