MO187: Healthcare Costs based on Risk of Progression in Patients with Chronic Kidney Disease
Notice bibliographique
Résumé
Abstract BACKGROUND AND AIMS Nephrologists follow patients with chronic kidney disease (CKD) stage G3 and G4 as a homogeneous group with the assumption that everyone had similar rates of progression with scheduled visits and lab investigations based on the stage of the disease. We now recognize that not all patients progress at similar rates to kidney failure and treatment and follow-up needs vary. The Kidney Failure Risk Equation (KFRE) identifies patients at different risks of progression to kidney failure (low, medium and high risk) in each stage of the disease. Previous studies had looked at resource utilization of patients based on the stage of the CKD. The purpose of our analysis was to examine resource utilization and associated costs based on the risk of progression by KFRE in the setting of a universal healthcare system. METHOD We conducted a retrospective cohort study of adults with CKD G3 and G4 enrolled in multidisciplinary CKD clinics in the province of Saskatchewan, Canada. Data was collected from January 2004 through December 2012 and patients were followed for 5 years. The predicted risk of kidney failure for each patient was calculated using the 8-variable KFRE. The equation used clinical and routine laboratory data, to stratify patients into three risk categories (low, medium and high risk) of progression. We compared the number and cost of hospital admissions, physician visits and prescription drugs by risk within G3 and G4. Negative binomial regression and generalized linear model were used to compare healthcare utilization and cost between the groups respectively (α = 0.05). RESULTS A total of 1003 adults with CKD G3 and G4 were included in the study. In patients with stage G3 CKD, 311 (59%), 150 (28%) and 68 (13%) were in low, medium and high-risk categories, respectively. Amongst patients with CKD stage G4, 275 (58%), 86 (18%) and 113 (24%) were in similar categories respectively. The cost of hospital admissions, physician visits and drug dispensations in stage G4 high risk in comparison to low risk over the 5-year study period was CAD $89 265 versus $48 374 (P = .008), $23 423 versus $11 231 (P < .001) and $21 853 versus $16 757 (P = .01), respectively. In stage G3, the cost of hospital admissions was CAD $55 944 versus $36 740 (P = 0.10), physician visits $13 414 versus $10 370 (P = .08) and prescription drugs $20 394 versus $14 902 (P = .02) in high-risk patients in comparison to low-risk patients (Figure 1). CONCLUSION In patients followed in multidisciplinary clinics with CKD stages G3 and G4, the cost of hospital admissions, physician visits and prescription drugs were higher in high-risk patients compared to patients in low-risk category. In our study, the KFRE, designed to predict the risk of progression to dialysis in patients with CKD, also assisted in identifying patients with higher health resource utilization and healthcare costs compared to those with lower health resource use. We additionally suggest that patients who are in medium and high-risk categories be followed in multidisciplinary clinics rather than individual physician offices to delay the trajectory of decline to kidney failure.
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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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».