Applied Predictive Modeling of Coronary Microvascular Disease using Coronary Doppler and Cardiac Echocardiography
Bibliographic record
Abstract
Introduction Type 2 diabetic (T2DM) coronary resistance microvessels (CRMs) show early inward hypertrophic remodeling and reduced wall stiffness, which are associated with decreased blood flow (CBF), coronary flow reserve, and disrupted flow patterns. These data may suggest that coronary microvascular disease (CMD) underlies the early pathophysiology of T2DM. Furthermore, distinct correlations have been identified between the Doppler coronary flow pattern in both normal and T2DM mice, potentially allowing for a predictive relationship between coronary flow patterns and CMD. Currently, CMD is extremely difficult to diagnose due to lack of non‐invasive methods. The goal of this study was to develop an applied predictive model for CMD non‐invasively in early T2DM using CBF combined with functional and structural measurements of the heart. Methods CBF of the left main coronary artery, aortic flow, E & A wave, left ventricular structural dimensions, and aortic diameter were recorded using high frequency, high resolution non‐invasive Doppler echocardiography (Vevo2100, Visual Sonics, Toronto, Canada) in 37 normal heterozygous Db/db mice and 36 T2DM homozygous db/db mice. Coronary flow patterns were analyzed using a Matlab program developed in our laboratory to identify 13 distinct parameters. All other flow patterns and structural and functional features were manually measured in the Vevo2100 software resulting in 13 additional parameters. All the parameters were feature engineered, and a factor analysis model was created and tested using various machine learning algorithms. Results Physiological data were subjected to a variety of machine learning models, and the “glmnet algorithm” in R software (generalized linear regression via penalized maximum likelihood) best predicted CMD with a cross validation accuracy of 80.19% and test dataset accuracy to date of 84.84%. Conclusion An applied predictive model for CMD in T2DM was developed using 73 mice, which shows a promising ability to accurately predict CMD in T2DM. Further studies will be undertaken to improve the predictive accuracy of the model to be suitable for clinical diagnostics and/or decision support. Support or Funding Information Supported by the National Institutes of Health (R00 HL‐116769 to AJT) and Nationwide Children's Hospital (to AJT and CWB). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".