Medical Costs of Chronic Kidney Disease and Type 2 Diabetes Among Newly Diagnosed Heart Failure Patients With Reduced, Mildly Reduced, and Preserved Ejection Fraction
Bibliographic record
Abstract
The economic burden of heart failure (HF) is enormous, but studies of HF costs typically consider the disease to be a single entity. We sought to distinguish the medical costs for patients with HF with reduced ejection fraction (HFrEF), mildly reduced ejection fraction (HFmrEF), and HF with preserved ejection fraction (HFpEF). We identified 16,516 adult patients with an incident HF diagnosis and an echocardiogram from 2005 to 2017 in the electronic medical record of Kaiser Permanente Northwest. Using the echocardiogram nearest to the first diagnosis date, we classified patients with HFrEF (ejection fraction [EF] ≤40%), HFmrEF (EF 41% to 49%), or HFpEF (EF ≥50%). We calculated annualized inpatient, outpatient, emergency, pharmaceutical medical utilization and costs and total costs in $2,020, adjusted for age and gender using generalized linear models, with further analysis of the effects of co-morbid chronic kidney disease (CKD) and type 2 diabetes (T2D). For all HF types, 1 in 5 patients were affected by both CKD and T2D, and costs were significantly higher when both co-morbidities were present. Total per-person costs were significantly higher for HFpEF ($33,740, 95% confidence interval $32,944 to $34,536) than HFrEF ($27,669, $25,649 to $29,689) or HFmrEF ($29,484, $27,166 to $31,800), driven by in- and outpatient visits. Across HF types, visits approximately doubled with the presence of both co-morbidities. Due to greater prevalence, HFpEF accounted for the majority of total and resource-specific treatment costs of HF, regardless of the presence of CKD and/or T2D. In summary, the economic burden was greater per HFpEF patient and was further amplified by co-morbid CKD and T2D. HFpEF accounted for the large majority of total HF costs, underscoring the need to implement effective treatments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".