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Performance of Phenotype Algorithms for the Identification of Opioid-Exposed Infants
Andrew D Wiese1,2, Julia C Phillippi2,3, Alexandra Muhar4,5
1Departments of Health Policy.
Insights
Accurate identification of opioid-exposed infants is possible using electronic health record data. Developed phenotype algorithms can aid research on infant outcomes, including those without neonatal opioid withdrawal syndrome (NOWS).
Area of Science:
- Perinatal Health
- Data Science in Healthcare
- Neonatal Research
Background:
- Observational studies on opioid-exposed infants are challenged by algorithms that may undercount infants without neonatal opioid withdrawal syndrome (NOWS).
- Accurate identification of opioid-exposed infants is crucial for understanding their health outcomes.
- Electronic health records (EHRs) offer a potential data source for identifying these infants.
Purpose of the Study:
- To develop and validate phenotype algorithms for identifying opioid-exposed infants using EHR data.
- To improve the accuracy of identifying opioid-exposed infants, particularly those without NOWS.
- To provide reliable tools for research on the outcomes of opioid-exposed infants.
Main Methods:
- Phenotype algorithms were developed using combinations of six indicators from infant and birthing person records (2010-2022).
- Indicators included diagnoses, toxicology results, and medication records related to opioid exposure.
- Positive predictive values (PPVs) were determined using medical record review as the gold standard.
Main Results:
- Among 41,047 dyads, 3.80% had evidence of opioid exposure; 0.08% met all six indicators.
- A phenotype algorithm using a single indicator achieved a PPV of 95.4% (CI: 93.3-96.8).
- Algorithms combining multiple indicators showed high PPVs ranging from 95.4% to 100.0%.
Conclusions:
- Opioid-exposed infants can be accurately identified through EHR data analysis.
- The developed phenotype algorithms are validated and publicly available.
- These algorithms can facilitate research on opioid-exposed infants, with or without NOWS.
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 data.
Methods:
We developed phenotype algorithms for the identification of opioid-exposed infants among a population of birthing person-infant dyads from an academic health care 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 or 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 for each phenotype algorithm using medical record review as the gold standard.
Results:
Among 41 047 dyads meeting exclusion criteria, we identified 1558 infants (3.80%) with evidence of at least 1 indicator for opioid exposure and 32 (0.08%) meeting all 6 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% (confidence interval: 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 electronic health record data. Our publicly available phenotype algorithms can be used to conduct research examining outcomes among opioid-exposed infants with and without NOWS.
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