Follow-up Care of Critically Ill Patients With Acute Kidney Injury: A Cohort Study
Bibliographic record
Abstract
Rationale & Objective To evaluate follow-up care of critically ill patients with acute kidney injury (AKI). Study Design Retrospective cohort study. Settings & Participants Patients admitted to the intensive care unit (ICU) with AKI in Alberta, Canada from 2005 to 2018, who survived to discharge without kidney replacement therapy or estimated glomerular filtration rate<15mL/min/1.73m 2 . Exposure AKI (defined as≥50% or≥0.3mg/dL serum creatinine increase). Outcomes The primary outcome was the cumulative incidence of an outpatient serum creatinine and urine protein measurement at 3 months postdischarge. Secondary outcomes included an outpatient serum creatinine or urine protein measurement or a nephrologist visit at 3 months postdischarge. Analytical Approach Patients were followed from hospital discharge until the first of each outcome of interest, death, emigration from the province, kidney replacement therapy (maintenance dialysis or kidney transplantation), or end of study period (March 2019). We used non-parametric methods (Aalen–Johansen) to estimate the cumulative incidence functions of outcomes accounting for competing events (death and kidney replacement therapy). Results There were 29,732 critically ill adult patients with AKI. The median age was 68 years (IQR, 57-77), 39% were female, and the median baseline estimated glomerular filtration rate was 72mL/min/1.73m 2 (IQR, 53-90). The cumulative incidence of having an outpatient creatinine and urine protein measurement at 3 months postdischarge was 25% (95% CI, 25-26). At 3 months postdischarge, 64% (95% CI, 64-65) had an outpatient creatinine measurement, 28% (95% CI, 27-28) had a urine protein measurement, and 5% (95% CI, 4-5) had a nephrologist visit. Limitations We lacked granular data, such as urine output. Conclusions Many critically ill patients with AKI do not receive the recommended follow-up care. Our findings highlight a gap in the transition of care for survivors of critical illness and 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".