A Prediction Model for Positive Infant Meconium and Urine Drug Tests

Abstract

Objective This study aimed to determine the factors associated with positive infant drug screen and create a shortened screen and a prediction model.

Study Design This is a retrospective cohort study of all infants who were tested for drugs of abuse from May 2012 through May 2014. The primary outcome was positive infant urine or meconium drug test. Multivariable logistic regression was used to identify independent risk factors. A combined screen was created, and test characteristics were analyzed.

Results Among the 3,861 live births, a total of 804 infants underwent drug tests. Variables associated with having a positive infant test were (1) positive maternal urine test, (2) substance use during pregnancy, (3) ≤ one prenatal visit, and (4) remote substance abuse; each p-value was less than 0.0001. A model with an indicator for having at least one of these four predictors had a sensitivity of 94% and a specificity of 69%. Application of this screen to our population would have decreased drug testing by 57%. No infants had a positive urine drug test when their mother's urine drug test was negative.

Conclusion This simplified screen can guide clinical decision making for determining which infants should undergo drug testing. Infant urine drug tests may not be needed when a maternal drug test result is negative.

Key Points

• Many common drug screening criteria are not predictive.

• Four criteria predicted positive infant drug tests.

• No infant urine drug test is needed if the mother tests negative.

Keywords

prediction model - drug tests - newborns - substance use disorder

Authors' Contribution

E.A.S., D.A.S., and S.E.S. conceptualized and designed the study. Statistical analysis was done by E.A.S. and A.K.S. All authors drafted and critically revised the manuscript for important intellectual content. They acquired, analyzed and interpreted the data. E.A.S. had full access to all the data and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Publication History

Received: 29 May 2020

Accepted: 02 November 2020

Article published online:
17 December 2020

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