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Record W4285285162 · doi:10.1177/20543581221103682

Use of Guideline-Based Therapy for Diabetes, Coronary Artery Disease, and Chronic Kidney Disease After Acute Kidney Injury: A Retrospective Observational Study

2022· article· en· W4285285162 on OpenAlexaffabout
Sunchit Madan, Patrick A. Norman, Ron Wald, Javier A. Neyra, Alejandro Meraz-Muñoz, Ziv Harel, Samuel A. Silver

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of TorontoSt. Joseph’s Healthcare HamiltonKingston Health Sciences CentreQueen's UniversityMcMaster UniversitySt. Michael's Hospital
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsMedicineKidney diseaseGuidelineCoronary artery diseaseDiabetes mellitusInternal medicineRetrospective cohort studyAcute kidney injuryIntensive care medicinePopulationEmergency medicinePathology

Abstract

fetched live from OpenAlex

Background: Survivors of acute kidney injury (AKI) are at a high risk for cardiovascular complications. An underrecognition of this risk may contribute to the low utilization of relevant guideline-based therapies in this population. Objective: We sought to assess accordance with guideline-based recommendations for survivors of AKI with diabetes, coronary artery disease (CAD), and preexisting chronic kidney disease (CKD) in a post-AKI clinic, and identify factors that may be associated with guideline accordance. Design: Retrospective cohort study. Setting: Post-AKI clinics at 2 tertiary care centers in Ontario, Canada. Patients: We included adult patients seen in both post-AKI clinics between 2013 and 2019 who had at least 2 clinic visits within 24 months of an index AKI hospitalization. Measurements: We assessed accordance to recommendations from the most recent North American and international guidelines available at the time of study completion for diabetes, CAD, and CKD. Methods: We compared guideline accordance between visits using the Cochran Mantel Haenszel test. We used multivariable Poisson regression to identify prespecified factors associated with accordance. Results: Of 213 eligible patients, 192 (90%) had Kidney Disease Improving Global Outcomes Stage 2-3 AKI, 91 (43%) had diabetes, 76 (36%) had CAD, and 88 (41%) had preexisting CKD. From the first clinic visit to the second, there was an increase in angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACE-I/ARB) use across all disease groups—from 33% to 46% ( P = .028) in patients with diabetes, from 30% to 57% ( P = .002) in patients with CAD, and from 16% to 35% ( P < .001) in patients with preexisting CKD. Statin use increased in patients with preexisting CKD from 64% to 71% ( P = .034). Every 25 μmol/L rise in the discharge serum creatinine was associated with a 19% (95% confidence interval [CI], 8%-28%) and 12% (95% CI, 2%-21%) lower likelihood of being on an ACE-I/ARB in patients with diabetes and preexisting CKD, respectively. Limitations: The study lacked a comparison group that received usual care. The small sample and multiple comparisons make false positives possible. Conclusion: There is room to improve guideline-based cardiovascular risk factor management in survivors of AKI, particularly ACE-I/ARB use in patients with an elevated discharge serum creatinine.

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)0.006
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 score0.151
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.057
GPT teacher head0.344
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

Citations1
Published2022
Admission routes2
Has abstractyes

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