Time-Weighted Lactate as a Predictor of Adverse Outcome in Acute Heart Failure
Bibliographic record
Abstract
Abstract Aims The role of dynamic changes in lactate concentrations on prognosis in acute heart failure has been poorly investigated. The aim of this study was to explore the predictive value of 24 h time-weighted lactate (LACTW) in patients with acute heart failure. Methods and results Ninety-six consecutive acute heart failure patients presenting to the Emergency Department of San Paolo Hospital, Naples, Italy, were prospectively enrolled. Arterial blood lactate was measured at admission and during the following 24 h at random time intervals. LACTW was obtained by the sum of the average lactate values among consecutive time points multiplied by the intervals between consecutive time points and dividing the sum by the total time (24 h). The outcome was a composite of need of admission to the intensive care unit, hospitalization duration >7 days, or intra-hospital death. Admission lactate, maximum measured lactate, and LACTW were collected. Univariate and multivariate Cox regression analysis was applied to determine the hazard ratio (HR) of developing the outcome. Forty-three patients experienced the pre-specified outcome. In sex-adjusted and age-adjusted multivariable analysis, LACTW predicted the outcome occurrence (HR: 1.51, 95% confidence interval: 1.24, 1.84, P < 0.001). Risk stratification analysis based on LACTW tertiles demonstrated a gradual increase in risk of developing the outcome (HR: 17.32, 95% confidence interval: 2.30, 130.23, P = 0.006) for the highest LACTW tertile. Conclusions In acute heart failure patients, 24 h LACTW had a significant independent predictive value for adverse intra-hospital outcome. LACTW could be a useful index at identifying high-risk patients who may require a more aggressive treatment during hospitalization.
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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 | 0.001 | 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".