PMID- 23294779
OWN - NLM
STAT- MEDLINE
DCOM- 20130828
LR  - 20240229
IS  - 1932-2968 (Electronic)
IS  - 1932-2968 (Linking)
VI  - 6
IP  - 6
DP  - 2012 Nov 1
TI  - Design of the health monitoring system for the artificial pancreas: low glucose 
      prediction module.
PG  - 1345-54
AB  - BACKGROUND: The purpose of this study was to design and evaluate a safety system 
      for the artificial pancreas device system (APDS). Safe operation of the APDS is a 
      critical task, where the safety system is engaged only as needed to ensure 
      reliable operation without positive feedback to the controller. METHODS: The 
      Health Monitoring System (HMS) was designed as a modular system to ensure the 
      safety of the APDS and the user. It was designed using a large set of ambulatory 
      data and evaluated in silico by inducing hypoglycemia with a missed meal [bolus 
      for a 65 g carbohydrate (CHO) meal] and administering rescue CHOs per HMS 
      alerting. The HMS was validated in-clinic with a real-life challenge of a subject 
      who overdosed insulin prior to admission. RESULTS: The HMS was evaluated for 
      clinical use with a 15 min prediction horizon. Retrospectively, 93.5% of episodes 
      were detected with 2.9 false alarms per day. During in silico evaluation, the HMS 
      reduced the time spent <70 mg/dl from 15% to 3%. When the HMS was first tested 
      in-clinic, the subject overdosed ~3 U of insulin prior to her arrival to a 
      closed-loop session (against protocol). The controller reduced insulin delivery, 
      and the HMS gave four alerts that were successfully received via clinical 
      software and text and multimedia messages. Even with insulin reduction and CHO 
      supplements, hypoglycemia was unavoidable but manageable due to the HMS, 
      confirming that a safety system to detect adverse events is an essential part of 
      the APDS. CONCLUSIONS: The ability of the HMS to be an effective alert system 
      that provides a safety layer to the APDS controller has been demonstrated in a 
      clinical setting.
CI  - (c) 2012 Diabetes Technology Society.
FAU - Harvey, Rebecca A
AU  - Harvey RA
AD  - Department of Chemical Engineering, University of California, Santa Barbara, 
      Santa Barbara, CA 93106, USA.
FAU - Dassau, Eyal
AU  - Dassau E
FAU - Zisser, Howard
AU  - Zisser H
FAU - Seborg, Dale E
AU  - Seborg DE
FAU - Jovanovic, Lois
AU  - Jovanovic L
FAU - Doyle, Francis J
AU  - Doyle FJ
LA  - eng
GR  - DK085628-01/DK/NIDDK NIH HHS/United States
GR  - DP3DK094331-01/DK/NIDDK NIH HHS/United States
PT  - Case Reports
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20121101
PL  - United States
TA  - J Diabetes Sci Technol
JT  - Journal of diabetes science and technology
JID - 101306166
RN  - 0 (Insulin)
SB  - IM
MH  - Algorithms
MH  - Blood Glucose Self-Monitoring/*instrumentation/methods
MH  - Diabetes Mellitus, Type 1/*therapy
MH  - Female
MH  - Humans
MH  - Hyperglycemia/*prevention & control
MH  - Hypoglycemia/*prevention & control
MH  - Insulin/therapeutic use
MH  - Insulin Infusion Systems
MH  - *Pancreas, Artificial
PMC - PMC3570874
EDAT- 2013/01/09 06:00
MHDA- 2013/08/29 06:00
PMCR- 2013/11/01
CRDT- 2013/01/09 06:00
PHST- 2013/01/09 06:00 [entrez]
PHST- 2013/01/09 06:00 [pubmed]
PHST- 2013/08/29 06:00 [medline]
PHST- 2013/11/01 00:00 [pmc-release]
AID - dst.6.6.1345 [pii]
AID - 10.1177/193229681200600613 [doi]
PST - epublish
SO  - J Diabetes Sci Technol. 2012 Nov 1;6(6):1345-54. doi: 10.1177/193229681200600613.