Key result
Wearable and chatbot models predict adverse cardiac events with an AUC of 0.75, outperforming SMART2.
Why the study?
The SMART2 risk score relies on static clinical parameters, whereas real-world recovery factors like sleep, activity, and autonomic function also affect recurrent CV risk and can be continuously monitored with digital health tools.
Do digital biomarkers from wearable and chatbot monitoring improve the prediction of adverse cardiac events compared to SMART2 risk score and lifestyle questionnaires in patients with established coronary artery disease after revascularization?
Comparison
SMART2 parameters vs lifestyle questionnaires vs wearable/chatbot data vs all combined
Design
Cohort study
Follow-up
two years
Authors
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Should not yet change post-revascularization care; leaves open whether wearable and chatbot data enhance adverse event prediction.
Do digital biomarkers from wearable and chatbot monitoring improve the prediction of adverse cardiac events compared to SMART2 risk score and lifestyle questionnaires in patients with established coronary artery disease after revascularization?
Early post-discharge digital monitoring of sleep, activity, and circadian HR dynamics predicts adverse cardiac events more accurately than standard clinical risk scores in patients recovering from coronary revascularization.
Blerck et al. (2026) studied this question. A wearable and chatbot model predicted adverse cardiac events with higher discrimination (AUC 0.75) than the SMART2 risk score (AUC 0.63) or lifestyle questionnaires (AUC 0.54) (p < 0.01).
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