Cost of Potentially Preventable Hospitalizations Among Adults With Chronic Kidney Disease: A Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Prior studies report high hospitalization rates among patients with chronic kidney disease (CKD) and approximately 10% to 20.9% of hospitalizations are potentially preventable. OBJECTIVE: To determine the rate, proportion, and cost of potentially preventable hospitalizations and whether this varied by CKD category. DESIGN: Retrospective cohort study using population-based data. SETTING: Alberta, Canada. PATIENTS: All adults with an outpatient serum creatinine measurement between January 1 and December 31, 2017 in the Alberta Kidney Disease Network data repository. MEASUREMENTS: CKD risk categories were based on measures of proteinuria (where available), eGFR, and use of dialysis. Patients were linked to administrative data to capture frequency and cost of hospital encounters and followed until death or end of study (December 31, 2018). The outcomes of interest were the rate and cost of potentially preventable hospitalizations, as identified using the Canadian Institute for Health Information (CIHI)-defined ambulatory care sensitive condition (ACSC) algorithm and a CKD-related ACSC algorithm. METHODS: Unadjusted and adjusted rates per 1000-patient years, proportions, and cost attributable to preventable hospitalizations were identified for the cohort as a whole and for patients within each CKD risk category. RESULTS: Of the 1,110,895 adults with eGFR and proteinuria measurements, 181,422 had CKD. During a median follow-up of 1 year, there were 62,023 hospitalizations among patients with CKD resulting in a total cost of $946 million CAD; 6907 (11.1%) of these hospitalizations were for CIHI-defined ACSCs while 4323 (7.0%) were for CKD-related ACSCs. Adjusted rates of hospitalization for ACSCs increased with CKD risk category and were highest among patients treated with dialysis. Among CKD patients, the total cost of potentially preventable hospitalizations was $79 million and $58 million CAD for CIHI-defined and CKD-related ACSCs (8.4% and 6.2% of total hospitalization cost, respectively). LIMITATIONS: Based on the ACSC construct, we were unable to determine if these hospitalizations were truly preventable. CONCLUSIONS: Potentially preventable hospitalizations have a substantial cost and burden on the health care system among people with CKD. Effective strategies that reduce preventable admissions among CKD patients may lead to significant cost savings. TRIAL REGISTRATION: Not applicable-observational study design.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".