Publications

UNLABELLED

Blood transcriptional signatures are promising for tuberculosis (TB) diagnosis but have not been evaluated among U.S.

PATIENTS

To be used clinically, transcriptional classifiers need reproducible accuracy in diverse populations that vary in genetic composition, disease spectrum and severity, and comorbidities. In a prospective case-control study, we identified novel transcriptional classifiers for active TB among U.S. patients and systematically compared their accuracy to classifiers from published studies. Blood samples from HIV-uninfected U.S. adults with active TB, pneumonia, or latent TB infection underwent whole-transcriptome microarray. We used support vector machines to classify disease state based on transcriptional patterns. We externally validated our classifiers using data from sub-Saharan African cohorts and evaluated previously published transcriptional classifiers in our population. Our classifier distinguishing active TB from pneumonia had an area under the concentration-time curve (AUC) of 96.5% (95.4% to 97.6%) among U.S. patients, but the AUC was lower (90.6% [89.6% to 91.7%]) in HIV-uninfected Sub-Saharan Africans. Previously published comparable classifiers had AUC values of 90.0% (87.7% to 92.3%) and 82.9% (80.8% to 85.1%) when tested in U.S.

PATIENTS

Our classifier distinguishing active TB from latent TB had AUC values of 95.9% (95.2% to 96.6%) among U.S. patients and 95.3% (94.7% to 96.0%) among Sub-Saharan Africans. Previously published comparable classifiers had AUC values of 98.0% (97.4% to 98.7%) and 94.8% (92.9% to 96.8%) when tested in U.S.

PATIENTS

Blood transcriptional classifiers accurately detected active TB among U.S. adults. The accuracy of classifiers for active TB versus that of other diseases decreased when tested in new populations with different disease controls, suggesting additional studies are required to enhance generalizability. Classifiers that distinguish active TB from latent TB are accurate and generalizable across populations and can be explored as screening assays.

Priorities in health care delivery are shifting, with a greater focus on enhancing value, incentivizing quality, and advancing population health. While measurement of quality in emergency department (ED) care is still in its infancy, performance measures are increasingly being linked to reimbursement to encourage the delivery of high-value care. With such changes, there will be growing oversight of diagnostic imaging in all clinical settings, including the ED. Here, the authors examine the current state of quality measurement as it pertains to ED imaging. The authors review relevant policies and discuss both the associated challenges and the facilitators of using quality measures to help optimize ED imaging. Understanding such factors will help ensure the delivery of diagnostic imaging that is appropriate, high-quality, and patient-centered.

Diagnostic imaging is integral to the evaluation of many emergency department (ED) patients. However, relatively little effort has been devoted to patient-centered outcomes research (PCOR) in emergency diagnostic imaging. This article provides background on this topic and the conclusions of the 2015 Academic Emergency Medicine consensus conference PCOR work group regarding "Diagnostic Imaging in the Emergency Department: A Research Agenda to Optimize Utilization." The goal was to determine a prioritized research agenda to establish which outcomes related to emergency diagnostic imaging are most important to patients, caregivers, and other key stakeholders and which methods will most optimally engage patients in the decision to undergo imaging. Case vignettes are used to emphasize these concepts as they relate to a patient's decision to seek care at an ED and the care received there. The authors discuss applicable research methods and approaches such as shared decision-making that could facilitate better integration of patient-centered outcomes and patient-reported outcomes into decisions regarding emergency diagnostic imaging. Finally, based on a modified Delphi process involving members of the PCOR work group, prioritized research questions are proposed to advance the science of patient-centered outcomes in ED diagnostic imaging.

Endogenous intestinal microbiota have wide-ranging and largely uncharacterized effects on host physiology. Here, we used reverse-phase liquid chromatography-coupled tandem mass spectrometry to define the mouse intestinal proteome in the stomach, jejunum, ileum, cecum and proximal colon under three colonization states: germ-free (GF), monocolonized with Bacteroides thetaiotaomicron and conventionally raised (CR). Our analysis revealed distinct proteomic abundance profiles along the gastrointestinal (GI) tract. Unsupervised clustering showed that host protein abundance primarily depended on GI location rather than colonization state and specific proteins and functions that defined these locations were identified by random forest classifications. K-means clustering of protein abundance across locations revealed substantial differences in host protein production between CR mice relative to GF and monocolonized mice. Finally, comparison with fecal proteomic data sets suggested that the identities of stool proteins are not biased to any region of the GI tract, but are substantially impacted by the microbiota in the distal colon.