The Characteristics, Dynamics, and the Risk of Death in COVID-19 Positive Dialysis Patients in London, UK
Bibliographic record
Abstract
Background Patients on dialysis with frequent comorbidities, advanced age, and frailty, who visit treatment facilities frequently, are perhaps more prone to SARS-CoV-2 infection and related death—the risk factors and dynamics of which are unknown. The aim of this study was to investigate the hospital outcomes in patients on dialysis infected with SARS-CoV-2. Methods Data on 224 patients on hemodialysis between February 29, 2020 and May 15, 2020 with confirmed SARS-CoV-2 were analyzed for outcomes and potential risk factors for death, using a competing risk-regression model assessed by subdistribution hazards ratio (SHR). Results Crude data analyses suggest an overall case-fatality ratio of 23% (95% CI, 17% to 28%) overall, but that varies across age groups from 11% (95% CI, 0.9% to 9.2%) in patients ≤50 years old and 32% (95% CI, 17% to 48%) in patients >80 years; with 60% of deaths occurring in the first 15 days and 80% within 21 days, indicating a rapid deterioration toward death after admission. Almost 90% of surviving patients were discharged within 28 days. Death was more likely than hospital discharge in patients who were more frail (WHO performance status, 3–4; SHR, 2.16 [95% CI, 1.25 to 3.74]; P =0.006), had ischemic heart disease (SHR, 2.28 [95% CI, 1.32 to 3.94]; P =0.003), cerebrovascular disease (SHR, 2.11 [95% CI, 1.20 to 3.72]; P =0.01), smoking history (SHR, 2.69 [95% CI, 1.33 to 5.45]; P =0.006), patients who were hospitalized (SHR, 10.26 [95% CI, 3.10 to 33.94]; P <0.001), and patients with high CRP (SHR, 1.35 [95% CI, 1.10 to 1.67]) and a high neutrophil:lymphocyte ratio (SHR, 1.03 [95% CI, 1.01 to 1.04], P <0.001). Our data did not support differences in the risk of death associated with sex, ethnicity, dialysis vintage, or other comorbidities. However, comparison with the entire dialysis population attending these hospitals, in which 13% were affected, revealed that patients who were non-White (62% versus 52% in all patients, P =0.001) and those with diabetes (54% versus 22%, P <0.001) were disproportionately affected. Conclusions This report discusses the outcomes of a large cohort of patients on dialysis. We found SARS-CoV-2 infection affected more patients with diabetes and those who were non-White, with a high case-fatality ratio, which increased significantly with age, frailty, smoking, increasing CRP, and neutrophil:lymphocyte ratio at presentation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".