Discharge Documentation and Follow-Up of Critically Ill Patients With Acute Kidney Injury Treated With Kidney Replacement Therapy: A Retrospective Cohort Study
Bibliographic record
Abstract
Leading organisations recommend follow-up of acute kidney injury (AKI) survivors, as these patients are at risk of long-term complications and increased mortality. Information transfer between specialties and from tertiary to primary care is essential to ensure timely and appropriate follow-up. Our aim was to examine the association between completeness of discharge documentation and subsequent follow-up of AKI survivors who received kidney replacement therapy (KRT) in the Intensive Care Unit (ICU). We retrospectively analysed the data of 433 patients who had KRT for AKI during ICU admission in a tertiary care centre in the UK between June 2017 and May 2018 and identified patients who were discharged from hospital alive. Patients with pre-existing end-stage kidney disease and patients who were transferred from hospitals outside the catchment area were excluded. The primary objective was to assess the completeness of discharge documentation from critical care and hospital; secondary objectives were to determine cardiovascular medications reconciliation after AKI, and to investigate kidney care and outcomes at 1 year. The development of AKI and the need for KRT were mentioned in 85 and 82% of critical care discharge letters, respectively. Monitoring of kidney function post-discharge was recommended in 51.6% of critical care and 36.3% of hospital discharge summaries. Among 35 patients who were prescribed renin-angiotensin-aldosterone system inhibitors before hospitalisation, 15 (42.9%) were not re-started before discharge from hospital. At 3 months, creatinine and urine protein were measured in 88.2 and 11.8% of survivors, respectively. The prevalence of chronic kidney disease stage III or worse increased from 27.2% pre-hospitalisation to 54.9% at 1 year ( p < 0.001). Our data demonstrate that discharge summaries of patients with AKI who received KRT lacked essential information. Furthermore, even in patients with appropriate documentation, renal follow-up was poor suggesting the need for more education and streamlined care pathways.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".