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Record W4412793365 · doi:10.1016/j.eclinm.2025.103391

Clinician underprescription of and patient nonadherence to clinical practice guideline-recommended medications for peripheral artery disease: a systematic review and meta-analysis

2025· review· en· W4412793365 on OpenAlexafffundabout
Aidan M. Kirkham, Maude Paquet, Dean Fergusson, Ian D. Graham, Justin Presseau, Daniel I. McIsaac, Sudhir Nagpal, David de Launay, Sami Aftab Abdul, Risa Shorr, Jeremy Grimshaw, Derek J. Roberts

Bibliographic record

VenueEClinicalMedicine · 2025
Typereview
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsUniversity of CalgaryInstitute for Clinical Evaluative SciencesOttawa HospitalUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchPhysicians' Services Incorporated Foundation
KeywordsMedicineGuidelineMeta-analysisArterial diseaseClinical PracticeDiseaseIntensive care medicineFamily medicineSurgeryInternal medicineVascular diseasePathology

Abstract

fetched live from OpenAlex

Background: Guidelines recommend that adults with peripheral artery disease (PAD) take antiplatelets, statins, and antihypertensives. However, it is unclear how frequently clinicians do not prescribe these medications (ie, underprescription), how often patients fail to fill/refill their prescriptions (ie, nonadherence), which factors increase underprescription/nonadherence risk, and whether underprescription/nonadherence are associated with outcomes. Methods: We searched MEDLINE, EMBASE, CENTRAL, and Evidence-Based Medicine Reviews (January 1, 2006-to-February 18th, 2025) for studies reporting cumulative incidences/point prevalences of clinician underprescription and/or patient nonadherence to antiplatelets, statins, and/or antihypertensives; adjusted-risk factors for underprescription/nonadherence; and adjusted-outcomes associated with underprescription/nonadherence among adults with PAD. Two investigators independently screened citations, extracted data, and assessed risk of bias. Data were pooled using random-effects models. Estimate certainty was communicated using GRADE. The study was registered on PROSPERO (CRD42022362801). Findings: Among 4206 citations identified, 125 studies (n = 14,681,801 participants; 37% female) were included. The pooled cumulative incidence of antiplatelet, statin, and antihypertensive (among those with PAD and hypertension) underprescription was 28% (95% confidence interval [CI] = 21-36%; moderate-certainty), 34% (95% CI = 31-38%; high-certainty), and 43% (95% CI = 33-53%; moderate-certainty), respectively. The cumulative incidence of antiplatelet, statin, and antihypertensive nonadherence was 27% (95% CI = 20-35%; moderate-certainty), 28% (95% CI = 24-33%; high-certainty), and 23% (95% CI = 22-24%; low-certainty), respectively. Underprescription was more common in population-based studies and those enrolling more females and past/current smokers while nonadherence was more common in studies enrolling more patients with diabetes. Underprescription risk factors included female sex, advanced age, malignancy history, and chronic limb-threatening ischemia (all moderate-certainty). Nonadherence risk factors included advanced age, comorbidity burden, and receiving specialist mental health care (all moderate-certainty). Underprescription was associated with increased major adverse cardiac events, all-cause mortality, and decreased amputation-free time (all moderate-certainty). Interpretation: One-quarter-to-one-half of adults with PAD are not prescribed antiplatelets, statins, and antihypertensives. Further, approximately one-quarter of these patients do not adhere to these medications after prescription. Funding: This research was supported by a 2024 Vanier Canada Graduate Scholarship (awarded to AMK and supervised by DJR), a Graham Farquharson Physician Services Incorporated Knowledge Translation Fellowship (awarded to DJR), and a Research Program Award, University of OttawaDepartment of Surgery Annual Competition (awarded to DJR).

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)0.004
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.700
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0130.003
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 head0.157
GPT teacher head0.497
Teacher spread0.340 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2025
Admission routes3
Has abstractyes

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