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Record W7132987259

Use of Troponin Testing after Non-cardiac Surgery

2023· dissertation· W7132987259 on OpenAlexaboutno aff
Paymon Azizi

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsTroponinAbdominal aortic aneurysmGuidelineVascular surgeryTroponin IAdverse effectCardiac surgeryLogistic regression
DOInot available

Abstract

fetched live from OpenAlex

Myocardial injury after noncardiac surgery (MINS), which is most often asymptomatic, is associated with increased mortality and morbidity. In 2017, the Canadian Cardiovascular Society (CCS) published guidelines recommending broad post-operative troponin surveillance in high-risk patients having major non-cardiac surgery. The objectives of this thesis were to: (1) evaluate the proportion of patients having major non-cardiac surgery in Ontario that would meet guideline recommendations for routine post-operative troponin testing; (2) determine the patient-, surgical-, and hospital-factors associated with post-operative troponin testing; and (3) evaluate whether high-intensity troponin testing practices at the hospital-level were associated with fewer adverse outcomes after major non-cardiac surgery. In Chapter 2, I identified 257,704 patients who underwent non-cardiac surgery in Ontario. Applying the CCS guidelines to this cohort, 71.2% of elective surgery patients and 81.0% of urgent surgery patients would have met recommendations for post-operative troponin screening, while only 10.8% and 27.1% of guideline recommended patients received post-operative troponin testing, respectively. In Chapter 3, I identified 176,454 patients undergoing orthopedic, colorectal, or vascular surgery in Ontario. Hierarchical logistic regression modeling was used to assess the association of patient-, surgery-, and hospital-factors with postoperative troponin testing. I found that troponin testing varied substantially across hospitals for selected major non-cardiac surgery procedures even after accounting for differences in patient-level cardiac risk factors. In Chapter 4, I identified 18,467 patients undergoing common vascular surgical procedures (carotid endarterectomies and abdominal aortic aneurysm repairs). Cox proportional hazards modeling was used to assess the association of hospital-specific testing intensity with 30-day and 1-year major adverse cardiovascular outcomes (MACE). Compared to patients at low-testing intensity hospitals, patients at high-testing intensity hospitals experienced a lower hazard of MACE over 30-days and 1-year. Overall, I established that CCS guidelines recommend routine troponin testing for most patients having major non-cardiac surgery. Prior to the publication of the CCS perioperative guidelines, the overall testing rate was low, and there was substantial hospital-level variation in the use of routine troponin testing. Finally, in a cohort of vascular surgery patients, I found that patients who had procedures at high-testing intensity hospitals experienced fewer adverse outcomes, thus supporting recommendations to increase testing to detect MINS in the future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.065
GPT teacher head0.337
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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