Prospective Study of COVID-19 in Patients Receiving Dialysis in Alberta Kidney Care South
Bibliographic record
Abstract
Background: People with kidney failure who are on facility-based hemodialysis (FBHD) are at high risk for COVID-19 infection due to inherent alterations in their immune system as well as the requirement to travel to a health care facility multiple times per week. In Alberta Kidney Care South, AKCS, public health measures and standardized screening of all patients entering clinics and HD units was initiated in March 2020 with COVID-19 testing of all patients who presented with a temperature, COVID related symptoms or a history of exposure to COVID-19. Methods: All COVID-19 test results performed for AKCS patients are tracked in the electronic kidney database. We performed a 14-month prospective observational study (March 2020 to May 2021) to determine the incidence of confirmed COVID-19 infections, the prevalence of symptoms amongst COVID + patients and outcomes of hospitalization and death for FBHD, home hemodialysis (HHD) and peritoneal dialysis (PD) patients within the Alberta Kidney Care South program. Results: We report on our preliminary results up to December 31, 2020. From a population of 1 329 patients, (931 FBHD, 102 HHD and 296 PD) 46(3.5%) patients were COVID positive. COVID-19 prevalence was 3.5% in FBHD (33/931), 4.4% in PD (13/296) and no HHD patients. The mean age of the cohort was 61 ± 16.5 years with 14(30%) female and comorbidities of hypertension 43(93%), diabetes 35(76%), coronary artery disease 16(35%) and heart failure 10(22%). COVID-19 testing was done for the following reasons: contact with a known COVID-19 person in 4(8.7%), resident of a long-term care facility in 3(6.5%) and for symptoms in 31(67%). The most common symptoms were fever (defined as T> 37.3C) with 20(43%), cough 10(22%) and sore throat 6(13%). Overall, 14 patients (30%) were admitted to hospital, 4 of whom went to the ICU and 5(11%) died. There were no differences in hospitalization between FBHD and PD (30% vs 31% respectively p = 0.971), ICU admissions (12% vs 0%, p=0.189) or death (12% vs 8 %, p=0.664). Conclusions: The prevalence of COVID-19 amongst FBHD and PD patients was similar to the general population but with higher rates of hospitalization, ICU admissions and death. People on HHD appear to have very low rates of COVID-19 as compared to either PD or FBHD.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".