Automatic data processing to achieve a safe telemedical artificial pancreas.
BACKGROUND
The use of telemedicine for diabetes care has evolved over time, proving that it contributes to patient self-monitoring, improves glycemic control, and provides analysis tools for decision support. The timely development of a safe and robust ambulatory artificial pancreas should rely on a telemedicine architecture complemented with automatic data analysis tools able to manage all the possible high-risk situations and to guarantee the patient's safety.


METHODS
The Intelligent Control Assistant system (INCA) telemedical artificial pancreas architecture is based on a mobile personal assistant integrated into a telemedicine system. The INCA supports four control strategies and implements an automatic data processing system for risk management (ADP-RM) providing short-term and medium-term risk analyses. The system validation comprises data from 10 type 1 pump-treated diabetic patients who participated in two randomized crossover studies, and it also includes in silico simulation and retrospective data analysis.


RESULTS
The ADP-RM short-term risk analysis prevents hypoglycemic events by interrupting insulin infusion. The pump interruption has been implemented in silico and tested for a closed-loop simulation over 30 hours. For medium-term risk management, analysis of capillary blood glucose notified the physician with a total of 62 alarms during a clinical experiment (56% for hyperglycemic events). The ADP-RM system is able to filter anomalous continuous glucose records and to detect abnormal administration of insulin doses with the pump.


CONCLUSIONS
Automatic data analysis procedures have been tested as an essential tool to achieve a safe ambulatory telemedical artificial pancreas, showing their ability to manage short-term and medium-term risk situations.
Bioengineering and Telemedicine Group, Polytechnic University of Madrid, Madrid, Spain. elena@gbt.tfo.upm.es
M Elena
ME
Hernando
Gema
G
García-Sáez
Iñaki
I
Martínez-Sarriegui
Agustín
A
Rodríguez-Herrero
Carmen
C
Pérez-Gandía
Mercedes
M
Rigla
Alberto
A
de Leiva
Ismael
I
Capel
Belén
B
Pons
Enrique J
EJ
Gómez
D000553:Ambulatory Care; D001331:Automation; D001786:Blood Glucose / Q000187:drug effects*; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation*; D056902:Clinical Alarms; D018592:Cross-Over Studies; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000175:diagnosis / Q000628:therapy*; D003936:Diagnosis, Computer-Assisted; D004040:Dietary Carbohydrates / Q000008:administration & dosage / Q000378:metabolism; D004868:Equipment Failure; D006801:Humans; D007003:Hypoglycemia / Q000139:chemically induced / Q000517:prevention & control; D007004:Hypoglycemic Agents / Q000008:administration & dosage* / Q000009:adverse effects; D007328:Insulin / Q000008:administration & dosage* / Q000009:adverse effects; D007332:Insulin Infusion Systems; D019397:Pancreas, Artificial; D011237:Predictive Value of Tests; D016032:Randomized Controlled Trials as Topic; D012189:Retrospective Studies; D012308:Risk Management; D012815:Signal Processing, Computer-Assisted; D018511:Systems Integration; D017216:Telemedicine / Q000295:instrumentation*; D013813:Therapy, Computer-Assisted; D013997:Time Factors; D016896:Treatment Outcome
D001786:Blood Glucose; D004040:Dietary Carbohydrates; D007004:Hypoglycemic Agents; D007328:Insulin