The use of computer decision support systems for the critical care environment

Performance of a closed-loop glucose control system, comprising a continuous glucose monitoring system and an AI-based controller in swine during severe hypo- and hyperglycemic provocations

Intensive care unit (ICU) patients develop stress induced insulin resistance causing hyperglycemia, large glucose variability and hypoglycemia. These glucose metrics have all been associated with increased rates of morbidity and mortality. The only way to achieve safe glucose control at a lower glucose range (e.g., 4.4–6.6 mmol/L) will be through use of an autonomous closed loop glucose control system (artificial pancreas). Our goal with the present study was to assess the safety and performance of an artificial pancreas system, composed of the EIRUS (Maquet Critical Care AB) continuous glucose monitor (CGM) and novel artificial intelligence-based glucose control software, in a swine model using unannounced hypo- and hyperglycemia challenges. Fourteen piglets (6 control, 8 treated) underwent sequential unannounced hypoglycemic and hyperglycemic challenges with 3 IU of NovoRapid and a glucose infusion at 17 mg/kg/min over the course of 5 h. In the Control animals an experienced ICU physician used every 30-min blood glucose values to maintain control to a range of 4.4–9 mmol/L. In the Treated group the artificial pancreas system attempted to maintain blood glucose control to a range of 4.4–6.6 mmol/L. Five of six Control animals and none of eight Treated animals experienced severe hypoglycemia (< 2.22 mmol/L). The area under the curve 3.5 mmol/L was 28.9 (21.1–54.2) for Control and 4.8 (3.1–5.2) for the Treated animals. The total percent time within tight glucose control range, 4.4–6.6 mmol/L, was 32.8% (32.4–47.1) for Controls and 55.4% (52.9–59.4) for Treated (p < 0.034). Data are median and quartiles. The artificial pancreas system abolished severe hypoglycemia and outperformed the experienced ICU physician in avoiding clinically significant hypoglycemic excursions.

Clinician satisfaction with computer decision support in the intensive care unit

The purpose of this study was to determine baseline user satisfaction for 2 computer decision support systems (DSSs) with demonstrated improvement in patient outcome used in a burn intensive care unit. We conducted a survey of staff members of a 16-bed burn intensive care unit (n = 82) using a written, anonymous questionnaire to determine satisfaction for 2 DSSs: a commercial glycemic management system and software program to guide initial burn fluid resuscitation. Staff members are not yet convinced of a positive correlation between DSS technology and patient outcomes. We suggest user satisfaction may be generally improved for DSS with concentration in the areas of interface, information, and communication.