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Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Abstract

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Nuclear Medicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Health Sciences > Medicine (miscellaneous)
Health Sciences > Health Informatics
Physical Sciences > Computer Science Applications
Health Sciences > Health Information Management
Language:English
Date:1 May 2023
Deposited On:11 Jul 2023 08:30
Last Modified:29 Oct 2024 02:41
Publisher:Nature Publishing Group
ISSN:2398-6352
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41746-023-00806-x
PubMed ID:37127660
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  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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