Venous Thromboembolism: A United States Cost Model for a Preventable and Costly Adverse Event,
Bibliographic record
Abstract
Abstract Abstract 4224 Introduction. Venous thromboembolism (VTE) is comprised of deep vein thrombosis (DVT) and pulmonary embolism (PE). VTE is a common cause of serious morbidity and mortality associated predominantly with hospitalization. The concept of “preventable” DVT has recently emerged in the medical literature. VTE remains the number one cause of preventable death in hospitalized patients. To date, VTE costs at a United States (US) national level for total costs, hospital-acquired costs, and “preventable” hospital-acquired costs have not yet been well-defined. Recently, investigators have defined US annual total, hospital-acquired, and preventable DVT costs ranged from $7.5 to $39.5 billion, $5 to $26.5 billion, and $2.5 to $19.5 billion, respectively, in 2010 US dollars. When a multi-way sensitivity analysis was applied, taking into consideration higher incidence rates and costs, annual US total, hospital-acquired, and “preventable” DVT costs ranged from $9.8 to $52 billion, $6.8 to $36 billion, and $3.4 to $27 billion, respectively. In addition, it was estimated that the US annual prophylaxis cost of at-risk patients is less than $600 million per year. PE costs have not yet been defined within the US. Defining PE costs would allow for definition of total US VTE costs on an annual basis. Methods. The authors undertook a thorough research review to identify morbidities, incident rates of morbidities, costs of morbidities and incidences of death associated with PE. Identified references were then hand-searched to ensure no pertinent publications had been overlooked. A decision tree and cost model were developed to estimate the United States healthcare costs for PE, total hospital-acquired PE, and total “preventable” PE. The decision tree contains probability information on: PE's that are hospital-acquired or community-acquired; fatal vs. non-fatal; readmissions; VTE recurrence; minor bleed; major bleed; heparin induced thrombocytopenia; chronic thromboembolic pulmonary hypertension; and resolution of symptoms. Based on the decision tree, a cost model with calculations performed via Microsoft Office Excel was developed. The cost model contains all potential outcomes, representing all branches, to reflect all possible outcomes for a PE patient. The product of each outcome's probabilities and costs yields the average cost of a patient going down that respective path of the PE decision tree. Similarly, each branch contains a sum that reflects the average cost of a patient in that branch. Results. Preliminary estimates of US annual direct total, hospital-acquired, and preventable PE costs are likely to range (at a minimum) from $5 to $27 billion, $2.5 to $18 billion, and $2.1 to $15.4 billion, respectively, in 2010 US dollars. Indirect costs, primarily from death due to PE, are estimated to be a minimum of $19.5 billion per year with approximately $11 billion per year of this being “preventable.” A multi-way sensitivity analysis will be applied which will take into consideration higher incidence rates and costs. Final results of the cost analysis, with the multi-way sensitivity analysis will be presented. Preliminary estimates suggest minimum total annualized, direct, VTE costs of approximately $12.5 to $66 billion per year with a minimum of $4.6 to $34.9 billion per year being “preventable.” When factoring in the indirect costs of $11 billion per year, minimum, “preventable” VTE costs within the US appear to range from $15.6 to $45.9 billion per year. Final results of the cost analysis with the multi-way sensitivity analysis will be presented. Conclusions. Considerable savings and reduced morbidity and mortality could be realized if improved prevention rates were achieved and systems were implemented throughout the US. To date, US VTE costs have been underestimated. The DVT and PE cost models may be applied to estimate costs in the European Union and other countries. VTE prophylaxis is cost effective and may be a good target for healthcare savings with healthcare reform on the horizon. Mandating VTE quality measures, such as those from the Joint Commission and National Quality Forum, would expedite reducing health care costs and reduce unnecessary morbidity and mortality. Disclosures: No relevant conflicts of interest to declare.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".