Evaluation of Different Strategies for Identifying Asymptomatic Left Ventricular Dysfunction and Pre-Clinical (stage B) Heart Failure in the Elderly. Results from ‘PREDICTOR’, a Population Based-Study in Central Italy
Bibliographic record
Abstract
AIMS: To evaluate the accuracy and cost-effectiveness of different screening strategies to identify systolic and/or diastolic asymptomatic LV dysfunction (ALVD), as well as pre-clinical (stage B) heart failure (HF), in a community of elderly subjects in Italy. METHODS AND RESULTS: A sample of 1452 subjects aged 65-84 years were chosen from the original cohort of 2001 randomly selected residents of the Lazio Region (Italy), as a part of the PREDICTOR survey. All subjects underwent physical examination, biochemistry/NT-proBNP assessment, 12-lead ECG, and Doppler transthoracic echocardiography (TE). Five strategies were evaluated including ECG, NT-proBNP, TE, and their combinations. Subjects older than 75 years, and with at least two additional risk factors, were defined as being high-risk for HF (435), whereas the remaining 1017 were defined at low risk. Screening characteristics and cost-effectiveness (cost per case) of the five strategies to predict systolic (EF <50% ) or diastolic ALVD and pre-clinical HF (stage B) were compared. NT-proBNP was the most accurate and cost-effective screening strategy to identify systolic and moderate to severe diastolic LV dysfunction without a difference between the high-risk and low-risk groups. Adding ECG to the NT-proBNP assessment did not improve the detection of pre-clinical LV dysfunction. TE-based screening was the least cost-effective strategy. In fact, all screening strategies were inadequate to identify stage B HF. CONCLUSIONS: In a community of elderly people, NT-proBNP is the most accurate and cost- effective pre-screening strategy to identify systolic and moderate to severe diastolic LV dysfunction.
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".