The high volume of Continuous Glucose Monitoring (CGM) data presents a significant bottleneck for large-scale clinical research. To address this computational challenge, we developed cgmguru, an R package that utilizes Rcpp to seamlessly integrate R and C++ for high-performance processing.
The cgmguru package calculates established CGM consensus metrics, including level 1/2 and extended hyper- and hypoglycemic events, based on CGM consensus statement for clinical trials. Furthermore, the package implements a novel algorithm for detecting post-prandial peak glucose, based on the Glucose Rate Increase Detector (GRID) algorithm. This detection method has been successfully applied in clinical research.
In this session, I will introduce how CGM research is used in the pharmaceutical industry. I will also demonstrate how the cgmguru package can be used in both clinical trials and observational studies.
Sang Ho Park
Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University
An R Package cgmguru for Automated Glycemic Event Detection From Continuous Glucose Monitoring Data
The high volume of Continuous Glucose Monitoring (CGM) data presents a significant bottleneck for large-scale clinical research. To address this computational challenge, we developed cgmguru, an R package that utilizes Rcpp to seamlessly integrate R and C++ for high-performance processing. The cgmguru package calculates established CGM consensus metrics, including level 1/2 and extended hyper- and hypoglycemic events, based on CGM consensus statement for clinical trials. Furthermore, the package implements a novel algorithm for detecting post-prandial peak glucose, based on the Glucose Rate Increase Detector (GRID) algorithm. This detection method has been successfully applied in clinical research. In this session, I will introduce how CGM research is used in the pharmaceutical industry. I will also demonstrate how the cgmguru package can be used in both clinical trials and observational studies.