Topological Data Analysis for Discovery in Preclinical Spinal Cord Injury and Traumatic Brain Injury

Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis

Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury.

Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis

Hypertension and Chronic Kidney Disease Progression: Why the Suboptimal Outcomes?

Current therapeutic interventions to retard the progression of chronic kidney disease have yielded disappointing outcomes despite adequate renin-angiotensin system blockade. The parameters to gauge the adequacy of blood pressure control need to be reassessed because clinic blood pressure constitutes a poor gauge of such control. The biologically relevant parameter for hypertensive target organ damage is total blood pressure burden, and reliance on isolated clinic blood pressure measurements per se does not accurately reflect the total blood pressure burden. This is particularly relevant to the population with chronic kidney disease in whom masked daytime or nocturnal hypertension and blood pressure lability are both widely prevalent and more difficult to control. Consequently, it is possible that the limited success currently being achieved in preventing or attenuating chronic kidney disease progression may be attributable in part to suboptimal 24-hour blood pressure control. Recent data and analyses also indicate that blood pressure variability, instability, episodic and nocturnal blood pressure elevations, and maximum systolic blood pressure may constitute additional strong predictors of the risk of target organ damage independently of mean systolic blood pressure. Accordingly, we suggest that future research should include the development of safe and effective strategies to achieve around-the-clock blood pressure control in addition to targeting mechanisms that reduce intrarenal blood pressure transmission or interrupt subsequent downstream pathways. Meanwhile, more aggressive use of patient education and home blood pressure monitoring with selection of longer-acting antihypertensive agents or nocturnal dosing should be considered to improve the current suboptimal results

Implementation of preemptive DNA sequence–based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study

The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping.Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response–related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug–gene pairs, were deposited preemptively in the Mayo electronic health record.For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping.Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources