Lung Sestamibi Uptake on Myocardial Perfusion Imaging and Outcomes in Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In patients with CKD and end-stage kidney disease (ESKD), cardiac stress testing has low sensitivity and specificity for coronary disease. Alternate markers that are derived during the stress testing may enhance the predictive characteristic of stress testing. The objective was to examine the predictive characteristic of lung-to-heart ratio (LHR) in patients with CKD and ESKD. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: Retrospective parallel cohort of ESKD and CKD not on dialysis (CKD-ND) who underwent stress testing with nuclear myocardial perfusion imaging utilizing sestamibi tracer and regadenoson. Stress LHR was calculated by the processing software and reported. Patients were analyzed by tertile of LHR (≤0.28, 0.29-0.32, ≥0.33). The primary outcome was a composite of all-cause mortality, hospitalization for myocardial infarction or unstable angina, or revascularization. RESULTS: There were 144 CKD-ND and 145 ESKD patients. Patients with ESKD had greater comorbidity burden than CKD-ND. Stress tests were more often performed for pre-operative risk assessment among ESKD versus CKD-ND (53.8 vs. 5.6%, p < 0.001). ESKD patients more likely had ischemia identified on stress testing (19.3 vs. 8.3%, p = 0.001). Mean LHR was 0.31 (Standard deviation - SD: 0.09) and was similar across CKD-ND stages and ESKD. Primary outcome in the lowest (23%) and highest (33.3%) LHR tertile was higher than the middle tertile (12.8%); p = 0.005. This finding was similar between CKD-ND and ESKD and persisted in multivariable analysis. CONCLUSIONS: LHR ≤0.28 and ≥0.33 are independently associated with higher risk for death in patients with CKD-ND and ESKD. Future studies are warranted to understand the association of extreme LHR values and outcomes in this high-risk population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".