Superior fitting of arterial resistance and compliance parameters with genetic algorithms in models of dynamic cerebral autoregulation

Abstract Objective: The capacity of discriminating between normal and impaired dynamic cerebral autoregulation (dCA), based on spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CBF), has considerable clinical relevance. This study aimed to quantify the separate contributions of vascular resistance and compliance as parameters that could reflect myogenic and metabolic mechanisms to dCA. Methods: Forty-five subjects were studied under normo and hypercapnic conditions induced by breathing a mixture of 5% carbon dioxide in air. Dynamic cerebrovascular resistance and compliance models with ABP as input and CBFV as output were fitted using Genetic Algorithms to identify parameter values for each subject, and respiratory condition. Results: The efficiency of dCA was assessed from the models generated CBFV response to an ABP step change, corresponding to an autoregulation index of 5.561.57 in normocapnia and 2.381.73 in hypercapnia, with an area under the ROC curve (AUC) of 0.9 between both conditions. Vascular compliance increased from 0.750.7 ml/mmHg in normocapnia to 5.8212.0 ml/mmHg during hypercapnia, with an AUC of 0.88. Conclusion: we demonstrated that Genetic Algorithms are a powerful tool to provide accurate identification of model parameters expressing the performance of human CA Significance: Further work is needed to validate this approach in clinical applications where individualised model parameters could provide relevant diagnostic and prognostic information about dCA impairment Index Terms arterial compliance, autoregulation impairment, cerebral blood flow, Genetic Algorithms, hypercapnia