Comparison of Acute Health Care Utilization Between Patients Receiving In-Center Hemodialysis and the General Population: A Population-Based Matched Cohort Study From Ontario, Canada
Notice bibliographique
Résumé
Background: Patients receiving maintenance hemodialysis have multiple comorbidities and are at high risk of presenting to the hospital. However, the incidence and cost of acute health care utilization in the in-center hemodialysis population and how this compares with other populations is poorly understood. Objective: To determine the rate, pattern, and cost of emergency department visits and hospitalizations in patients receiving in-center hemodialysis compared with a matched general population. Design: Population-based matched cohort study. Setting: We used linked administrative health care databases from Ontario, Canada. Patients: We included 25 379 patients (incident and prevalent) receiving in-center hemodialysis between January 1, 2010, and December 31, 2018. Patients were matched on birth date (±2 years), sex, and cohort entry date using a 1:4 ratio to 101 516 individuals from the general population. Measurements: Our primary outcomes were emergency department visits (allowing for multiple visits per individual) and hospital admissions from the emergency department. We also assessed all-cause hospitalizations, all-cause readmissions within 30 days of discharge from the original hospitalization, length of stay for hospital admissions (including multiple visits per individual), and the financial cost of these admissions. Methods: We presented the rate, percentage, median (25th, 75th percentiles), and incidence rate per 1000 person-years for emergency department visits and hospitalizations. Individual-level health care costs for emergency department visits and all-cause hospitalization were estimated using resource intensity weights multiplied by the cost per weighted case. Results: Patients receiving in-center hemodialysis had substantially more comorbidities (eg, diabetes) than the matched general population. Eighty percent (n = 20 309) of patients receiving in-center hemodialysis had at least 1 emergency department visit compared with 56% (n = 56 452) of individuals in the matched general population, over a median follow-up of 1.8 years (25th, 75th percentiles: 0.7, 3.6) and 5.2 (2.5, 8.4) years, respectively. The incidence rate of emergency department visits, allowing for multiple visits per individual, was 2274 per 1000 person-years (95% confidence interval [CI]: 2263, 2286) for patients receiving in-center hemodialysis, which was almost 5 times as high as the matched general population (471 per 1000 person-years; 95% CI: 469, 473). The rate of hospital admissions from the emergency department and the rate of all-cause hospital admissions in the in-center hemodialysis population was more than 7 times as high as the matched general population (hospital admissions from the emergency department: 786 vs 101 per 1000 person-years; all-cause hospital admissions: 1056 vs 139 per 1000 person-years). The median number of all-cause hospitalization days per patient year was 4.0 (0, 16.5) in the in-center hemodialysis population compared with 0 (0, 0.5) in the matched general population. The cost per patient-year for emergency department visits in the in-center hemodialysis population was approximately 5.5 times as high as the matched general population while the cost of hospitalizations in the in-center hemodialysis population was approximately 11 times as high as the matched general population (emergency department visits: CAN$ 1153 vs CAN$ 209; hospitalizations: CAN$ 21 151 vs CAN$ 1873 [all costs in 2023 CAN$]). Limitations: External generalizability and we could not determine whether emergency department visits and hospitalizations were preventable. Conclusions: Patients receiving in-center hemodialysis have high acute health care utilization. These results improve our understanding of the burden of disease and the associated costs in the in-center hemodialysis population, highlight the need to improve acute outcomes, and can aid health care capacity planning. Additional research is needed to address the risk of hospitalization after controlling for patient comorbidities. Trial registration: This is not applicable as this is a population-based matched cohort study and not a clinical trial.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».