End‐stage kidney disease patients from ethnic minorities and mortality in coronavirus disease 2019
Bibliographic record
Abstract
INTRODUCTION: Coronavirus disease 2019 (COVID-19) adversely affects patients who are older, multimorbid, and from Black, Asian or minority ethnicities (BAME). We assessed whether being from BAME is independently associated with mortality in end-stage kidney disease (ESKD) patients with COVID-19. METHODS: Prospective observational study in a single UK renal center. A study was conducted between March 10, 2020 and April 30, 2020. Demographics, socioeconomic deprivation (index of multiple deprivation), co-morbidities (Charlson comorbidity index [CCI]), and frailty data (clinical frailty score) were collected. The primary outcome was all-cause mortality. Data were censored on the 1st June 2020. FINDINGS: Overall, 191 of our 3379 ESKD patients contracted COVID-19 in the 8-week observation period; 84% hemodialysis, 5% peritoneal dialysis, and 11% kidney transplant recipients (KTR). Of these, 57% were male and 67% were from BAME groups (43% Asian, 17% Black, 2% mixed race, and 5% other). Mean CCI was 7.45 (SD 2.11) and 3.90 (SD 2.10) for dialysis patients and KTR, respectively. In our cohort, 60% of patients lived in areas classified as being in the most deprived 20% in the United Kingdom, and of these, 77% of patients were from BAME groups. The case fatality rate was 29%. Multivariable cox regression demonstrated that BAME (hazard ratio [HR]: 2.37, 95% CI: 1.22-4.61) was associated with all-cause mortality after adjustment for age, deprivation, co-morbidities, and frailty. Associations with all-cause mortality persisted in sensitivity analyses in patients from South Asian (HR: 2.52, 95% CI: 1.24-5.12) and Black (HR: 2.43, 95% CI: 1.04-5.67) ethnic backgrounds. DISCUSSION: BAME ESKD patients with COVID-19 are just over twice as likely to die compared to White patients, despite adjustment for age, deprivation, comorbidity, and frailty. This study highlights the need to develop strategies to improve BAME patient outcomes in future outbreaks of COVID-19.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".