Functional data analysis and prediction tools for continuous glucose-monitoring studies.
Functional data analysis and prediction tools for continuous glucose-monitoring studies.
复制标题
DOI：
10.1017/cts.2020.545
复制
发表时间：
2020-09-22
影响因子：
2.6
通讯作者：
Szczesniak RD
中科院分区：
文献类型：
--
作者：
Gecili E;Huang R;Khoury JC;King E;Altaye M;Bowers K;Szczesniak RD
To identify phenotypes of type 1 diabetes based on glucose curves from continuous glucose-monitoring (CGM) using functional data (FD) analysis to account for longitudinal glucose patterns. We present a reliable prediction model that can accurately predict glycemic levels based on past data collected from the CGM sensor and real-time risk of hypo-/hyperglycemic for individuals with type 1 diabetes. A longitudinal cohort study of 443 type 1 diabetes patients with CGM data from a completed trial. The FD analysis approach, sparse functional principal components (FPCs) analysis was used to identify phenotypes of type 1 diabetes glycemic variation. We employed a nonstationary stochastic linear mixed-effects model (LME) that accommodates between-patient and within-patient heterogeneity to predict glycemic levels and real-time risk of hypo-/hyperglycemic by creating specific target functions for these excursions. The majority of the variation (73%) in glucose trajectories was explained by the first two FPCs. Higher order variation in the CGM profiles occurred during weeknights, although variation was higher on weekends. The model has low prediction errors and yields accurate predictions for both glucose levels and real-time risk of glycemic excursions. By identifying these distinct longitudinal patterns as phenotypes, interventions can be targeted to optimize type 1 diabetes management for subgroups at the highest risk for compromised long-term outcomes such as cardiac disease or stroke. Further, the estimated change/variability in an individual’s glucose trajectory can be used to establish clinically meaningful and patient-specific thresholds that, when coupled with probabilistic predictive inference, provide a useful medical-monitoring tool.
登录
查看更多内容
影响因子：
7.7
作者：
SERVICE, FJ
通讯作者：
SERVICE, FJ
混合效应机器学习：预测血红蛋白A1c纵向变化的框架。
影响因子：
4.5
作者：
Ngufor C;Van Houten H;Caffo BS;Shah ND;McCoy RG
通讯作者：
McCoy RG
影响因子：
4
作者：
Frøslie KF;Røislien J;Qvigstad E;Godang K;Bollerslev J;Voldner N;Henriksen T;Veierød MB
通讯作者：
Veierød MB
影响因子：
2
作者：
Dmitrienko, A;Wang, MD
通讯作者：
Wang, MD
影响因子：
4.3
作者：
Gupta, Resmi;Khoury, Jane C.;Szczesniak, Rhonda D.
通讯作者：
Szczesniak, Rhonda D.