Scoring System Based on Electrocardiogram Features to Predict the Type of Heart Failure in Patients With Chronic Heart Failure
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is divided into heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF). Mortality from HF is inversely related to left ventricular function. Additional studies are required to distinguish between these two types of HF. A previous study showed that HFrEF is less likely when electrocardiogram (ECG) findings are normal. This study aims to create a scoring system based on ECG findings that will predict the type of HF. METHODS: We performed a cross-sectional study analyzing ECG and echocardiographic data from 110 subjects with chronic HF. HFrEF was defined as an ejection fraction ≤ 40%. RESULTS: Fifty people were diagnosed with HFpEF and 60 people suffered from HFrEF. Multiple logistic regression analysis revealed certain ECG variables that were independent predictors of HFrEF, i.e., left atrial hypertrophy (LAH), QRS duration > 100 ms, right bundle branch block (RBBB), ST-T segment changes and prolongation of the QT interval. Based on receiver operating characteristic (ROC) curve analysis, we obtained a score for HFpEF of -1 to +3, while HFrEF had a score of +4 to +6 with 76% sensitivity, 96% specificity, a 95% positive predictive value, an 80% negative predictive value and an accuracy of 86%. CONCLUSIONS: The scoring system derived from this study, including the presence or absence of LAH, QRS duration > 100 ms, RBBB, ST-T segment changes and prolongation of the QT interval can be used to predict the type of HF with satisfactory sensitivity and specificity.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 0.001 |
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".