Predictability of buprenorphine-naloxone treatment retention: A multi-site analysis combining electronic health records and machine learning.

BACKGROUND AND AIMS: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors. DESIGN: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data. SETTING AND CASES: Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively. MEASUREMENTS: Predict continuous prescription of buprenorphine-naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC-AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time-to-event analysis on a single site to estimate the duration of buprenorphine-naloxone treatment continuity evaluated by the C-index and a comparative evaluation against predictions made by three human clinical experts. FINDINGS: Attrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC-AUCs up to 75.8 (95% confidence interval [CI] = 73.6-78.0). Addiction medicine specialists' predictions show a ROC-AUC of 67.8 (95% CI = 50.4-85.2). Time-to-event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C-index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence. CONCLUSIONS: US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts.

Identification of novel lncRNAs regulated by the TAL1 complex in T-cell acute lymphoblastic leukemia.

TAL1/SCL is one of the most prevalent oncogenes in T-cell acute lymphoblastic leukemia (T-ALL). TAL1 and its regulatory partners (GATA3, RUNX1, and MYB) positively regulate each other and coordinately regulate the expression of their downstream target genes in T-ALL cells. However, long non-coding RNAs (lncRNAs) regulated by these factors are largely unknown. Here we established a bioinformatics pipeline and analyzed RNA-seq datasets with deep coverage to identify lncRNAs regulated by TAL1 in T-ALL cells. Our analysis predicted 57 putative lncRNAs that are activated by TAL1. Many of these transcripts were regulated by GATA3, RUNX1, and MYB in a coordinated manner. We identified two novel transcripts that were activated in multiple T-ALL cell samples but were downregulated in normal thymocytes. One transcript near the ARID5B gene locus was specifically expressed in TAL1-positive T-ALL cases. The other transcript located between the FAM49A and MYCN gene locus was also expressed in normal hematopoietic stem cells and T-cell progenitor cells. In addition, we identified a subset of lncRNAs that were negatively regulated by TAL1 and positively regulated by E-proteins in T-ALL cells. This included a known lncRNA (lnc-OAZ3-2:7) located near the RORC gene, which was expressed in normal thymocytes but repressed in TAL1-positive T-ALL cells.