Effect of COVID-19 on Dialysis Practices on the Ground: Early Results from an International DOPPS Program Survey
Bibliographic record
Abstract
Background: The COVID-19 pandemic caused unprecedented disruption to dialysis patients care globally. Facility surveys were distributed to assess the impact of COVID-19 pandemic on hemodialysis (HD) and peritoneal dialysis (PD) practices. Methods: Medical Director (MD) and Nurse Manager (NM) Surveys (MDS, NMS) are being distributed in May/June 2020 to 723 clinics enrolled in the Dialysis (in-center HD, DOPPS) or Peritoneal (PDOPPS) Dialysis Outcomes and Practice Patterns Study in Canada, China, Japan, the United States, 7 European countries, 5 Gulf Cooperative Council countries, and China metropolitan areas (Beijing, Guangzhou, Shanghai). Surveys content includes the number of COVID-19 cases, testing, and clinical management, screening, infection control, staffing, patient transportation, and psychological support. Results: As of 27 May 2020, we have 80 MDS (China, Europe, US = 33, 38, 5) and 101 NMS (45, 46, 9) responses from DOPPS sites. The following percentages are presented sequentially for China, Europe, and US. Among MDs, 0%, 67%, 67% reported at least one confirmed COVID-19 case among dialysis patients, and 85%, 70%, 66% reported being on the late phase of the COVID-19 curve. 40%, 23%, 100% of MDs were more likely to recommend home dialysis; 19%, 5%, 29% reported an increase in missed dialysis treatments; 30%, 24%, 50% were more likely to prescribe potassium binders; and 75%, 68%, 43% had greater challenges obtaining vascular access interventions. Among NMs, 30%, 9%, 40% reported current limitations in access to COVID-19 testing; and 61%, 51%, 29% reported having, or risk of, shortage in staffing. Conclusions: Early results indicate many clinics in Europe and US have had COVID-19 cases, but sites in the three DOPPS-China cities have avoided COVID-19 to date. In all regions, shortages of human and medical resources were common, as were changes to dialysis delivery/practice including more skipped sessions, greater use of potassium binders, and preferentially recommending home dialysis. Over the next month, we expect hundreds more responses, and will compare approaches in PD and HD clinics. These data will inform guidance for dialysis care as the COVID-19 pandemic ensues. Funding: NIDDK Support, Commercial Support - Support for the DOPPS Program (including CKDopps, DOPPS, and PDOPPS) is provided by Amgen (founding sponsor, since 1996), Kyowa Kirin Co. (since 1999, in Japan), and Baxter Healthcare (since 2011). Additional support is provided for specific projects and/or countries by Akebia Therapeutics, AstraZeneca, Bard Peripheral Vascular, Bayer Yakuhin, Chugai Pharmaceutical, DialyzeDirect, Japanese Society for PD, JMS Co., Kidney Research UK, Kidney Foundation Japan, Kissei Pharmaceutical Co., Medice, Nikkiso Co., ONO Pharmaceutical Co., Sanofi-Aventis Deutschland GmbH, Terumo Corporation, Torii Pharmaceutical Co., and Vifor Fresenius Renal Pharma.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".