Variability in response to drug use is common and heritable, suggesting that genome-wide pharmacogenomics studies may help explain the “missing heritability” of complex traits. Here, we describe four independent analyses in 33,781 participants of European ancestry from ten cohorts that were designed to identify genetic variants modifying the effects of drugs on QT interval duration (QT). Each analysis cross-sectionally examined four therapeutic classes: thiazide diuretics (prevalence of use=13.0%), tri/tetracyclic antidepressants (2.6%), sulfonylurea hypoglycemic agents (2.9%), and QT prolonging drugs as classified by the University of Arizona Center for Education and Research on Therapeutics (4.4%). Drug-gene interactions were estimated using covariable adjusted linear regression and results were combined with fixed-effects meta-analysis. Although drug-SNP interactions were biologically plausible and variables were well-measured, findings from the four cross-sectional meta-analyses were null (Pinteraction>5.0×10−8). Simulations suggested that additional efforts, including longitudinal modeling to increase statistical power, are likely needed to identify potentially important pharmacogenomic effects.
SummaryBackground: In determining whether clinical decision support (CDS) should be interruptive or noninterruptive, CDS designers need more guidance to balance the potential for interruptive CDS to overburden clinicians and the potential for non-interruptive CDS to be overlooked by clinicians. Objectives: (1)To compare performance achieved by clinicians using interruptive CDS versus using similar, non-interruptive CDS. (2)To compare performance achieved using non-interruptive CDS among clinicians exposed to interruptive CDS versus clinicians not exposed to interruptive CDS. Methods: We studied 42 emergency medicine physicians working in a large hospital where an interruptive CDS to help identify patients requiring contact isolation was replaced by a similar, but non-interruptive CDS. The first primary outcome was the change in sensitivity in identifying these patients associated with the conversion from an interruptive to a non-interruptive CDS. The second primary outcome was the difference in sensitivities yielded by the non-interruptive CDS when used by providers who had and who had not been exposed to the interruptive CDS. The reference standard was an epidemiologist-designed, structured, objective assessment. Results: In identifying patients needing contact isolation, the interruptive CDS-physician dyad had sensitivity of 24% (95% CI: 17%-32%), versus sensitivity of 14% (95% CI: 9%-21%) for the non-interruptive CDS-physician dyad (p = 0.04). Users of the non-interruptive CDS with prior exposure to the interruptive CDS were more sensitive than those without exposure (14% [95% CI: 9%-21%] versus 7% [95% CI: 3%-13%], p = 0.05). Limitations: As with all observational studies, we cannot confirm that our analysis controlled for every important difference between time periods and physician groups. Conclusions: Interruptive CDS affected clinicians more than non-interruptive CDS. Designers of CDS might explicitly weigh the benefits of interruptive CDS versus its associated increased clinician burden. Further research should study longer term effects of clinician exposure to interruptive CDS, including whether it may improve clinician performance when using a similar, subsequent non-interruptive CDS. A Retrospective Analysis of Interruptive versus Non-interruptive
e15105 Background: ALK-rearrangements are a powerful and highly prevalent driver in cancer biology. The advent of potent tyrosine kinase inhibitors has led to the development of numerous laboratory tests and FDA approved therapies for the detection and treatment of ALK-positive cancers. The vast majority of reported ALK rearrangements involve exon 20 with fusions involving exons 17-19 occasionally reported. While less common, ALK-rearrangements occurring earlier in the ALK gene have not been well characterized. This is largely because most clinically available biomarker testing is only optimized for detection of rearrangements involving the common regions. To this end, we aimed to investigate the prevalence of early exon ALK rearrangements (eALKr). Methods: Two pathology databases, including the Tempus deidentified database, were retrospectively queried to characterize eALKr, which were defined as any rearrangement that involved ALK exons 1-16. Demographic information and tumor type were recorded. Identification of eALKr was performed via targeted amplicon-based RNA sequencing, targeted hybrid capture DNA sequencing and whole transcriptome sequencing. Select specimens with early ALK fusions were also evaluated for ALK detection using available FDA-approved methods (immunohistochemistry [IHC] and fluorescent in-situ hybridization [FISH]). Results: Out of the specimens that underwent NGS testing in the two databases, the prevalence of eALKr was 0.05% (73/143,959) across the databases and 10.3% (73/709) for all tumors with ALK rearrangements. Across the 73 rearrangements, there were 58 unique 5’ partner genes of which 46 were only identified once. eALK rearrangements were identified in all exons from exons 1 to 16 except for exon 13, with exon 4 being the most common. Prostatic, breast and ovarian serous carcinoma were the most common tumor types (Table 1). In three cases IHC and FISH were unreliable due to the early rearrangement location (0/3 IHC positive, 1/3 FISH positive). FISH was negative for samples with rearrangements in exon 2 and 4 and showed an atypical (deletion) pattern with a rearrangement involving exon 12. Conclusions: A non-trivial number of eALKr were detected within two database using next generation sequencing (0.05% of total cancer cases; 10.3% of ALK fusion cases). These novel eALK rearrangements were predominantly in tumor types not normally associated with ALK fusions. FDA approved testing modalities are only optimized to detect common ALK rearrangements, which in part may be a reason why eALK-rearrangements are poorly characterized, with limited prevalence and treatment data available. [Table: see text]
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.