An Enhanced Automatic Algorithm for Estimation of Respiratory Variations in Arterial Pulse Pressure During Regions of Abrupt Hemodynamic Changes

Abstract

We describe an improved automatic algorithm to estimate the pulse-pressure-variation (PPV) index from arterial blood pressure (ABP) signals. This enhanced algorithm enables for PPV estimation during periods of abrupt hemodynamic changes. Numerous studies have shown PPV to be one of most specific and sensitive predictors of fluid responsiveness in mechanically ventilated patients. The algorithm uses a beat detection algorithm to perform beat segmentation, kernel smoothers for envelope detection, and a suboptimal Kalman filter for PPV estimation and artifact removal. In this paper, we provide a detailed description of the algorithm and assess its performance on over 40 h of ABP signals obtained from 18 mechanically ventilated crossbred Yorkshire swine. The subjects underwent grade V liver injury after splenectomy, while receiving mechanical ventilation, and general anesthesia with isoflurane. All subjects in the database underwent a period of abrupt hemodynamic change after an induced grade V liver injury involving severe blood loss resulting in hemorrhagic shock, followed by fluid resuscitation with either 0.9% normal saline or lactated ringers solutions. Trained experts manually calculated PPV at five time instances during the period of abrupt hemodynamic changes. We report validation results comparing the proposed algorithm against a commercial system (pulse contour cardiac output, PICCO) with continuous PPV monitoring capabilities. Both systems were assessed during periods of abrupt hemodynamic changes against the "gold-standard" PPV, calculated and manually annotated by experts. Our results indicate that the proposed algorithm performs considerably better than the PICCO system during regions of abrupt hemodynamic changes.