Dynamic changes in cardiovascular and systemic parameters prior to sudden cardiac death in heart failure with reduced ejection fraction: a <scp>PARADIGM‐HF</scp> analysis
Bibliographic record
Abstract
Abstract Aims Prognostic models of sudden cardiac death (SCD) typically incorporate data at only a single time‐point. We investigated independent predictors of SCD addressing the impact of integrating time‐varying covariates to improve prediction assessment. Methods and results We studied 8399 patients enrolled in the PARADIGM‐HF trial and identified independent predictors of SCD ( n = 561, 36% of total deaths) using time‐updated multivariable‐adjusted Cox models, classification and regression tree (CART), and logistic regression analysis. Compared with patients who were alive or died from non‐sudden cardiovascular deaths, patients who suffered a SCD displayed a distinct temporal profile of New York Heart Association (NYHA) class, heart rate and levels of three biomarkers (albumin, uric acid and total bilirubin), with significant differences observed more than 1 year prior to the event ( P interaction < 0.001). In multivariable models adjusted for baseline covariates, seven time‐updated variables independently contributed to SCD risk (incremental likelihood chi‐square = 46.2). CART analysis identified that baseline variables (implantable cardioverter‐defibrillator use and N‐terminal prohormone of B‐type natriuretic peptide levels) and time‐updated covariates (NYHA class, total bilirubin, and total cholesterol) improved risk stratification. CART‐defined subgroup of highest risk had nearly an eightfold increment in SCD hazard (hazard ratio 7.7, 95% confidence interval 3.6–16.5; P < 0.001). Finally, changes over time in heart rate, NYHA class, blood urea nitrogen and albumin levels were associated with differential risk of sudden vs. non‐sudden cardiovascular deaths ( P < 0.05). Conclusions Beyond single time‐point assessments, distinct changes in multiple cardiac‐specific and systemic variables improved SCD risk prediction and were helpful in differentiating mode of death in chronic heart failure.
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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 | 0.003 | 0.002 |
| 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 | 0.001 | 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".