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med.glucose - Diabetes & Glucose Management

HbA1c estimation, CGM analytics, insulin dosing algorithms, DKA assessment, and hypoglycemia classification.

HbA1c / eAG Conversions​

hba1c_from_glucose(mean_glucose_mgdl) → dict​

Estimate HbA1c from mean glucose using the ADAG equation (Nathan et al., Diabetes Care 2008).

eA1C = (mean_glucose + 46.7) / 28.7

Returns estimated_hba1c and category (NORMAL, PREDIABETES, DIABETES).

lib.glucose.hba1c_from_glucose(154)
# {'estimated_hba1c': 7.0, 'category': 'DIABETES'}

glucose_from_hba1c(hba1c) → dict​

Reverse ADAG: estimate mean glucose from HbA1c.

lib.glucose.glucose_from_hba1c(7.0)
# {'estimated_mean_glucose_mgdl': 154.2, 'estimated_mean_glucose_mmol': 8.6}

CGM Analytics​

time_in_range(readings, low=70, high=180) → dict​

Calculate Time In Range (TIR) from CGM readings. International consensus targets: 70-180 mg/dL.

Returns TIR, time below range (TBR), time above range (TAR), and assessment (EXCELLENT, GOOD, NEEDS_IMPROVEMENT, POOR).

readings = [85, 120, 140, 190, 210, 95, 130, 72, 180, 65]
lib.glucose.time_in_range(readings)
# {'time_in_range_pct': 70.0, 'time_below_range_pct': 10.0,
#  'time_above_range_pct': 20.0, 'assessment': 'EXCELLENT'}

gmi(mean_glucose_mgdl) → dict​

Glucose Management Indicator (Bergenstal et al. 2018). Replaces "estimated A1C" for CGM data.

GMI = 3.31 + 0.02392 * mean_glucose

glycemic_variability(readings) → dict​

Calculate variability metrics from CGM data:

MetricDescriptionTarget
CVCoefficient of variationunder 36%
MAGEMean Amplitude of Glycemic ExcursionsLower is better
SDStandard deviation-

Returns stability: STABLE, MODERATE_VARIABILITY, or HIGH_VARIABILITY.

lib.glucose.glycemic_variability([100, 150, 200, 120, 180, 90, 160])
# {'cv_percent': 28.5, 'mage': 65.0, 'stability': 'STABLE'}

Insulin Dosing​

insulin_sensitivity_factor(tdd, insulin_type="rapid") → dict​

Calculate ISF (how much 1 unit drops glucose):

  • Rapid-acting: 1800 rule (ISF = 1800 / TDD)
  • Regular: 1500 rule (ISF = 1500 / TDD)

carb_ratio(tdd) → dict​

Insulin-to-Carb Ratio using the 500 rule (ICR = 500 / TDD).

correction_dose(current_bg, target_bg, isf) → dict​

Calculate correction insulin dose: (current_BG - target_BG) / ISF.

basal_rate(tdd) → dict​

Basal insulin using the 50% rule. Returns daily dose and hourly pump rate.

sliding_scale(current_bg) → dict​

Traditional sliding scale dosing. Returns recommended dose and severity classification.

full_regimen(tdd, insulin_type="rapid") → dict​

Complete insulin regimen from TDD: basal dose, hourly rate, ISF, and ICR.

lib.glucose.full_regimen(40)
# {'basal_units_per_day': 20.0, 'basal_hourly_rate': 0.83,
#  'isf': 45.0, 'icr': 12.5}

DKA & Hypoglycemia​

dka_check(glucose, ph=7.4, bicarb=24, ketones=0) → dict​

Diabetic Ketoacidosis diagnostic assessment.

CriteriaThreshold
Glucoseover 250 mg/dL
pHunder 7.3
Bicarbonateunder 18 mEq/L
Ketonesover 0.6 mmol/L

Returns severity (MILD, MODERATE, SEVERE, NOT_DKA) and management recommendations.

lib.glucose.dka_check(350, 7.1, 12, 3.2)
# {'diagnosis': 'DKA', 'severity': 'MODERATE',
#  'management': 'IV insulin drip, fluid resuscitation, hourly monitoring'}

hypo_check(glucose) → dict​

ADA hypoglycemia classification:

  • Level 1 (under 70 mg/dL): Alert value
  • Level 2 (under 54 mg/dL): Clinically significant

MOISSCode Example​

protocol DiabetesReview {
    input: Patient p;

    let a1c = med.glucose.hba1c_from_glucose(154);
    let regimen = med.glucose.full_regimen(40);
    let dka = med.glucose.dka_check(350, 7.1, 12, 3.2);

    if dka.severity != "NOT_DKA" {
        alert "DKA detected" severity: critical;
    }
}

See Also​

  • med.lab — interpret glucose and HbA1c lab panels
  • med.pk — insulin and metformin dosing adjustments
  • med.scores — cardiovascular risk scoring for diabetic patients
  • med.icd — ICD-10 codes for diabetes diagnosis
  • Diabetes CGM Dashboard — full walkthrough combining all diabetes modules