Key Points Question Are medication monitoring programs within a hospital associated with more accurate identification of patients with opioid use disorder through the use of proxy Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) ( DSM-5) criteria for opioid use disorder extracted from electronic health records? Findings This cross-sectional study demonstrated that DSM-5 criteria for opioid use disorder can be extracted through review of electronic health records and that patients who are part of a drug monitoring program had a higher mean prevalence of opiod use disorder and a higher mean number of psychiatric comorbidities associated with opioid use disorder. Meaning Proxy measures that rely on multiple sources of data, including prescription drug history and notes in the electronic health record, may help identify patients with opioid use disorder who have not received a diagnosis.

Background Prescription opioids (POs) are commonly used to treat moderate to severe chronic pain in the health system setting. Although they improve quality of life for many patients, more work is needed to identify both the clinical and genetic factors that put certain individuals at high risk for developing opioid use disorder (OUD) following use of POs for pain relief. With a greater understanding of important risk factors, physicians will be better able to identify patients at highest risk for developing OUD for whom non-opioid alternative therapies and treatments should be considered. Methods We are conducting a prospective observational study that aims to identify the clinical and genetic factors most stongly associated with OUD. The study design leverages an existing biobank that includes whole exome sequencing and array genotyping. The biobank is maintained within an integrated health system, allowing for the large-scale capture and integration of genetic and non-genetic data. Participants are enrolled into the health system biobank via informed consent and then into a second study that focuses on opioid medication use. Data capture includes validated self-report surveys measuring addiction severity, depression, anxiety, and nicotine use, as well as additional clinical, prescription, and brain imaging data extracted from electronic health records. Discussion We will harness this multimodal data capture to establish meaningful patient phenotypes in order to understand the genetic and non-genetic contributions to OUD.

BackgroundVariations in regional cortical folds across individuals have been examined using computationally-derived morphological measures, or by manual characterization procedures that map distinct variants of a regional fold to a set of human-interpretable shapes. Although manual mapping approaches have proven useful for identifying morphological differences of clinical relevance, such procedures are subjective and not amenable to scaling.New MethodWe propose a 3-step pipeline to develop computational models of manual mapping. The steps are: represent regional folds as feature vectors, manually map each feature vector to a shape-variant that the underlying fold represents, and train classifiers to learn the mapping.ResultsFor demonstration, we chose a 2D-problem of detecting within slice discontinuity of medial and lateral sulci of orbitofrontal cortex (OFC); the discontinuity may be visualized as a broken H-shaped pattern, and is fundamental to OFC-type-characterization. The classifiers predicted discontinuities with 86-95% test-accuracy.Comparison with Existing MethodsThere is no existing pipeline that automates a manual characterization process. For the current demonstration problem, we conduct multiple analyses using existing softwares to explain our design decisions, and present guidelines for using the pipeline to examine other regional folds using conventional or non-conventional morphometric measures.ConclusionWe show that this pipeline can be useful for determining axial-slice discontinuity of sulci in the OFC and can learn structural-features that human-raters may rely on during manual-characterization.The pipeline can be used for examining other regional folds and may facilitate discovery of various statistically-reliable 2D or 3D human-interpretable shapes that are embedded throughout the brain.

BACKGROUND AND PURPOSE: Brain volumetrics have historically been obtained from MR imaging data. However, advances in CT, along with refined publicly available software packages, may support tissue-level segmentations of clinical CT images. Here, brain volumetrics obtained by applying two publicly available software packages to paired CT-MR data are compared. MATERIALS AND METHODS:In a group of patients (n ¼ 69; 35 men) who underwent both MR imaging and CT brain scans within 12 months of one another, brain tissue was segmented into WM, GM, and CSF compartments using 2 publicly available software packages: Statistical Parametric Mapping and FMRIB Software Library. A subset of patients with repeat imaging sessions was used to assess the repeatability of each segmentation. Regression analysis and Bland-Altman limits of agreement were used to determine the level of agreement between segmented volumes.RESULTS: Regression analysis showed good agreement between volumes derived from MR images versus those from CT. The correlation coefficients between the 2 methods were 0.93 and 0.98 for Statistical Parametric Mapping and FMRIB Software Library, respectively. Differences between global volumes were significant (P , .05) for all volumes compared within a given segmentation pipeline. WM bias was 36% (SD, 38%) and 18% (SD, 18%) for Statistical Parametric Mapping and FMRIB Software Library, respectively, and 10% (SD, 30%) and 6% (SD, 20%) for GM (bias 6 limits of agreement), with CT overestimating WM and underestimating GM compared with MR imaging. Repeatability was good for all segmentations, with coefficients of variation of ,10% for all volumes. CONCLUSIONS:The repeatability of CT segmentations using publicly available software is good, with good correlation with MR imaging. With careful study design and acknowledgment of measurement biases, CT may be a viable alternative to MR imaging in certain settings.

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