cgmguru-package: Advanced Continuous Glucose Monitoring Analysis and...

In cgmguru: Advanced Continuous Glucose Monitoring Analysis with High-Performance C++ Backend

Advanced Continuous Glucose Monitoring Analysis and GRID-Based Event Detection

Description

A high-performance R package for comprehensive Continuous Glucose Monitoring (CGM) data analysis with optimized C++ implementations. The package provides advanced tools for CGM data analysis with two primary capabilities: GRID and postprandial peak detection, and extended glycemic events detection aligned with international consensus CGM metrics.

Details

The package implements several key algorithms for CGM analysis:

• GRID Algorithm: Detects rapid glucose rate increases (commonly \geq 90–95 mg/dL/hour) with configurable thresholds and gaps for postprandial peak detection

• Postprandial Peak Detection: Finds peak glucose after GRID points using local maxima and configurable time windows

• Consensus CGM Metrics Event Detection: Level 1/2 hypo- and hyperglycemia detection with duration validation (default minimum 15 minutes) aligned with Battelino et al. (2023) international consensus

• Advanced Analysis Tools: Local maxima finding, excursion analysis, and robust episode validation utilities

Core algorithms are implemented in optimized C++ via 'Rcpp' for accurate and fast analysis on large datasets, making the package suitable for both research and clinical applications.

Main Functions

grid

GRID algorithm for detecting rapid glucose rate increases

maxima_grid

Combined maxima detection and GRID analysis for postprandial peaks

detect_hyperglycemic_events

Hyperglycemic event detection (Level 1/2/Extended)

detect_hypoglycemic_events

Hypoglycemic event detection (Level 1/2/Extended)

detect_all_events

Comprehensive detection of all glycemic event types

find_local_maxima

Local maxima identification in glucose time series

orderfast

Fast dataframe ordering utility

Data Requirements

Input dataframes should contain:

• id: Patient identifier (character or factor)

• time: POSIXct timestamps

• gl: Glucose values in mg/dL

All function arguments and return values are expected to be in tibble format. For convenience, single-column parameters can be passed as vectors in R, which will be automatically converted to single-column tibbles.

Examples

# Basic GRID analysis
result <- grid(cgm_data, gap = 15, threshold = 130)

# Postprandial peak detection (GRID-based)
maxima <- maxima_grid(cgm_data, threshold = 130, gap = 60, hours = 2)

# Level 1 Hyperglycemic event detection
events <- detect_hyperglycemic_events(cgm_data, start_gl = 180, 
                                     dur_length = 15, end_length = 15, 
                                     end_gl = 180)

# Comprehensive event detection
all_events <- detect_all_events(cgm_data, reading_minutes = 5)

Author(s)

Sang Ho Park [e-mail]

References

• Battelino, T., et al. "Continuous glucose monitoring and metrics for clinical trials: an international consensus statement." *The Lancet Diabetes & Endocrinology* 11.1 (2023): 42-57.

• Harvey, Rebecca A., et al. "Design of the glucose rate increase detector: a meal detection module for the health monitoring system." *Journal of diabetes science and technology* 8.2 (2014): 307-320.

• Chun, E., et al. "iglu: interpreting glucose data from continuous glucose monitors." R package version 3.0 (2023).

For more information about the GRID algorithm and CGM analysis methodologies, see the package vignette: vignette("intro", package = "cgmguru")

See Also

grid, maxima_grid, detect_hyperglycemic_events, detect_all_events

cgmguru documentation built on Nov. 6, 2025, 1:07 a.m.