Integrating Risk-Based Care for Patients With Chronic Kidney Disease in the Community: Study Protocol for a Cluster Randomized Trial
Notice bibliographique
Résumé
BACKGROUND: A risk-based model of care for managing patients with chronic kidney disease (CKD) using the Kidney Failure Risk Equation (KFRE) has been successfully integrated into nephrology care pathways in several jurisdictions. However, as most patients with CKD can be managed in primary care, the next pertinent steps would be to integrate the KFRE into primary care pathways. OBJECTIVE: Using a risk-based approach for guiding CKD care in the primary care setting, the objective of the study is to develop, implement, and evaluate tools that can be used by patients and providers. DESIGN: This study is a multicenter cluster randomized control trial. SETTING: Thirty-two primary care clinics belonging to the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) across Manitoba and Alberta. PATIENTS: All patients at least 18 years old or older with CKD categories G3-G5 attending the participating clinics; we estimate each clinic will have an average of 185 patients with CKD. METHODS: Thirty-two primary care clinics will be randomized to receive either an active knowledge translation intervention or no intervention. The intervention involves the addition of the KFRE and decision aids to clinics' Data Presentation Tool (DPT), as well as patient-facing visual aids, a medical detailing visit, and sentinel feedback reports. Control clinics will only be exposed to current guidelines for CKD management, without active dissemination. MEASUREMENTS: Data from the CPCSSN repository will be used to assess whether a risk-based care approach affected management of CKD. Primary outcomes are as follows: the proportion of patients with measured urine albumin-to-creatinine ratio, and the proportion of patients being appropriately treated with angiotensin-converting enzyme inhibitor or angiotensin receptor blockers. Secondary outcomes are as follows: the optimal management of diabetes (hemoglobin A1C <8.5%, and the use of sodium-glucose cotransporter-2 inhibitors in CKD G3 patients), hypertension (office blood pressure <130/80 for patients with diabetes, 140/90 for those without), and cardiovascular risk (statin prescription); prescriptions of nonsteroidal anti-inflammatory drugs; and decline in estimated glomerular filtration rate (eGFR). In addition, in a substudy, we will measure CKD-specific health literacy and trust in physician care via surveys administered in the clinic post-visit. At the provider level, we will measure satisfaction with the risk prediction tools. Lastly, at the health system level, outcomes include cost of CKD care, and appropriate referrals for patients at high risk of kidney failure based on provincial guidelines. Primary and secondary outcomes will be measured at the patient level and enumerated at the clinic level 1 year after the intervention implementation, except for decline in eGFR, which will be measured 2 years postintervention. LIMITATIONS: Limitations include scalability of the proposal in other health care systems. CONCLUSIONS: If successful, this intervention has the potential to improve the management of patients with CKD within Canadian primary care settings, leading to health and economic benefits, and influencing practice guidelines. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03365063.
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 | 0,036 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,003 |
| Méta-épidémiologie (sens large) | 0,014 | 0,006 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,066 | 0,009 |
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 ».