Dataset Information

Identification of Hip BMD Loss and Fracture Risk Markers Through Population-Based Serum Proteomics.

ABSTRACT: Serum proteomics analysis may lead to the discovery of novel osteoporosis biomarkers. The Osteoporotic Fractures in Men (MrOS) study comprises men ?65 years old in the US who have had repeated BMD measures and have been followed for incident fracture. High-throughput quantitative proteomic analysis was performed on baseline fasting serum samples from non-Hispanic white men using a multidimensional approach coupling liquid chromatography, ion-mobility separation, and mass spectrometry (LC-IMS-MS). We followed the participants for a mean of 4.6 years for changes in femoral neck bone mineral density (BMD) and for incident hip fracture. Change in BMD was determined from mixed effects regression models taking age and weight into account. Participants were categorized into three groups: BMD maintenance (no decline; estimated change ?0?g/cm2 , n?=?453); expected loss (estimated change 0 to 1 SD below the estimated mean change, -0.034?g/cm2 for femoral neck, n?=?1184); and accelerated loss (estimated change ?1 SD below mean change, n?=?237). Differential abundance values of 3946 peptides were summarized by meta-analysis to determine differential abundance of each of 339 corresponding proteins for accelerated BMD loss versus maintenance. Using this meta-analytic standardized fold change at cutoffs of ?1.1 or ?0.9 (p?

SUBMITTER: Nielson CM

PROVIDER: S-EPMC5489383 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Identification of Hip BMD Loss and Fracture Risk Markers Through Population-Based Serum Proteomics.

Nielson Carrie M CM Wiedrick Jack J Shen Jian J Jacobs Jon J Baker Erin S ES Baraff Aaron A Piehowski Paul P Lee Christine G CG Baratt Arie A Petyuk Vladislav V McWeeney Shannon S Lim Jeong Youn JY Bauer Douglas C DC Lane Nancy E NE Cawthon Peggy M PM Smith Richard D RD Lapidus Jodi J Orwoll Eric S ES

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research 20170406 7

Serum proteomics analysis may lead to the discovery of novel osteoporosis biomarkers. The Osteoporotic Fractures in Men (MrOS) study comprises men ≥65 years old in the US who have had repeated BMD measures and have been followed for incident fracture. High-throughput quantitative proteomic analysis was performed on baseline fasting serum samples from non-Hispanic white men using a multidimensional approach coupling liquid chromatography, ion-mobility separation, and mass spectrometry (LC-IMS-MS) ...[more]

PMID: 28316103

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Project description:ObjectivesTo determine whether the use of total hip arthroplasty (THA) among individuals with a displaced intracapsular fracture of the femoral neck is based on national guidelines or if there are systematic inequalities.DesignObservational cohort study using the National Hip Fracture Database (NHFD).SettingAll hospitals that treat adults with hip fractures in England, Wales, and Northern Ireland.ParticipantsPatients within the national database (all aged ≥60) who received operative treatment for a non-pathological displaced intracapsular hip fracture from 1 July 2011 to 31 April 2015.Main outcome measuresProvision of THA to patients considered eligible under criteria published by the National Institute for Health and Care Excellence (NICE).Results114 119 patients with hip fracture were included, 11 683 (10.2%) of whom underwent THA. Of those who satisfied the NICE criteria, 32% (6780) : received a THA. Of patients who underwent THA, 42% (4903) did not satisfy the NICE criteria. A recursive partitioning algorithm found that the NICE eligibility criteria did not optimally explain which patients underwent THA. A model with superior explanatory power drew distinctions that are not supported by NICE, which were an age cut off at 76 and a different ambulation cut off. Among patients who satisfied the NICE eligibility, the use of THA was less likely with higher age (odds ratio 0.88, 95% confidence interval 0.87 to 0.88), worsening abbreviated mental test scores (0.49 (0.41 to 0.58) for normal cognition v borderline cognitive impairment)), worsening American Society of Anesthesiologists score (0.74, 0.66 to 0.84), male sex (0.85, 0.77 to 0.93), worsening ambulatory status (0.32, 0.28 to 0.35 for walking with a stick v independent ambulation), and fifths of worsening socioeconomic area deprivation (0.76 (0.66 to 0.88) for least v most deprived fifth). Patients receiving treatment during the working week were more likely to receive THA than at the weekend (0.90, 0.83 to 0.98).ConclusionsThere are wide disparities in the use of THA among individuals with hip fractures, and compliance with NICE guidance is poor. Patients with higher levels of socioeconomic deprivation and those who require surgery at the weekend were less likely to receive THA. Inconsistent compliance with NICE recommendations means that the optimal treatment for older adults with hip fractures can depend on where and when they present to hospital.

Project description:PurposeTo investigate the feasibility of automatic identification and classification of hip fractures using deep learning, which may improve outcomes by reducing diagnostic errors and decreasing time to operation.Materials and methodsHip and pelvic radiographs from 1118 studies were reviewed, and 3026 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous open reduction and internal fixation, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (or DenseNet) was trained on a subset of the bounding box images, and its performance was evaluated on a held-out test set and by comparison on a 100-image subset with two groups of human observers: fellowship-trained radiologists and orthopedists; senior residents in emergency medicine, radiology, and orthopedics.ResultsThe binary accuracy for detecting a fracture of this model was 93.7% (95% confidence interval [CI]: 90.8%, 96.5%), with a sensitivity of 93.2% (95% CI: 88.9%, 97.1%) and a specificity of 94.2% (95% CI: 89.7%, 98.4%). Multiclass classification accuracy was 90.8% (95% CI: 87.5%, 94.2%). When compared with the accuracy of human observers, the accuracy of the model achieved an expert-level classification, at the very least, under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance in the multiclass classification.ConclusionA deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians.Supplemental material is available for this article.© RSNA, 2020.

Dataset Information

Identification of Hip BMD Loss and Fracture Risk Markers Through Population-Based Serum Proteomics.

Publications

Identification of Hip BMD Loss and Fracture Risk Markers Through Population-Based Serum Proteomics.

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