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Record W3170124186 · doi:10.1177/20543581211021742

Retrospective Analysis of Tacrolimus Intrapatient Variability as a Measure of Medication Adherence

2021· article· en· W3170124186 on OpenAlexaffabout
Jordana Herblum, Niki Dacouris, Michael Huang, Jeffrey S. Zaltzman, G. V. Ramesh Prasad, Michelle M. Nash, Lucy Chen

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2021
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineTacrolimusInternal medicineTrough levelRetrospective cohort studyPopulationCohortKidney transplantationTransplantation

Abstract

fetched live from OpenAlex

Background: Increased intrapatient variability (IPV) in tacrolimus levels is associated with graft rejection, de novo donor-specific antibodies, and graft loss. Medication nonadherence may be a significant contributor to high IPV. Objective: The objective of this study is to determine the utility of tacrolimus IPV in detecting nonadherence by examining the relationship between self-reported adherence and tacrolimus coefficient of variability (COV), a measure of IPV. Design: Retrospective cohort study. Setting: St. Michael’s Hospital, Toronto, Ontario. Patients: All patients who were at least 1-year post-kidney transplant as of March 31, 2019, prescribed tacrolimus as an immunosuppressant and had a self-reported adherence status. Patients were excluded from the primary analysis of examining the correlation between COV and self-reported adherence if they lacked a calculatable COV. Measurements: Self-reported adherence, COV, demographic data, transplant, and medication history. Methods: A modified Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS) administered by healthcare professionals to assess self-reported adherence was used. The COV of tacrolimus trough levels was calculated and its correlation to BAASIS response was noted. The median COV was used as a cutoff to examine the characteristics of patients deemed “high COV” and “low COV.” Results: A total of 591 patients fit the initial criteria; however, only 525 had a recent calculatable COV. Overall, 92.38% of the population were adherent by self-report. Primary analysis identified a COV of 25.2% and 29.6% in self-reported adherent and nonadherent patients, respectively, though the result was not significant ( P = .2). Secondary analyses showed a significant correlation between younger age at transplant and at the time of adherence self-reporting with nonadherence ( P = .01). In addition, there was a strong correlation between those nonadherent with routine post-transplant blood work and younger age ( P < .01). Limitations: The limitations included modified nonvalidated BAASIS questionnaire, social desirability bias, BAASIS only administered in English, and patients with graft failure not active in clinic not being captured. Conclusions: The COV should not be used as the sole method for determining medication adherence. However, COV may have some utility in capturing individuals who are not adherent to their blood work or patients who are having a poor response to tacrolimus and should be switched to another medication.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.303
Teacher spread0.286 · 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

Citations12
Published2021
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Kidney Health and DiseaseSame topicRenal Transplantation Outcomes and TreatmentsFrench-language works237,207