Prediction of left ventricular ejection fraction improvement in patients with ischemic cardiomyopathy after coronary artery bypass grafting based on cardiac magnetic resonance
Bibliographic record
Abstract
Background: To investigate the risk factors of left ventricular ejection fraction (LVEF) improvement in patients with ischemic cardiomyopathy (ICM) after coronary artery bypass grafting (CABG), and to construct a model that predicts LVEF improvement. Methods: A retrospective analysis was performed on 106 ICM patients who received CABG and underwent cardiac magnetic resonance (CMR) at Beijing Anzhen Hospital, Capital Medical University from January 2017 to June 2022. Patients were divided into two groups with improved LVEF and no improved LVEF based on the results of postoperative 6-month transthoracic echocardiography. To analyze the risk factors affecting the LVEF non-improvement after CABG and establish a prediction model. Results: There was LVEF non-improvement in 30.2% (32/106) of patients. Multivariate analysis showed that the number of transmural scar segments and left ventricular end-systolic volume index (LVESVI) were independent risk factors in LVEF non-improvement after CABG [odds ratio (OR) =2.398, 95% confidence interval (CI): 1.607–3.579, P<0.001; OR =1.036, 95% CI: 1.009–1.063, P=0.008]. The model is built and internally verified. ROC showed that the area under the curve (AUC) was 0.866 (95% CI: 0.792–0.940), calibration curve showed that the probability predicted by the model matched well with the clinical results, and decision curve analysis (DCA) showed that the model had good clinical applicability. During the mean follow-up time of 1.5 years, the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) in the LVEF non-improvement group was higher (5.4% vs. 25.0%, P=0.009), and the NYHA grading was higher (P=0.016), when compared to the LVEF improvement group. Conclusions: The prediction model based on the number of transmural scar segments and LVESVI has good diagnostic efficacy. Our findings help to identify patients with improved LVEF and thus guide the selection of clinical treatment strategies.
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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 | 0.003 |
| 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".