Resumen
Aims/hypothesis Diabetes Type 1 (DT1) therapy by means of artificial pancreas consisting on insulin pump with CGM and hybrid "closed-loop"control algorithm trained with machine learning technology provides better glycemia control than multi-daily injection, insulin pump without CGM and "sensor assisted"insulin pump therapies.Methods Using Accu-Chek smart pix software to analyze the own data collected in-vivo by JC Peiro, author and DT1 patient, in the period August 2004 to August 2019, collecting glycemia control data using the different therapies. Accu-Chek smart pix has been used to collect 4.621 glycemia tests for a period of 1.241 days. The period measured with CGM contains data with +90% sensor coverage. Control graphics measure mean and median glycemia, standard deviation, time in range %, above range%, below range %, % hypoglycemia, high glucose blood index and low glucose blood index. Finally, analysis is validated in-silico using the UVA/Padova T1DMS simulator, with MATLAB and a population of 30 individuals under Multi-daily Injection and under "closed-loop"artificial pancreas therapies.Results The improvement of therapy with artificial pancreas with hybrid "closed-loop"control algorithm, when comparing to MDI reduces-70.7% the periods above range, reduces-67.2% periods below range, TIR% increases 75% reaching 84%, hypoglycemia's % reduced-91.2%, HGBI reduces-67%, LGBI reduces-73.8% reaching 2.3 and 1.6 respectively, reducing the mean glycemia in-16% reaching mean of 121mg/dL with SD 42mg/dL, reducing median glycemia in-20% reaching median of 115 mg/dL with SD 55 mg/dL and reducing the Glycated Hemoglobin (HbA1c)-20.1% reaching a 6.6% value.Conclusions/interpretation We can conclude that therapy for DT1 using "hybrid closed-loop"artificial pancreas controlled by ML trained algorithm provides statistically better glycemic control results. The improvement of the treatment is significantly better regarding all the analyzed therapies: MDI, insulin pump without CGM and "sensor assisted"insulin pump.
| Idioma original | Inglés |
|---|---|
| Título de la publicación alojada | 2020 International Conference on Data Analytics for Business and Industry |
| Subtítulo de la publicación alojada | Way Towards a Sustainable Economy, ICDABI 2020 |
| Editorial | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (versión digital) | 9781728196756 |
| DOI | |
| Estado | Publicada - 26 oct 2020 |
| Publicado de forma externa | Sí |
| Evento | 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020 - Sakheer, Bahréin Duración: 26 oct 2020 → 27 oct 2020 |
Serie de la publicación
| Nombre | 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020 |
|---|
Conferencia
| Conferencia | 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020 |
|---|---|
| País/Territorio | Bahréin |
| Ciudad | Sakheer |
| Período | 26/10/20 → 27/10/20 |
ODS de las Naciones Unidas
Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible
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ODS 3: Salud y bienestar
Huella
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