Cardiac contractility: Correction strategies applied to telemetry data from a HESI-sponsored consortium
Bibliographic record
Abstract
QT has a long history of heart rate (HR) correction but limited investigations have been undertaken to assess the impact of cardiovascular parameters on left ventricular (LV) contractility in drug safety testing. Cardiac contractility is affected by preload (Cyon-Frank-Starling law), afterload (Anrep effect) and HR (Bowditch effect). We evaluated multi-parameter correction methods to help with dP/dtmax interpretation. Modeling was undertaken using data from dogs in single or double 4 × 4 Latin square studies. Correction models (16 fitting formulas × 2 modeling approaches (universal and individualized) × 2 correction approaches (linear or proportional)) were evaluated. 3D/2D cloud analysis of the beat-to-beat data for the control, pimobendan, and either itraconazole or atenolol groups were used to evaluate correlations between parameters and derive an optimal correction method. Cardiac contractility (i.e., dP/dtmax) was best correlated to HR and systolic LV pressure with a correlation coefficient of 0.8. In decreasing order, dP/dtmin, mean arterial blood pressure (BP), systolic BP, diastolic BP, arterial pulse pressure and LV end diastolic pressure (LVEDP) showed a reduced correlation to dP/dtmax. Subject-specific models improved the correction by up to 14% when compared to universal correction models. The non-linear correction model was superior to the linear model. Results suggest that the optimal correction formula for dP/dtmax would be subject-specific, non-linear and would include HR and LV systolic pressure. Correcting contractility for HR and systolic LV pressure may enhance data interpretation in non-clinical drug safety assessments. Similar correction methods could be evaluated for other species used in safety pharmacology.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".