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Record W4411672608 · doi:10.1097/pcc.0000000000003776

Hyperlactatemia in Critically Ill Children: Modeling Early Recovery Kinetics After Initiation of Extracorporeal Membrane Oxygenation

2025· article· en· W4411672608 on OpenAlexaff
Cheuk C. Au, Frederick W. Vonberg, Matthew Luchette, Kerri L. LaRovere, Ravi R. Thiagarajan, Robert C. Tasker, Alireza Akhondi‐Asl

Bibliographic record

VenuePediatric Critical Care Medicine · 2025
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsExtracorporeal membrane oxygenationMedicineInterquartile rangeCritically illHyperlactatemiaAnesthesiaResuscitationRetrospective cohort studyOxygenationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Blood lactate concentration ([Lac] b ) reflects the balance among production, clearance (C l[Lac] ), and volume of distribution. We have observed dramatic improvement in [Lac] b in critically ill patients after starting support with extracorporeal membrane oxygenation (ECMO) and discontinuing vasopressors. Here, we evaluated such [Lac] b profiles to develop a mathematical model of recovery kinetics. We then examined the interrelationships between maximum [Lac] b and model-derived parameters of lactate production, endogenous lactate transfer, and C l[Lac] . DESIGN: Mathematical modeling using a convenience sample. SETTING: Quaternary U.S. academic children's hospital. PARTICIPANTS: A retrospective sample of 25 ECMO patients (from birth to < 18 yr) with serial [Lac] b measurements during the first 30 hours after initiation of ECMO. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: The median (interquartile range [IQR]) age of ptients was 17 days (IQR 3-152 d), and the median weight was 3.3 kg (IQR 2.7-4.7 kg). At the initiation of ECMO, the mean peak [Lac] b was 16.7 mmol/L (95% CI, 14.3-20.0 mmol/L). Recovery in [Lac] b could be described using a one-compartment, bi-exponential, open model of kinetics. Solving the model equation showed starting lactate load was 17.7 mmol/kg (95% CI, 14.6-20.7 mmol/kg) and C l[Lac] was 19.7 mL/min (95% CI, 3.0-36.4 mL/min). The interrelationship between maximum [Lac] b and model-derived parameters in children requiring ECMO at the limits of cardiopulmonary survival showed: 1) lactate production ranged from 2.3 to 6.4 µmol/kg/min (95% CI), 2) initial endogenous lactate transfer velocity, 82.5-1301.0 µmol/kg/min, 3) high initial [Lac] b levels suggested severely impaired C l[Lac] , 4) a strong correlation was observed between model-derived velocity and transfer parameters (rho 0.75; p < 0.0001), at levels exceeding those seen in high-intensity endurance exercise, and 5) upon achieving steady state, lactate production and C l[Lac] were balanced. CONCLUSIONS: At the time of maximal cardiopulmonary instability requiring ECMO initiation, our model of [Lac] b recovery indicated that high initial [Lac] b reflected severely impaired and reduced C l[Lac] . This modeling approach may also be applicable to assessing changes in lactate kinetics in other forms of critical illness.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.255
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2025
Admission routes1
Has abstractyes

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