Follow-Up Care of Critically Ill Patients With AKI
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) occurs in more than half of critically ill patients in the intensive care unit and is associated with adverse outcomes. The 2012 Kidney Disease Improving Global Outcomes guideline recommends follow-up at 3 months post-discharge for assessment of kidney health. It remains unclear whether these recommendations are followed. Our objective was to determine processes of follow-up care for critically ill patients with AKI. Methods: We conducted a retrospective cohort study in Alberta, Canada, using linked healthcare databases within the Alberta Kidney Disease Network. We included critically ill adult patients with evidence of AKI (defined as ≥50% or ≥26.5 μmol/L serum creatinine increase from baseline) from 2005-2018. The primary outcome was an outpatient nephrology follow-up visit within 3 months of discharge. Secondary outcomes were an outpatient serum creatinine or urine protein measurement, and a follow-up visit by a family physician within 3 months of discharge. Results: There were 29,732 critically ill adult patients with AKI. The median age was 68 years, 39% were female, and the median estimated glomerular filtration rate was 72 mL/min/1.73 m2. The cumulative incidence of receiving nephrology follow-up within 3 months before dying or requiring maintenance kidney replacement therapy was 5%. At 3 months, 64% and 28% of patients had an outpatient creatinine and urine protein measurement, respectively, and 89% received follow-up by a family physician. Factors associated with nephrology follow-up were younger age, urban residence, lower baseline estimated glomerular filtration rate, higher baseline albuminuria, previous nephrology visit, shorter hospitalization stay, higher severity of AKI, receipt of acute dialysis, inpatient nephrology consultation, kidney biopsy, and worse kidney function at the time of discharge. Conclusions: Many critically ill patients with AKI do not receive the recommended follow-up care. Our findings illustrate a significant gap in the transition of care for critically ill patients with AKI. Further research is needed to determine if follow-up care is associated with improved patient outcomes. Funding: Private Foundation Support
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".