Concurrently Low Coronary Flow Reserve and Low Index of Microvascular Resistance Are Associated With Elevated Resting Coronary Flow in Patients With Chest Pain and Nonobstructive Coronary Arteries
Bibliographic record
Abstract
Background: Coronary microvascular function can be distinctly quantified using the coronary flow reserve (CFR) and index of microvascular resistance (IMR). Patients with low CFR can present with low or high IMR, although the prevalence and clinical characteristics of these patient groups remain unclear. Methods: One hundred ninety-nine patients underwent coronary microvascular assessments using coronary thermodilution techniques. A pressure-temperature sensor-tipped guidewire measured proximal and distal coronary pressure, whereas the inverse of the mean transit time to room temperature saline was used to measure coronary blood flow. The CFR and IMR were quantified during adenosine and acetylcholine hyperemia. Results: Low adenosine and acetylcholine CFR was observed in 70 and 49 patients, respectively, whereas low CFR/low IMR to adenosine and acetylcholine was observed in 39(56%) and 19(39%) patients, respectively. Despite similar adenosine CFR, patients with low CFR/low IMR had increased resting (2.8±1.2 versus 1.3±0.4s -1 ) and hyperemic coronary blood flow (4.8±1.5 versus 2.1±0.5s -1 ) compared with patients with low CFR/high IMR (both P <0.01). The same pattern was observed in response to acetylcholine. Patients with low CFR/low IMR to adenosine were younger (56±12 versus 63±10 years), women (84% versus 66%), had fewer coronary risk factors (1.1±1.0 versus 1.6±1.1), lower hemoglobin A1c (5.8±0.7 versus 6.1±0.9 mmol/L), and thinner septal thickness (8.5±2.5 versus 9.9±1.6 mm) compared with patients with low CFR/high IMR to adenosine (all P <0.05). Conclusions: Low CFR/low IMR to adenosine and acetylcholine are associated with elevated resting coronary blood flow and preserved hyperemic coronary blood flow. These patients present with distinct phenotypic characteristics. Simultaneous CFR and IMR measures appear necessary to differentiate these endotypes.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".