Long-Term Morbidity and Mortality of Coronavirus Disease 2019 in Patients Receiving Maintenance Dialysis: A Multicenter Population-Based Cohort Study
Bibliographic record
Abstract
Key Points The rates of long-term mortality, reinfection, cardiovascular outcomes, and hospitalization were high among coronavirus disease 2019 (COVID-19) survivors on maintenance dialysis. Several risk factors, including intensive care unit admission related to COVID-19 and reinfection, were found to have a prolonged effect on survival. This study shows that the burden of COVID-19 remains high after the period of acute infection in the population receiving maintenance dialysis. Background Many questions remain about the population receiving maintenance dialysis who survived coronavirus disease 2019 (COVID-19). Previous literature has focused on outcomes associated with the initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it may underestimate the effect of disease. This study describes the long-term morbidity and mortality among patients receiving maintenance dialysis in Ontario, Canada, who survived SARS-CoV-2 infection and the risk factors associated with long-term mortality. Methods We conducted a population-based cohort study of patients receiving maintenance dialysis in Ontario, Canada, who tested positive for SARS-CoV-2 and survived 30 days between March 14, 2020, and December 1, 2021 (pre-Omicron), with follow-up until September 30, 2022. Our primary outcome was all-cause mortality while our secondary outcomes included reinfection, composite of cardiovascular (CV)–related death or hospitalization, all-cause hospitalization, and admission to long-term care or complex continuing care. We also examined risk factors associated with long-term mortality using multivariable Cox proportional hazards regression. Results We included 798 COVID-19 survivors receiving maintenance dialysis. After the first 30 days of infection, death occurred at a rate of 15.0 per 100 person-years (95% confidence interval [CI], 12.9 to 17.5) over a median follow-up of 1.4 years (interquartile range, 1.1–1.7) with a nadir of death at approximately 0.5 years. Reinfection, composite CV death or hospitalization, and all-cause hospitalization occurred at a rate (95% CI) of 15.9 (13.6 to 18.5), 17.4 (14.9 to 20.4), and 73.1 (66.6 to 80.2) per 100 person-years, respectively. In addition to traditional predictors of mortality, intensive care unit admission for COVID-19 had a prolonged effect on survival (adjusted hazard ratio, 2.6; 95% CI, 1.6 to 4.3). Reinfection with SARS-CoV-2 among 30-day survivors increased all-cause mortality (adjusted hazard ratio, 2.2; 95% CI, 1.4 to 3.3). Conclusions The burden of COVID-19 persists beyond the period of acute infection in the population receiving maintenance dialysis in Ontario with high rates of death, reinfection, all-cause hospitalization, and CV disease among COVID-19 survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".