Multiparametric Prognostic Scores in Chronic Heart Failure with Reduced Ejection Fraction: A Long-Term Comparison
Bibliographic record
Abstract
AIMS: Risk stratification in heart failure (HF) is crucial for clinical and therapeutic management. A multiparametric approach is the best method to stratify prognosis. In 2012, the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score was proposed to assess the risk of cardiovascular mortality and urgent heart transplantation. The aim of the present study was to compare the prognostic accuracy of MECKI score to that of HF Survival Score (HFSS) and Seattle HF Model (SHFM) in a large, multicentre cohort of HF patients with reduced ejection fraction. METHODS AND RESULTS: We collected data on 6112 HF patients and compared the prognostic accuracy of MECKI score, HFSS, and SHFM at 2- and 4-year follow-up for the combined endpoint of cardiovascular death, urgent cardiac transplantation, or ventricular assist device implantation. Patients were followed up for a median of 3.67 years, and 931 cardiovascular deaths, 160 urgent heart transplantations, and 12 ventricular assist device implantations were recorded. At 2-year follow-up, the prognostic accuracy of MECKI score was significantly superior [area under the curve (AUC) 0.781] to that of SHFM (AUC 0.739) and HFSS (AUC 0.723), and this relationship was also confirmed at 4 years (AUC 0.764, 0.725, and 0.720, respectively). CONCLUSION: In this cohort, the prognostic accuracy of the MECKI score was superior to that of HFSS and SHFM at 2- and 4-year follow-up in HF patients in stable clinical condition. The MECKI score may be useful to improve resource allocation and patient outcome, but prospective evaluation is needed.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".