Burden of hospitalization for heart failure in the United States: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) affects approximately 6 million Americans, with prevalence projected to increase by 46% and direct medical costs to reach $53 billion by 2030. Hospitalizations are the largest component of direct costs for HF; however, recent syntheses of the economic and clinical burden of hospitalization for heart failure (HHF) are lacking. OBJECTIVE: To synthesize contemporary estimates of cost and clinical outcomes of HHF in the United States. METHODS: A systematic literature review was conducted using MEDLINE and Embase to identify articles reporting cost or charge per HHF in the United States published between January 2014 and May 2019. Subgroups of interest were those with both HF and renal disease or diabetes, as well as HF with reduced or preserved ejection fraction (HFrEF or HFpEF). RESULTS: 23 studies reporting cost and/or charge per HHF were included. Sample sizes ranged from 989 to approximately 11 million (weighted), mean age from 65 to 83 years, and 39% to 74% were male. Cost per HHF ranged from $7,094 to $9,769 (median) and $10,737 to $17,830 (mean). Charge per HHF ranged from $22,162 to $40,121 (median), and $50,569 to $50,952 (mean). Among patients with renal disease, HHF mean cost ranged from $9,922 to $41,538. For those with HFrEF or HFpEF, mean cost ranged from $11,600 to $17,779 and $7,860 to $10,551, respectively. No eligible studies were identified that reported HHF costs or charges among patients with HF and diabetes. Cost and charge per HHF increased with length of stay, which ranged from 3 to 5 days (median) and 4 to 7 days (mean). CONCLUSIONS: This synthesis demonstrates the substantial economic burden of HHF and the variability in estimates of this burden. Factors contributing to variability in estimates include length of stay, age and sex of the sample, HF severity, and frequencies of comorbidities. Further research into cost drivers of HHF is warranted to understand potential mechanisms to reduce associated costs. DISCLOSURES: This study was funded by Boehringer Ingelheim Pharmaceuticals. Osenenko, Deighton, and Szabo are employees of Broadstreet HEOR, which received funds from Boehringer Ingelheim Pharmaceuticals for this work. Kuti and Pimple are employees of Boehringer Ingelheim Pharmaceuticals. This study was presented in abstract form at the 2020 American Heart Association (AHA) Quality of Care and Outcomes Research (QCOR) 2020 Scientific Sessions (May 15-16, Virtual Meeting).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".