SaO022REINTERPRETING CIRCULATING CARDIAC BIOMARKERS IN ADVANCED CHRONIC KIDNEY DISEASE: ASSOCIATION WITH LONG-TERM CARDIOVASCULAR EVENTS
Bibliographic record
Abstract
INTRODUCTION AND AIMS: The clinical interpretation of a raised level of high-sensitivity troponin T (hsTnT) or N-terminal pro-B-type natriuretic peptide (NT-pro-BNP) is uncertain in patients with chronic kidney disease (CKD) due to the relationship of these biomarkers to reduced kidney function. We sought to examine the distribution of hsTnT and NT-pro-BNP with decreasing eGFR in the setting of advanced CKD, and investigate the association between eGFR-specific distributions of both biomarkers and long-term cardiovascular (CV) events. METHODS: This was a prospective analysis of 1977 participants from a pan-Canadian cohort of individuals with moderate to advanced CKD who were all under the care of a nephrologist. We describe age- and sex-adjusted predicted mean values of hsTnT and NT-pro-BNP (both log-transformed) per 1 mL/min/1.73m2 decrease in eGFR. Within each tertile of eGFR (9-21, 22-30, 31-50 mL/min/1.73m2) we created tertiles of hsTnT and NT-pro-BNP. We used Cox Proportional Hazards regression to examine the association between eGFR-specific distributions of each biomarker and time to first CV event, a composite of ischaemic heart disease (fatal or non-fatal myocardial infarction or the need for coronary revascularization), congestive heart failure, stroke and sudden cardiac death. All outcomes were independently adjudicated by a panel of physicians using source documentation. RESULTS: Mean age of the cohort was 68 years, 63.5% were male and 49% had a diagnosis of diabetes. The majority (76%) of patients had a value of hsTnT above the upper limit of the laboratory reference range (>14 ng/L). Predicted mean (95% confidence interval) values of hsTnT were 15.3 (14.2-16.4) and 37.1 (35.1-39.2) ng/L at an eGFR of 45 and 15 mL/min/1.73m2 respectively. A total of 339 CV events were recorded during a median follow-up time of 4.3 years (6948 person years at risk). In the lowest eGFR tertile, after adjusting for demographics and CV risk factors, the association between hsTnT and CV events only became evident (hazard ratio 1.9 [95% confidence interval 1.1-3.5]) in the highest tertile of hsTnT (range 43-566 ng/L). In contrast, the relationship between NT-pro-BNP tertiles and CV events was strong and graded across the range of eGFR, was independent of CV risk factors, and did not vary by the presence or absence of baseline cardiac disease. Even in the lowest eGFR tertile, each unit increase in NT-pro-BNP was associated with steadily increased risk (HR 3.8 [1.7-8.1]) and 7.0 [3.2-15.2] for tertiles 2 and 3 respectively). Results were unchanged after censoring for the onset of renal replacement therapy. CONCLUSIONS: Our findings have several important implications. From an epidemiological perspective, our data suggest that the utility of hsTnT for CV risk discrimination is lower in advanced CKD as compared to earlier stages. The robust risk estimates for NT-pro-BNP indicate that subclinical volume overload, rather than ischaemia, is the dominant pathophysiological mechanism contributing to CV risk in these patients.From a clinical perspective, the use of eGFR-specific reference ranges, built on the risk of CV endpoints, represents an alternative to laboratory reference ranges that could refine the interpretation of cardiac biomarkers in CKD. Finally, this approach may be useful for inclusion of CKD patients into CV event-driven clinical trials, by using eGFR-specific thresholds of hsTnT or NT-pro-BNP to stratify CV risk at the time of study enrolment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".