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Record W2066631463 · doi:10.1186/1532-429x-15-s1-p77

The effect of septal basal segments in the assessment of systolic dyssynchrony index

2013· article· en· W2066631463 on OpenAlexaff
Avan Suinesiaputra, David A. Bluemke, Brett R. Cowan, Pau Medrano−Gracia, João A.C. Lima, Perry Radau, Alistair A. Young

Bibliographic record

VenueJournal of Cardiovascular Magnetic Resonance · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Institutes of Health
KeywordsMedicineCardiologyAngiologyInternal medicineAsymptomaticCardiac resynchronization therapyIndex (typography)Ventricular dyssynchronyBasal (medicine)Heart failureEjection fraction

Abstract

fetched live from OpenAlex

LV intraventricular dyssynchrony is commonly assessed by systolic dyssynchrony index (SDI), which is defined by the dispersion of time of minimum regional cavity volume. Several studies have reported conflicting results on cardiac resynchronization therapy (CRT) responses based on SDI values. In this study, we aim to characterise SDI distributions in asymptomatic patients and to analyse how particular regions affect the global SDI measurement in different patient groups. We randomly selected 292 asymptomatic patients (EF:60.9±5.9%, LVM:135.2±36.2gr) from the MESA cohort [ 1 ], and 36 patients from the Sunnybrook Cardiac dataset [ 2 ]: 12 patients with hypertrophy (EF:63.0±3.7%, LVM:176.1±86.2gr) and 24 heart failure subjects (EF:33.6±12.3%, LVM:197.1±41.0gr). Three-dimensional LV models were semi-automatically generated using dedicated software (CIM v6.0, AMRG, Auckland) from cine MRI. To register and normalise the temporal domain (0≤t≤1), LV models between frames were interpolated using smooth periodic spline method. ED frames were registered at t=0. The AHA 17-segment models were determined at ED and then propagated to all frames to get consistent regions. Segment 17 was excluded. SDI was computed by taking the standard deviation of normalised time to reach peak temporal ejection fraction (EF) from all regions. The distributions of regional EF in asymptomatic patients in Fig. 1 (left) suggest a wider range of peak time and a significant median shift to late systole in two basal septal regions (S2 and S3). Pairwise paired t-test of SDI values between these segments with the others were all significantly different (P<0.001, adjusted for multiple testing). The mean temporal EF curves in Fig. 1 (right) also confirm this phenomenon. This had a large effect on the SDI measurement (Table 1 ). When these segments were excluded, SDI values from all groups were significantly different (P<0.05) compared to when all segments included. The average SDI values were all decreased and the variations within each group were all smaller when S2 and S3 were excluded. Left: The distributions of the peak time of regional EF values. Right (four figures): temporal profiles of the average regional EF values grouped in four regions of myocardium (septum, anterior, lateral and inferior). All of these plots were computed from the asymptomatic patient group (n=292). Late regional contraction in septal base has a significant effect on the SDI values. Since SDI is measured by the dispersion of time, this effect was dominant. The delayed, as well as the increased variability, time-to-peak regional EF in these segments both seem to contribute to SDI variability. This study shows that eliminating septal base segments significantly decreased SDI values and narrowed their variation. Further validation is still needed to investigate whether this elimination protocol will better predict CRT responses.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.262
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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