Long-Term Mortality Risk of Hemodialysis Patients Surviving Initial COVID-19: A Report from the Quebec Renal Network COVID-19 Study
Bibliographic record
Abstract
Background: Dialysis patients are at high-risk of morbidity and mortality early after SARS-CoV-2 infection. Long-term consequences of SARV-CoV-2 infection are however still not well described in this population. We aimed to compare long-term mortality between dialysis patients who survived 30-day after a SARS-CoV-2 infection and dialysis patients negative to SARS-CoV-2. Methods: We included patients with SARS-CoV-2 PCR tests performed between March 1st 2020 and February 30th 2021 from 7 dialysis centers in Quebec. Patients alive at 30 days after SARS-CoV-2 diagnosis were matched by age, sex, center and PCR test date to patients negative for SARS-CoV-2 and followed for up to one year, starting at 30 days after initial infection (or negative test). We assessed mortality risk in unadjusted and adjusted multivariable Cox regressions. Results: Ninety-eight patients with SARS-CoV-2 infection alive 30-day after diagnosis were matched to 166 SARS-CoV-2-negative patients. Baseline characteristics were similar between the two groups. Patients were followed for a median of 331 (301-347) days. Overall, 32 patients died during the study period (15 [15%] in the SARS-CoV-2-positive group and 17 [10%] in the SARS-CoV-2-negative group, p=0.22). There was no statistically significant association between mortality risk and previous SARS-CoV-2 infection (HR 1.5, 95% CI 0.8-3.1), even after adjustment for residual imbalance (aHR 1.4, 95% CI 0.7-3.1). Results remained similar after exclusion of 4 patients who died of SARS-CoV-2 infection > 30-day after diagnosis (Table 1). Conclusions: One-year survival of dialysis patients surviving SARS-CoV-2 infection was similar to those never infected. Funding: Government Support - Non-U.S. - Baseline Characteristics SARS-CoV-2 positive (n=98) SARS-CoV-2 negative (n=166) p-value Age, years 72 (62; 79) 71 (61; 78) 0.76 Sex male 58 (59) 149 (61) 0.71 Long-term care residency 19 (19) 26 (16) 0.44 Primary kidney disease 0.65 Diabetic nephropathy 43 (44) 83 (50) Hypertensive disease 21 (21) 27 (16) Glomerulonephritis 11 (11) 21 (13) Others 23 (23) 35 (21) Diabetes 58 (59) 103 (62) 0.65 Cardiovascular disease 62 (63) 102 (61) 0.77 Respiratory disease 14 (14) 32 (19) 0.30 Cancer (previous or active) 11 (11) 35 (21) 0.04 Previous kidney transplantation 7 (7) 6 (4) 0.20 Kidney replacement therapy duration, in years 2.6 (1.0;6.5) 2.7 (1.2;4.9) 0.88 Adjusted mortality predictors aHR 95% CI p-value Positive SARS-CoV-2 infection 1.4 0.7-3.1 0.33 Long-term care housing 3.5 1.7-7.3 0.001 Kidney replacement therapy duration, per year 1.05 0.99-1.11 0.09 Diabetic kidney disease (vs. other) 1.9 0.9-3.9 0.09
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".