Error simulation modeling to assess the effects of bias and precision on bilirubin measurements used to screen for neonatal hyperbilirubinemia

Abstract Objectives Error simulation models have been used to understand the relationship between analytical performance and clinical outcomes. We developed an error simulation model to understand the effects of method bias and precision on misclassification rate for neonatal hyperbilirubinemia using an age-adjusted risk assessment tool. Methods For each of 176 measured total bilirubin (TSBM) values, 10,000 simulated total bilirubin (TBS) values were generated at each combination of bias and precision conditions for coefficient of variation (CV) between 1 and 15%, and for biases between −51.3 μmol/L and 51.3 μmol/L (−3 and 3 mg/dL) fixed bias. TBS values were analyzed to determine if they were in the same risk zone as the TSBM value. We then calculated sensitivity and specificity for prediction of ≥75th percentile for postnatal age values as a function of assay bias and precision, and determined the rate of critical errors (≥95th percentile for age TSBM with <75th percentile TBS). Results A sensitivity >95% for predicting ≥75th percentile bilirubin values was observed when there is a positive fixed bias of greater than 17.1 μmol/L (1.0 mg/dL) and CV is maintained ≤10%. A specificity >70% for predicting <75th percentile bilirubin values was observed when positive systematic bias was 17.1 μmol/L (1 mg/dL) or less at CV ≤ 10%. Critical errors did not occur with a frequency >0.2% until negative bias was −17.1 μmol/L (−1 mg/dL) or lower. Conclusions A positive systematic bias of 17.1 μmol/L (1 mg/dL) may be optimal for balancing sensitivity and specificity for predicting ≥75th percentile TSB values. Negative systematic bias should be avoided to allow detection of high risk infants and avoid critical classification errors.