NOVEL SPECT RNA QUANTIFICATION OF MECHANICAL DYSSYNCHRONY FOR THE PREDICTION OF CRT RESPONSE
Bibliographic record
Abstract
Mechanical dyssynchrony (MD) occurs when different regions of the heart wall contract out of phase, reducing the pumping efficiency of the heart and possibly leading to congestive heart failure (CHF).Cardiac resynchronization therapy (CRT) is a possible medical intervention for CHF that resynchronizes the heart, effectively reducing MD.However, CRT has been shown to provide no clinical benefit in 20-50% of recipients.Current CRT patient selection protocols do not include assessment of MD despite evidence that the mechanism of CRT response is to improve MD.Single-photon emission computed tomography (SPECT) radionuclide angiography (RNA) is an imaging technique that is capable of quantifying MD by accurately assessing cardiac wall motion.In this thesis, new methods of quantifying MD with SPECT RNA were developed and evaluated as a means to predict response to CRT.Novel Fourier-analysis (FA) amplitude values were compared to left ventricular (LV) scar size in the lateral wall to determine whether they can serve as surrogate markers for predicting response to CRT.Moderate but significant correlations (r=0.51,p≤0.05) were shown to exist between amplitude-and scar-based parameters.Lateral wall amplitude ii analysis was equivalent to lateral wall scar analysis for predicting response to CRT, but there were indications that amplitude may also be partly complementary to scar.Novel MD parameters were developed using FA amplitude parameters and by developing novel clustering algorithms.These novel MD parameters were evaluated alongside preestablished FA phase-based parameters in both global and segmental regions for their ability to predict CRT response.Using FA, septal wall amplitude standard deviation (SD), global synchrony and global phase-SD were significantly predictive of CRT response.Similar to the FA amplitude results, cluster analysis was predictive of CRT response using septal wall analysis.Based on the area under a receiver operating characteristic curve (AUC), there was an indication of improved predictive ability using cluster analysis (AUC=0.82)over scar analysis (AUC=0.73) and FA (AUC=0.78),but differences were not significant in the small test population.Novel amplitude-based FA parameters and cluster analysis approaches provide promising tools for the assessment of MD and the prediction of CRT response with SPECT RNA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".