Cost of Acute Kidney Injury in Hospitalized Patients
Bibliographic record
Abstract
BACKGROUND: The economic burden of acute kidney injury (AKI) is not well understood. OBJECTIVE: To estimate the effects of AKI on hospitalization costs and length of stay (LOS). DESIGN: Using data from the 2012 National Inpatient Sample, we compared hospitalization costs and LOS with and without AKI. We used a generalized linear model with a gamma distribution and a log link fitted to AKI to adjust for demographics, hospital differences, and comorbidities. SETTING: United States. PATIENTS: 29,763,649 adult hospitalizations without endstage renal disease. EXPOSURE: AKI determined using International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) diagnosis codes.. MEASUREMENTS: Hospitalization costs and LOS. RESULTS: AKI was associated with an increase in hospitalization costs of $7933 (95% confidence interval [CI], $7608-$8258) and an increase in LOS of 3.2 (95% CI, 3.2-3.3) days compared to patients without AKI. When adjusted for patient and hospital characteristics, the associated increase in costs was $1795 (95% CI, $1692-$1899) and in LOS, it was 1.1 (95% CI, 1.1-1.1) days. Corresponding results among patients hospitalized with AKI requiring dialysis were $42,077 (95% CI, $39,820-$44,335) and 11.5 (95% CI, 11.2-11.8) days and $11,016 (95% CI, $10,468-$11,564) and 3.9 (95% CI, 3.8-4.1) days. AKI was associated with higher hospitalization costs than myocardial infarction and gastrointestinal bleeding, and costs were comparable to those for stroke, pancreatitis, and pneumonia.. CONCLUSIONS: In the United States, AKI is associated with excess hospitalization costs and prolonged LOS. The economic burden of AKI warrants further attention from hospitals and policymakers to enhance processes of care and develop novel treatment strategies. Journal of Hospital Medicine 2017;12:70-76.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".