Positive Predictive Value of Administrative Data for Neonatal Abstinence Syndrome

Abstract

Objectives: Neonatal abstinence syndrome (NAS) is a postnatal withdrawal syndrome experienced by some infants with opioid exposure. Hospital administrative data are commonly used for research and surveillance but have not been validated for NAS. Our objectives for this study were to validate the diagnostic codes for NAS and to develop an algorithm to optimize identification.

Methods: Tennessee Medicaid claims from 2009 to 2011 (primary sample) and 2016 (secondary sample; post-International Classification of Diseases, 10th Revision, Clinical Modification [ICD-10-CM]) were obtained. Cases of NAS were identified by using International Classification of Diseases, Ninth Revision, Clinical Modification code (2009-2011) 779.5 and ICD-10-CM code (2016) P96.1. Medical record review cases were then conducted by 2 physicians using a standardized algorithm, and positive predictive value (PPV) was calculated. Algorithms were developed for optimizing the identification of NAS in administrative data.

Results: In our primary sample of 112 029 mother-infant dyads, 950 potential NAS cases were identified from Medicaid claims data and reviewed. Among reviewed records, 863 were confirmed as having NAS (including 628 [66.1%] cases identified as NAS requiring pharmacotherapy, 224 [23.5%] as NAS not requiring pharmacotherapy, and 11 [1.2%] as iatrogenic NAS), and 87 (9.2%) did not meet clinical criteria for NAS. The PPV of the International Classification of Diseases, Ninth Revision, Clinical Modification code for NAS in clinically confirmed NAS was 91% (95% confidence interval: 88.8%-92.5%). Similarly, the PPV for the ICD-10-CM code in the secondary sample was 98.2% (95% confidence interval: 95.4%-99.2%). Algorithms using elements from the Medicaid claims and from length of stay improved PPV.

Conclusions: In a large population-based cohort of Medicaid participants, hospital administrative data had a high PPV in identifying cases of clinically diagnosed NAS.

FIGURE 1

Algorithm used to identify different cases of NAS. ^a Arms stay up 3 to 4 seconds and/or pronounced jitteriness of the hands during or at the end of the Moro reflex. ^b Described in any extremity. ^c Difficult to straighten or bend the arms (but is possible) and head lag is present, or no head lag noted and/or arms or legs will not straighten or bend.

FIGURE 2

Process for identifying different cases of NAS in the primary sample.

FIGURE 3

Algorithm for identifying NAS cases from administrative data.