Clinician underprescription of and patient nonadherence to clinical practice guideline-recommended medications for peripheral artery disease: a systematic review and meta-analysis
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,021 | 0,040 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».