The clinical and cost burden of coronary calcification in a Medicare cohort: An economic model to address under-reporting and misclassification
Bibliographic record
Abstract
BACKGROUND: Coronary artery calcification (CAC) is a well-established risk factor for the occurrence of adverse ischemic events. However, the economic impact of the presence of CAC is unknown. OBJECTIVES: Through an economic model analysis, we sought to estimate the incremental impact of CAC on medical care costs and patient mortality for de novo percutaneous coronary intervention (PCI) patients in the 2012 cohort of the Medicare elderly (≥65) population. METHODS: This aggregate burden-of-illness study is incidence-based, focusing on cost and survival outcomes for an annual Medicare cohort based on the recently introduced ICD9 code for CAC. The cost analysis uses a one-year horizon, and the survival analysis considers lost life years and their economic value. RESULTS: For calendar year 2012, an estimated 200,945 index (de novo) PCI procedures were performed in this cohort. An estimated 16,000 Medicare beneficiaries (7.9%) were projected to have had severe CAC, generating an additional cost in the first year following their PCI of $3500, on average, or $56 million in total. In terms of mortality, the model projects that an additional 397 deaths would be attributable to severe CAC in 2012, resulting in 3770 lost life years, representing an estimated loss of about $377 million, when valuing lost life years at $100,000 each. CONCLUSIONS: These model-based CAC estimates, considering both moderate and severe CAC patients, suggest an annual burden of illness approaching $1.3 billion in this PCI cohort. The potential clinical and cost consequences of CAC warrant additional clinical and economic attention not only on PCI strategies for particular patients but also on reporting and coding to achieve better evidence-based decision-making.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".