Abstract 16771: Patient Selection for Therapies to Prevent Major Adverse Limb Events
Bibliographic record
Abstract
Introduction: Recently medical therapies have shown benefit in reducing major adverse limb events (MALE) including acute limb ischemia (ALI), urgent revascularization for ischemia, and ischemic amputation in patients with atherosclerotic vascular disease. Defining predictors of MALE may facilitate patient selection for application of novel therapies. Hypothesis: Clinical characteristics can be used to develop a risk score that will predict MALE Methods: Clinical characteristics independently predictive of ALI were identified among 3,985 patients with PAD randomized to placebo in TRA 2°P-TIMI 50 and were used to develop a risk score (c-statistic 0.82). This score was prospectively validated in 7,053 and 13,723 placebo patients from the PEGASUS-TIMI 54 and FOURIER trials respectively. Risk in each trial was evaluated stratified by the investigational treatment (vorapaxar, ticagrelor, or evolocumab). Results: In the broad PAD placebo population of TRA 2°P-TIMI 50, seven independent predictors of ALI with relative risks ranging from 1.69 to ~10 fold were identified including claudication, prior peripheral revascularization or amputation, ankle brachial index ≤ 0.5, heart failure, low body weight, elevated CRP and ASA monotherapy and assigned a point score.There was a gradient of MALE risk in placebo treated patients from 0.1% to 19.7% with increasing score (p<0.0001; Figure, Top). When validated in PEGASUS-TIMI 54 and FOURIER, discrimination remained high (c-statistic of 0.81 in both datasets). A risk of 4 generally correlated to an annualized risk of MALE of ~ 1% (1.1% TRA2P-TIMI 50, 0.93% PEGASUS-TIMI 54, 1.2% FOURIER). A score <4 identified patients at low risk of MALE, whereas those with a score ≥ 4 had greater baseline risk and benefit of randomized therapy (Figure, Bottom). Conclusions: A simple risk score can identify patients at greater risk of MALE who derive greater absolute reductions with effective therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".