Dataset Information

Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control with repeated large unannounced meals.

ABSTRACT:

Background

Closed-loop control of blood glucose levels in people with type 1 diabetes offers the potential to reduce the incidence of diabetes complications and reduce the patients' burden, particularly if meals do not need to be announced. We therefore tested a closed-loop algorithm that does not require meal announcement.

Materials and methods

A multiple model probabilistic predictive controller (MMPPC) was assessed on four patients, revised to improve performance, and then assessed on six additional patients. Each inpatient admission lasted for 32?h with five unannounced meals containing approximately 1?g/kg of carbohydrate per admission. The system used an Abbott Diabetes Care (Alameda, CA) Navigator(®) continuous glucose monitor (CGM) and Insulet (Bedford, MA) Omnipod(®) insulin pump, with the MMPPC implemented through the artificial pancreas system platform. The controller was initialized only with the patient's total daily dose and daily basal pattern.

Results

On a 24-h basis, the first cohort had mean reference and CGM readings of 179 and 167?mg/dL, respectively, with 53% and 62%, respectively, of readings between 70 and 180?mg/dL and four treatments for glucose values <70?mg/dL. The second cohort had mean reference and CGM readings of 161 and 142?mg/dL, respectively, with 63% and 78%, respectively, of the time spent euglycemic. There was one controller-induced hypoglycemic episode. For the 30 unannounced meals in the second cohort, the mean reference and CGM premeal, postmeal maximum, and 3-h postmeal values were 139 and 132, 223 and 208, and 168 and 156?mg/dL, respectively.

Conclusions

The MMPPC, tested in-clinic against repeated, large, unannounced meals, maintained reasonable glycemic control with a mean blood glucose level that would equate to a mean glycated hemoglobin value of 7.2%, with only one controller-induced hypoglycemic event occurring in the second cohort.

SUBMITTER: Cameron F

PROVIDER: S-EPMC4201242 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Publications

Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control with repeated large unannounced meals.

Cameron Fraser F Niemeyer Günter G Wilson Darrell M DM Bequette B Wayne BW Benassi Kari S KS Clinton Paula P Buckingham Bruce A BA

Diabetes technology & therapeutics 20140926 11

<h4>Background</h4>Closed-loop control of blood glucose levels in people with type 1 diabetes offers the potential to reduce the incidence of diabetes complications and reduce the patients' burden, particularly if meals do not need to be announced. We therefore tested a closed-loop algorithm that does not require meal announcement.<h4>Materials and methods</h4>A multiple model probabilistic predictive controller (MMPPC) was assessed on four patients, revised to improve performance, and then asse ...[more]

PMID: 25259939

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Project description:BACKGROUND:Machine-learning or deep-learning algorithms for clinical diagnosis are inherently dependent on the availability of large-scale clinical datasets. Lack of such datasets and inherent problems such as overfitting often necessitate the development of innovative solutions. Probabilistic modeling closely mimics the rationale behind clinical diagnosis and represents a unique solution. OBJECTIVE:The aim of this study was to develop and validate a probabilistic model for differential diagnosis in different medical domains. METHODS:Numerical values of symptom-disease associations were utilized to mathematically represent medical domain knowledge. These values served as the core engine for the probabilistic model. For the given set of symptoms, the model was utilized to produce a ranked list of differential diagnoses, which was compared to the differential diagnosis constructed by a physician in a consult. Practicing medical specialists were integral in the development and validation of this model. Clinical vignettes (patient case studies) were utilized to compare the accuracy of doctors and the model against the assumed gold standard. The accuracy analysis was carried out over the following metrics: top 3 accuracy, precision, and recall. RESULTS:The model demonstrated a statistically significant improvement (P=.002) in diagnostic accuracy (85%) as compared to the doctors' performance (67%). This advantage was retained across all three categories of clinical vignettes: 100% vs 82% (P<.001) for highly specific disease presentation, 83% vs 65% for moderately specific disease presentation (P=.005), and 72% vs 49% (P<.001) for nonspecific disease presentation. The model performed slightly better than the doctors' average in precision (62% vs 60%, P=.43) but there was no improvement with respect to recall (53% vs 56%, P=.27). However, neither difference was statistically significant. CONCLUSIONS:The present study demonstrates a drastic improvement over previously reported results that can be attributed to the development of a stable probabilistic framework utilizing symptom-disease associations to mathematically represent medical domain knowledge. The current iteration relies on static, manually curated values for calculating the degree of association. Shifting to real-world data-derived values represents the next step in model development.

Dataset Information

Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control with repeated large unannounced meals.

Background

Materials and methods

Results

Conclusions

Publications

Inpatient trial of an artificial pancreas based on multiple model probabilistic predictive control with repeated large unannounced meals.

Similar Datasets

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