Cerebral Autoregulation Real-Time Monitoring

Abstract

Cerebral autoregulation is a mechanism which maintains constant cerebral blood flow (CBF) despite changes in mean arterial pressure (MAP). Assessing whether this mechanism is intact or impaired and determining its boundaries is important in many clinical settings, where primary or secondary injuries to the brain may occur. Herein we describe the development of a new ultrasound tagged near infra red light monitor which tracks CBF trends, in parallel, it continuously measures blood pressure and correlates them to produce a real time autoregulation index. Its performance is validated in both in-vitro experiment and a pre-clinical case study. Results suggest that using such a tool, autoregulation boundaries as well as its impairment or functioning can be identified and assessed. It may therefore assist in individualized MAP management to ensure adequate organ perfusion and reduce the risk of postoperative complications, and might play an important role in patient care.

Conflict of interest statement

The study was sponsored by Ornim Medical LTD. Adi Tsalach, Eliahu Ratner, Stas Lokshin, Zmira Silman, Ilan Breskin, Nahum Budin and Moshe Kamar are eomplyed by Ornim Medical LTD. Ornim Medical LTD. holds the patent of the UT-NIRS technology (US 20150327779). There are no additional patents, products in development or marketed products to declare. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Figures

Fig 1. Schematic illustration of the flow modeling experimental setup.

The C-FLOW’s measuring sensor is placed above the tissue phantom. Flow within the phantom’s tubes is generated using the syringe pump.

Fig 2

(A) Schematic illustration of the pressure modeling experimental setup comprising of a water column connected to a computer controlled hydraulic pump and a peristaltic pump. Pressure was measured using a standard disposable pressure transducer (DPT) connected directly to the C-FLOW-AR. (B) An example of a pressure wave generated by the hydraulic pressure system designed to mimic blood pressure. All pressure properties, such as average pressure (MAP), oscillations magnitude (Systolic-Diastolic pressures), and frequency (HR), are controlled and can be predetrmined.

Fig 3

MAP (C), CFI (B,D) and ARI (A,E) data obtained in the in-vitro experiment. MAP (panel C) was significantly increased and decreased, followed by a similar flow protocol which was applied to sensor 1 (panel B) versus constant flow which was measured by sensor 2 (panel D). Sensor 1 which experienced a pressure passive flow protocol was used to model No AutoRegulation (NAR) state, while sensor 2 which measured constant flow was utilized to model intact AutoRegulation (AR) state. Corresponding calculated autoregulation indexes are depicted in panels A and E respectively. Delayed ARI values relative to MAP changes are due to data collection and buffering required for ARI calculation.

Fig 4. Boxplot for autoregulation index values calculated for the two to cFLOW-AR sensors.

Pink astrics represent averaged ARI for each condition. A distinct separation between the conditions is apparent.

Fig 5

Left—MAP and CFI data over time throughout the study. MAP was increased to 140mmHg followed by a return to baseline and a decrease to 40mmHg. Dashed green lines represent initial injections of Phnylephrine and Nitroprusside respectively. Blue points represent all MAP values. Red points are associated with periods in which the algorithm identified a significant MAP change and a correlation index (ARI) can be calculated. Right—Scatter plot of CFI versus MAP revealing two distinct slopes obtained for values under of over 100mmHg. This point was defined as the upper limit of autoregulation (ULA).

Fig 6

Left–Bar Diagram of averaged autoregulation index for each 10mmHg MAP segment. Error bars represent the standard error of the mean. Threshold value is 52. Right–ROC analysis for MAP classification as over or under the ULA. Area Under the Curve (AUC) is 0.848 (95% confidence interval [0.783, 0.912]).