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Enregistrement W3033303100 · doi:10.1093/ndt/gfaa142.p0687

P0687DO ALL PATIENTS BENEFIT FROM MULTIDISCIPLINARY CHRONIC KIDNEY DISEASE CLINICS?

2020· article· en· W3033303100 sur OpenAlexaffabout
Bhanu Prasad, Maryam Jafari, Lexis Gordon, Navdeep Tangri, Joanne Kappel

Notice bibliographique

RevueNephrology Dialysis Transplantation · 2020
Typearticle
Langueen
DomaineMedicine
ThématiquePharmaceutical Practices and Patient Outcomes
Établissements canadiensSt. Paul's HospitalSeven Oaks General HospitalRegina General HospitalUniversity of SaskatchewanSaskatchewan HealthSaskatchewan Health Authority
Organismes subventionnairesnon disponible
Mots-clésMedicineKidney diseasePsychological interventionMultidisciplinary approachDialysisNephrologySpecialtyIntensive care medicinePolypharmacyHealth careInternal medicineFamily medicineEmergency medicineNursing

Résumé

récupéré en direct d'OpenAlex

Abstract Background and Aims: Multidisciplinary clinics (MDC’s) were established in Canada to offer a variety of support systems (diabetes care, social support, easy access to pharmacists, dietitians, specialty trained nurses), to monitor and delay progression through timed lab investigations and visits in conjunction with the Nephrologist. The reasons for better outcomes have been identified as better education, focus on self-care, dietary interventions, timely transplant referrals, modality education, lower hospitalizations and mortality. Treating all patients with chronic kidney disease (CKD) as part of a multidisciplinary care team runs the risk of adding unwarranted labs, interventions, polypharmacy and costs. Kidney Failure Risk Equation (KFRE) uses routine laboratory and clinical data, to stratify patients into three risk categories (low, medium, and high risk) of progression. KFRE has been shown to accurately estimate progression to kidney failure in adults with CKD. The objectives of the study were to i) validate the KFRE in our CKD patients, ii) evaluate health care utilization of patients based on the risk of progression in our province, Saskatchewan. iii) identify the subgroup of patients that benefit most from follow up in MDC. Methods: We conducted a retrospective study on 1007 patients with CKD stages G3 and G4 in two CKD multidisciplinary clinics in the province of Saskatchewan, Canada (January 2004-December 2012). The predicted risk of kidney failure (low, medium high) for each patient was calculated using the 8-variable KFRE. Patients were followed for five years to validate the KFRE; data on initiation of dialysis or death was collected. Cost of delivery of care per patient per year in the CKD clinic was determined. Health care utilization was evaluated by measuring the number/cost of hospital admissions, cardiovascular and thoracic (CVT) surgery, non-nephrology specialist appointments, and medications. Results: There were more patients in G 3 (n= 533) than in G 4 (n=474). 313 (59%), 150 (28%), and 70 (13%) were in low, medium and high-risk categories for G 3 CKD. 275 (58%), 86 (18%), and 113 (24%) were in similar categories for G 4. The mean age (SD) was 71 (12.8) years. The number of patients > 65 years of age was 75%. 57% were men, mean GFR (mls/min/1.73m2) for G3 was 40 (7.8) and 23 (4) for G4. Of the G3 patients, 4% of low risk, 11% of the medium risk and 26% of the high risk progressed to dialysis by 5 years. In G 4 patients, 7% of low risk, 17% of medium risk and 48% of high risk progressed to dialysis over 2 years. These results validate the KFRE in our population. The cost of care per patient in MDC was $ 3800 (CAD) per year. There was a difference in the cost of medications, number and cost of (inpatient hospitalizations, cardiovascular surgeries, non-Nephrology specialist visits, and day surgeries) between low risk patients vs high risk patients in G4 patients. Conclusion: We performed a cost-effectiveness analysis of our MDC’s and show that very few patients at low-risk of progression advance to ESRD. They are also unlikely to benefit from intensive care management and better managed in primary care with advice from tertiary centres. Individual programs have significant opportunity to improve health care delivery by identifying the sub- groups that benefit the most from MDC based on the risk of progression to allow optimal utilization of resources. At $ 3800 (CAD) per patient, we suggest that MDC’s are best utilized by patients with medium and high risk of progression. Further, we show that patients that the low-risk patients were older, had fewer inpatient visits, had lesser drug costs, underwent fewer cardiovascular surgeries, had fewer day surgery visits, and fewer non-nephrology specialist visits. This is the first study to our knowledge that focuses on health care utilization based on the risk of disease progression rather than the stage of CKD.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,138
Score d'incertitude au seuil0,275

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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.

Tête enseignante Opus0,057
Tête enseignante GPT0,353
Écart entre enseignants0,296 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission2
Résumé présentoui

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