58 Percutaneous coronary intervention in patients turned down for surgical revascularization: a single-centre experience
Bibliographic record
Abstract
Aim We aimed to evaluate the reasons for surgical ineligibility and in-hospital outcome of percutaneous coronary intervention (PCI) in these patients at a large tertiary centre. Methods We performed a retrospective analysis of prospectively collected data on surgical turndown patients who underwent PCI between April 2013 and November 2020. Data were collected from the institutional electronic database. Results Of 473 patients, 69.8% were male with mean age of 72±11 years and mean body mass index of 29±6 kg/m2. Turndown reasons were documented in 52.4 % of patients and mainly included the perceived frailty of the patient with associated comorbidities and/or the quality of distal coronary arteries. Elective cases with stable angina constituted 216 patients (45.7%) and urgent cases with acute coronary syndromes constituted 257 patients (54.3%). Mean hospital stay was 4±5 days. Procedural success was documented in 457 out of 473 patients (96.6%). Adjunctive tools included physiological assessments in 34 patients (7.2%), intracoronary imaging in 97 patients (20.5%), rotational atherectomy in 96 patients (20.3%), laser atherectomy in 12 patients (2.5%) and lithotripsy in 3 patients (0.6%). In-hospital complications included major adverse cardiac events in 32 patients (6.8%), death in 12 patients (2.5%), myocardial infarction in 21 patients (4.4%), ischaemic stroke in 1 patient (0.2%), coronary perforation in 7 patients (1.5%), repeat target vessel revascularization in 6 patients (1.3%), major access-site bleeding in 2 patients (0.4%), aortic dissection in 1 patient (0.2%) and new acute kidney injury requiring dialysis in 1 patient (0.2%). Conclusions In real-world data, the process of determining suitability for surgical revascularization is often complex. PCI in surgically ineligible patients is generally safe and effective. Conflict of Interest None
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".