The Feasibility of a Transitional Care Unit for Patients Newly Started on In-Center Hemodialysis: A Research Letter
Bibliographic record
Abstract
Background: Patients with end-stage kidney disease face high mortality and morbidity after dialysis initiation. Transitional care units (TCUs) are typically 4- to 8-week structured multidisciplinary programs targeted toward patients starting hemodialysis during this high-risk time in their care. The goals of such programs are to provide psychosocial support, provide dialysis modality education, and reduce risks of complications. Despite apparent benefits, the TCU model may be challenging to implement, and the effect on patient outcomes is unclear. Objective: To assess a newly created multidisciplinary TCUs' feasibility for patients newly started on hemodialysis. Design: Before-and-after study. Setting: Kingston Health Sciences Centre hemodialysis unit in Ontario, Canada. Patients: We considered all adult patients (age 18+) who initiated in-center maintenance hemodialysis eligible for the TCU program, although patients on infection control precautions and evening shifts were not able to receive TCU care due to staffing limitations. Measurements: We defined feasibility as eligible patients completing the TCU program in a timely fashion without additional need for space, no signal of harm, and without explicit concerns from TCU staff or patients at weekly meetings. Key outcomes at 6 months included mortality, proportion hospitalized, dialysis modality, vascular access, initiation of transplant workup, and code status. Methods: The TCU care consisted of 1:1 nursing and education until predefined clinical stability and dialysis decisions were satisfied. We compared outcomes among the pre-TCU cohort who initiated hemodialysis between June 2017 and May 2018, and TCU patients who initiated dialysis between June 2018 and March 2019. We summarized outcomes descriptively, along with unadjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results: We included 115 pre-TCU patients and 109 post-TCU patients, of whom 49/109 (45%) entered and completed the TCU. The most common reasons for not participating in the TCU included evening hemodialysis shifts (18/60, 30%) or contact precautions (18/60, 30%). The TCU patients completed the program in a median of 35 (25-47) days. We observed no differences in mortality (9% vs 8%; OR = 0.93, 95% CI = 0.28-3.13) or proportion hospitalized (38% vs 39%; OR = 1.02, 95% CI = 0.51-2.03) between the pre-TCU cohort and TCU patients. There was also no difference in use of home dialysis (16% vs 10%; OR = 1.67, 95% CI = 0.64-4.39), non-catheter access (32% vs 25%; OR = 1.44, 95% CI = 0.69-2.98), initiation of transplant workup (14% vs 12%; OR 1.67; 95% CI = 0.64-4.39), and choosing "do not resuscitate" (DNR) orders (22% vs 19%; OR = 1.22, 95% CI = 0.54-2.77). There was no negative patient or staff feedback on the program. Limitations: Small sample size and potential for selection bias given inability to provide TCU care for patients on infection control precautions or evening shifts. Conclusions: The TCU accommodated a large number of patients, who completed the program in a timely fashion. The TCU model was determined to be feasible at our center. There was no difference in outcomes due to the small sample size. Future work at our center is required to expand the number of TCU dialysis chairs to evening shifts and evaluate the TCU model in prospective, controlled studies.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| 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".