Novel Approach to Cardiovascular Outcome Prediction in Haemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: Cardiovascular mortality is high in haemodialysis (HD) patients. Arterial stiffness and global longitudinal strain (GLS) are important non-atheromatous cardiovascular risk predictors. No study has encompassed both parameters in a combined model for prediction of outcomes in HD patients. This is important because left ventricular (LV) dysfunction can result from fibrotic remodelling secondary to increased arterial stiffness. METHODS: Two hundred and nineteen HD patients had pulse wave velocity (PWV) and echocardiography (including GLS) assessments. Patients were followed-up until death, transplantation or November 16, 2015, whichever happened first. Pearson's correlation coefficient was used to determine factors associated with PWV and GLS. A multivariable Cox regression model investigated factors associated with all-cause, cardiac death and events. RESULTS: One hundred and ninety eight HD patients had full datasets (median age 64.2, 68.7% males) with a mean LV ejection fraction (LVEF) of 61.7 ± 10.1% and GLS -13.5 ± 3.3%; 51% had LV hypertrophy. Forty eight deaths (15 cardiac) and 44 major cardiac events occurred during a median follow-up of 27.6 (25th-75th percentile, 17.3-32.7) months. In separate survival models, PWV and GLS were independently associated with all-cause mortality; however, in a combined model, LV mass indexed to height2.7 (LVMI/HT2.7; adjusted hazard ratio (HR) 1.02, 95% CI 1.00-1.04) and PWV (adjusted HR 1.23, 95% CI 1.03-1.47) were significant. PWV was neither associated with cardiac death nor associated with related cardiac events. However, GLS was associated with cardiac death (adjusted HR 1.24, 95% CI 1.00-1.54) and cardiac events (adjusted HR 1.13, 95% CI 1.03-1.25). CONCLUSIONS: PWV and LVMI/HT2.7 were superior to GLS in prediction of all-cause mortality. However, GLS was associated with cardiac death and events even when accounting for LVEF and LVMI/HT2.7.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".