Diabetic Utils

• Teams reinventing the same glucose conversion algorithms (mg/dL to mmol/L)
• A1C calculation formulas copy-pasted from medical papers without validation
• Time in Range (TIR) metrics implemented inconsistently across apps
• No type-safe APIs for glucose data structures
• Clinical calculation errors that could impact patient care

Quick Start

import {
  mgDlToMmolL,
  mmolLToMgDl,
  estimateGMI,
  estimateA1CFromAverage
} from 'diabetic-utils'

// Glucose unit conversions
mgDlToMmolL(180)  // -> 10.0
mmolLToMgDl(5.5)  // -> 99

// GMI calculation (multiple input formats)
estimateGMI(100, 'mg/dL')              // -> 5.4
estimateGMI('5.5 mmol/L')              // -> 5.4
estimateGMI({ value: 100, unit: 'mg/dL' })  // -> 5.4

// A1C estimation
estimateA1CFromAverage(154, 'mg/dL')  // -> 7.0

Glucose Conversions

• mgDlToMmolL() - Convert mg/dL to mmol/L with proper rounding
• mmolLToMgDl() - Convert mmol/L to mg/dL
• Handles edge cases and validates input ranges
• Uses standard conversion factor (18.0182) from clinical guidelines

A1C Calculations

• calculateA1C() - Convert average glucose to estimated A1C percentage
• Based on ADAG Study formula: A1C = (avg_glucose + 46.7) / 28.7
• Supports both mg/dL and mmol/L input
• Returns A1C as percentage (e.g., 7.2%)

Time in Range (TIR)

• calculateTIR() - Percentage of readings in target range (70-180 mg/dL)
• calculateTBR() - Time below range (hypoglycemia detection)
• calculateTAR() - Time above range (hyperglycemia detection)
• Configurable target ranges for personalized therapy goals
• Handles missing data and validates reading counts

Enhanced TIR (5-range)

import { calculateEnhancedTIR } from 'diabetic-utils'
import type { GlucoseReading } from 'diabetic-utils'

const readings: GlucoseReading[] = [
  { value: 120, unit: 'mg/dL', timestamp: '2024-01-01T08:00:00Z' },
  { value: 95, unit: 'mg/dL', timestamp: '2024-01-01T08:05:00Z' },
  { value: 180, unit: 'mg/dL', timestamp: '2024-01-01T08:10:00Z' },
  // ... more readings
]

const result = calculateEnhancedTIR(readings)

console.log(`TIR: ${result.inRange.percentage}%`)
console.log(`Very Low: ${result.veryLow.percentage}%`)
console.log(`Assessment: ${result.meetsTargets.overallAssessment}`)
console.log(result.meetsTargets.recommendations)

100% Test Coverage

• All calculation formulas against known medical values
• Edge cases: boundary values, negative numbers, zero handling
• Input validation and error handling
• Precision and rounding behavior

TypeScript-First Design

• Strongly typed glucose reading objects
• Enum types for units (mg/dL, mmol/L)
• Generic functions supporting both unit systems
• Full IntelliSense support in IDEs

Clinical Accuracy

• A1C calculation uses ADAG Study consensus formula
• TIR ranges follow ADA/ATTD 2019 guidelines
• Conversion factors match international standards
• Test cases derived from published medical examples

Developer Experience

• Zero dependencies - pure TypeScript implementation
• Tree-shakeable exports for minimal bundle size
• Comprehensive JSDoc documentation
• Published on npm with semantic versioning

Use Cases

• CGM (Continuous Glucose Monitor) companion apps
• Diabetes management dashboards
• Apple Watch and wearable health apps (like GlucoseIQ)
• HealthKit integration layers
• Clinical research data processing

• Featured in Google AI Overview for diabetes developer tools--recognized as a go-to resource for glucose calculations
• Adopted by health tech teams building production CGM apps and diabetes management platforms
• 100% test coverage ensures clinical calculation accuracy, critical for patient-facing applications
• Type-safe API prevents unit confusion bugs (mg/dL vs mmol/L) that plague glucose apps
• Open-source contribution to the diabetes developer community, reducing duplicated effort
• Zero dependencies makes it safe to adopt in production health apps with strict security requirements