IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i11p1200-d561964.html
   My bibliography  Save this article

Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts

Author

Listed:
  • Sayyar Ahmad

    (Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain)

  • Charrise M. Ramkissoon

    (Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain)

  • Aleix Beneyto

    (Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain)

  • Ignacio Conget

    (Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 28001 Madrid, Spain
    Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08023 Barcelona, Spain)

  • Marga Giménez

    (Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 28001 Madrid, Spain
    Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08023 Barcelona, Spain)

  • Josep Vehi

    (Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain)

Abstract

Preclinical testing and validation of therapeutic strategies developed for patients with type 1 diabetes (T1D) require a cohort of virtual patients (VPs). However, current simulators provide a limited number of VPs, lack real-life scenarios, and inadequately represent intra- and inter-day variability in insulin sensitivity and blood glucose (BG) profile. The generation of a realistic scenario was achieved by using the meal patterns, insulin profiles (basal and bolus), and exercise sessions estimated as disturbances using clinical data from a cohort of 14 T1D patients using the Medtronic 640G insulin pump provided by the Hospital Clínic de Barcelona. The UVa/Padova’s cohort of adult patients was used for the generation of a new cohort of VPs. Insulin model parameters were optimized and adjusted in a day-by-day fashion to replicate the clinical data to create a cohort of 75 VPs. All primary and secondary outcomes reflecting the BG profile of a T1D patient were analyzed and compared to the clinical data. The mean BG 166.3 versus 162.2 mg/dL ( p = 0.19), coefficient of variation 32% versus 33% ( p = 0.54), and percent of time in range (70 to 180 mg/dL) 59.6% versus 66.8% ( p = 0.35) were achieved. The proposed methodology for generating a cohort of VPs is capable of mimicking the BG metrics of a real cohort of T1D patients from the Hospital Clínic de Barcelona. It can adopt the inter-day variations in the BG profile, similar to the observed clinical data, and thus provide a benchmark for preclinical testing of control techniques and therapy strategies for T1D patients.

Suggested Citation

  • Sayyar Ahmad & Charrise M. Ramkissoon & Aleix Beneyto & Ignacio Conget & Marga Giménez & Josep Vehi, 2021. "Generation of Virtual Patient Populations That Represent Real Type 1 Diabetes Cohorts," Mathematics, MDPI, vol. 9(11), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1200-:d:561964
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/11/1200/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/11/1200/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Navid Resalat & Joseph El Youssef & Nichole Tyler & Jessica Castle & Peter G Jacobs, 2019. "A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-17, July.
    2. Mounir Djouima & Ahmad Taher Azar & Saïd Drid & Driss Mehdi, 2018. "Higher Order Sliding Mode Control for Blood Glucose Regulation of Type 1 Diabetic Patients," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 7(1), pages 65-84, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Omer Mujahid & Ivan Contreras & Aleix Beneyto & Ignacio Conget & Marga Giménez & Josep Vehi, 2022. "Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models," Mathematics, MDPI, vol. 10(20), pages 1-15, October.
    2. Shu-Rong Yan & Khalid A. Alattas & Mohsen Bakouri & Abdullah K. Alanazi & Ardashir Mohammadzadeh & Saleh Mobayen & Anton Zhilenkov & Wei Guo, 2022. "Generalized Type-2 Fuzzy Control for Type-I Diabetes: Analytical Robust System," Mathematics, MDPI, vol. 10(5), pages 1-20, February.
    3. Alexis Alonso-Bastida & Manuel Adam-Medina & Rubén Posada-Gómez & Dolores Azucena Salazar-Piña & Gloria-Lilia Osorio-Gordillo & Luis Gerardo Vela-Valdés, 2022. "Dynamic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors," IJERPH, MDPI, vol. 19(2), pages 1-19, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alexis Alonso-Bastida & Manuel Adam-Medina & Rubén Posada-Gómez & Dolores Azucena Salazar-Piña & Gloria-Lilia Osorio-Gordillo & Luis Gerardo Vela-Valdés, 2022. "Dynamic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors," IJERPH, MDPI, vol. 19(2), pages 1-19, January.
    2. Turki Alsuwian & Muhammad Tayyeb & Arslan Ahmed Amin & Muhammad Bilal Qadir & Saleh Almasabi & Mohammed Jalalah, 2022. "Design of a Hybrid Fault-Tolerant Control System for Air–Fuel Ratio Control of Internal Combustion Engines Using Genetic Algorithm and Higher-Order Sliding Mode Control," Energies, MDPI, vol. 15(15), pages 1-23, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:11:p:1200-:d:561964. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.