High-Sensitivity Troponin I Measurement in a Large Contemporary Cohort: Implications for Clinical Care
Bibliographic record
Abstract
INTRODUCTION: Contemporary methods of cardiovascular (CV) risk stratification are frequently inaccurate. Biomarkers such as high-sensitivity troponin I (hsTnI) have the potential to improve risk stratification. However, uncertainties exist regarding factors that determine hsTnI concentration. Our aim was to investigate the prevalence of elevated hsTnI in a large contemporary Canadian cohort and describe the effect of comorbidities on hsTnI concentration. METHODS: We report a large dataset of 41,602 visits in which hsTnI was measured routinely in ambulatory outpatients. hsTnI was remeasured in 28% of patients, with a mean time between measurements of 387 days (IQR 364-441). Low-, medium-, and high-risk categories were created based on hsTnI cutoffs for each sex. Laboratory data, blood pressure, and anthropomorphic measures were extracted from the electronic medical record. RESULTS: Remeasurement of hsTnI did not change risk category in 92.7% of cases. Male sex, higher HDL-C, higher Hgb A1c, decreasing eGFR, and increasing systolic blood pressure were significant predictors of increased hsTnI. High non-HDL-C and the use of statins were associated with lower hsTnI. The inverse relationship between hsTnI and non-HDL-C was partially corrected when the confounding effect of statin therapy was considered. Model fit was poor (adjusted R-squared = 0.0091). CONCLUSION: Traditional CV risk factors were predictors of serum hsTnI levels; however, a significant amount of the variance in hsTnI cannot be explained by these factors alone. This suggests that hsTnI adds additional information that is not provided by traditional risk stratification methods and supports ongoing study of hsTnI as a biomarker for CV risk stratification.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".