Assessment of Probable Opioid Use Disorder Using Electronic Health Record Documentation.

Importance: Electronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD). Objective: To evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria. Design, Setting, and Participants: This retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020. Main Outcomes and Measures: The primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system. Results: Among the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes. Conclusions and Relevance: These results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.

Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study.

The DiscovEHR collaboration between the Regeneron Genetics Center and Geisinger Health System couples high-throughput sequencing to an integrated health care system using longitudinal electronic health records (EHRs). We sequenced the exomes of 50,726 adult participants in the DiscovEHR study to identify ~4.2 million rare single-nucleotide variants and insertion/deletion events, of which ~176,000 are predicted to result in a loss of gene function. Linking these data to EHR-derived clinical phenotypes, we find clinical associations supporting therapeutic targets, including genes encoding drug targets for lipid lowering, and identify previously unidentified rare alleles associated with lipid levels and other blood level traits. About 3.5% of individuals harbor deleterious variants in 76 clinically actionable genes. The DiscovEHR data set provides a blueprint for large-scale precision medicine initiatives and genomics-guided therapeutic discovery.

Modeling and analysis of molecularinteraction between Smurf1-WW2 domain and various isoforms of LIM mineralization protein.

LIM Mineralization Protein-1 (LMP-1) has been cloned and shown to be osteoinductive. Our efforts to understand the mode of action of LMP-1 led to the determination that LMP-1 interacts with Smad Ubiquitin Regulatory Factor-1 (Smurf1). Smurf1 targets osteogenic Smads, Smad1/5, for ubiquitin-mediated proteasomal degradation. Smurf1 interaction with LMP-1 or Smads is based on the presence of unique WW-domain interacting motif in these target molecules. By performing site-directed mutagenesis and binding studies in vitro on purified recombinant proteins, we identified a specific motif within the osteogenic region of several LMP isoforms that is necessary for Smurf1 interaction. Similarly, we have identified that the WW2 domain of Smurf1 is necessary for target protein interaction. Here, we present a homology-based modeling of the Smurf1 WW2 domain and its interacting motif of LMP-1. We performed computational docking of the interacting domains in Smurf1 and LMPs to identify the key amino acid residues involved in their binding regions. In support of the computational predictions, we also present biochemical evidence supporting the hypothesis that the physical interaction of Smurf1 and osteoinductive forms of LMP may prevent Smurf1 from targeting osteogenic Smads by ubiquitin-mediated proteasomal degradation.