Occurrence of CPPopt values in uncorrelated ICP and ABP time series

Abstract

Objectives: Optimal cerebral perfusion pressure (CPPopt) is a concept that uses the pressure reactivity (PRx)–CPP relationship over a given period to find a value of CPP at which PRx shows best autoregulation. It has been proposed that this relationship be modelled by a U-shaped curve, where the minimum is interpreted as being the CPP value that corresponds to the strongest autoregulation. Owing to the nature of the calculation and the signals involved in it, the occurrence of CPPopt curves generated by non-physiological variations of intracranial pressure (ICP) and arterial blood pressure (ABP), termed here “false positives”, is possible. Such random occurrences would artificially increase the yield of CPPopt values and decrease the reliability of the methodology. In this work, we studied the probability of the random occurrence of false-positives and we compared the effect of the parameters used for CPPopt calculation on this probability. Materials and methods: To simulate the occurrence of false-positives, uncorrelated ICP and ABP time series were generated by destroying the relationship between the waves in real recordings. The CPPopt algorithm was then applied to these new series and the number of false-positives was counted for different values of the algorithm’s parameters. Results: The percentage of CPPopt curves generated from uncorrelated data was demonstrated to be 11.5%. Conclusion: This value can be minimised by tuning some of the calculation parameters, such as increasing the calculation window and increasing the minimum PRx span accepted on the curve.