Clinical Outcomes and Health Care Utilization in Patients with Advanced Chronic Kidney Disease not on Dialysis After the Onset of the COVID-19 Pandemic in Ontario, Canada
Notice bibliographique
Résumé
Background: The COVID-19 pandemic caused considerable disruption to health care services. Limited data exist on its impacts on clinical outcomes and health care utilization in patients with advanced chronic kidney disease (CKD). Objective: To compare the rates of all-cause mortality, cardiovascular-related hospitalizations, kidney-related outcomes, and health care utilization in patients with advanced CKD before and during the first 21 months of the COVID-19 pandemic. Design: Population-based, repeated cross-sectional study from March 15, 2017 to November 15, 2021, with follow-up until December 14, 2021 (preceding the Omicron variant). Setting: Linked administrative health care databases from Ontario, Canada. Participants: (excluding patients receiving maintenance dialysis). Measurements: The pre-COVID-19 period was from March 15, 2017 to March 14, 2020 and the COVID-19 period was from March 15, 2020 to December 14, 2021. Poisson generalized estimating equations were used to predict post-COVID-19 patient outcomes and health utilization based on pre-COVID trends, estimating relative changes between the observed and expected outcomes. The multivariable model incorporated age group-sex interaction terms, a continuous variable denoting time in months to capture general trends, and pre-COVID month indicators to adjust for seasonal changes. Methods: Our primary outcome was all-cause mortality. Secondary outcomes included all-cause hospitalizations, non-COVID-19-related deaths and hospitalizations, intensive care unit (ICU) admissions, mechanical ventilation, and emergency room visits. We also examined cardiovascular-related hospitalizations, kidney-related outcomes, and ambulatory visits. Results: We included 101 688 adults with advanced CKD. The incidence of all-cause mortality was 147.4 (95% confidence interval [CI] = 145.1, 149.7) per 1000 person-years in the pre-COVID-19 period compared to 150.8 (95% CI = 147.9, 153.7) per 1000 person-years in the COVID-19 period. After adjustment, there was an 8% higher rate of all-cause mortality during the COVID-19 (adjusted relative rate [aRR] = 1.08, 95% CI = 1.03, 1.12). Non-COVID-19-related deaths did not increase substantially (aRR = 1.02, 95% CI = 0.97, 1.07). The COVID-19 period was associated with a lower rate of all-cause hospitalizations, ICU admissions, and emergency room visits. There were declines in long-term care admissions and non-nephrology physician visits in the first 3 months of the pandemic. In contrast, nephrology visits remained stable throughout the study period, including the first 3 months of the pandemic. Similarly, the monthly rates of acute kidney injury requiring dialysis initiation showed little variation compared with pre-pandemic levels. Limitations: Due to data availability at the time of analysis, we did not examine the impact of the COVID-19 pandemic on patients with advanced CKD beyond December 2021. Conclusions: Non-COVID-19-related deaths did not increase during the first 21 months of the pandemic, despite reduced health care utilization. The study informs health service planning in future health care emergencies.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 ».