Troponin T monitoring to detect myocardial injury after noncardiac surgery: a cost–consequence analysis
Bibliographic record
Abstract
BACKGROUND: Myocardial injury after noncardiac surgery (MINS) is a mostly asymptomatic condition that is strongly associated with 30-day mortality; however, it remains mostly undetected without systematic troponin T monitoring. We evaluated the cost and consequences of postoperative troponin T monitoring to detect MINS. METHODS: We conducted a model-based cost-consequence analysis to compare the impact of routine troponin T monitoring versus standard care (troponin T measurement triggered by ischemic symptoms) on the incidence of MINS detection. Model inputs were based on Canadian patients enrolled in the Vascular Events in Noncardiac Surgery Patients Cohort Evaluation (VISION) study, which enrolled patients aged 45 years or older undergoing inpatient noncardiac surgery. We conducted probability analyses with 10 000 iterations and extensive sensitivity analyses. RESULTS: The data were based on 6021 patients (48% men, mean age 65 [standard deviation 12] yr). The 30-day mortality rate for MINS was 9.6%. We determined the incremental cost to avoid missing a MINS event as $1632 (2015 Canadian dollars). The cost-effectiveness of troponin monitoring was higher in patient subgroups at higher risk for MINS, e.g., those aged 65 years or more, or with a history of atherosclerosis or diabetes ($1309). CONCLUSION: The costs associated with a troponin T monitoring program to detect MINS were moderate. Based on the estimated incremental cost per health gain, implementation of postoperative troponin T monitoring seems appealing, particularly in patients at high risk for MINS.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".