<scp>COVID</scp>‐19 vaccination status impact on mortality in end‐stage kidney disease
Bibliographic record
Abstract
To the Editor: Patients with end-stage kidney disease (ESKD) are particularly vulnerable to adverse clinical outcomes associated with COVID-19, with an estimated 20%-30% mortality risk.[1][2][3][4] There is limited information on clinical outcomes, including mortality, following vaccination among patients on dialysis, with many relevant phase 3 trials excluding patients with "serious kidney disease" and chronic conditions.5 To characterize the impact of COVID-19 vaccination on all-cause mortality, we conducted a retrospective analysis using data from all US adults (i.e., aged ≥18 years) with ESKD receiving dialysis through Fresenius Medical Care (FMC) between March 1 and September 18, 2021.Data for a total of 239,660 patients were available with clinical and demographic, including vaccination status, being updated throughout the study period.As of March 1, 2021, 16,140 patients were classified as fully vaccinated (defined as the time period beginning 2 weeks after either an Ad26.COV2.S vaccination or a second mRNA vaccination), 39,938 were partially vaccinated (defined as the time period up to 2 weeks after vaccination with Ad26.COV2.S or the period from initial vaccination to 2 weeks after a second mRNA vaccination), and 114,403 were unvaccinated (defined as having no COVID-19 vaccination history).Among patients at the start of the study period (N = 170,481), 13.8% were on peritoneal dialysis, 3.8% were on home hemodialysis, and 82.4% were on in-center hemodialysis, 42.6% were female, 63.9% had a history of diabetes, and 51.6% were younger than age 65.Patient demographics remained relatively consistent throughout the study period.Overall, 102,717 patient-years of follow-up were available for analysis: 30,689 for unvaccinated patients, 14,478 for partially vaccinated patients, and 57,550 for fully vaccinated patients.During the analysis period, 19,356 deaths occurred, equating to an overall mortality rate of 18.8 deaths per 100 patient-years.The unadjusted rate of death (per 100 patient-years) was 29.6, 13.8, and 14.4 among unvaccinated, partially vaccinated, and fully vaccinated patients, respectively.When adjusted for sex, age, race/ ethnicity, diabetes history, and US geographic region, the
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".