Assessment of Mitral Annular Plane Systolic Excursion in Patients With Left Ventricular Diastolic Dysfunction
Bibliographic record
Abstract
BACKGROUND: Mitral annular plane systolic excursion (MAPSE) is a well-known surrogate measurement of left ventricular ejection fraction (LVEF) and prognostic factor for many cardiac conditions. However, little is known about its role in assessing LV diastolic function; we therefore sought to identify potential determinants of MAPSE in patients with LV diastolic dysfunction (LVDD). METHODS: Our echocardiographic database was queried for studies of patients with normal sinus. Patients were allocated into three groups: LVDD 0, LVDD 1 and LVDD 2 in accordance with LVDD stages recommended by the American Society of Echocardiography guidelines. RESULTS: A total of 107 echocardiographic studies were included in the study. The mean MAPSE was 1.22 ± 0.32 cm. Groups LVDD 0 (n = 23), LVDD 1 (n = 43), and LVDD 2 (n = 41) were significantly different in most of the studied variables. Particularly, MAPSE differed between the three groups (P < 0.01). A multiple regression analysis showed that age, LVEF and LV mass index were predictors of MAPSE instead of LVDD and left atrial measurements. Finally, a regression model was constructed to predict MAPSE in the studied group showing that age and LVEF explained a 46% of the MAPSE variation. A two-way contour plot was illustrated to ease the model interpretation. CONCLUSIONS: Age and measures of LV systolic function correlated well with MAPSE. A simplified model to predict MAPSE based on age and LVEF is proposed. Additional studies are needed to examine the potential role of MAPSE in identifying symptoms and overall prognosis in LVDD patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".