Assessing sensor accuracy for non-adjunct use of continuous glucose monitoring

Abstract

Background: The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes.

Materials and methods: A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to "replay" real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows.

Results: In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels ≤50 mg/dL and BG levels ≤39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD =10%. Similarly, hyperglycemia (frequency of BG levels ≥250 mg/dL and BG levels ≥400 mg/dL) increased and displayed an abrupt slope change at MARD=10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively.

Conclusions: Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD=10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.

Figures

FIG. 1.

Hypoglycemia outcomes from six treatment modalities plotted (A and C) against sensor mean absolute relative difference (MARD) and (B and D) against the frequency of sensor errors exceeding 20%: (A and B) percentage of blood glucose values below 50 mg/dL and (C and D) frequency of blood glucose readings ≤39 mg/dL within a 12-h time period. POC, point-of-care treatment equivalent to direct dosing using a sensor value; Threshold, addition of threshold alarms to prevent hypoglycemia; Predictive, addition of predictive alarms to prevent hypoglycemia. POC+Arrow, Threshold+Arrow, and Predictive+Arrow are settings that use additional insulin reduction based on downward trend arrows displayed by the sensor.

FIG. 2.

Hyperglycemic outcomes from six treatment modalities plotted (A and C) against sensor mean absolute relative difference (MARD) and (B and D) against the frequency of sensor errors exceeding 20%: (A and B) percentage of blood glucose values above 250 mg/dL and (C and D) percentage of blood glucose readings above 400 mg/dL. POC, point-of-care treatment equivalent to direct dosing using a sensor value; Threshold, addition of threshold alarms to prevent hyperglycemia; Predictive, addition of predictive alarms to prevent hyperglycemia. POC+Arrow, Threshold+Arrow, and Predictive+Arrow are settings that use additional insulin increase based on upward trend arrows displayed by the sensor.

FIG. 3.

Average blood glucose (BG) levels resulting from six treatment modalities plotted against sensor mean absolute relative difference (MARD): (A) point-of-care (POC) treatment equivalent to direct dosing using a sensor value; (B) the effect of threshold alarms; and (C) the effect of predictive alarms. All treatment modalities were tested twice, without and with additional insulin increase based on upward trend arrows displayed by the sensor.

FIG. 4.

Characteristics of the distribution of continuous glucose monitoring errors implemented in this study plotted by mean absolute relative difference (MARD): (A) percentage errors greater than 20% and 50% and (B) quartiles and bias of the error distribution in mg/dL.