The Quality of Discharge Summaries After AKI
Bibliographic record
Abstract
Background: Patients who survive acute kidney injury (AKI) are at increased risk of hospital readmission, chronic kidney disease (CKD), and death. However, most patients are unaware they experienced AKI, emphasizing the importance of high-quality communication between inpatient and outpatient health care providers. Our objectives were to determine how often different elements of AKI were mentioned in discharge summaries and to identify predictors of discharge summary quality after AKI. Methods: We performed a retrospective chart review of 300 randomly selected discharge summaries from 2015 to 2019. We included 150 hospitalizations before and after introduction of a post-AKI clinic in August 2017, with 50 patients from each Kidney Disease Improving Global Outcomes (KDIGO) AKI stage. We assessed each discharge summary for 10 elements, including AKI course and follow-up recommendations. We used multivariable logistic regression to determine predictors of discharge summary quality. Results: The median number of AKI elements mentioned was 4/10 (IQR, 2-6). Follow-up with nephrology was documented for 33 (11%) patients. AKI-specific recommendations for labs and medication changes were noted in 66 (22%) and 80 (27%) discharge summaries, respectively. The odds of having a higher quality discharge summary (AKI elements ≥4/10) were greater for every increase in baseline creatinine (Cr) of 25 umol/L (OR, 1.86; 95% CI, 1.42-2.43); intrarenal etiology (OR, 2.33; 95% CI, 1.23-4.41); increased AKI severity (stage 3 or kidney replacement therapy (KRT)) (OR, 6.85 and 4.39; 95% CI, 2.83-16.59 and 1.53-12.58, respectively); inpatient nephrology consultation (OR, 10.53; 95% CI, 4.82-22.98); and discharge Cr ≥100% above baseline (OR, 4.88; 95% CI, 1.80-13.26). Discharge summary quality did not improve with the introduction of a post-AKI clinic (OR, 0.76; 95% CI, 0.44-1.31). Conclusions: Overall discharge summary quality in AKI survivors is poor, improving modestly for patients with baseline CKD, intrarenal etiology, severe AKI, higher discharge Cr, and inpatient nephrology involvement. Most discharge summaries are missing key post-AKI elements, including Cr trajectory and AKI-specific follow-up recommendations, even in patients receiving KRT. These gaps suggest an opportunity exists to improve discharge summary quality and communication post-AKI, especially for patients not assessed by nephrology as inpatients.
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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 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".