Emergency Department Use among Patients with CKD: A Population-Based Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although prior studies have observed high resource use among patients with CKD, there is limited exploration of emergency department use in this population and the proportion of encounters related to CKD care specifically. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: (including dialysis-dependent patients) in Alberta, Canada between April 1, 2010 and March 31, 2011. Patients with CKD were linked to administrative data to capture clinical characteristics and frequency of emergency department encounters and followed until death or end of study (March 31, 2013). Within each CKD category, we calculated adjusted rates of overall emergency department use as well as rates of potentially preventable emergency department encounters (defined by four CKD-specific ambulatory care-sensitive conditions: heart failure, hyperkalemia, volume overload, and malignant hypertension). RESULTS: During mean follow-up of 2.4 years, 111,087 patients had 294,113 emergency department encounters; 64.2% of patients had category G3A CKD, and 1.6% were dialysis dependent. Adjusted rates of overall emergency department use were highest among patients with more advanced CKD; 5.8% of all emergency department encounters were for CKD-specific ambulatory care-sensitive conditions, with approximately one third resulting in hospital admission. Heart failure accounted for over 80% of all potentially preventable emergency department events among patients with categories G3A, G3B, and G4 CKD, whereas hyperkalemia accounted for almost one half (48%) of all ambulatory care-sensitive conditions among patients on dialysis. Adjusted rates of emergency department events for heart failure showed a U-shaped relationship, with the highest rates among patients with category G4 CKD. In contrast, there was a graded association between rates of emergency department use for hyperkalemia and CKD category. CONCLUSIONS: Emergency department use is high among patients with CKD, although only a small proportion of these encounters is for potentially preventable CKD-related care. Strategies to reduce emergency department use among patients with CKD will, therefore, need to target conditions other than CKD-specific ambulatory care-sensitive conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".