Comparison of baseline and 6-month post-PCI data.

Background
Persistent low-grade inflammation following percutaneous coronary intervention (PCI) is a known contributor to major adverse cardiovascular events (MACE). While biomarkers such as high-sensitivity C-reactive protein (hs-CRP) are routinely assessed, the predictive role of behavioral factors derived from wearable devices remains underutilized.
Aim
This study aimed to develop and validate a multimodal predictive model integrating wearable-derived behavioral data and physiologic biomarkers to assess sustained inflammatory risk in post-PCI patients.
Methods
In this prospective observational study, data from 312 adult patients who underwent PCI between January 2022 and December 2024 were analyzed. Data sources included electronic health records, blood-based inflammatory markers (hs-CRP, IL-6, NLR), and continuous wearable-based lifelog variables (step count, sleep efficiency, HRV, SpO₂) collected for up to 6 months. Four machine learning approaches—including logistic regression, random forest, LSTM, and Transformer—were compared for predicting ≥1.0 mg/L reduction in hs-CRP. SHAP and attention weight analyses were used to assess feature importance and model interpretability.
Results
Participants with improved inflammation (59.3%) demonstrated significantly higher step count (8,050 vs. 6,140 steps/day), sleep efficiency (87.1% vs. 78.2%), HRV (64.7 vs. 51.1 ms), and SpO₂ (97.1% vs. 95.2%) compared to non-responders (all p < 0.001). The Transformer model yielded the best performance (AUC 0.88, F1-score 0.81), outperforming other models. SHAP results confirmed the strong predictive contribution of modifiable behavioral features.
Conclusions
Multimodal integration of wearable-informed behavioral and physiologic data enhances the prediction of inflammatory outcomes after PCI. The strong association of behavioral metrics with inflammation supports the development of patient-centered, self-regulatory interventions for long-term cardiovascular risk management.