Advancing Community Care and Access to Follow-up After Acute Kidney Injury Hospitalization: Design of the AFTER AKI Randomized Controlled Trial
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) is a common complication among hospitalized patients with long-term implications including chronic kidney disease (CKD). Although models are available to predict the risk of advanced CKD after AKI, there is limited evidence regarding follow-up for patients with AKI after hospital discharge, resulting in variable follow-up care. A risk-stratified follow-up approach may improve appropriateness and efficiency of management for CKD among patients at risk of declining kidney function following AKI. Objective: The objective was to compare and evaluate the use of a risk-stratified approach to follow-up care vs usual care for patients with AKI after hospital discharge. Design: This study was a pragmatic randomized controlled trial. Setting: This study was conducted in 2 large urban hospitals in Alberta, Canada. Patients: Hospitalized patients with AKI (KDIGO stage 2 or 3) not previously under the care of a nephrologist, expected to survive greater than 90 days being discharged home. Measurements: . We will also assess the feasibility of recruitment and the proportion of patients completing the recommended blood and urine tests at 90 days. Methods: Patients with AKI will be enrolled and randomized near the time of hospital discharge. In the intervention group, low risk patients will receive information regarding AKI, medium risk patients will additionally receive follow-up guidance sent to their primary care physician, and high-risk patients will additionally receive follow-up with a nephrologist. Participants in the intervention and usual care group will receive a requisition for urine testing and bloodwork at 90 days following hospital discharge. Telephone follow-up will be conducted for all study participants at 90 days and 1 year after hospital discharge. Bivariate tests of association will be conducted to evaluate group differences at the follow-up time points. Limitations: We expect there may be challenges with recruitment due to the significant co-existence of comorbidity in this population. Conclusions: If the trial shows a positive effect on these processes for kidney care, it will inform larger-scale trial to determine whether this intervention reduces the incidence of long-term clinical adverse events, including CKD progression, cardiovascular events, and mortality following hospitalization with AKI.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".