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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".