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

BackgroundPersistent 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.AimThis 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.MethodsIn 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.ResultsParticipants 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 ConclusionsMultimodal 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.