High-Sensitivity Troponin for Suspected Acute Coronary Syndrome in Patients With Chronic Kidney Disease Versus Patients Without Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Heart disease is the leading cause of death in the United States. Patients with acute coronary syndrome (ACS) who have chronic kidney disease (CKD) have a twofold increase in mortality compared to patients with normal kidney function. Patients with CKD tend to have elevated baseline high-sensitivity cardiac troponin-T (hs-cTnT) levels. We studied patients with or without CKD to find out if a higher baseline hs-cTnT influenced the change in hs-cTnT (delta) when ruling in or ruling out ACS. METHODS: -test were used for the continuous variables. Mann-Whitney U test was used to examine the variables between the two groups. Chi-square test was used to compare the categorical variables between the two groups. RESULTS: The mean ages of patients with CKD and without CKD were 61.2 and 58.9 years, respectively (P = 0.508). We found that although there were differences in the sensitivities, specificities, positive predictive values and negative predictive values of delta hs-cTnT > 5 for ACS between the patients with CKD and without CKD, the differences were not statistically significant. Subgroup analysis showed that in patients with CKD, the positive predictive values and sensitivities of delta hs-cTnT > 5 for CAD requiring percutaneous coronary intervention (PCI) and stent were significantly higher compared to the patients without CKD (82.4% vs. 27.3%, and 82.4% vs. 40.0%, respectively) (P < 0.05). CONCLUSIONS: In calculating delta hs-cTnT to rule in or rule out ACS, the presence of CKD does not influence the delta. Patients with CKD and a delta hs-cTnT > 5 have significantly higher risk of undergoing PCI.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".