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Published on: August 24, 2011
A Simulation Study to Assess the Effect of Analytic Error on Neonatal Glucose Measurements Using the Canadian
Mark Inman1, Kayla Parker1, Lannae Strueby1
1Department of Pediatrics, University of Saskatchewan, Saskatoon, SK, Canada.
Insights
Glucose meter errors can lead to misclassification of neonatal hypoglycemia risk. This study quantizes the risk of inappropriate clinical actions due to bias and imprecision in glucose meter readings for at-risk newborns.
Area of Science:
- Neonatal medicine
- Clinical chemistry
- Biomedical engineering
Background:
- The Canadian Pediatric Society (CPS) recommends an algorithm for neonatal hypoglycemia screening and management.
- This algorithm utilizes time-dependent glucose concentration action thresholds.
- Neonatal intensive care units (NICUs) rely on glucose meters for monitoring.
Purpose of the Study:
- To evaluate the impact of glucose analytic error (bias and imprecision) on the misclassification of glucose meter results.
- To assess the risk of inappropriate clinical actions based on CPS guidelines in NICU settings.
Main Methods:
- A simulation dataset of 100,000 true glucose values was generated from NICU data.
- Bias and imprecision were introduced to simulate measured glucose values.
- Monte Carlo simulations determined misclassification rates at CPS action thresholds.
Main Results:
- At 5% CV and +10 mg/dL bias, misclassification rates ranged from 0.8% to 5% at 32 and 47 mg/dL thresholds.
- At 5% CV and -10 mg/dL bias, misclassification rates ranged from 3% to 12.5% at 32 and 47 mg/dL thresholds.
- Specific bias and imprecision values predicted the proportion of neonates at risk of overtreatment or undertreatment.
Conclusions:
- Glucose meter analytic errors can lead to misclassification of neonatal glucose levels.
- This misclassification poses a risk of inappropriate clinical actions (overtreatment or failure to treat).
- Understanding these risks is crucial for accurate neonatal hypoglycemia management.
Background:
The Canadian Pediatric Society (CPS) has endorsed an algorithm for the screening and immediate management of babies at risk of neonatal hypoglycemia that provides time-dependent glucose concentration action thresholds. The objective of this study was to evaluate the impact of glucose analytic error (bias and imprecision) on the misclassification of glucose meter results from a neonatal intensive care unit (NICU) using the CPS guidelines.
Methods:
A simulation dataset of true glucose values (N = 100 000) was derived by finite mixture model analysis of NICU glucose data (N = 23 749). Bias and imprecision were added to create measured glucose values. The percentages of measured glucose values that were misclassified at CPS action thresholds were determined by Monte Carlo simulation.
Results:
Measurement biases ranging from -20 to +20 mg/dL combined with coefficients of variation 0% to 20% were evaluated to predict misclassification rates at 32, 36, and 47 mg/dL. The models demonstrated low risk of false normoglycemia-at 5% CV and +10 mg/dL bias: 0.8% to 5% misclassification at the 32 and 47 mg/dL thresholds due to bias. The models demonstrated risk of false hypoglycemia-at 5% CV and -10 mg/dL bias: 3% to 12.5% misclassification at 32 and 47 mg/dL thresholds due to both bias and imprecision.
Conclusion:
Using CPS action thresholds, the simulation model predicted the proportion of neonates at risk of inappropriate clinical action-both of omission or "failure to treat" and commission or "overtreatment" in response to NICU glucose meter results at specific bias and imprecision values.
