Hospital Health Care Costs Following Incident CKD in Japan, Sweden, and the United States
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) affects an estimated 10% of the global population. It is associated with cardiorenal complications (e.g. end stage kidney disease and heart failure [HF]), premature mortality, and high healthcare burden and costs. Here we assess hospital healthcare costs following incident CKD. Methods: This study uses secondary data from electronic health records or claims data sources from Japan, Sweden and the US. Adult patients with incident CKD (defined as having either: two estimated glomerular filtration rate [eGFR] measurements ≥ 90 days apart of ≤ 60 mL/min/1.73 m2 or an eGFR measurement ≤ 60 mL/min/1.73 m2 followed by a CKD diagnosis) were identified during 2016-2023. Cumulative costs per patient for hospitalizations associated with a main diagnosis of HF, CKD, myocardial infarction (MI), stroke or peripheral artery disease (PAD) were summarized for up to 5 years after index (date of second eGFR measurement or CKD diagnosis). Results: Overall, 549 884 patients were included (Japan, 74 285; Sweden, 76 133; US, 399 466). In Japan, Sweden and the US, respectively: median ages were 81, 78 and 74 years; 54%, 48% and 37% were males; and across countries the majority (68%) of patients did not have type 2 diabetes. Median eGFR measurements were similar across countries. Hospital healthcare costs associated with cardiorenal events (CKD and HF) were high (Figure). Atherosclerotic cardiovascular disease (MI, stroke and PAD) contributed less to hospital healthcare costs. Conclusions: Hospital healthcare costs were high and largely driven by cardiorenal events (CKD and HF) among patients with incident CKD. This was consistent across all three countries, despite differences in healthcare systems. Funding: Commercial Support - AstraZeneca
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".