Regional tissue perfusion index (RTPI): a new optical-based metric for quantifying regional tissue perfusion

Accurate, continuous assessment of regional tissue perfusion remains a significant clinical challenge, as most existing modalities are invasive, indirect, or impractical for routine monitoring. Near-infrared spectroscopy (NIRS) has been widely adopted to assess tissue oxygenation; however, conventional NIRS-derived indices are insufficient surrogates for true perfusion and often fail to capture rapid hemodynamic changes. This study aimed to introduce and validate the Regional Tissue Perfusion Index (RTPI), a novel NIRS-derived metric that integrates multiple features of the NIRS signal to provide continuous, non-invasive, and physiologically relevant assessment of tissue perfusion. RTPI was developed using principal component analysis (PCA) of multiple NIRS-derived parameters, including pulse amplitude ratio, derivative, and area under the curve. Its performance was evaluated in healthy volunteers during controlled ischemia–reperfusion protocols and compared with established reference standards, including laser Doppler flowmetry (LDF) and photoplethysmography (PPG). RTPI showed acceptable correlations with LDF Flux and PPG perfusion index (PI) during dynamic perfusion changes. Unlike conventional NIRS-derived oxygenation and hemodynamic indices, which often exhibited delayed responses, RTPI demonstrated immediate and significant sensitivity to both complete and partial ischemia–reperfusion episodes in the analyzed datasets. Intraclass correlation indicated stronger session-to-session reliability for RTPI than for both Flux and PI. RTPI represents a multiparametric, physiologically meaningful, and computationally efficient metric for real-time monitoring of tissue perfusion. Its ability to detect perfusion compromise independently of oxygenation indices highlights its translational potential for bedside implementation in critical care, trauma, perioperative, and vascular medicine, where improved diagnostic accuracy could significantly impact patient outcomes.