Mark Inman1, Kayla Parker1, Lannae Strueby1, Andrew W Lyon2, Martha E Lyon2. 1. Department of Pediatrics, University of Saskatchewan, Saskatoon, SK, Canada. 2. Division of Clinical Biochemistry, Department of Pathology and Laboratory Medicine, Saskatchewan Health Authority, Royal University Hospital, Saskatoon, SK, Canada.
Abstract
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.
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.
Entities:
Keywords:
Monte Carlo simulation; analytic error; hypoglycemia; neonate; point-of-care testing