Use of continuous glucose monitoring systems in pediatric patients in the perioperative environment: Challenges and machine learning opportunities

Table 1 Currently available continuous glucose monitoring systems used in children in the United States

Company	Dexcom, Inc	Abbott laboratories	Medtronic	Senseonics
Product	Dexcom G6	FreeStyle Libre 2	Guardian connect	Eversense
Approved age of use (years)	≥ 2	≥ 4	≥ 14	≥ 18
Sensor location	Abdomen	Upper arm	Abdomen or upper arm	Upper arm
Communication technique	Cloud-based wireless system	Bluetooth with smartphone. Uploads to cloud with app	Bluetooth to smartphone. Uploads to cloud	Bluetooth to smartphone. Cloud storage
Year of FDA approval	2018	2020	2018	2018
Special features	Glucose streaming to smartphone, customizable alarms, compatible with smartwatches	14-day wear, real-time alarms. Option for high and low glucose alerts	Predictive alerts (60 minutes before reaching low/high levels, real-time glucose values, predictive alerts, critical alerts customizable alerts)	Long wear sensor (up to 90 days in United States), real-time glucose data, alerts and alarms signaled with on-body vibration, customizable alarms through app
Ability to integrate with insulin pump	Yes	Some	Yes	Yes
Smartphone requirement	Dexcom Clarity	LibreView	Carelink	Eversense data management system pro
Pharmacologic interferences	Acetaminophen, hydroxyurea	Vitamin C	Acetaminophen	Acetaminophen, mannitol or sorbitol
Contraindications	MRI, X ray, diathermy	MRI, X ray, diathermy	MRI, X ray, diathermy	MRI, diathermy, lithotripsy
Device classification	Real-time glucose display	Intermittent scanned	Real-time glucose display	Implant
EHR compatibility potential	Yes	Yes	Yes	Yes

EHR: Electronic health record; MRI: Magnetic resonance imaging; FDA: Food and Drug Administration.

Table 2 Preprocedural checklist recommendations for type 1 diabetes pediatric surgical patients

	Preprocedural visit checklist for pediatric T1D with CGMS
	Preop checklist for T1D patient as obtained by endocrinologist:
1	Verify type of insulin pump, CGM device and manufacturer
2	Verify patients’ insulin type and basal rate settings
3	Draw HbA1c and electrolyte levels
4	Verify insulin: Carbohydrate ratio, correction factor
5	Check for interference of operative site with CGMS equipment
6	Ensure patient has new sensor, working battery, and pump supplies on day of surgery

T1D: Type 1 diabetes; CGMS: Continuous glucose monitors systems; HbA1c: Hemoglobin A1c.

Table 3 Summary of International Society for Pediatric and Adolescent Diabetes recommendations

ISPAD 2022 clinical practice guidelines for management of children with diabetes having surgery
Glycemic goals for surgery
BG should be maintained between 90-180 mg/dL
Prevent perioperative hypoglycemia and DKA
Assessment of pediatric T1D prior to surgery/or anesthesia
Preoperative endocrine consultation: Thorough assessment of glycemia, assess for ketones (blood and urine) formalize perioperative glycemic plan
Preoperative care
If patient expected to receive GA, can be admitted to hospital or same-day clinic
Insulin is required, even in fasting state, to avoid DKA
POC BG should be checked and recorded every hour
Intraoperative care
Monitor POC BG every hour and continue in recovery
CGM can be continued intraoperatively but validated with POC BG levels
Postoperative care
Give short or rapid acting insulin (based upon insulin: Carbohydrate ratio or correction factor)
More frequent CGM/POC BG levels recommended for 24-48 hours after surgery due to surgical stressors
Validate CGM readings after exposure to anesthetic agents with POC BG
Target BG levels in postoperative period between 140-180 mg/dL

ISPAD: International Society for Pediatric and Adolescent Diabetes; CGM: Continuous glucose monitors; BG: Blood glucose; POC: Point-of-care; T1D: Type 1 diabetes; DKA: Diabetic ketoacidosis.

Table 4 Common medication interferences on continuous glucose monitor readings

Medication	Effect on continuous glucose monitor reading
Acetaminophen	↑
Aspirin	↓
Ascorbic acid (Vitamin C)	↓
Lisinopril	↑ or ↓
Losartan	↑ or ↓
Atenolol	↑ or ↓
Albuterol	↑ or ↓
Hydroxyurea	↑ or ↓

Table 5 Summary of the health level 7 fast healthcare interoperability resource protocol criteria

	HL7 FHIR protocol development criteria
1	Establishing a platform that can connect the technologies between different IT systems
2	Ensure data security for HIPPA regulations while maintaining highest level of patient privacy
3	Adopt or establish protocols which standardize data for ease of interpretation by EHR
4	Application programming interfaces are needed to facilitate communication between CGM cloud-based repository and the hospital EHR
5	Ensure data accuracy with data mapping and integration
6	User interface adjustments-allows personnel to follow data trends and gain insights into patient care specifics
7	Training and support-ongoing education of healthcare providers on how to interpret and use blood glucose data

CGM: Continuous glucose monitors; EHR: Electronic health record; IT: Information technology; HIPPA: Health Insurance Portability and Accountability Act; HL7 FHIR: Health level 7 fast healthcare interoperability resources.

Table 6 Machine learning applications commonly used in continuous glucose monitors

AI technique	Predictive modeling	Pattern recognition	Event prediction
Methodology	(1) Supervised learning: Use of labeled data in training models to predict future glucose levels (Hemoglobin A1c); (2) Case based reasoning: Adapts solutions based on previous data history	Unsupervised learning: Use of unlabeled data in training models used to identify patterns and relationships in the data	Supervised learning: Uses datasets with specific outcomes (hypo and hyperglycemia) for event prediction
Algorithms used	(1) Linear regression, decision trees, random forests, neural networks; (2) Similarity measures, nearest neighbor	k-means clustering, isolation forests	support vector machines, logistic regression and deep learning (require more resources for efficiency)
Diabetic applications	Strength: Provides early alerts for high and low glucose readings. Weakness: Requires copious data points for accuracy	Strength: Detects glucose patterns and atypical readings. Weakness: Clustering can require interpretation, can result in false + especially when readings are varied	Strength: Prompts critical alerts in hypoglycemia. Weakness: May produce false positive alarms
Examples in diabetic care	Predictive modeling, self-management tools, retinal screening	Decision support, prediction models, self-management tools, retinal screening	Predictive modeling, patient self-management tools, retinal screening

AI: Artificial intelligence.