Development and validation of imaging-free myocardial fibrosis prediction models, association with outcomes, and sample size estimation for phase 3 trials

Black, N. et al. (2024) Development and validation of imaging-free myocardial fibrosis prediction models, association with outcomes, and sample size estimation for phase 3 trials. medRxiv, (doi: 10.1101/2024.02.07.24302443)

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Abstract

Background and Aims Phase 3 trials testing whether pharmacologic interventions targeting myocardial fibrosis (MF) improve outcomes require MF measurement that does not rely on tomographic imaging with intravenous contrast. Methods We developed and externally validated extracellular volume (ECV) prediction models incorporating readily available data (comorbidity and natriuretic peptide variables), excluding tomographic imaging variables. Survival analysis tested associations between predicted ECV and incident outcomes (death or hospitalization for heart failure). We created various sample size estimates for a hypothetical therapeutic clinical trial testing an anti-fibrotic therapy using: a) predicted ECV, b) measured ECV, or c) no ECV. Results Multivariable models predicting ECV had reasonable discrimination (optimism corrected C-statistic for predicted ECV ≥27% 0.78 (95%CI 90.75-0.80) in the derivation cohort (n=1663) and 0.74 (95%CI 0.71-0.76) in the validation cohort (n=1578)) and reasonable calibration. Predicted ECV associated with adverse outcomes in Cox regression models: ECV ≥27% (binary variable) HR 2.21 (1.84–2.66). For a hypothetical clinical trial with an inclusion criterion of ECV ≥27%, use of predicted ECV (with probability threshold of 0.69 and 80% specificity) compared to measured ECV would obviate the need to perform 3940 CMR scans, at the cost of an additional 3052 participants screened and 705 participants enrolled. Conclusions Predicted ECV (derived without tomographic imaging) associates with outcomes and efficiently identifies vulnerable patients who might benefit from treatment. Predicted ECV may foster the design of phase 3 trials targeting MF with higher numbers of screened and enrolled participants, but with simplified eligibility criteria, avoiding the complexity of tomographic imaging. Key Question Phase 3 trials targeting myocardial fibrosis (MF) to improve outcomes require MF measurement that does not rely on tomographic imaging with intravenous contrast. So, we developed and validated extracellular volume (ECV) prediction models incorporating clinical data, excluding tomographic imaging. Key Finding Predicted ECV had reasonable discrimination and associated with outcomes. For a hypothetical trial with an ECV ≥27% inclusion criterion, using predicted ECV versus measured ECV would avoid 3940 cardiovascular magnetic resonance (CMR) scans, but require an additional 3052 participants screened and 705 enrolled. Take-home Message Predicted ECV (derived without imaging) associates with outcomes and efficiently identifies vulnerable patients. Predicted ECV may foster phase 3 trials targeting MF with higher numbers of screened and enrolled participants, but simplified eligibility criteria, avoiding the complexity of tomographic imaging.

Item Type:Articles (Pre-print)
Additional Information:CAM, Advanced Fellowship, NIHR301338 is funded by the National Institute for Health and Care Research (NIHR). The views expressed in this publication are those of the authors and not necessarily those of the NIHR, NHS or the UK Department of Health and Social Care. CAM acknowledges support from the University of Manchester British Heart Foundation Accelerator Award (AA/18/4/34221) and the NIHR Manchester Biomedical Research Centre (NIHR203308).
Status:Published
Refereed:No
Glasgow Author(s) Enlighten ID:Petrie, Professor Mark
Authors: Black, N., Bradley, J., Lewis, G., Lagan, J., Orsborne, C., Soltani, F., Farrant, J. P., McDonagh, T., Schmitt, M., Cavalcante, J. L., Ugander, M., Butler, J., Petrie, M. C., Miller, C. A., and Schelbert, E. B.
College/School:College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
Journal Name:medRxiv

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