Abstract 165: Temporal Trends in FFR Utilization in Patients Undergoing Coronary Angiography: A Population Based Study
Bibliographic record
Abstract
Background: Invasive fractional flow reserve (FFR) has emerged as an important tool to identify a subset of patients in whom coronary revascularization may be beneficial. Our objective was to evaluate temporal trends in FFR utilization. Methods: In this population-based study, we identified all coronary angiograms in the CorHealth Ontario Cardiac Registry between January 1 st , 2010 to December 31 st , 2015. The primary and secondary outcomes were the age-sex adjusted monthly rate of FFR per 100,000 population and per 100 angiograms respectively. Piecewise regression analysis was used to evaluate the temporal trends in FFR utilization, for the entire cohort, and then stratified by indication (stable coronary disease (SCAD) versus acute coronary syndrome (ACS)). Results: The study cohort included 379,688 angiograms, of which 122,571 were for SCAD (32%), and 134,769 were for ACS (35%). FFR was performed in 3.2% of all angiograms (4.6% in SCAD; 2.7% in ACS). Monthly age-sex adjusted FFR utilization rates increased significantly over the study period, from 0.4 to 2.1 per 1000,000 people/month. The monthly FFR utilization rate per 100 angiograms increased from 1.3 to 4.8 per 100 angiograms/month; however, the proportion of positive FFR results was relatively constant at 27%. There was a more dramatic increase in the use of FFR in the SCAD (1.4 to 7.5 per 100 angiograms/month) compared to the ACS population (1.3 to 3.4 per 100 angiograms/month). Conclusions: Over time, there was a 5-fold increase in the use of FFR in patients across Ontario, which was predominantly driven by use in patients with SCAD. Case selection for FFR use was relatively unchanged with approximately a quarter of FFR cases being positive over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".