Predicting Chronic Opioid Use Among Patients With Osteoarthritis Using Electronic Health Record Data.

OBJECTIVE: To estimate the risk of a patient with osteoarthritis (OA) developing chronic opioid use (COU) within 1 year of a new opioid prescription by using electronic health record (EHR) data and predictive models. METHODS: We used EHR data from 13 health care organizations to identify patients with OA with an opioid prescription between March 1, 2017 and February 28, 2019 and no record of opioid use in the prior 6 months. We evaluated 4 machine learning models to estimate patients' risk of COU (≥3 prescriptions ≥84 days, maximum gap ≤60 days). We also estimated the transportability of models to organizations outside the training set. RESULTS: The cohort consisted of 33,894 patients with OA, of whom 2,925 (8.6%) developed COU within 1 year. All models demonstrated good discrimination, with the best-performing model (random forest) achieving an area under the receiver operating characteristic curve (AUC) of 0.728 (95% CI 0.711-0.745), but the simplest regression model performed nearly as well (AUC 0.717 [95% CI 0.699-0.734]). Predicted risk deciles spanned a range of 2% risk for the 10th percentile to 18% risk for the 90th percentile for well-calibrated models. Models showed highly variable discrimination across organizations (AUC 0.571-0.842). CONCLUSIONS: We found that EHR-based predictive models could estimate the risk of future COU among patients with OA to help inform care decisions. Black-box methods did not have significant advantages over more interpretable models. Care should be taken when extending all models into organizations not included in model training because of a high variability in performance across held-out organizations.

Differential HIV epitope processing in monocytes and CD4 T cells affects cytotoxic T lymphocyte recognition.

The ability of cytotoxic T lymphocytes (CTLs) to clear virus-infected cells is dependent on the presentation of viral peptides processed intracellularly and displayed by major histocompatibility complex class I. Most CTL functional assays use exogenously added peptides, a practice that does not account for the kinetics and quantity of antigenic peptides produced by infectable cells. Here, we examined the relative ability of 2 major human immunodeficiency virus-infectable cell subsets-CD4 T lymphocytes and monocytes-to produce antigenic peptides, using cytosol as a source of peptidases and mass spectrometry to define the degradation products. We show clear subset-specific differences in the kinetics of peptide production and the ability of the peptides produced to sensitize cells for lysis by CTLs, with primary CD4 T lymphocytes having significantly lower proteolytic activity than monocytes. These differences in epitope processing by cell subsets may affect the efficiency of CTL-mediated clearance of infected subsets and contribute to the establishment of chronic infection.