Dataset Information

Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric.

ABSTRACT:

Background

Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typically several metrics are reported, including time in range, time in mild and severe hypoglycemia, coefficient of variation, and others. To date, there is no one metric that combines all of these individual metrics to give a number indicative of overall performance. We proposed a composite metric that combines 5 commonly reported metrics, and we used this composite metric to compare 6 glucose controllers.

Methods

We evaluated the following controllers: Ideal Medical Technologies (IMT) artificial-intelligence-based controller, Yale protocol, Glucommander, Wintergerst et al PID controller, GRIP, and NICE-SUGAR. We evaluated each controller across 80 simulated patients, 4 clinically relevant exogenous dextrose infusions, and one nonclinical infusion as a test of the controller's ability to handle difficult situations. This gave a total of 2400 5-day simulations, and 585?604 individual glucose values for analysis. We used a random walk sensor error model that gave a 10% MARD. For each controller, we calculated severe hypoglycemia (<40 mg/dL), mild hypoglycemia (40-69 mg/dL), normoglycemia (70-140 mg/dL), hyperglycemia (>140 mg/dL), and coefficient of variation (CV), as well as our novel controller metric.

Results

For the controllers tested, we achieved the following median values for our novel controller scoring metric: IMT: 88.1, YALE: 46.7, GLUC: 47.2, PID: 50, GRIP: 48.2, NICE: 46.4.

Conclusion

The novel scoring metric employed in this study shows promise as a means for evaluating new and existing ICU-based glucose controllers, and it could be used in the future to compare results of glucose control studies in critical care. The IMT AI-based glucose controller demonstrated the most consistent performance results based on this new metric.

SUBMITTER: DeJournett J

PROVIDER: S-EPMC5951048 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric.

DeJournett Jeremy J DeJournett Leon L

Journal of diabetes science and technology 20170622 6

<h4>Background</h4>Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates and thereby decrease health care expenditures. To evaluate what constitutes effective glucose control, typically several metrics are reported, including time in range, time in mild and severe hypoglycemia, coefficient of variation, and others. To date, there is no one metric that combines all of these individual metrics to give a number indicative of o ...[more]

PMID: 28637358

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Project description:BackgroundMultiple scoring systems have been developed for both the intensive care unit (ICU) and the emergency department (ED) to risk stratify patients and predict mortality. However, it remains unclear whether the additional data needed to compute ICU scores improves mortality prediction for critically ill patients compared to the simpler ED scores.MethodsWe studied a prospective observational cohort of 227 critically ill patients admitted to the ICU directly from the ED at an academic, tertiary care medical center. We compared Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, Simplified Acute Physiology Score (SAPS) II, Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS), Prince of Wales Emergency Department Score (PEDS), and a pre-hospital critical illness prediction score developed by Seymour et al. (JAMA 2010, 304(7):747-754). The primary endpoint was 60-day mortality. We compared the receiver operating characteristic (ROC) curves of the different scores and their calibration using the Hosmer-Lemeshow goodness-of-fit test and visual assessment.ResultsThe ICU scores outperformed the ED scores with higher area under the curve (AUC) values (p = 0.01). There were no differences in discrimination among the ED-based scoring systems (AUC 0.698 to 0.742; p = 0.45) or among the ICU-based scoring systems (AUC 0.779 to 0.799; p = 0.60). With the exception of the Seymour score, the ED-based scoring systems did not discriminate as well as the best-performing ICU-based scoring system, APACHE III (p = 0.005 to 0.01 for comparison of ED scores to APACHE III). The Seymour score had a superior AUC to other ED scores and, despite a lower AUC than all the ICU scores, was not significantly different than APACHE III (p = 0.09). When data from the first 24 h in the ICU was used to calculate the ED scores, the AUC for the ED scores improved numerically, but this improvement was not statistically significant. All scores had acceptable calibration.ConclusionsIn contrast to prior studies of patients based in the emergency department, ICU scores outperformed ED scores in critically ill patients admitted from the emergency department. This difference in performance seemed to be primarily due to the complexity of the scores rather than the time window from which the data was derived.

Project description:Background Despite many studies in the field examining excessive noise in the intensive care unit, this issue remains an ongoing problem. A limiting factor in the progress of the field is the inability to draw conclusions across studies due to the different and poorly reported approaches used. Therefore, the first goal is to present a method for the general measurement of sound pressure levels and sound sources, with precise details and reasoning, such that future studies can use these procedures as a guideline. The two procedures used in the general method will outline how to record sound pressure levels and sound sources, using sound level meters and observers, respectively. The second goal is to present the data collected using the applied method to show the feasibility of the general method and provide results for future reference. Methods The general method proposes the use of two different procedures for measuring sound pressure levels and sound sources in the intensive care unit. The applied method uses the general method to collect data recorded over 24-h, examining two beds in a four-bed room, via four sound level meters and four observers each working one at a time. Results The interrater reliability of the different categories was found to have an estimate of >0.75 representing good and excellent estimates, for 19 and 16 of the 24 categories, for the two beds examined. The equivalent sound pressure levels (LAeq) for the day, evening, and night shift, as an average of the sound level meters in the patient room, were 54.12, 53.37, and 49.05 dBA. In the 24-h measurement period, talking and human generated sounds occurred for a total of 495 (39.29% of the time) and 470 min (37.30% of the time), at the two beds of interest, respectively. Conclusion A general method was described detailing two independent procedures for measuring sound pressure levels and sound sources in the ICU. In a continuous data recording over 24 h, the feasibility of the proposed general method was confirmed. Moreover, good and excellent interrater reliability was achieved in most categories, making them suitable for future studies.

Dataset Information

Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric.

Background

Methods

Results

Conclusion

Publications

Comparative Simulation Study of Glucose Control Methods Designed for Use in the Intensive Care Unit Setting via a Novel Controller Scoring Metric.

Similar Datasets

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