Fully automated life support: an implementation and feasibility pilot study in healthy pigs

Abstract

Background: Automated systems are available in various application areas all over the world for the purpose of reducing workload and increasing safety. However, such support systems that would aid caregivers are still lacking in the medical sector. With respect to workload and safety, especially, the intensive care unit appears to be an important and challenging application field. Whereas many closed-loop subsystems for single applications already exist, no comprehensive system covering multiple therapeutic aspects and interactions is available yet. This paper describes a fully closed-loop intensive care therapy and presents a feasibility analysis performed in three healthy pigs over a period of 72 h each to demonstrate the technical and practical implementation of automated intensive care therapy. Methods: The study was performed in three healthy, female German Landrace pigs under general anesthesia with endotracheal intubation. An arterial and a central venous line were implemented, and a suprapubic urinary catheter was inserted. Electrolytes, glucose levels, acid-base balance, and respiratory management were completely controlled by an automated fuzzy logic system based on individual targets. Fluid management by adaption of the respective infusion rates for the individual parameters was included. Results: During the study, no manual modification of the device settings was allowed or required. Homoeostasis in all animals was kept stable during the entire observation period. All remote-controlled parameters were maintained within physiological ranges for most of the time (free arterial calcium 73%, glucose 98%, arterial base excess 89%, and etCO2 98%). Subsystem interaction was analyzed. Conclusions: In the presented study, we demonstrate the feasibility of a fully closed-loop system, for which we collected high-resolution data on the interaction and response of the different subsystems. Further studies should use big data approaches to analyze and investigate the interactions between the subsystems in more detail.