Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis

被引:1
|
作者
Cabrera, Alvis [1 ]
Biagi, Lyvia [2 ]
Beneyto, Aleix [1 ]
Estremera, Ernesto [1 ]
Contreras, Ivan [1 ]
Gimenez, Marga [3 ,4 ]
Conget, Ignacio [3 ,4 ]
Bondia, Jorge [4 ,5 ]
Martin-Fernandez, Josep Antoni [6 ]
Vehi, Josep [1 ,4 ]
机构
[1] Univ Girona, Dept Elect Elect & Automat Engn, Girona 17003, Spain
[2] Fed Univ Technol Parana UTFPR, Campus Guarapuava, BR-85053525 Guarapuava, Brazil
[3] Hosp Clin Barcelona, Endocrinol & Nutr Dept, Diabet Unit, Barcelona 08036, Spain
[4] Inst Salud Carlos III, Ctr Invest Biomed Red Diabet & Enfermedades Metab, Madrid 28029, Spain
[5] Univ Politecn Valencia, Inst Univ Automat Informat Ind, Valencia 46022, Spain
[6] Univ Girona, Dept Comp Sci Appl Math & Stat, Girona 17003, Spain
关键词
compositional data; continuous glucose monitoring; prediction model; time in range; type; 1; diabetes;
D O I
10.3390/math11051241
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Glycemia assessment in people with type 1 diabetes (T1D) has focused on the time spent in different glucose ranges. As this time reflects the relative contributions to the finite duration of a day, it should be treated as compositional data (CoDa) that can be applied to T1D data. Previous works presented a tool for the individual categorization of days and proposed a probabilistic transition model between categories, although validation has hitherto not been presented. In this study, we consider data from eight real adult patients with T1D obtained from continuous glucose monitoring (CGM) sensors and introduce a methodology based on compositional methods to validate the previously presented probability transition model. We conducted 5-fold cross-validation, with both the training and validation data being CoDa vectors, which requires developing new performance metrics. We design new accuracy and precision measures based on statistical error calculations. The results show that the precision for the entire model is higher than 95% in all patients. The use of a probabilistic transition model can help doctors and patients in diabetes treatment management and decision-making. Although the proposed method was tested with CoDa applied to T1D data obtained from CGM, the newly developed accuracy and precision measures apply to any other data or validation based on CoDa.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Model Identification with Incomplete Input Data in Type 1 Diabetes
    Ozaslan, Basak
    Aiello, Eleonora M.
    Doyle, Francis J., III
    Dassau, Eyal
    IFAC PAPERSONLINE, 2023, 56 (02): : 6518 - 6524
  • [32] Development and validation of a lifetime prediction model for incident type 2 diabetes in patients with established cardiovascular disease: the CVD2DM model
    Helmink, Marga A. G.
    Peters, Sanne A. E.
    Westerink, Jan
    Harris, Katie
    Tillmann, Taavi
    Woodward, Mark
    van Sloten, Thomas T.
    van der Meer, Manon G.
    Teraa, Martin
    Dorresteijn, Jannick A. N.
    Ruigrok, Ynte M.
    Visseren, Frank L. J.
    Hageman, Steven H. J.
    EUROPEAN JOURNAL OF PREVENTIVE CARDIOLOGY, 2024, 31 (14) : 1671 - 1678
  • [33] Prediction of type 1 diabetes using a genetic risk model in the Diabetes Autoimmunity Study in the Young
    Frohnert, Brigitte I.
    Laimighofer, Michael
    Krumsiek, Jan
    Theis, Fabian J.
    Winkler, Christiane
    Norris, Jill M.
    Ziegler, Anette-Gabriele
    Rewers, Marian J.
    Steck, Andrea K.
    PEDIATRIC DIABETES, 2018, 19 (02) : 277 - 283
  • [34] Time in range and complications of diabetes: a cross-sectional analysis of patients with Type 1 diabetes
    Bezerra, Marta Fernandes
    Neves, Celestino
    Neves, Joao Sergio
    Carvalho, Davide
    DIABETOLOGY & METABOLIC SYNDROME, 2023, 15 (01)
  • [35] Influence of obesity on blood glucose control using continuous glucose monitoring data among patients with type 1 diabetes
    Nicolau, Joana
    Romano, Andrea
    Rodriguez, Irene
    Sanchis, Pilar
    Puga, Maria
    Masmiquel, Lluis
    ENDOCRINOLOGIA DIABETES Y NUTRICION, 2024, 71 (05): : 202 - 207
  • [36] Prediction of Type 2 Diabetes using Metagenomic Data and Identification of Taxonomic Biomarkers
    Temiz, Mustafa
    Kuzudisli, Cihan
    Yousef, Malik
    Bakir-Gungor, Burcu
    32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024, 2024,
  • [37] Development and Validation of a Risk Prediction Model for Sarcopenia in Chinese Older Patients with Type 2 Diabetes Mellitus
    Wang, Xinming
    Gao, Shengnan
    DIABETES METABOLIC SYNDROME AND OBESITY, 2024, 17 : 4611 - 4626
  • [38] Novel Data Mining Analysis Method on Risk Prediction of Type 2 Diabetes
    Guo, Hong
    Fan, ZhiChao
    Zeng, Yan
    JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 2022, 94 (11): : 1183 - 1198
  • [39] Predicting major outcomes in type 1 diabetes: a model development and validation study
    Soedamah-Muthu, Sabita S.
    Vergouwe, Yvonne
    Costacou, Tina
    Miller, Rachel G.
    Zgibor, Janice
    Chaturvedi, Nish
    Snell-Bergeon, Janet K.
    Maahs, David M.
    Rewers, Marian
    Forsblom, Carol
    Harjutsalo, Valma
    Groop, Per-Henrik
    Fuller, John H.
    Moons, Karel G. M.
    Orchard, Trevor J.
    DIABETOLOGIA, 2014, 57 (11) : 2304 - 2314
  • [40] Prediction and Prevention of Type 1 Diabetes
    Primavera, Marina
    Giannini, Cosimo
    Chiarelli, Francesco
    FRONTIERS IN ENDOCRINOLOGY, 2020, 11