Abstract
Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool to identify fractures, predict BMD, and ...evaluate fracture risk using plain radiographs. The tool performance is evaluated on 5164 and 18175 patients with pelvis/lumbar spine radiographs and Hologic DXA. The model is well calibrated with minimal bias in the hip (slope = 0.982, calibration-in-the-large = −0.003) and the lumbar spine BMD (slope = 0.978, calibration-in-the-large = 0.003). The area under the precision-recall curve and accuracy are 0.89 and 91.7% for hip osteoporosis, 0.89 and 86.2% for spine osteoporosis, 0.83 and 95.0% for high 10-year major fracture risk, and 0.96 and 90.0% for high hip fracture risk. The tool classifies 5206 (84.8%) patients with 95% positive or negative predictive value for osteoporosis, compared to 3008 DXA conducted at the same study period. This automated tool may help identify high-risk patients for osteoporosis.
To describe trends in the epidemiology of gout and patterns of urate-lowering treatment (ULT) in the UK general population from 1997 to 2012.
We used the Clinical Practice Research Datalink to ...estimate the prevalence and incidence of gout for each calendar year from 1997 to 2012. We also investigated the pattern of gout management for both prevalent and incident gout patients.
In 2012, the prevalence of gout was 2.49% (95% CI 2.48% to 2.51%) and the incidence was 1.77 (95% CI 1.73 to 1.81) per 1000 person-years. Prevalence and incidence both were significantly higher in 2012 than in 1997, with a 63.9% increase in prevalence and 29.6% increase in incidence over this period. Regions with highest prevalence and incidence were the North East and Wales. Among prevalent gout patients in 2012, only 48.48% (95% CI 48.08% to 48.89%) were being consulted specifically for gout or treated with ULT and of these 37.63% (95% CI 37.28% to 38.99%) received ULT. In addition, only 18.6% (95% CI 17.6% to 19.6%) of incident gout patients received ULT within 6 months and 27.3% (95% CI 26.1% to 28.5%) within 12 months of diagnosis. The management of prevalent and incident gout patients remained essentially the same during the study period, although the percentage of adherent patients improved from 28.28% (95% CI 27.33% to 29.26%) in 1997 to 39.66% (95% CI 39.11% to 40.22%) in 2012.
In recent years, both the prevalence and incidence of gout have increased significantly in the UK. Suboptimal use of ULT has not changed between 1997 and 2012. Patient adherence has improved during the study period, but it remains poor.
An investigation was conducted to develop an effective automated tool to deploy micro-fabricated stretchable networks of distributed sensors onto the surface of large structures at macroscale to ...create “smart” structures with embedded distributed sensor networks. Integrating a large network of distributed sensors with structures has been a major challenge in the design of so-called smart structures or devices for cyber-physical applications where a large amount of usage data from structures or devices can be generated for artificial intelligence applications. Indeed, many “island-and-serpentine”-type distributed sensor networks, while promising, remain difficult to deploy. This study aims to enable such networks to be deployed in a safe, automated, and efficient way. To this end, a scissor-hinge controlled system was proposed as the basis for a deployment mechanism for such stretchable sensor networks (SSNs). A model based on a kinematic scissor-hinge mechanism was developed to simulate and design the proposed system to automatically stretch a micro-scaled square network with uniformly distributed sensor nodes. A prototype of an automatic scissor-hinge stretchable tool was constructed during the study with an array of four scissor-hinge mechanisms, each belt-driven by a single stepper motor. Two micro-fabricated SSNs from a 100 mm wafer were fabricated at the Stanford Nanofabrication Facility for this deployment study. The networks were designed to be able to cover an area 100 times their manufacturing size (from a 100 mm diameter wafer to a 1 m2 active area) once stretched. It was demonstrated that the proposed deployment tool could place sensor nodes in prescribed locations efficiently within a drastically shorter time than in current labor-intensive manual deployment methods.
A multiscale method that allows a noninvasive and precise integration of thousands of nano‐ or microscale electronic devices into large macroscopic materials or structures of any 3D shape and ...rigidity is demonstrated. An array of microsensors (for strain and temperature) is built on a spider‐web‐like, highly expandable and flexible polyimide substrate that is engineered in such a way as to allow unique area dilatations.
Background
Whether non–vitamin K antagonist oral anticoagulants (NOACs) are superior to warfarin among Asians with nonvalvular atrial fibrillation remains unclear.
Methods and Results
In this ...nationwide retrospective cohort study collected from Taiwan National Health Insurance Research Database, there were 5843, 20 079, 27 777, and 19 375 nonvalvular atrial fibrillation patients taking apixaban, dabigatran, rivaroxaban and warfarin, respectively, from June 1, 2012 to December 31, 2016. Propensity‐score weighting was used to balance covariates across study groups. Patients were followed until the first occurrence of any efficacy or safety outcome or the end date of study. Hazard ratios (95% confidence intervals) comparing apixaban, dabigatran, and rivaroxaban with warfarin were: ischemic stroke/systemic embolism (IS/SE), 0.55 (0.43–0.69), 0.82 (0.68–0.98), and 0.81 (0.67–0.97); major bleeding, 0.41 (0.31–0.53), 0.65 (0.53–0.80), and 0.58 (0.46–0.72); and all‐cause mortality, 0.58 (0.51–0.66), 0.61 (0.54–0.68), and 0.57 (0.51–0.65). A total of 3623 (62%), 17 760 (88%), and 26 000 (94%) patients were taking low‐dose apixaban (2.5 mg twice daily), dabigatran (110 mg twice daily), and rivaroxaban (10–15 mg once daily), respectively. Similar to all‐dose NOACs, all low‐dose NOACs had lower risk of IS/SE, major bleeding, and mortality when compared with warfarin. In contrast to other standard‐dose NOACs, apixaban was associated with lower risks of IS/SE (0.45 0.31–0.65), major bleeding (0.29 0.18–0.46), and mortality (0.23 0.17–0.31) than warfarin.
Conclusions
All NOACs were associated with lower risk of IS/SE, major bleeding, and mortality compared with warfarin in the largest real‐world practice among Asians with nonvalvular atrial fibrillation. All low‐dose NOACs had lower risk of IS/SE, major bleeding, and mortality when compared with warfarin. Standard‐dose apixaban caused a lower risk of IS/SE, major bleeding, and mortality compared with warfarin.
Reliability quantification is a critical and necessary process for the evaluation and assessment of any inspection technology that may be classified either as a nondestructive evaluation or ...structural health monitoring technique. Based on the sensitivity characterization of nondestructive evaluation techniques, appropriate processes have been developed and established for the reliability quantification of their performance with respect to damage/flaw detection in materials or structures. However, in the case of structural health monitoring methods, no such well-defined and general applicable approaches have been established for neither active nor passive sensing techniques that allow for their accurate reliability quantification. The objective of this study is to characterize the sensitivity of active-sensing acousto-ultrasound-based structural health monitoring techniques with respect to damage detection, as well as to identify the parameters that influence their sensitivity. With such an understanding, it is believed that adequate quantitative methods could then be established to enable the practical use of acousto-ultrasound structural health monitoring methods in the aerospace and mechanical engineering communities. In order to evaluate the sensitivity of a pre-selected active-sensing acousto-ultrasound structural health monitoring system, both numerical simulations and experiments were performed on 30 aluminum coupons each outfitted with a pair of lead zirconate titanate piezoelectric sensors/actuators. A damage index versus damage size relationship was investigated numerically and experimentally to assess the applicability of the traditional nondestructive evaluation linear regression framework for probability of detection for an active-sensing structural health monitoring system. The results of the study show that the position of each sensor–actuator pair with respect to a known damage location and the damage growth pattern are the two most critical parameters influencing the reliability of the same structural health monitoring system applied to identical structural components under the same environmental conditions.
Relatives of patients with systemic lupus erythematosus (SLE) appear to be at higher risk of SLE and other autoimmune diseases, but estimates of individual familial risks are largely unavailable or ...unreliable. Furthermore, relative contributions of genetic, shared, and unshared environmental factors to SLE susceptibility remain unclear.
To examine familial aggregation and heritability of SLE and the relative risks (RRs) of other autoimmune diseases in relatives of patients with SLE.
A population-based family study using the Taiwan National Health Insurance Research Database was conducted. Participants included all individuals (N = 23,658,577) registered with that database in 2010; of these, 18,283 had SLE. We identified 21,009,551 parent-child relationships, 17,168,340 full sibling pairs, and 342,066 twin pairs. Diagnoses of SLE were ascertained from March 1, 1995, to December 31, 2010, and analysis was conducted between March 1 and August 15, 2014.
The prevalence and RRs of SLE and other autoimmune diseases in relatives and spouses of patients with SLE as well as the relative contributions of heritability, shared, and nonshared environmental factors to SLE susceptibility.
Among the more than 23 million participants, the RRs (95% CIs) for SLE were 315.94 (210.66-473.82) for twins of the patients, 23.68 (20.13-27.84) for siblings, 11.44 (9.74-13.43) for parents, 14.42 (12.45-16.70) for offspring, and 4.44 (2.38-8.30) for spouses without genetic similarity. The accountability for phenotypic variance of SLE was 43.9% for heritability, 25.8% for shared environmental factors, and 30.3% for nonshared environmental factors. The RRs (95% CIs) in individuals with a first-degree relative with SLE were 5.87 (4.89-7.05) for primary Sjögren syndrome, 5.40 (3.37-8.65) for systemic sclerosis, 2.95 (2.04-4.26) for myasthenia gravis, 2.77 (1.45-5.32) for idiopathic inflammatory myositis, 2.66 (2.28-3.11) for rheumatoid arthritis, 2.58 (1.16-5.72) for multiple sclerosis, 1.68 (1.22-2.32) for type 1 diabetes mellitus, 1.39 (0.66-2.91) for inflammatory bowel diseases, and 0.86 (0.43-1.71) for vasculitis.
The individual risks of SLE and other autoimmune diseases were increased in families that included patients with SLE. The heritability of SLE was estimated to be 43.9%. These data should be considered when counseling families with affected members.
This study presents active sensing methods in structural health monitoring, for detecting cracks and debonds in metallic and composite structures, which can be potentially implemented into airframe ...structures. First, a pitch-catch method using a pair of piezoelectric actuator and sensor is introduced to generate a damage indeX which can be used to characterize damage at a known location. Tests on airbus fuselage panels are conducted to verify the method and damage indeX. The damage indeX relates changes in the energy content of a specific Lamb wave mode selected by group velocity analysis to the eXtent of damage. Second, an imaging method based on multiple pitch-catch information, a network of piezoelectric actuator/sensors, is presented for characterizing damage (location and size) without need for a structural or damage model. The imaging method with an autofocusing feature is applied to aluminum plates and a stiffened composite panel for method verification.
Objective
One in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease‐modifying antirheumatic drugs (DMARDs) complicates ...surgical planning, and it is difficult to predict which patients may ultimately require surgery. We used machine learning to develop predictive models for the likelihood of undergoing an operation related to RA and which type of operation patients who require surgery undergo.
Methods
We used electronic health record data to train two extreme gradient boosting machine learning models. The first model predicted patients’ probabilities of undergoing surgery ≥5 years after their initial clinic visit. The second model predicted whether patients who underwent surgery would undergo a major joint replacement versus a less intensive procedure. Predictors included demographics, comorbidities, and medication data. The primary outcome was model discrimination, measured by area under the receiver operating characteristic curve (AUC).
Results
We identified 5,481 patients, of whom 278 (5.1%) underwent surgery. There was no significant difference in the frequency of DMARD or steroid prescriptions between patients who did and did not have surgery, though nonsteroidal anti‐inflammatory drug prescriptions were more common among patients who did have surgery (P = 0.03). The model predicting use of surgery had an AUC of 0.90 ± 0.02. The model predicting type of surgery had an AUC of 0.58 ± 0.10.
Conclusions
Predictive models using clinical data have the potential to facilitate identification of patients who may undergo rheumatoid‐related surgery, but not what type of procedure they will need. Integrating similar models into practice has the potential to improve surgical planning.
Objective
To investigate familial aggregation of Sjögren's syndrome (SS) and the relative risks (RRs) of other autoimmune disease in relatives of patients with SS.
Methods
We identified 23,658,577 ...beneficiaries enrolled in the Taiwan National Health Insurance system in 2010, of whom 12,754 had SS. We identified 21,009,551 parent–child relationships and 17,168,340 pairs of full siblings. The familial risks of SS and other autoimmune diseases, tetrachoric correlation, and familial transmission were estimated.
Results
We identified 105 patients with SS who had an affected first‐degree relative. The RR of SS was 18.99 (95% confidence interval 95% CI 9.76–36.93) in siblings of patients with SS, 11.31 (95% CI 8.34–15.33) in offspring, and 12.46 (95% CI 9.34–16.62) in parents. Tetrachoric correlation coefficients were 0.53 (95% CI 0.41–0.65) for cotwins of affected individuals and 0.21 (95% CI 0.16–0.26) for full siblings. The familial transmission (heritability plus shared environmental contribution) was 0.54 (95% CI 0.44–0.77). In first‐degree relatives of patients with SS, the RRs were 2.95 (95% CI 2.33–3.73) for rheumatoid arthritis, 6.25 (95% CI 5.15–7.58) for systemic lupus erythematosus, 2.39 (95% CI 0.77–7.41) for systemic sclerosis, 0.71 (95% CI 0.10–5.07) for idiopathic inflammatory myopathy, 1.97 (95% CI 1.29–3.02) for type 1 diabetes mellitus, 3.38 (95% CI 1.26–9.05) for multiple sclerosis, 1.67 (95% CI 0.83–3.33) for myasthenia gravis, 1.25 (95% CI 1.04–1.50) for psoriasis, 1.21 (95% CI 0.39–3.76) for inflammatory bowel disease, and 2.29 (95% CI 1.19–4.40) for vasculitis.
Conclusion
The risk of SS and other autoimmune diseases is increased in relatives of patients with SS, and more than one‐half of phenotypic variance in SS can be explained by familial factors.
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