R-Wave Peak Time and Subclinical Left Ventricular Dysfunction in Hypertensive Patients: Insights From Speckle-Tracking Echocardiography
Bibliographic record
Abstract
BACKGROUND: Hypertension (HT) is one of the most common causes of myocardial dysfunction. Although early detection of myocardial impairment remains challenging, left ventricular global longitudinal strain (LV-GLS) is a sensitive echocardiographic parameter that can identify subclinical myocardial damage. However, its application is limited in routine clinical settings. R-wave peak time (RWPT) is a simple and widely available electrocardiographic parameter that may reflect intramyocardial conduction delay and early structural remodeling. This study aimed to investigate the association between RWPT and LV-GLS in patients with HT. METHODS: This prospective study included 403 patients with a confirmed diagnosis of HT. All participants underwent transthoracic echocardiography and 12-lead surface ECG. LV-GLS was assessed using speckle-tracking echocardiography. ECG images were digitized and analyzed using ImageJ software, and RWPT was defined as the interval from the onset of the QRS complex to the peak of the R-wave. RESULTS: Patients were divided into two groups according to their LV-GLS value of -15.9%, which is defined as the cutoff value of myocardial impairment. Patients with a lower LV-GLS had significantly longer RWPT and QRS durations. In multivariate analysis, RWPT was found to be an independent predictor of impaired LV-GLS (OR: 1.085; 95% CI: 1.056-1.114; P < 0.001). ROC analysis demonstrated an AUC of 0.715 (95% CI: 0.665-0.765; P < 0.001) with a sensitivity of 64.9% and a specificity of 67.7% at a cutoff value of 45.5 ms. CONCLUSIONS: RWPT may serve as a practical, accessible, and sensitive electrocardiographic marker for detecting subclinical myocardial dysfunction in patients with HT.
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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 | 0.001 | 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".