An artificial intelligence decision support system for the management of type 1 diabetes.
Adult
Algorithms
Artificial Intelligence
Blood Glucose
/ analysis
Computer Simulation
Decision Support Systems, Clinical
Diabetes Mellitus, Type 1
/ drug therapy
Disease Management
Glycemic Control
Humans
Hyperglycemia
/ blood
Hypoglycemia
/ blood
Hypoglycemic Agents
/ administration & dosage
Insulin
/ administration & dosage
Insulin Infusion Systems
Reproducibility of Results
Journal
Nature metabolism
ISSN: 2522-5812
Titre abrégé: Nat Metab
Pays: Germany
ID NLM: 101736592
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
25
09
2019
accepted:
23
04
2020
entrez:
23
7
2020
pubmed:
23
7
2020
medline:
1
1
2021
Statut:
ppublish
Résumé
Type 1 diabetes (T1D) is characterized by pancreatic beta cell dysfunction and insulin depletion. Over 40% of people with T1D manage their glucose through multiple injections of long-acting basal and short-acting bolus insulin, so-called multiple daily injections (MDI)
Identifiants
pubmed: 32694787
doi: 10.1038/s42255-020-0212-y
pii: 10.1038/s42255-020-0212-y
pmc: PMC7384292
mid: NIHMS1587540
doi:
Substances chimiques
Blood Glucose
0
Hypoglycemic Agents
0
Insulin
0
Banques de données
ClinicalTrials.gov
['NCT03443713']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
612-619Subventions
Organisme : NIDDK NIH HHS
ID : R01 DK120367
Pays : United States
Références
Paris, C. A. et al. Predictors of insulin regimens and impact on outcomes in youth with type 1 diabetes: the SEARCH for diabetes in youth study. J. Pediatr. 155, 183–189.e1 (2009).
doi: 10.1016/j.jpeds.2009.01.063
Miller, K. M. et al. Current state of type 1 diabetes treatment in the U.S.: updated data from the T1D Exchange clinic registry. Diabetes Care 38, 971–978 (2015).
doi: 10.2337/dc15-0078
Resalat, N., El Youssef, J., Tyler, N., Castle, J. & Jacobs, P. G. A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model. PLoS One 14, e0217301 (2019).
doi: 10.1371/journal.pone.0217301
Cover, T. & Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13, 21–27 (2006).
doi: 10.1109/TIT.1967.1053964
Nimri, R. et al. Adjusting insulin doses in patients with type 1 diabetes who use insulin pump and continuous glucose monitoring: Variations among countries and physicians. Diabetes Obes. Metab. 20, 2458–2466 (2018).
doi: 10.1111/dom.13408
Man, C. D. et al. The UVA/PADOVA type 1 diabetes simulator: new features. J. Diabetes Sci. Technol. 8, 26–34 (2014).
doi: 10.1177/1932296813514502
The Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Study Research Group. Intensive diabetes treatment and cardiovascular outcomes in type 1 diabetes: the DCCT/EDIC study 30-year follow-up. Diabetes Care 39, 686–693 (2016).
Schwartz, F. L., Guo, A., Marling, C. R. & Shubrook, J. H. Analysis of use of an automated bolus calculator reduces fear of hypoglycemia and improves confidence in dosage accuracy in type 1 diabetes mellitus patients treated with multiple daily insulin injections. J. Diabetes Sci. Technol. 6, 150–152 (2012).
doi: 10.1177/193229681200600118
Roze, S. et al. Cost-effectiveness of continuous subcutaneous insulin infusion versus multiple daily injections of insulin in type 1 diabetes: a systematic review. Diabet. Med. 32, 1415–1424 (2015).
doi: 10.1111/dme.12792
McNally, K., Rohan, J., Pendley, J. S., Delamater, A. & Drotar, D. Executive functioning, treatment adherence, and glycemic control in children with type 1 diabetes. Diabetes Care 33, 1159–1162 (2010).
doi: 10.2337/dc09-2116
Sarbacker, G. B. & Urteaga, E. M. Adherence to insulin therapy. Diabetes Spectr. 29, 166–170 (2016).
doi: 10.2337/diaspect.29.3.166
Kirwan, M., Vandelanotte, C., Fenning, A. & Duncan, J. M. Diabetes self-management smartphone application for adults with type 1 diabetes: randomized controlled trial. J. Med. Internet Res. 15, e235 (2013).
doi: 10.2196/jmir.2588
Charpentier, G. et al. The Diabeo software enabling individualized insulin dose adjustments combined with telemedicine support improves HbA1c in poorly controlled type 1 diabetic patients: a 6-month, randomized, open-label, parallel-group, multicenter trial (TeleDiab 1 Study). Diabetes Care 34, 533–539 (2011).
doi: 10.2337/dc10-1259
Wu, Y. et al. Mobile app-based interventions to support diabetes self-management: a systematic review of randomized controlled trials to identify functions associated with glycemic efficacy. JMIR Mhealth Uhealth 5, e35 (2017).
doi: 10.2196/mhealth.6522
Veazie, S et al. Rapid evidence review of mobile applications for self-management of diabetes. J. Gen. Intern. Med. 33, 1167–1176 (2018).
doi: 10.1007/s11606-018-4410-1
Beck, R. W. et al. Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: the DIAMOND randomized clinical trial. JAMA 317, 371–378 (2017).
doi: 10.1001/jama.2016.19975
Steil, G. M. et al. Use of automated clinical decision support (CDS) to effect glycemic control in elderly patients with T1D. Diabetes 67, 921-P (2018).
doi: 10.2337/db18-921-P
Palerm, C. C., Zisser, H., Jovanovic, L. & Doyle, F. J. 3rd A run-to-run control strategy to adjust basal insulin infusion rates in type 1 diabetes. J. Process Control 18, 258–265 (2008).
doi: 10.1016/j.jprocont.2007.07.010
Herrero, P., Bondia, J., Gimenez, M., Oliver, N. & Georgiou, P. Automatic adaptation of basal insulin using sensor-augmented pump therapy. J. Diabetes Sci. Technol. 12, 282–294 (2018).
doi: 10.1177/1932296818761752
Toffanin, C., Messori, M., Cobelli, C. & Magni, L. Automatic adaptation of basal therapy for type 1 diabetic patients: a run-to-run approach. Biomed. Signal Process. Control 31, 539–549 (2017).
doi: 10.1016/j.bspc.2016.09.002
Zisser, H., Palerm, C. C., Bevier, W. C., Doyle, F. J. 3rd & Jovanovic, L. Clinical update on optimal prandial insulin dosing using a refined run-to-run control algorithm. J. Diabetes Sci. Technol. 3, 487–491 (2009).
doi: 10.1177/193229680900300312
Herrero, P. et al. Advanced insulin bolus advisor based on run-to-run control and case-based reasoning. IEEE J. Biomed. Health Inform. 19, 1087–1096 (2015).
pubmed: 24956470
Perez-Gandia, C. et al. Decision support in diabetes care: the challenge of supporting patients in their daily living using a mobile glucose predictor. J. Diabetes Sci. Technol. 12, 243–250 (2018).
doi: 10.1177/1932296818761457
Breton, M. D. et al. Continuous glucose monitoring and insulin informed advisory system with automated titration and dosing of insulin reduces glucose variability in type 1 diabetes mellitus. Diabetes Technol. Ther. 20, 531–540 (2018).
doi: 10.1089/dia.2018.0079
Reddy, M. et al. Clinical safety and feasibility of the advanced bolus calculator for type 1 diabetes based on case-based reasoning: a 6-week nonrandomized single-arm pilot study. Diabetes Technol. Ther. 18, 487–493 (2016).
doi: 10.1089/dia.2015.0413
Resalat, N., El Youssef, J., Reddy, R., Castle, J. & Jacobs, P. G. Adaptive tuning of basal and bolus insulin to reduce postprandial hypoglycemia in a hybrid artificial pancreas. J. Process Control 80, 247–254 (2019).
doi: 10.1016/j.jprocont.2019.05.018
Sørensen, T. J A Method of Establishing Groups of Equal Amplitude in Plant Sociology Based on Similarity of Species Content and its Application to Analyses of the Vegetation on Danish Commons (I kommission hos E. Munksgaard, 1948).
Davidson, M. B., Duran, P., Davidson, S. J. & Lee, M. Comparison of insulin dose adjustments by primary care physicians and endocrinologists. Clin. Diabetes 36, 39–43 (2018).
doi: 10.2337/cd17-0021
Bashan, E. & Hodish, I. Frequent insulin dosage adjustments based on glucose readings alone are sufficient for a safe and effective therapy. J. Diabetes Complicat. 26, 230–236 (2012).
doi: 10.1016/j.jdiacomp.2012.03.012
Reddy, R. et al. The effect of exercise on sleep in adults with type 1 diabetes. Diabetes Obes. Metab. 20, 443–447 (2018).
doi: 10.1111/dom.13065
Castle, J. R. et al. Randomized outpatient trial of single- and dual-hormone closed-loop systems that adapt to exercise using wearable sensors. Diabetes Care 41, 1471–1477 (2018).
doi: 10.2337/dc18-0228
Pettus, J. & Edelman, S. V. Recommendations for using real-time continuous glucose monitoring (rtCGM) data for insulin adjustments in type 1 diabetes. J. Diabetes Sci. Technol. 11, 138–147 (2017).
doi: 10.1177/1932296816663747
Whitney, A. W. A direct method of nonparametric measurement selection. IEEE Trans. Comput. 20, 1100–1103 (1971).
doi: 10.1109/T-C.1971.223410
Scheiner, G. Practical CGM: Improving Patient Outcomes Through Continuous Glucose Monitoring (American Diabetes Association, 2015).
White, J. W., Rassweiler, A., Samhouri, J. F., Stier, A. C. & White, C. Ecologists should not use statistical significance tests to interpret simulation model results. Oikos 123, 385–388 (2014).
doi: 10.1111/j.1600-0706.2013.01073.x