Objective
Observational studies examining outcomes among opioid-exposed infants are limited by phenotype algorithms that may under identify opioid-exposed infants without neonatal opioid withdrawal syndrome (NOWS). We developed and validated the performance of different phenotype algorithms to identify opioid-exposed infants using electronic health record (EHR) data.
Patients and Methods
We developed phenotype algorithms for the identification of opioid-exposed infants among a population of birthing person-infant dyads from an academic healthcare system (2010-2022). We derived phenotype algorithms from combinations of 6 unique indicators of in-utero opioid exposure, including those from the infant record (NOWS/opioid-exposure diagnosis, positive toxicology) and birthing person record (opioid use disorder diagnosis, opioid drug exposure record, opioid listed on medication reconciliation, positive toxicology). We determined the positive predictive value (PPV) and 95% confidence interval (CI) for each phenotype algorithm using medical record review as the gold standard.
Results
Among 41,047 dyads meeting exclusion criteria, we identified 1,558 infants (3.80%) with evidence of at least one indicator for opioid-exposure and 32 (0.08%) meeting all six indicators of the phenotype algorithm. Among the sample of dyads randomly selected for review (n=600), the PPV for the phenotype requiring only a single indicator was 95.4% (CI: 93.3-96.8) with varying PPVs for the other phenotype algorithms derived from a combination of infant and birthing person indicators (PPV range: 95.4-100.0).
Conclusions
Opioid-exposed infants can be accurately identified using EHR data. Our publicly available phenotype algorithms can be used to conduct research examining outcomes among opioid-exposed infants with and without NOWS.
[Link to Publication]: *Pending Publication*
GitHub URL: https://github.com/Precision-Phenotyping-Core/opioid-exposed-infants
PheKB URL: https://phekb.org/phenotype/opioid-exposed-infants
