Correlation between thermodilution-based IMR and angio-IMR in the post-heart transplant population
Bibliographic record
Abstract
Abstract Background Coronary microvascular dysfunction (CMD), as defined by an elevated index of microcirculatory resistance (IMR), has been demonstrated to be an adverse prognostic indicator in patients early post-heart transplant (HT). Non-wire-based diagnostic methods, like angio-IMR, may improve access, as well as reduce risk and healthcare costs associated with invasive coronary physiology testing. However, angio-IMR has not been evaluated in HT patients. Purpose The objective of this study is to determine the diagnostic performance of angio-IMR in HT patients for identifying CMD using invasive wire-based IMR as a reference standard. Methods HT patients who underwent invasive coronary physiology study at our Institute as part of the ECAV and PET-CAV studies were included. QAngio Xa 2D was used to obtain angio-IMR measures. The relationship between angio-IMR and IMR was evaluated by Pearson correlation analysis and Bland-Altman plots. Receiver operating characteristic curve analysis was used to assess the diagnostic performance of angio-IMR in conjunction with Youden’s index to determine optimal cut points for detecting CMD (defined as an IMR >=25). Results 135 angio-IMR measures were obtained from 76 patients, from whom 95 (70.37%), 22 (16.30%) and 18 (13.33%) were obtained in the left anterior descending, left circumflex and right coronary artery, respectively. CMD was evident in 38 (38.15%) vessels. Mean IMR was 23.05 ± 18.66 and mean angio-IMR was 22.70 ± 13.11. There was a significant correlation between angio-IMR and IMR (r= 0.93; 95% CI 0.90-0.95; p<0.01) (Figure 1A). The mean difference between IMR and angio-IMR was 0.31 ± 9.27 (p=0.70) (Figure 1B), with increased differences observed with high IMR values. Angio-IMR displayed high diagnostic performance for detection coronary microvascular dysfunction (IMR >=25), with an area under the curve of 0.93 (95% CI 0.87-0.99) (Figure 2). The optimal angio-IMR cutoff for CMD was >=25. On the basis of this threshold, angio-IMR showed high accuracy (90.37%; 95% CI 84.10-94.77), sensitivity (84.21%; 95% CI 68.75- 93.98) and specificity (92.78%; 95% 85.70-97.05) Conclusion Angio-IMR has a high diagnostic performance for detection of microvascular dysfunction in HT patients. Angio-IMR enables additional coronary physiology assessment during routine invasive angiography that may improve HT patients risk stratification and enable tailored management.Figure 1 Figure 2
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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) | 0.002 | 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".