A Single-Center Experience of Paclitaxel in the Treatment of Femoropopliteal Disease—No Evidence for an Association With Mortality
Bibliographic record
Abstract
PURPOSE: Paclitaxel-coated devices have been increasingly used in endovascular treatment of femoropopliteal disease as they limit recurrence of lesions and improve patient outcomes. However, a recent meta-analysis reported that these devices increase mortality risk at 2 years post-intervention but did not account for confounding variables. Therefore, our goal was to evaluate mortality after paclitaxel treatment of femoropopliteal disease using patient-level data. METHODS: We performed a retrospective review of all patients who received endovascular treatment for femoropopliteal lesions at our center between December 2009 and July 2017. There were 388 patients in the paclitaxel group and 314 control patients. RESULTS: Survival analysis with hazard ratios showed no difference between mortality in the paclitaxel and control groups. Age, renal insufficiency, and chronic limb-threatening ischemia were significant predictors of mortality. We also used logistic regression to evaluate mortality at 1, 2, and 5 years post-intervention and found no difference between the paclitaxel and control groups at any time point, while age, renal insufficiency, and chronic limb-threatening ischemia at the time of intervention were all associated with the risk of death. Finally, we tallied the causes of death in our cohort and found no difference in the distribution of causes between groups. CONCLUSION: Our single-center, retrospective study provides no evidence of increased risk of death with paclitaxel treatment in femoropopliteal disease. Contrastingly, age, renal insufficiency, and chronic limb-threatening ischemia were the most important factors contributing to mortality and therefore should be included as potential confounders in future studies assessing mortality in femoropopliteal disease.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".