We present Attractor-Vascular Coupling Theory (AVCT), a theory-guided framework for deriving interpretable blood pressure (BP) predictors from photoplethysmography (PPG) attractor geometry. AVCT connects vascular dynamics, PPG observation, delay-coordinate reconstruction, and attractor descriptors within a unified framework for formulating testable hypotheses, without presupposing the physiological sufficiency of a particular low-dimensional feature set. We evaluate a LightGBM-based estimator using leave-one-subject-out cross-validation (LOSO-CV) across two complementary experimental settings. Experiment 1 comprises 46 ICU/surgical subjects from BIDMC and VitalDB, with ECG, PPG, and repeated reference BP measurements. The calibrated full model achieves systolic/diastolic BP (SBP/DBP) mean absolute errors (MAEs) of 2.05/1.67 mmHg, compared with 2.02/1.63 mmHg for the PPG-attractor-only model. Experiment 2 includes 217 ambulatory subjects from the PPG-BP dataset (651 PPG windows; one cuff-based BP reference per subject) and assesses cross-subject calibrated BP reconstruction rather than within-subject BP tracking. The PPG-attractor model achieves SBP/DBP MAEs of 1.98/0.96 mmHg. Across both cohorts, incorporating ECG-derived features provides no practically meaningful improvement in MAE. Calibration substantially reduces between-subject offset error, while morphological and dynamical descriptors contribute complementary predictive information. Although the reported numerical errors fall within commonly cited thresholds associated with AAMI/ISO and BHS criteria, these findings do not establish compliance with device-validation standards. Collectively, the results support AVCT as a mechanistically motivated representation framework for interpretable PPG-based BP estimation and provide a foundation for prospective validation using repeated reference BP measurements and smartphone-acquired PPG.

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