000002963 001__ 2963
000002963 005__ 20190326164008.0
000002963 022__ $$a2352-6483
000002963 0247_ $$2DOI$$a10.1016/j.smhl.2018.07.003
000002963 037__ $$aARTICLE
000002963 041__ $$aeng
000002963 245__ $$aRobustness analysis of personalised delivery rate computation for IV administered anesthetic
000002963 260__ $$c2018-12
000002963 269__ $$a2018-12
000002963 300__ $$a14 p.
000002963 506__ $$avisible
000002963 520__ $$9eng$$aControlled delivery of intravenous (IV) anesthetics aims at fast and safe achievement and maintenance of a suitable depth of hypnosis (DOH), by ensuring appropriate effect site (i.e. brain) exposure to the drug. Today, such drugs are regularly injected by Target Controlled Infusion (TCI) systems, piloted by an open-loop algorithm based on Pharmacokinetic (PK) models. Yet the inaccuracy of concentration prediction of current TCI can reach up to 100%. The situation could be improved by closing the loop with sensors providing regular real measurements of the anesthetic concentration in body fluids. In this paper we present a closed-loop algorithm based on the classic open-loop algorithm combined with a Kalman filter. The latter estimates plasma drug concentration based on PK model and sensor measurements. The estimates are then used in the open-loop algorithm. To validate our approach measurements are generated by means of modulating the population-based plasma concentration values with the maximum inter- and intrapatient variability of the statistical Eleveld׳s (Eleveld et al., 2014) PK model. This allows us to stress the system to a maximum level prior to testing it on patients. We also perform robustness analysis of this algorithm by accounting for realistic measurement periods and delays.
000002963 540__ $$acorrect
000002963 592__ $$aHEI-VS
000002963 592__ $$bInstitut Systèmes industriels
000002963 592__ $$cIngénierie et Architecture
000002963 65017 $$aIngénierie
000002963 655__ $$ascientifique
000002963 6531_ $$9eng$$aindividualized anesthesia
000002963 6531_ $$9eng$$adrug delivery
000002963 6531_ $$9eng$$aclosed-loop control
000002963 6531_ $$9eng$$aKalman filer
000002963 6531_ $$9eng$$aRobustness analysis
000002963 700__ $$aSimalatsar, Alena$$uSchool of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland ; Service of Clinical Pharmacology, University Hospital of lausanne (CHUV), Lausanne, Switzerland
000002963 700__ $$aGuidi, Monica$$uService of Clinical Pharmacology, University Hospital of lausanne (CHUV), Lausanne, Switzerland ; School of Pharmaceutical Sciences, Unviersity of Geneva, University of Lausanne, Geneva, Switzerland
000002963 700__ $$aRoduit, Pierre$$uSchool of Engineering, HES-SO Valais-Wallis, HEI, HES-SO // University of Applied Sciences Western Switzerland
000002963 700__ $$aBuclin, Thierry$$uService of Clinical Pharmacology, University Hospital of lausanne (CHUV), Lausanne, Switzerland
000002963 773__ $$g2018, vol. 9-10, pp. 101-114$$tSmart Health
000002963 8564_ $$uhttps://hesso.tind.io/record/2963/files/Simalatsar_2018_robustness_analysis_personalized_IV_anesthetic.pdf$$s3294573
000002963 906__ $$aNONE
000002963 909CO $$pGLOBAL_SET$$ooai:hesso.tind.io:2963
000002963 950__ $$aI2
000002963 981__ $$ascientifique
000002963 980__ $$ascientifique