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Record W2782011346 · doi:10.1213/ane.0000000000002785

Predicting Heparin Responsiveness in Children Before Cardiopulmonary Bypass: A Retrospective Cohort Study

2018· article· en· W2782011346 on OpenAlexaff
Sayaka Nakamura, Osami Honjo, Lynn Crawford‐Lean, Celeste Foreman, Minako Sano, James D. O’Leary

Bibliographic record

VenueAnesthesia & Analgesia · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac and Coronary Surgery Techniques
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsMedicineActivated clotting timeHeparinConcordanceCardiopulmonary bypassRetrospective cohort studyConcordance correlation coefficientHemostasisPercentileAnesthesiaAnticoagulantPharmacodynamicsCohortInternal medicinePharmacokineticsStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Inadequate or excess administration of unfractionated heparin for cardiopulmonary bypass (CPB) can cause significant harm. Age-dependent differences in the pharmacodynamics and pharmacokinetics of heparin contribute to increased variability of heparin responsiveness in children. The aims of the current study were to (1) examine the correlation between predicted and observed heparin responsiveness in children before CPB measured using the Hemostasis Management System (HMS) Plus (Medtronic, Minneapolis, MN), (2) describe age-specific reference intervals for heparin sensitivity index (HSI) observed in children, and (3) test predictive models of HSI using preoperative clinical and laboratory data. METHODS: In this retrospective cohort study, children (ages ≤17 years) who required therapeutic heparinization for CPB in a 40-month period between September 2010 and December 2013 were investigated. Children weighing ≥45 kg or with a height ≥142 cm were excluded. HSI was defined as the difference between activated clotting time after heparin administration and the baseline activated clotting time divided by the heparin-loading dose (IU) per kilogram. Lin's concordance correlation coefficient was used for the primary analysis of the relationship between predicted and observed HSI. Reference intervals were calculated for HSI using medians and 2.5% and 97.5% percentiles according to established guidelines for clinical and laboratory standards. Nonparametric regression analyses were used to model the relationship between HSI (dependent variable) and preoperative covariates (independent variables). RESULTS: A total of 1281 eligible children were included in the final analysis. Overall, there was a moderate correlation between predicted and observed HSI measured using HMS Plus System (rho_c = 0.46; 95% confidence interval, 0.41-0.50; P < .001). Sixty-five percent (829 of 1281) of predicted HSI values were less than observed. From adjusted regression models, HSI was best predicted by preoperative international normalized ratio, platelet count, and weight, but this model accounted for only 25% of the variance in HSI. CONCLUSIONS: In a large cohort of children, heparin responsiveness before CPB was not reliably predicted by either in vitro measurement using the HMS Plus System or commonly available preoperative clinical and laboratory data. We describe age-specific reference intervals for HSI in children, and we anticipate that these data will aid the identification of heparin resistance in this population.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.006
GPT teacher head0.253
Teacher spread0.246 · 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 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

Citations10
Published2018
Admission routes1
Has abstractyes

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