The problem of artifacts in patient monitor data during surgery: a clinical and methodological review.

Artifacts are a significant problem affecting the accurate display of information during surgery. They are also a source of false alarms. A secondary problem is the inadvertent recording of artifactual and inaccurate information in automated record keeping systems. Though most of the currently available patient monitors use techniques to minimize the effect of artifacts, their success is limited. We reviewed the problem of artifacts affecting patient monitor data during surgical cases. Methods adopted by currently marketed patient monitors to eliminate and minimize artifacts due to technical and environmental factors are reviewed and discussed. Also discussed are promising artifact detection and correction methods that are being investigated. These might be used to detect and eliminate artifacts with improved accuracy and specificity.

A novel insulin delivery algorithm in rats with type 1 diabetes: the fading memory proportional-derivative method.

An algorithm designed to automatically control insulin delivery was tested in rats with Type 1 diabetes. This nonlinear algorithm included a fading memory component of proportional and derivative errors in order to simulate normal insulin secretion. Error-weighting functions for the proportional and derivative terms were used with a performance index designed for error adaptation. In the first version of the algorithm, the proportional gain was adaptively varied. In the second version, a low rate of basal insulin delivery was adaptively varied. Six 6-h studies with each version were conducted using frequent blood sampling and intravenous insulin delivery. In Version 2 studies, blood glucose levels during the last two hours were well-controlled and significantly lower than in Version 1 (118 +/- 2.0 vs. 130 +/- 2.9 mg/dL). Neither version produced hypoglycemia. Future research using this algorithm needs to focus on automated glucose sensing in combination with insulin delivery.

Bispectral Index monitoring correlates with sedation scales in brain-injured patients.

OBJECTIVE: Monitoring critically ill, brain-injured patients with a decreased level of consciousness is challenging. Our goal is to determine in this population the correlation between the Bispectral Index (BIS) and three commonly used sedation agitation scales: the Richmond Agitation-Sedation Scale (RASS), the Sedation-Agitation Scale (SAS) and the Glasgow Coma Scale (GCS) scores. DESIGN: Prospective, single-blinded observational study. SETTING: Eight-bed neurology-neurosurgery intensive care unit at the Cleveland Clinic Foundation. PATIENTS: Thirty critically ill patients admitted to the neurointensive care unit with primary brain injury and a decreased level of consciousness. MEASUREMENTS AND MAIN RESULTS: Patients were prospectively evaluated for level of consciousness using the RASS, SAS, and GCS every hour and simultaneously were monitored continuously with a BIS monitor for 6 hrs. A Spearman's correlation coefficient was used to correlate the BIS scores with clinical scales. In 15 patients monitored with the newer BIS XP version, the BIS values correlated significantly with the RASS (R2 = .810; p < .0001), SAS (R2 = .725; p < .0001), and GCS (R2 = .655; p < .0001). In 15 patients monitored with the older BIS 2.1.1 software, the correlation was as follows: for RASS, R2 = .30 (p < .008), for SAS: R2 = .376 (p < .001), and for GCS: R2 = .274 (p < .015). This correlation was maintained in patients who received sedative medications. CONCLUSIONS: A statistically significant correlation existed between BIS values and the RASS, SAS, and GCS scores in critically ill brain-injured patients, with and without sedation. The newer BIS XP software package may be a useful adjunctive tool in objective assessment of level of consciousness in brain-injured patients.