Potentially Preventable Hospitalization among Patients with CKD and High Inpatient Use
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although patients with CKD are commonly hospitalized, little is known about those with frequent hospitalization and/or longer lengths of stay (high inpatient use). The objective of this study was to explore clinical characteristics, patterns of hospital use, and potentially preventable acute care encounters among patients with CKD with at least one hospitalization. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: ) in Alberta, Canada between January 1 and December 31, 2009, excluding those with prior kidney failure. Patients with CKD were linked to administrative data to capture clinical characteristics and frequency of hospital encounters, and they were followed until death or end of study (December 31, 2012). Patients with one or more hospital encounters were categorized into three groups: persistent high inpatient use (upper 5% of inpatient use in 2 or more years), episodic high use (upper 5% in 1 year only), or nonhigh use (lower 95% in all years). Within each group, we calculated the proportion of potentially preventable hospitalizations as defined by four CKD-specific ambulatory care sensitive conditions: heart failure, hyperkalemia, volume overload, and malignant hypertension. RESULTS: During a median follow-up of 3 years, 57,007 patients with CKD not on dialysis had 118,671 hospitalizations, of which 1.7% of patients were persistent high users, 12.3% were episodic high users, and 86.0% were nonhigh users of hospital services. Overall, 24,804 (20.9%) CKD-related ambulatory care sensitive condition encounters were observed in the cohort. The persistent and episodic high users combined (14% of the cohort) accounted for almost one half (45.5%) of the total ambulatory care sensitive condition hospitalizations, most of which were attributed to heart failure and hyperkalemia. Risk of hospitalization for any CKD-specific ambulatory care sensitive condition was higher among older patients, higher CKD stage, lower income, registered First Nations status, and those with poor attachment to primary care. CONCLUSIONS: Many hospitalizations among patients with CKD and high inpatient use are ambulatory care sensitive condition related, suggesting opportunities to improve outcomes and reduce cost by focusing on better community-based care for this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".