Identifying Patients with Group 3 Pulmonary Hypertension Associated with COPD or ILD Using an Administrative Claims Database

Lung - Group 3 pulmonary hypertension (PH) describes a subpopulation of patients with PH due to chronic lung disease and/or hypoxia, with chronic obstructive pulmonary disease (COPD) and...     

Forecasting Migraine Attacks and the Utility of Identifying Triggers

Purpose of Review

This review synthesizes the utility of measuring migraine triggers for the purpose of forecasting future headache attacks. The nature of forecasting models, headache triggers as inputs to such models, and how these trigger exposures can be measured for forecasting are reviewed. A critical evaluation of the existing forecasting models in the context of their potential application for preemptive treatment is considered.

Recent Findings

A substantial pool of candidate trigger factors could be considered in the creation of forecasting models. However, because mechanistic information about causal factors that precede a migraine attack is not well understood, and such factors are difficult to measure, empirical models that are based on trigger factors that are merely associated with the onset of headache activity are likely to be the focus of forecasting efforts in the near future. Of such factors, stress has considerable empirical support and has been used to successfully forecast future headache attacks within individuals over time. However, at present, existing models possess only modest levels of discrimination and lack strong resolution in generated predictions.

Summary

Current headache forecasting models represent an important first step in accurately predicting future headache activity. However, to utilize these models in a preemptive treatment paradigm where the risk of headache is treated prior to the actual experience of pain, these models must achieve greater precision with good calibration and generate predictions that are clinically actionable by individuals in their real-time home environments.

     

Biology‐Driven Gene‐Gene Interaction Analysis of Age‐Related Cataract in the eMERGE Network

Bioinformatics approaches to examine gene‐gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge‐driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty‐three SNP‐SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell‐to‐cell adhesion signaling, cell‐cell junction organization, and cell‐cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP‐SNP models, which included signal transduction and PI3K‐Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology‐driven method, applicable for any genome‐wide association study dataset.     

Assessment of Nociception and Pain in Participants in an Unresponsive or Minimally Conscious State After Acquired Brain Injury: The Relation Between the Coma Recovery Scale–Revised and the Nociception Coma Scale–Revised

Objectives

To investigate the relation between consciousness and nociceptive responsiveness (ie, Nociception Coma Scale–Revised [NCS-R]), to examine the suitability of the NCS-R for assessing nociception in participants with disorders of consciousness (DOC), and to replicate previous findings on psychometric properties of the scale.