Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

Studying physiology and pathophysiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data can be intrusive, dangerous, and expensive. One solution is to use data that have been collected for a different purpose. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse populations and allow correlation with clinical outcomes, but limitations in the data have thus far thwarted such use. For example, using uncontrolled population-scale EHR data to verify the outcome of time dependent behavior of mechanistic, constructive models can be difficult because: (i) aggregation of the population can obscure or generate a signal, (ii) there is often no control population with a well understood health state, and (iii) diversity in how the population is measured can make the data difficult to fit into conventional analysis techniques. This paper shows that it is possible to use EHR data to test a physiological model for a population and over long time scales. Specifically, a methodology is developed and demonstrated for testing a mechanistic, time-dependent, physiological model of serum glucose dynamics with uncontrolled, population-scale, physiological patient data extracted from an EHR repository. It is shown that there is no observable daily variation the normalized mean glucose for any EHR subpopulations. In contrast, a derived value, daily variation in nonlinear correlation quantified by the time-delayed mutual information (TDMI), did reveal the intuitively expected diurnal variation in glucose levels amongst a random population of humans. Moreover, in a population of continuously (tube) fed patients, there was no observable TDMI-based diurnal signal. These TDMI-based signals, via a glucose insulin model, were then connected with human feeding patterns. In particular, a constructive physiological model was shown to correctly predict the difference between the general uncontrolled population and a subpopulation whose feeding was controlled

Pathway analysis of genome-wide data improves warfarin dose prediction

Background: Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations. Results: Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association. Conclusions: Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning

Explaining the footsteps, belly dancer, Wenceslas, and kickback illusions

The footsteps illusion (FI) demonstrates that an object's background can have a profound effect on the object's perceived speed. This illusion consists of a yellow bar and a blue bar that move over a black-and-white, striped background. Although the bars move at a constant rate, they appear to repeatedly accelerate and decelerate in antiphase with each other. Previously, this illusion has been explained in terms of the variations in contrast at the leading and trailing edges of the bars that occur as the bars traverse the striped background. Here, we show that this explanation is inadequate and instead propose that for each bar, the bar's leading edge, trailing edge, lateral edges, and the surrounding background edges all contribute to the bar's perceived speed and that the degree to which each edge contributes to the motion percept is determined by that edge's contrast. We show that this theory can explain all the data on the FI as well as the belly dancer and Wenceslas illusions. We conclude by presenting a new illusion, the kickback illusion, which, although geometrically similar to the FI, is mediated by a different mechanism, namely, reverse phi motion

Liver Imaging Reporting and Data System (LI-RADS) v2018: diagnostic value of ancillary features favoring malignancy in hypervascular observations ≥ 10 mm at intermediate (LR-3) and high probability (LR-4) for hepatocellular carcinoma

Objective: This study was conducted in order to assess the diagnostic accuracy of LI-RADS v2018 ancillary features (AFs) favoring malignancy applied to LR-3 and LR-4 observations on gadoxetate-enhanced MRI. Methods: In this retrospective dual-institution study, we included consecutive patients at high risk for hepatocellular carcinoma (HCC) imaged with gadoxetate disodium-enhanced MRI between 2009 and 2014 fulfilling the following criteria: (i) at least one LR-3 or LR-4 observation ≥ 10 mm; (ii) nonrim arterial phase hyperenhancement; and (iii) confirmation of benignity or malignancy by pathology or imaging follow-up. We compared the distribution of AFs between HCCs and benign observations and the diagnostic performance for the diagnosis of HCC using univariate and multivariate analyses. Significance was set at p value < 0.05. Results: Two hundred five observations were selected in 155 patients (108 M, 47 F) including 167 (81.5%) LR-3 and 38 (18.5%) LR-4. There were 126 (61.5%) HCCs and 79 (28.5%) benign lesions. A significantly larger number of AFs favoring malignancy were found in LR-3 and LR-4 lesions that progressed to HCC compared to benign lesions (p < 0.001 and p = 0.003, respectively). The most common AFs favoring malignancy in HCCs were hepatobiliary phase (HBP) hypointensity (p < 0.001), transitional phase hypointensity (p < 0.001), and mild–moderate T2 hyperintensity (p < 0.001). Sensitivity and specificity of AFs for the diagnosis of HCC ranged 0.8–76.2% and 86.1–100%, respectively. HBP hypointensity yielded the highest sensitivity but also the lowest specificity and was the only AF remaining independently associated with the diagnosis of HCC at multivariate logistic regression analysis (OR 14.83, 95% CI 5.81–42.76, p < 0.001). Conclusions: Among all AFs, HBP hypointensity yields the highest sensitivity for the diagnosis of HCC. Key Points: • LR-3 and LR-4 observations diagnosed as HCC have a significantly higher number of ancillary features favoring malignancy compared to observations proven to be benign. • The presence of three or more ancillary features favoring malignancy has a high specificity (96.2%) for the diagnosis of HCC. • Among all ancillary features favoring malignancy, hepatobiliary phase hypointensity yields the highest sensitivity, but also the lowest specificity for the diagnosis of HCC