A Novel Method for Cardiac Output and Vascular Parameters Estimation Using Peripheral Arterial Waveforms: Integrating...

Conventional pulse-contour analysis estimates cardiac output ( $\mathrm{CO}$ ) from arterial pressure waveforms but often relies on demographic calibration or black-box modeling, which limits physiological interpretability and generalizability. This study aims to develop and validate a structurally identifiable model that simultaneously estimates $\mathrm{CO}$ and vascular parameters from peripheral arterial pressure waveforms.

The proposed framework is based on a four-element Windkessel model reformulated through $\alpha$ -parameters ( ${\alpha }_C,{\alpha }_R,{\alpha }_L,{\alpha }_{\tau }$ ) that encapsulate arterial compliance, resistive and inertial loads, and pressure decay dynamics. Radial peripheral arterial pressure (pABP) waveforms are preprocessed, smoothed, converted into a periodic representation, and fitted to the Windkessel model to extract $\alpha$ -parameters. Combined with biometric covariates, these parameters serve as inputs to a generalized linear model (Gamma distribution, identity link) trained to estimate $\mathrm{CO}$ . The estimated $\mathrm{CO}$ is subsequently reinjected into the $\alpha$ -parameter expressions to derive arterial compliance ( $C$ ), characteristic impedance ( $R_z$ ), distal resistance ( $R_{\mathrm{dis}}$ ), and inertance ( $L$ ).

Internal validation against the EV1000 pulse-contour reference yields an $R^2=0.82$ , negligible bias (- 0.02 $\text{L}.{\text{min}}^{-1}$ ), and a percentage error ( $\mathrm{PE}$ ) of 26.17%, meeting the clinical interchangeability criterion ( $\mathrm{PE}$ < 30%). External evaluation on an independent Vigileo dataset achieves $R^2=0.72$ and $\mathrm{PE}$ = 28.41% without retraining, confirming robustness across platforms.

The $\alpha$ -parameterized Windkessel framework provides a physiologically interpretable, data-efficient, and calibration-free alternative for $\mathrm{CO}$ estimation. Beyond $\mathrm{CO}$ , it simultaneously quantifies $C$ , $R_z$ , $R_{\mathrm{dis}}$ , and $L$ , offering a comprehensive and mechanistically grounded hemodynamic profile from a single peripheral arterial pressure signal, suitable for real-time integration into perioperative and critical care monitoring.