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Record W2982047021 · doi:10.1093/eurheartj/ehz746.0221

P5250Diastolic strain rate ratio as determined on MRI on detecting left ventricle stiffness and predict heart failure in post-STEMI patients

2019· article· en· W2982047021 on OpenAlexaboutno aff
Kaiyue Diao, Shan Huang, Yu Gao, Shuai He, Zhi‐gang Yang, Yong He

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVentricleCardiologyInternal medicineHeart failureDiastoleMyocardial infarctionReceiver operating characteristicBlood pressure

Abstract

fetched live from OpenAlex

Abstract Background ST-elevation myocardial infarction (STEMI) Patients suffered from progressive heart failure, for which progressive and aggravated Left ventricle stiffness was one of the major culprit. Myocardial deformation in the early diastole is largely affected by the left ventricle compliance which could partly reflect chamber stiffness and potentially predict left ventricular remodeling for post-STEMI patients. Purpose To determine the value of diastolic strain rate in detecting left ventricle stiffness and early heart failure in post-STEMI patients. Methods A number of 52 (M/F: 46/6, age: 54.27 [46.8–62.3]yrs) patients with STEMI three months ago were prospectively recruited from 2016 to 2017. Follow-up was done until 2018. The primary end points were the symptoms of heart failure (NYHA II-IV). Consent was acquired from each patient and 3.0 T MRI was arranged. Imaging analysis was performed on Cvi 42 (V5.9.3 Canada). Peak radial strain (PS) and strain rate (SR) were extracted both from 2D short- and long-axis cine images, while peak circumferential parameters only from the short axis slices and longitudinal the long axis slices. The diastolic strain rate ratios (DSRRs) were calculated as the peak early diastolic SR divided by the peak late diastolic SR, which were derived from the two peak points on the corresponding curve of time-to-SR curve in the diastole (Figure 1a). Receiver-operating characteristics curve analysis and Logistic regression test were done for statistical analysis on R project and P<0.05 was considered as significant. Results Three patients were excluded due to unsatisfied cine images. Among the 52 patients, none of the patients died or had congestive heart failure. 23/52 (44.2%) patients complained of heart failure symptoms at the one-year follow-up. No significant difference was found in LVEF and three directional peak strain values or systolic peak strain rates between the patients with and without heart failure. Patients with symptoms had lower Longitudinal PS (P=0.049), early diastolic radial SR (P=0.01798), longitudinal SR (P=0.0042), and decreased DSRR in all directions (Figure 1b). Multivariate Logistic regression test showed that only DSRR in the radial direction on the short axis (DSRR-SR) was the independent predictor of the heart failure symptoms (6.59; range, 6.71–3.68; P=0.026). ROC analysis demonstrated that the DSRR-SR of 2.35 had sensitivity 91.3% and specificity 58.6% for the prediction of heart failure (Figure 1c). Figure 1 Conclusion DSRR especially DSRR-SR was more sensitive to left ventricle stiffness change and help predict the progression of heart failure for Post-STEMI patients. Further studies were needed to verify the its association with other cardiovascular clinical events. Acknowledgement/Funding the National Natural Science Foundation of China (81600299,81471721, 81471722, 81771887, and 81771897,);

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.260
Teacher spread0.249 · 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
Published2019
Admission routes1
Has abstractyes

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