Predictive Value of Combining Angio-Based Index of Microcirculatory Resistance and Fractional Flow Reserve in Patients with STEMI
Bibliographic record
Abstract
BACKGROUND: The assessment of coronary function and microcirculation in patients with ST-segment elevation myocardial infarction (STEMI) may be useful to guide long-term prognosis, but the research is limited. This study aimed to investigate the value of angio-based fraction flow reserve (AccuFFR) and an index of microcirculatory resistance (AccuIMR) after percutaneous coronary intervention (PCI) for evaluating the long-term prognosis of STEMI patients. METHODS: Data of patients with STEMI who underwent PCI at Peking University Third Hospital between January 2017 and March 2022 were retrospectively analyzed. AccuFFR and AccuIMR were analyzed immediately after primary PCI. According to AccuFFR and AccuIMR, patients were classified into 4 groups: normal coronary function, macrovascular disorder, microvascular disorder, and mixed disorder. RESULTS: A total of 1297 patients were enrolled. The median follow-up time was 35 (24, 58) months. The risks of major adverse cardiovascular events (MACE), all-cause death, cardiovascular death, and readmission for heart failure in the mixed disorder group were significantly higher than those in the other 3 groups (all P < 0.001). Both AccuFFR (hazard ratio [HR], 0.948 per 0.01 increase; 95% confidence ratio [CI], 0.914-0.983; P = 0.004) and AccuIMR (HR, 1.018; 95% CI, 1.009-1.027; P < 0.001) were independent predictors of MACE. A nomogram model was established to predict MACE after PCI in patients with STEMI at 1, 3, and 5 years. The receiver-operating characteristic (ROC) curve, C-index, and calibration curve showed that the model had high discrimination. CONCLUSIONS: Coronary function and microcirculation assessment immediately after primary PCI are important in evaluating the prognosis of patients with STEMI. TRIAL REGISTRATION NUMBER: NCT06435728.
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 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 | 0.001 |
| 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) | 0.001 | 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".