Évaluation de la pertinence de l’application de l’algorithme d’estimation du risque cardiaque périopératoire de la Société canadienne de cardiologie publié en 2016 chez les patients subissant une chirurgie élective non cardiaque avec insuffisance rénale chronique de stade 3b et plus
Bibliographic record
Abstract
RésuméEn 2016, la Société canadienne de cardiologie (SCC) a publié de nouvelles lignes directrices périopératoires s’appliquant aux patients qui doivent subir une intervention chirurgicale non cardiaque. Elle propose, entre autres, un algorithme décisionnel d’estimation du risque cardiaque périopératoire et de surveillance post-opératoire chez les patients subissant une chirurgie élective non cardiaque. Or, cet algorithme pourrait être moins pertinent à appliquer chez les patients avec une insuffisance rénale chronique (IRC) de stade 3b et plus (débit de filtration glomérulaire estimé (DGFe) < 45 mL/min/1,73m2) subissant une chirurgie élective non cardiaque. De fait, dans la présente étude, 145 sur 249 patients avec une IRC de stade 3b et plus, soit 58,2% (IC 95% : 51%–64%, p = 0,011) ont présenté un dosage préopératoire de NT-proBNP ≥ 300 mg/L. Parmi ces patients, 71,0% ont eu un dosage de troponines postopératoire complété. Bien que 24,3% d’entre eux aient subi un Myocardial Injury after Noncardiac Surgery (MINS), seulement 7,8% de ces patients ont bénéficié d’une intensification de leur thérapie antiplaquettaire ou hypolipidémiante afin de réduire leur mortalité à 30 jours postopératoire. La pertinence de l’application de cet algorithme décisionnel pourrait donc être remise en question chez les patients avec une IRC de stade 3b et plus, notamment quant au rapport coût-avantage. AbstractIn 2016, the Canadian Cardiovascular Society (CCS) released new perioperative guidelines for patients undergoing noncardiac surgery. The guidelines push on, among other things, a tree algorithm allowing estimation of perioperative cardiac risk and postoperative monitoring in patients undergoing elective noncardiac surgery. However, little is known about applying this algorithm to patients with stages 3b-5 chronic kidney disease (CKD) undergoing elective noncardiac surgery. In fact, the present study shows that 145 out of 249 patients with stages 3b-5 CKD (58.2% [95% CI: 51%-64%, p = 0.011]) presented preoperative NT-proBNP levels ≥ 300 mg/L. Of these 145 patients, 103 participants (71.0%) had a postoperative troponin measurement. Although 25 patients (24.3%) of the latter underwent myocardial injury after noncardiac surgery (MINS), only 8 patients (7.8%) benefited from a treatment intensification of their antiplatelet or lipid-lowering therapy to reduce mortality within 30 days following surgery. The CCS’ algorithm’s relevance can therefore be called into question in patients with stages 3b-5 CKD, particularly in regard to the benefit-cost ratio.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".