# Fully automated, deep learning, cardiac CT-based multimodal network for cardiovascular risk stratification in high-risk perioperative patients

## TL;DR

A deep learning system combining CT scans and patient data improves prediction of heart risks before surgery.

## Contribution

A fully automated multimodal deep learning system for cardiovascular risk prediction in perioperative patients.

## Key findings

- The multimodal DL system outperformed RCRI and automated CAD-RADS in predicting MACE (AUROC = 0.82).
- Automated CAD-RADS showed comparable performance to human analysis (AUROC = 0.69 vs. 0.67).
- The system achieved 83% sensitivity and 79% specificity in predicting MACE.

## Abstract

Major adverse cardiac events (MACE) significantly impact perioperative morbidity and mortality. We aimed to develop a fully automated multimodal deep learning (DL) system integrating patient demographics, comorbidities, and coronary computed tomography angiography (CCTA) findings to optimize risk prediction.

We included 639 patients undergoing CCTA as part of perioperative risk assessment for elective non-cardiac surgery. Convolutional neural networks automatically identified coronary artery disease reporting and data system (CAD-RADS) scores and segmented the left ventricle, aorta, and heart. These imaging features were combined with patient demographics and comorbidities to predict MACE risk. We evaluated the performance of our multimodal model against the revised cardiac risk index (RCRI) using gradient boosting decision tree modelling and area under the receiver operating characteristic (AUROC) curves. Among 639 patients (mean age 70 ± 9 years, 56% males, median RCRI 1), 61% underwent orthopaedic surgery, 27% vascular surgery and the rest abdominal/pelvic or spine surgery. 45 patients experienced MACE within 30 days. Automated CAD-RADS (AUROC = 0.69) demonstrated comparable performance to human analysis (AUROC = 0.67, P = 0.77). The multimodal DL system (AUROC = 0.82) outperformed CAD-RADS (delta-AUROC = 0.13, CI: 0.02, 0.26, P = 0.02), and RCRI (delta-AUROC =0.22, CI: 0.05, 0.46; P = 0.001) in predicting MACE and demonstrated robust sensitivity (83%) and specificity (79%).

Our multimodal system built using automated CAD-RADS, anatomical segmentations and patient demographics outperforms both human expert and automated CAD-RADS for MACE prediction. This approach has the potential to enhance patient outcomes by leveraging the synergy between automated imaging and clinical data.

Graphical AbstractFor image description, please refer to the figure legend and surrounding text.