Predictors of cardiovascular events in hemodialysis patients after stress myocardial perfusion imaging
Bibliographic record
Abstract
Cardiovascular prognosis in patients under normal stress myocardial perfusion images (MPI) is generally excellent. However, this is not true for patients with chronic kidney disease (CKD) treated by hemodialysis. This study evaluated prognostic factors of adverse cardiovascular events in hemodialysis patients in whom stress MPI was performed. Pharmacological stress MPI was performed in 88 hemodialysis patients, and we retrospectively followed-up for 26 months. Cardiovascular events included cardiac death, nonfatal myocardial infarction, and unstable angina. Cardiovascular events occurred in 16 patients (18%). Univariate Cox regression analysis revealed that peripheral artery disease (PAD) and parameters of stress MPI were significant predictors of cardiovascular events. Multivariate Cox regression analysis revealed that only PAD (hazard ratio=6.54; P=0.002), and abnormal stress MPI (hazard ratio=8.26; P=0.008) were independent and significant predictors of cardiovascular events. Kaplan-Meier analysis showed better prognosis in patients with normal stress MPI than in patients with abnormal stress MPI (P<0.001, log-rank test). However, in patients with normal stress MPI, cardiovascular events occurred in 10 of the 76 patients (13%). Among patients with normal stress MPI, Kaplan-Meier analysis showed that patients with no PAD had better prognosis than patients with PAD (P=0.001, log-rank test). In hemodialysis patients, both PAD and stress MPI were powerful cardiovascular predictors. Normal stress MPI alone cannot guarantee good prognosis in terms of cardiovascular events. Consideration of PAD may improve the predictive value of stress MPI in some patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".