Left atrial and ventricular systolic and diastolic myocardial mechanics in patients with end‐stage renal disease
Bibliographic record
Abstract
BACKGROUND: Diastolic cardiac dysfunction is an important complication of end-stage renal disease (ESRD), but quantification remains a challenge. Given that diastolic dysfunction is reflected in both left atrial (LA) and ventricular (LV) function, we aimed to identify abnormalities in LV and LA volume and function using measures of myocardial mechanics. METHODS: We retrospectively studied 53 incident ESRD patients (46±16 y/o 44% male) and compared them to 85 normal controls. LA phasic volumes and functional parameters were obtained from the apical 4CH view. Global ventricular peak longitudinal and circumferential strain, strain rate (GLS, GL-SR, CS), and rotation were obtained from apical and short-axis views. LA and LV measurements were taken off line using dedicated software (eSie VVI). RESULTS: ESRD patients had abnormal systolic function with lower LV ejection fraction and peak endocardial strain parameters (mean: GLS -16.6% vs -19.9%, GL-SR -0.91 vs -1.04, and CS -25.6% vs 27.9%, P≤.01 for all). Traditional Doppler parameters remained similar between groups, while diastolic mechanics were abnormal in ESRD. Reduced LV-derived diastolic parameters, fractional early reverse rotation, a marker of ventricular relaxation (P<.006), and ratio of early diastolic SR to systolic SR (P<.04) denote significant diastolic dysfunction. Increased LA volumes (P<.001), decreased LA reservoir (P<.001), conduit (P<.0004), and contractile (P<.02) function reflect diastolic dysfunction. CONCLUSION: Myocardial strain measurements quantitated the abnormalities in both LV diastolic and LA function associated with the uremic state. The distinct abnormal diastolic parameters were suggestive of abnormal relaxation and increased filling pressures. Early and accurate assessment of diastolic function may help tailor patient management ESRD.
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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 | 0.002 |
| 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 | 0.000 | 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".