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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.
There is currently no generally accepted formula for the optimal timing of health technology assessments (HTAs). This paper presents some of the relevant issues and then reviews the existing literature on timing of HTAs. It finds that the literature that specifically addresses these issues is limited. There is a consensus that HTAs should be initiated at an early stage of the development of a new health technology, and repeated during the life cycle of the technology. However, the questions of reliably identifying new technologies at an early stage in their development and of deciding on a detectable critical point for starting evaluation are not resolved. It is proposed that a system of categorization and prioritization of health technologies should be developed to allow decisions to be made as to when a strongly precautionary approach is required and how the limited resources available for HTA could be optimally deployed.
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