Assessing Discharge Communication and Follow-up of Acute Kidney Injury in British Columbia: A Retrospective Chart Review
Bibliographic record
Abstract
Background and objective:Acute kidney injury (AKI) affects up to 20% of hospitalizations and is associated with chronic kidney disease, cardiovascular disease, increased mortality, and increased health care costs. Proper documentation of AKI in discharge summaries is critical for optimal monitoring and treatment of these patients once discharged. Currently, there is limited literature evaluating the quality of discharge communication after AKI. This study aimed to evaluate the accuracy and quality of documentation of episodes of AKI at a tertiary care center in British Columbia, Canada.Methods, design, setting, patients, and measurements:This was a retrospective chart review study of adult patients who experienced AKI during hospital admission between January 1, 2018, and December 31, 2018. Laboratory data were used to identify all admissions to the cardiac and general medicine ward complicated by AKI defined by the Kidney Disease Improving Global Outcomes (KDIGO) criteria. A random sample of 300 AKI admissions stratified by AKI severity (eg, stages 1, 2, and 3) were identified for chart review. Patients were excluded if they required ongoing renal replacement therapy after admission, had a history of kidney transplant, died during their admission, or did not have a discharge summary available. Discharge summaries were reviewed for documentation of the following: presence of AKI, severity of AKI, AKI status at discharge, practitioner and laboratory follow-up plans, and medication changes.Results:A total of 1076 patients with 1237 AKI admissions were identified. Of the 300 patients selected for discharge summary review, 38 met exclusion criteria. In addition, AKI was documented in 140 (53%) discharge summaries and was more likely to be documented in more severe AKI: stage 1, 38%; stage 2, 51%; and stage 3, 75%. Of those with their AKI documented, 94 (67%) documented AKI severity, and 116 (83%) mentioned the AKI status or trajectory at the time of discharge. A total of 239 (91%) of discharge summaries mentioned a follow-up plan with a practitioner, but only 23 (10%) had documented follow-up with nephrology. Patients with their AKI documented were more likely to have nephrology follow-up than those without AKI documented (17% vs 1%). Regarding laboratory investigations, 92 (35%) of the summaries had documented recommendations. In summaries that included medications typically held during AKI, only about half made specific reference to those medications being held, adjusted, or documented a post-discharge plan for that medication. For those with nonsteroidal anti-inflammatory drugs (NSAIDs) listing, 64% of discharge summaries mentioned holding, and 9% mentioned a discharge plan. For those with angiotensin converting enzyme inhibitor (ACEi)/angiotensin II receptor blocker (ARB) listing, 38% mentioned holding these medications, and 46% mentioned a discharge plan. In summaries with diuretics listed, 35% mentioned holding, and 51% included a discharge plan.Conclusions and limitations:We found suboptimal quality and completeness of discharge reporting in patients hospitalized with AKI. This may contribute to inadequate follow-up and post-hospitalization care for this patient population. Strategies are required for increasing the presence and quality of AKI reporting in discharge summaries. Limitations include our definition of AKI based on lab criteria, which may have missed some of the injuries that met the criteria based on urine output. Another limitation is that our definition of AKI based on the highest and lowest creatinine during admission may have led to some overclassification. In addition, without outpatient laboratories, it is possible that we have not captured the true baseline creatinine in some patients.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".