BACKGROUND: We developed a model-based control system using end-tidal carbon dioxide fraction (FE'(CO(2))) to adjust a ventilator during clinical anaesthesia. METHODS: We studied 16 ASA I-II patients (mean age 38 (range 20-59) yr; weight 67 (54-87) kg) during i.v. anaesthesia for elective surgery. After periods of normal ventilation the patients were either hyper- or hypoventilated to assess precision and dynamic behaviour of the control system. These data were compared with a previous group where a fuzzy-logic controller had been used. Responses to different clinical events (invalid carbon dioxide measurement, limb tourniquet release, tube cuff leak, exhaustion of carbon dioxide absorbent, simulation of pulmonary embolism) were also noted. RESULTS: The model-based controller correctly maintained the setpoint. No significant difference was found for the static performance between the two controllers. The dynamic response of the model-based controller was more rapid (P<0.05). The mean rise time after a setpoint increase of 1 vol% was 313 (sd 90) s and 142 (17) s for fuzzy-logic and model-based control, respectively, and after a 1 vol% decrease was 355 (127) s and 177 (36) s, respectively. The new model-based controller had a consistent response to clinical artefacts. CONCLUSION: A model-based FE'(CO(2)) controller can be used in a clinical setting. It reacts appropriately to artefacts, and has a better dynamic response to setpoint changes than a previously described fuzzy-logic controller.
BACKGROUND: We developed a model-based control system using end-tidal carbon dioxide fraction (FE'(CO(2))) to adjust a ventilator during clinical anaesthesia. METHODS: We studied 16 ASA I-II patients (mean age 38 (range 20-59) yr; weight 67 (54-87) kg) during i.v. anaesthesia for elective surgery. After periods of normal ventilation the patients were either hyper- or hypoventilated to assess precision and dynamic behaviour of the control system. These data were compared with a previous group where a fuzzy-logic controller had been used. Responses to different clinical events (invalid carbon dioxide measurement, limb tourniquet release, tube cuff leak, exhaustion of carbon dioxide absorbent, simulation of pulmonary embolism) were also noted. RESULTS: The model-based controller correctly maintained the setpoint. No significant difference was found for the static performance between the two controllers. The dynamic response of the model-based controller was more rapid (P<0.05). The mean rise time after a setpoint increase of 1 vol% was 313 (sd 90) s and 142 (17) s for fuzzy-logic and model-based control, respectively, and after a 1 vol% decrease was 355 (127) s and 177 (36) s, respectively. The new model-based controller had a consistent response to clinical artefacts. CONCLUSION: A model-based FE'(CO(2)) controller can be used in a clinical setting. It reacts appropriately to artefacts, and has a better dynamic response to setpoint changes than a previously described fuzzy-logic controller.
Authors: Ibrahim M Mehedi; Heidir S M Shah; Ubaid M Al-Saggaf; Rachid Mansouri; Maamar Bettayeb Journal: J Healthc Eng Date: 2021-06-23 Impact factor: 2.682