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BACKGROUND - Circulating biomarkers can facilitate diagnosis and risk stratification for complex conditions such as heart failure (HF). Newer molecular platforms can accelerate biomarker discovery, but they require significant resources for data and sample acquisition.
OBJECTIVES - The purpose of this study was to test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics.
METHODS - Using the electronic health record, the authors identified patients with and without HF, retrieved their discarded plasma samples, and screened these specimens using a DNA aptamer-based proteomic platform (1,129 proteins). Candidate biomarkers were validated in 3 different prospective cohorts.
RESULTS - In an automated manner, plasma samples from 1,315 patients (31% with HF) were collected. Proteomic analysis of a 96-patient subset identified 9 candidate biomarkers (p < 4.42 × 10). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in 3 separate validation cohorts. In an emergency department-based registry of 852 dyspneic patients, the 2 biomarkers improved discrimination of acute HF compared with a clinical score (p < 0.0001) or clinical score plus B-type natriuretic peptide (p = 0.02). In a community-based cohort (n = 768), both biomarkers predicted incident HF independent of traditional risk factors and N-terminal pro-B-type natriuretic peptide (hazard ratio per SD increment: 1.35 [95% confidence interval: 1.14 to 1.61; p = 0.0007] for angiopoietin-2, and 1.37 [95% confidence interval: 1.06 to 1.79; p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80% to 84%) following cardiac transplant (p < 0.001 for both).
CONCLUSIONS - A novel strategy integrating electronic health records, discarded clinical specimens, and proteomics identified 2 biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.
Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
OBJECTIVE - To utilize electronic health records (EHRs) to study SLE, algorithms are needed to accurately identify these patients. We used machine learning to generate data-driven SLE EHR algorithms and assessed performance of existing rule-based algorithms.
METHODS - We randomly selected subjects with ≥ 1 SLE ICD-9/10 codes from our EHR and identified gold standard definite and probable SLE cases by chart review, based on 1997 ACR or 2012 SLICC Classification Criteria. From a training set, we extracted coded and narrative concepts using natural language processing and generated algorithms using penalized logistic regression to classify definite or definite/probable SLE. We assessed predictive characteristics in internal and external cohort validations. We also tested performance characteristics of published rule-based algorithms with pre-specified permutations of ICD-9 codes, laboratory tests and medications in our EHR.
RESULTS - At a specificity of 97%, our machine learning coded algorithm for definite SLE had 90% positive predictive value (PPV) and 64% sensitivity and for definite/probable SLE, 92% PPV and 47% sensitivity. In the external validation, at 97% specificity, the definite/probable algorithm had 94% PPV and 60% sensitivity. Adding NLP concepts did not improve performance metrics. The PPVs of published rule-based algorithms ranged from 45-79% in our EHR.
CONCLUSION - Our machine learning SLE algorithms performed well in internal and external validation. Rule-based SLE algorithms did not transport as well to our EHR. Unique EHR characteristics, clinical practices and research goals regarding the desired sensitivity and specificity of the case definition must be considered when applying algorithms to identify SLE patients.
Copyright © 2019 Elsevier Inc. All rights reserved.
Single-cell technologies that can quantify features of individual cells within a tumor are critical for treatment strategies aiming to target cancer cells while sparing or activating beneficial cells. Given that key players in protein networks are often the primary targets of precision oncology strategies, it is imperative to transcend the nucleic acid message and read cellular actions in human solid tumors. Here, we review the advantages of multiplex, single-cell mass cytometry in tissue and solid tumor investigations. Mass cytometry can quantitatively probe nearly any cellular feature or target. In discussing the ability of mass cytometry to reveal and characterize a broad spectrum of cell types, identify rare cells, and study functional behavior through protein signaling networks in millions of individual cells from a tumor, this review surveys publications of scientific advances in solid tumor biology made with the aid of mass cytometry. Advances discussed include functional identification of rare tumor and tumor-infiltrating immune cells and dissection of cellular mechanisms of immunotherapy in solid tumors and the periphery. The review concludes by highlighting ways to incorporate single-cell mass cytometry in solid tumor precision oncology efforts and rapidly developing cytometry techniques for quantifying cell location and sequenced nucleic acids.
© 2018 Federation of European Biochemical Societies.
In BriefPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling-in patients who will have clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based mechanism for safe discharge. Here, using data from 12,902 patients from the Pediatric Emergency Care Applied Research Network (PECARN) TBI data set, the authors utilize artificial intelligence to predict CRTBI using radiologist-interpreted CT information with > 99% sensitivity and an AUC of 0.99.
Importance - Because of the recurrent nature of idiopathic subglottic stenosis, routine follow-up is necessary for monitoring progression of stenosis. However, no easily accessible, standardized objective measure exists to monitor disease progression.
Objective - To determine whether peak expiratory flow (PEF) can be used as a reliable and easily accessible biometric indicator of disease progression relative to other validated spirometry measures in patients with idiopathic subglottic stenosis.
Design, Setting, and Participants - Prospectively collected data on PEF, expiratory disproportion index (EDI), and total peak flow (TPF) from 42 women with idiopathic subglottic stenosis without comorbid lower airway or parenchymal lung disease who were treated at a single tertiary referral center between 2014 and 2018 were analyzed. The mean follow-up period was 18.2 months (range, 2-40 months). Ten patients initially screened were not included in the analysis owing to comorbid glottic or supraglottic stenosis or nonidiopathic etiology.
Main Outcomes and Measures - Measurements of PEF, EDI, and TPF were taken at preoperative visits and at all other visits.
Results - Forty-two women (mean age, 51.5 years; 98% white [n = 41]) met the inclusion criteria. The area under the curve for PEF was 0.855 (95% CI, 0.784-0.926). The optimal cutoff value was 4.4 liters per second (264 L/min), with a sensitivity and specificity of 84.4% and 82.0%, respectively. The area under the curve for EDI was 0.853 (95% CI, 0.782-0.925). For TPF, this was 0.836 (95% CI, 0.757-0.916).
Conclusions and Relevance - This study provides evidence supporting the use of PEF as a simple, efficient, and accessible way of monitoring progression of idiopathic subglottic stenosis and predicting receipt of surgical intervention. Sensitivity and specificity of PEF were comparable to those of the more complex measures of TPF and EDI.
OBJECTIVE - To characterize the phenotype and function of fibroblasts derived from airway scar in idiopathic subglottic stenosis (iSGS) and to explore scar fibroblast response to interleukin 17A (IL-17A).
STUDY DESIGN - Basic science.
SETTING - Laboratory.
SUBJECTS AND METHODS - Primary fibroblast cell lines from iSGS subjects, idiopathic pulmonary fibrosis subjects, and normal control airways were utilized for analysis. Protein, molecular, and flow cytometric techniques were applied in vitro to assess the phenotype and functional response of disease fibroblasts to IL-17A.
RESULTS - Mechanistically, IL-17A drives iSGS scar fibroblast proliferation ( P < .01), synergizes with transforming growth factor ß1 to promote extracellular matrix production (collagen and fibronectin; P = .04), and directly stimulates scar fibroblasts to produce chemokines (chemokine ligand 2) and cytokines (IL-6 and granulocyte-macrophage colony-stimulating factor) critical to the recruitment and differentiation of myeloid cells ( P < .01). Glucocorticoids abrogated IL-17A-dependent iSGS scar fibroblast production of granulocyte-macrophage colony-stimulating factor ( P = .02).
CONCLUSION - IL-17A directly drives iSGS scar fibroblast proliferation, synergizes with transforming growth factor ß1 to promote extracellular matrix production, and amplifies local inflammatory signaling. Glucocorticoids appear to partially abrogate fibroblast-dependent inflammatory signaling. These results offer mechanistic support for future translational study of clinical reagents for manipulation of the IL-17A pathway in iSGS patients.
For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.
Copyright © 2018 Elsevier Inc. All rights reserved.
OBJECTIVES - We aimed to validate an algorithm using both primary discharge diagnosis (International Classification of Diseases Ninth Revision (ICD-9)) and diagnosis-related group (DRG) codes to identify hospitalisations due to decompensated heart failure (HF) in a population of patients with diabetes within the Veterans Health Administration (VHA) system.
DESIGN - Validation study.
SETTING - Veterans Health Administration-Tennessee Valley Healthcare System PARTICIPANTS: We identified and reviewed a stratified, random sample of hospitalisations between 2001 and 2012 within a single VHA healthcare system of adults who received regular VHA care and were initiated on an antidiabetic medication between 2001 and 2008. We sampled 500 hospitalisations; 400 hospitalisations that fulfilled algorithm criteria, 100 that did not. Of these, 497 had adequate information for inclusion. The mean patient age was 66.1 years (SD 11.4). Majority of patients were male (98.8%); 75% were white and 20% were black.
PRIMARY AND SECONDARY OUTCOME MEASURES - To determine if a hospitalisation was due to HF, we performed chart abstraction using Framingham criteria as the referent standard. We calculated the positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for the overall algorithm and each component (primary diagnosis code (ICD-9), DRG code or both).
RESULTS - The algorithm had a PPV of 89.7% (95% CI 86.8 to 92.7), NPV of 93.9% (89.1 to 98.6), sensitivity of 45.1% (25.1 to 65.1) and specificity of 99.4% (99.2 to 99.6). The PPV was highest for hospitalisations that fulfilled both the ICD-9 and DRG algorithm criteria (92.1% (89.1 to 95.1)) and lowest for hospitalisations that fulfilled only DRG algorithm criteria (62.5% (28.4 to 96.6)).
CONCLUSIONS - Our algorithm, which included primary discharge diagnosis and DRG codes, demonstrated excellent PPV for identification of hospitalisations due to decompensated HF among patients with diabetes in the VHA system.
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Ultrasound imaging for kidney stones suffers from poorer sensitivity, diminished specificity, and overestimation of stone size compared to computed tomography (CT). The purpose of this study was to demonstrate in vitro feasibility of novel ultrasound imaging methods comparing traditional B-mode to advanced beamforming techniques including plane wave synthetic focusing (PWSF), short-lag spatial coherence (SLSC) imaging, mid-lag spatial coherence (MLSC) imaging with incoherent compounding, and aperture domain model image reconstruction (ADMIRE). The ultrasound techniques were evaluated using a research-based ultrasound system applied to an in vitro kidney stone model at 4 and 8 cm depths. Stone diameter sizing and stone contrast were compared among the different techniques. Analysis of variance was used to analyze the differences among group means, with p < 0.05 considered significant, and a Student's t test was used to compare each method with B-mode, with p < 0.0025 considered significant. All stones were detectable with each method. MLSC performed best with stone sizing and stone contrast compared to B-mode. On average, B-mode sizing error ± SD was > 1 mm (1.2 ± 1.1 mm), while those for PWSF, ADMIRE, and MLSC were < 1 mm (- 0.3 ± 2.9 mm, 0.6 ± 0.8, 0.8 ± 0.8, respectively). Subjectively, MLSC appeared to suppress the entire background thus highlighting only the stone. The ADMIRE and SLSC techniques appeared to highlight the stone shadow relative to the background. The detection and sizing of stones in vitro are feasible with advanced beamforming methods with ultrasound. Future work will include imaging stones at greater depths and evaluating the performance of these methods in human stone formers.
Tract-based spatial statistics (TBSS) has proven to be a popular technique for performing voxel-wise statistical analysis that aims to improve sensitivity and interpretability of analysis of multi-subject diffusion imaging studies in white matter. With the advent of advanced diffusion MRI models - e.g., the neurite orientation dispersion density imaging (NODDI), it is of interest to analyze microstructural changes within gray matter (GM). A recent study has proposed using NODDI in gray matter based spatial statistics (N-GBSS) to perform voxel-wise statistical analysis on GM microstructure. N-GBSS adapts TBSS by skeletonizing the GM and projecting diffusion metrics to a cortical ribbon. In this study, we propose an alternate approach, known as gray matter surface based spatial statistics (GS-BSS), to perform statistical analysis using gray matter surfaces by incorporating established methods of registration techniques of GM surface segmentation on structural images. Diffusion microstructure features from NODDI and GM surfaces are transferred to standard space. All the surfaces are then projected onto a common GM surface non-linearly using diffeomorphic spectral matching on cortical surfaces. Prior post-mortem studies have shown reduced dendritic length in prefrontal cortex region in schizophrenia and bipolar disorder population. To validate the results, statistical tests are compared between GS-BSS and N-GBSS to study the differences between healthy and psychosis population. Significant results confirming the microstructural changes are presented. GS-BSS results show higher sensitivity to group differences between healthy and psychosis population in previously known regions.