Event-Based Assessments of Sensor Accuracy during Exercise-Induced Reductions in Glucose in Children with Type 1 Diabetes Craig Kollman, 1 Darrell Wilson, 2 Roy Beck 1 and William Tamborlane 3 for the Diabetes Research in Children Network (DirecNet) Study Group 1. Tampa, FL; 2. Stanford, CA; 3. New Haven, CT;

Event-Based Assessments of Sensor Accuracy during Exercise-Induced Reductions in Glucose in Children with Type 1 Diabetes Craig Kollman, 1 Darrell Wilson, 2 Roy Beck 1 and William Tamborlane 3 for the Diabetes Research in Children Network (DirecNet) Study Group 1 Tampa, FL; 2 Stanford, CA; 3 New Haven, CT;. Currently, the accuracy of near-continuous glucose sensors is typically assessed using point-by-point comparisons. While useful in evaluating meters, these methods can be skewed by important temporal aspects of hypo- or hyperglycemic excursions. To compare event-based vs. point-by-point analyses of accuracy during exercise-induced falls in plasma glucose, we studied 22 children with T1D 8-17 y who wore a Freestyle Navigator Continuous Glucose Monitoring System (Abbott Diabetes Care, Alameda, CA) during a 24h CRC admission that included an exercise session. Blood samples for reference laboratory glucose measurements were taken q15 min during exercise. Three subjects were excluded from analysis because the blood samples were insufficient for laboratory measurements (N=1) or the glucose did not fall during exercise (N=2). Point-by-point analysis resulted in a median relative absolute difference (RAD) of 17% during exercise compared with 12% during non-exercise. Event analysis looking at the entire glycemic excursion (baseline to nadir) shows this discrepancy was primarily accounted for by a slower rate of fall in Navigator vs reference values (median 0.8 vs 1.4 mg/dL/min; p<0.001) and by a 5-30 min lag between the nadir Navigator and reference glucose levels (median 100 vs 75 min, respectively p<0.001). The sensor-measured fall was similar to that of the reference in each patient (figure). Point-by-point estimates of error can be unduly inflated by sensor lag during periods of rapid change. Event-based methods offer a simple way to incorporate the time dimension into analyses without a complicated or arbitrary adjustment for lag. Abstract

Introduction Many techniques used to assess the accuracy of near-continuous glucose sensors are borrowed from methods developed for meters. Rely on point-by-point comparisons. Do not assess trends over time. Event-based methods intended to incorporate the time element into analyses in a clinically meaningful way.

Point-by-Point Analysis Each glucose reading is paired to a reference measurement (within a few minutes). Sensor readings often adjusted for a fixed time lag. Difference measures calculated for each pair used to describe accuracy.

Point-by-Point Analysis Example with Hourly Reference Sampling Pair #1 Pair #2Pair #3

Point-by-Point Analysis Example Measures of Accuracy Difference: Sensor minus reference. Absolute Difference: Absolute value of Difference (always positive). Relative Absolute Difference (RAD): Absolute Difference divided by reference (expressed as a percentage). ISO Criteria: If reference ≤75 mg/dL sensor within ±15mg/dL; if reference>75 mg/dL sensor within ±20%. Error Grid: (Clarke or Consensus): Zone A or B.

Point-by-Point Analysis Previous Example Revisited Pair #1Pair #2Pair #3 Sensor Glucose Reference Glucose Difference+17+30–14 Absolute Difference RAD12%21%8% ISO Criteria MetYesNoYes Zone A or BYesYesYes

Point-by-Point Analysis Consensus Error Grid

Event Analysis Combine multiple points into clinically meaningful events. Define events to capture more of the time element. Example events would be hypo- or hyperglycemic episodes or hypo- or hyperglycemic excursions. Define difference measures based on the entire event rather than individual points. Requires more frequent reference sampling.

Event Analysis Defining Excursions Define the hypoglycemic excursion as the drop in glucose from baseline to nadir. Calculate separately for sensor vs. reference. Calculate rate of change from baseline until the nadir. Note the sensor and reference nadirs will often occur at different times. Rates of change therefore calculated over slightly different time periods.

Event Analysis Example Comparison of Hypoglycemic Excursions Reference Nadir Sensor Baseline Sensor Nadir Reference Baseline

Event Analysis Calculate Excursion Summaries from Previous Graph SensorReference Baseline Glucose Nadir Glucose Drop in Glucose = =61 Minutes to Nadir8050 Rate of Change*58÷80=0.761÷50=1.2 * mg/dL/minute.

Event Analysis Hypoglycemic Events Multiple glucose readings below the hypoglycemic threshold (e.g., 70 mg/dL) considered part of the same event. Calculation of false positive and sensitivity rates based on events rather than individual points. Allow ±10 mg/dL margin of error (e.g., sensor=68/reference=72 not counted as a false positive). Allow ± 30 minute range from beginning of event to confirm a sensor low or detect a low reference. Analogous rules for hyperglycemic events.

Illustration Using Actual Data DirecNet Pilot Study of the FreeStyle Navigator™ Continuous Glucose Monitoring System, (Abbott Diabetes Care, Alameda, CA). One of the objectives was to assess accuracy. Type 1 diabetes for at least 1 year. Age 3 to <18 years. Using a downloadable insulin infusion pump for a least 6 months.

Study Procedures Admitted to Clinical Research Center –Approximately 24 hours Exercise session for those older than 7 yrs –Four 15-min sessions walking on a treadmill at a target heart rate 140 bpm interspersed with three 5- min rest breaks Venous blood sampling –Just prior to start of exercise (Baseline) –Each rest period –End of exercise Reference glucoses measured at DirecNet Central Laboratory.

Point-by-Point Analysis N=19 subjects Median (25 th, 75 th percentiles) DuringOther ExerciseTimes N=97 pairsN=1,203 pairs Difference+17 (+3, +27)0 (-16, +16) Absolute Difference22 (12, 33)16 (8, 28) RAD17% (9%, 26%)12% (6%, 20%) ISO Criteria58%75%

Event Analysis Hypoglycemic Excursions during Exercise N=19 Subjects Drop in Glucose from Baseline to Nadir (mg/dL)

Event Analysis Hypoglycemic Excursions during Exercise N=19 Subjects Rate of Change from Baseline to Nadir (mg/dL/minute)

Event Analysis Hypoglycemic Excursions during Exercise N=19 subjects median (25 th -75 th percentiles) NavigatorLabDifference Abs. Diff. Baseline (-26, +18) 23 (17, 36) Glucose Drop a (-27, +4) 17 (8, 28) Minutes to Nadir (-7, +14) 22 (13, 42) Rate of Change b (-0.8, -0.2) 0.4 (0.2, 0.8) a-baseline minus nadir; b-mg/dL/minute (timed from baseline to nadir)

Event Analysis Detection of Hypo- and Hyperglycemic events SensitivityFalse Positive Inpatient Hypoglycemia*49%37% Hyperglycemia**83%18% Outpatient Hypoglycemia*62%28% Hyperglycemia**87%12% * Multiple readings ≤70 mg/dL within 30 min treated as same event. ** Multiple readings ≥200 mg/dL within 30 min treated as same event.

Remarks Point-by-point analysis may give the impression that the Navigator did not perform well during exercise. However, the Navigator generally tracked the exercise-induced drops in glucose fairly well. Rate of change was less when estimated by the Navigator which may have reflected the longer time necessary for the compartment it measures to reach the nadir.

Summary Event-based analyses can be used to supplement point-by-point comparisons to further describe the clinical utility of near- continuous glucose sensors. Requires more frequent reference sampling than is necessary for point-by- point analysis. Comparison of glucose excursions does not require adjustment for a fixed lag time. Amount of lag can vary by subject and within the same subject over time.

Contact Information Craig Kollman Address: Jaeb Center for Health Research Amberly Dr. – Suite 350 Tampa, FL Phone:

Barbara Davis Center –H. Peter Chase –Rosanna Fiallo-Scharer –Laurel Messer –Barbara Tallant University of Iowa –Eva Tsalikian –Michael Tansey –Linda Larson –Julie Coffey –Joanne Cabbage Nemours Children’s Clinic –Tim Wysocki –Nelly Mauras –Larry Fox –Keisha Bird –Kim Englert Stanford University –Bruce Buckingham –Darrell Wilson –Jennifer Block –Paula Clinton Yale University –William Tamborlane –Stuart Weinzimer –Elizabeth Doyle –Melody Martin –Amy Steffen Jaeb Center for Health Research –Roy Beck –Katrina Ruedy –Craig Kollman –Dongyuan Xing –Cynthia Stockdale