Considering trends of restraints and seclusions by patient race

2021 ◽  
Vol 37 (9) ◽  
pp. 1-4
Author(s):  
Colleen Victor ◽  
Elizabeth Brannan ◽  
Jeffrey Hunt ◽  
Jennifer Wolff
Keyword(s):  
2021 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Laura B. Scheinfeldt ◽  
Andrew Brangan ◽  
Dara M. Kusic ◽  
Sudhir Kumar ◽  
Neda Gharani

Pharmacogenomics holds the promise of personalized drug efficacy optimization and drug toxicity minimization. Much of the research conducted to date, however, suffers from an ascertainment bias towards European participants. Here, we leverage publicly available, whole genome sequencing data collected from global populations, evolutionary characteristics, and annotated protein features to construct a new in silico machine learning pharmacogenetic identification method called XGB-PGX. When applied to pharmacogenetic data, XGB-PGX outperformed all existing prediction methods and identified over 2000 new pharmacogenetic variants. While there are modest pharmacogenetic allele frequency distribution differences across global population samples, the most striking distinction is between the relatively rare putatively neutral pharmacogene variants and the relatively common established and newly predicted functional pharamacogenetic variants. Our findings therefore support a focus on individual patient pharmacogenetic testing rather than on clinical presumptions about patient race, ethnicity, or ancestral geographic residence. We further encourage more attention be given to the impact of common variation on drug response and propose a new ‘common treatment, common variant’ perspective for pharmacogenetic prediction that is distinct from the types of variation that underlie complex and Mendelian disease. XGB-PGX has identified many new pharmacovariants that are present across all global communities; however, communities that have been underrepresented in genomic research are likely to benefit the most from XGB-PGX’s in silico predictions.


Author(s):  
Morgan Congdon ◽  
Stephanie A. Schnell ◽  
Tatiana Londoño Gentile ◽  
Jennifer A. Faerber ◽  
Christopher P. Bonafide ◽  
...  

2009 ◽  
Vol 24 (9) ◽  
pp. 1057-1064 ◽  
Author(s):  
Crystal W. Cené ◽  
Debra Roter ◽  
Kathryn A. Carson ◽  
Edgar R. Miller ◽  
Lisa A. Cooper

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Alison L Herman ◽  
Adam H De Havenon ◽  
Guido J Falcone ◽  
Shadi Yaghi ◽  
Shyam Prabhakaran ◽  
...  

Introduction: White matter hyperintensities (WMH) are linked to cognitive decline and stroke. We hypothesized that Black race would be associated with greater WMH progression in the ACCORDION MIND trial. Methods: The primary outcome is WMH progression in mL, evaluated by fitting linear regression to WMH volume on the month 80 MRI and including the WMH volume on the baseline MRI. The primary predictor is patient race, with the exclusion of patients defined as “other” race. We also derived predicted probabilities of our outcome for systolic blood pressure (SBP) levels. Results: We included 276 patients who completed the baseline and month 80 MRI, of which 207 were white, 48 Black, and 21 Hispanic. During follow-up, the mean number of SBP, LDL, and A1c measurements per patient was 21, 8, and 15. The mean (SD) WMH progression was 3.3 (5.4) mL for blacks, 2.5 (3.2) mL for Hispanics, and 2.4 (3.3) mL for whites. In the multivariate regression model (Table 1), Black, compared to white, patients had significantly more WMH progression (β Coefficient 1.26, 95% CI 0.45-2.06, p=0.002). Hispanic, compared to white, patients did not have significantly different WMH progression (p=0.392), nor was there a difference when comparing Hispanic to Black patients (p=0.162). The predicted WMH progression was significantly higher for Black compared to white patients across a mean SBP of 117 to 139 mm Hg (Figure 1). Conclusions: Black diabetic patients in ACCORDION MIND have a higher risk of WMH progression than white patients across a normal range of systolic blood pressure.


2000 ◽  
Vol 108 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Russell S Phillips ◽  
Mary Beth Hamel ◽  
Joan M Teno ◽  
Jane Soukup ◽  
Joanne Lynn ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Sarah K. Calabrese ◽  
David A. Kalwicz ◽  
Djordje Modrakovic ◽  
Valerie A. Earnshaw ◽  
E. Jennifer Edelman ◽  
...  

2018 ◽  
Vol 131 ◽  
pp. 115S ◽  
Author(s):  
Isa Ryan ◽  
Kimberly A. Martin ◽  
Shari M. Lawson ◽  
Kristin L. Martin ◽  
Betty Chou

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