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Although tumor necrosis factor inhibitors (TNFi) have favorably altered the treatment landscape for patients with axial spondyloarthritis (axSpA), there is limited data regarding TNFi persistence and reasons for discontinuation. This is an observational time-to-event study utilizing data collected for a prospective multiple-disease registry of US Veterans with axSpA treated with TNFi therapies and recruited over a 10 year period. Clinical, serological, and comorbid parameters were collected. Corporate Data Warehouse Pharmacy files provided courses of the 5 TNFi agents, and response to treatment was documented. Individual TNFi persistence was established utilizing univariate and multivariate Cox proportional models, and reasons for discontinuation were obtained by physician chart review. Two-hundred and fifty-five axSpA patients received 731 TNFi courses. A majority of patients (84.3%) had TNFi persistence at 12 months; 63.5% and 47.1% at 24 and 36 months, respectively. Compared to adalimumab, infliximab demonstrated greater persistence, certolizumab the least. Age, smoking status, BMI, comorbidity burden, inflammatory markers and HLA-B27 did not predict TNFi persistence or discontinuation. Stroke and peripheral arterial disease increased the probability of TNFi discontinuation. Secondary non-response (SNR) was the most common reason for discontinuation (46% of all courses); non-adherence (6%) and clinical remission (2%) were uncommon. Pain score at enrollment, myocardial infarction, African American race and inflammatory bowel disease (IBD) predicted TNFi response. While initial persistence of TNFi treatment was high, a large proportion of the patients discontinued initial TNFi therapy by 3 years, primarily due to loss of efficacy. While further research identifying potential predictors of TNFi discontinuation in axSpA is warranted, access to alternate disease-modifying therapies is needed.

This was a multicenter retrospective analysis comparing intravenous push (IVP) analgesia versus patient-controlled analgesia (PCA) in patients admitted for sickle cell pain crisis. The primary objective was to compare the analgesic management, measured in total daily morphine milligram equivalents (MME). Secondary objectives included length of hospitalization, 30-day hospital readmissions and pain scores. Of the 98 patients identified between August 2017 and August 2018, 68 patients were included in this study. There were 51% (

This study was conducted to assess the utility of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in predicting radiographic sacroiliitis and active disease in axial spondyloarthritis (SpA) and to explore the association between use of a tumor necrosis factor inhibitor (TNFi) and these laboratory values compared with traditional inflammatory markers.Observational data from the Program to Understand the Longterm Outcomes in Spondyloarthritis (PULSAR) registry were analyzed. We generated receiver operating characteristic curves to calculate laboratory cutoff values; we used these values in multivariable logistic regression models to identify associations with radiographically confirmed sacroiliitis and active disease. We also used logistic regression to determine the likelihood of elevated laboratory values after initiation of TNFi.Most study participants (n = 354) were White, male, and HLA-B27 positive. NLR (odds ratio [OR] 1.459, P = 0.034), PLR (OR 4.842, P 0.001), erythrocyte sedimentation rate (OR 4.397, P 0.001), and C-reactive protein (CRP) level (OR 2.911, P = 0.001) were independent predictors of radiographic sacroiliitis. Models that included PLR with traditional biomarkers performed better than those with traditional biomarkers alone. NLR (OR 6.931, P = 0.002) and CRP (OR 2.678, P = 0.004) were predictors of active disease, but the model that included both NLR and CRP performed better than CRP alone. TNFi use reduced the odds of elevated NLR (OR 0.172, P 0.001), PLR (OR 0.073, P 0.001), erythrocyte sedimentation rate (OR 0.319, P 0.001), and CRP (OR 0.407, P 0.001), but models that included NLR or PLR and traditional biomarkers performed best.These findings demonstrate an association between NLR and PLR and sacroiliitis and disease activity, with NLR and PLR showing response after TNFi treatment and adding useful clinical information to established biomarkers, thus perhaps assisting in management of axial SpA.

Protein-based medications are expensive and susceptible to damage from mechanical shock, which may occur during shipping to patients from pharmacies. Our objectives were to evaluate the performance of 2 packaging systems and to describe the mechanical shock that occurs during shipments. The packaging systems evaluated were boxes containing expanded polystyrene (EPS) or soft lint-like foam, prepared with and without polymer cooling packs. In laboratory-based studies, accelerometers measured g forces in boxes dropped from varying heights. Transportation studies evaluated the EPS cooler when sent to locations via 2 vendors. The relationship between drop height and maximum force vector was approximately linear for drop heights of 7.5″ through 30″, with lower magnitude forces at 45″ and 60″. Soft foam reduced force by 9.8 g on average compared to EPS (p < 0.009). The presence of polymer packs mitigated forces; frozen packs reduced forces by 28 g versus thawed packs (p < 0.001). Transportation experiments demonstrated most impacts were in the low (10-24 g) and low-medium (25-49 g) range (95% of all impacts). There was no difference between impacts during shipments with the vendors, and there was no correlation between distance traveled and number of impacts. Overall, mechanical shock during shipping is both prevalent and contingent upon the packing materials used.

Although tumor necrosis factor inhibitors (TNFi) have favorably altered the treatment landscape for patients with axial spondyloarthritis (axSpA), there is limited data regarding TNFi persistence and reasons for discontinuation. This is an observational time-to-event study utilizing data collected for a prospective multiple-disease registry of US Veterans with axSpA treated with TNFi therapies and recruited over a 10 year period. Clinical, serological, and comorbid parameters were collected. Corporate Data Warehouse Pharmacy files provided courses of the 5 TNFi agents, and response to treatment was documented. Individual TNFi persistence was established utilizing univariate and multivariate Cox proportional models, and reasons for discontinuation were obtained by physician chart review. Two-hundred and fifty-five axSpA patients received 731 TNFi courses. A majority of patients (84.3%) had TNFi persistence at 12 months; 63.5% and 47.1% at 24 and 36 months, respectively. Compared to adalimumab, infliximab demonstrated greater persistence, certolizumab the least. Age, smoking status, BMI, comorbidity burden, inflammatory markers and HLA-B27 did not predict TNFi persistence or discontinuation. Stroke and peripheral arterial disease increased the probability of TNFi discontinuation. Secondary non-response (SNR) was the most common reason for discontinuation (46% of all courses); non-adherence (6%) and clinical remission (2%) were uncommon. Pain score at enrollment, myocardial infarction, African American race and inflammatory bowel disease (IBD) predicted TNFi response. While initial persistence of TNFi treatment was high, a large proportion of the patients discontinued initial TNFi therapy by 3 years, primarily due to loss of efficacy. While further research identifying potential predictors of TNFi discontinuation in axSpA is warranted, access to alternate disease-modifying therapies is needed.

Rural outplacement programs have been offered by Australian universities to encourage dental students to consider rural careers. The dental curricula should be designed to provide graduates with a good understanding of oral health issues that rural oral health care workers face. Pre-outplacement perceptions of dental students on rural practice are considered imperative to review and update the outplacement programs, however, they have not been investigated in detail. An online, anonymous, voluntary survey was conducted among the first-year dental students. The responses were solicited on the level of awareness and understanding of rural oral health, intention to practice in rural locations and factors informing the appeal of rural dental practice. The mean of a seven-point Likert scale revealed that most students had some level of perceived knowledge and awareness of rural communities and oral health. Students with rural affiliation were more likely to have the intent to practise rurally after graduation (p = 0.001). For short-term rural practice, students overall expressed positive intention. Greater job opportunities were the main motivating factor, while the distance from family and friends was the main deterring factor for practising rurally. Rural affiliation influences the intent to practise rurally on graduation. The pre-outplacement perceptions, in this study, are comparable with post-outplacement observations reported in the literature.

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. Current treatments for AF remain suboptimal due to a lack of understanding of the underlying atrial structures that directly sustain AF. Existing approaches for analyzing atrial structures in 3-D, especially from late gadolinium-enhanced (LGE) magnetic resonance imaging, rely heavily on manual segmentation methods that are extremely labor-intensive and prone to errors. As a result, a robust and automated method for analyzing atrial structures in 3-D is of high interest. We have, therefore, developed AtriaNet, a 16-layer convolutional neural network (CNN), on 154 3-D LGE-MRIs with a spatial resolution of 0.625 mm $\times0.625$ mm $\times1.25$ mm from patients with AF, to automatically segment the left atrial (LA) epicardium and endocardium. AtriaNet consists of a multi-scaled, dual-pathway architecture that captures both the local atrial tissue geometry and the global positional information of LA using 13 successive convolutions and three further convolutions for merging. By utilizing computationally efficient batch prediction, AtriaNet was able to successfully process each 3-D LGE-MRI within 1 min. Furthermore, benchmarking experiments have shown that AtriaNet has outperformed the state-of-the-art CNNs, with a DICE score of 0.940 and 0.942 for the LA epicardium and endocardium, respectively, and an inter-patient variance of

The purpose of this study is to analyze the electrical propagation patterns from patients with persistent atrial fibrillation (AF). Unipolar electrograms $(\sim 1$ minute) were recorded sequentially in atrial chambers from 5 patients with persistent AF prior to ablation using a 64-electrode basket catheter. Raw unipolar signals were processed using a suite of signal processing approaches including: 1) A Savitzky-Golay filter, 2) a standard QRS subtraction and 3) a local variability approach which uses a local integral to enhance the signals. Recurring patterns of electrode activation and AF drivers were then identified by using activation time maps and dominant frequency analysis, and by screening through the entire isopotential movies of each basket recording. Localized sources in the form of either localized reentries $(N=1)$ or focal impulses $(N=4)$ were observed in all of the patients studied. Interestingly, all localized sources were found in the left atrium (LA) near the pulmonary veins with the exception of 1 patient where an additional focal impulse was also detected in the right atrium (RA). Disorganized activity was observed in the RA of 3 patients, though their respective LAs were driven by localized sources. These results suggest that either localized sources in the LA or disorganized activity in the RA or both of these mechanisms may be the predominant triggers of persistent AF

OBJECTIVE: The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases, such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the general population in industrialized countries. Automatic AF detection in clinics remains a challenging task due to the high inter-patient variability of ECGs, and unsatisfactory existing approaches for AF diagnosis (e.g., atrial or ventricular activity based analyses). APPROACH: We have developed RhythmNet, a 21-layer 1D convolutional recurrent neural network, trained using 8,528 single lead ECG recordings from the 2017 PhysioNet/Computing in Cardiology (CinC) Challenge, to classify ECGs of different rhythms including AF automatically. Our RhythmNet architecture contained 16 convolutions to extract features directly from raw ECG waveforms, followed by three recurrent layers to process ECGs of varying lengths and to detect arrhythmia events in long recordings. Large 15 × 1 convolutional filters were used to effectively learn the detailed variations of the signal within small time-frames such as the P-waves and QRS complexes. We employed residual connections throughout RhythmNet, along with batch-normalization and rectified linear activation units to improve convergence during training. MAIN RESULTS: We evaluated our algorithm on 3,658 testing data and obtained an F(1) accuracy of 82% for classifying sinus rhythm, AF, and other arrhythmias. RhythmNet was also ranked 5(th) in the 2017 CinC challenge. SIGNIFICANCE: Potentially, our approach could aid AF diagnosis in clinics and be used for patient self-monitoring to improve the early detection and effective treatment of AF.

RATIONALE, AIMS, AND OBJECTIVES Long-term exposure to glucocorticoids can cause adverse drug reactions of long latency (ADRLLs), including glucocorticoid-induced diabetes mellitus (GID). Providers can monitor for GID using the glycosylated haemoglobin blood (HbA1C) test. This study examined the utility of decisional support to improve HbA1C-based screening for GID. US veterans were identified as chronic users of oral glucocorticoids (>120 days of oral glucocorticoids in the last 2 years). The primary care providers caring for these patients were the target of the intervention. Providers were randomized to receive automatic HbA1C orders for their patients receiving chronic glucocorticoid or usual care. METHODS This study was a pilot two-arm, group-randomized, controlled trial (n = 12 providers, n = 38 patients). Data collection occurred from 5 May 2013 until 10 January 2014. A pharmacist generated the order for an HbA1C through the electronic medical record. The time between the intervention start date and the date on which an HbA1C order was signed were compared using Cox proportional and hierarchical linear regression. RESULTS The time to sign HbA1C orders (mean 12.0 days for the intervention arm; 104.0 days for control arm) was associated with significant differences favouring the intervention [HR (Hazard Ratio) 50.2, P

Studies show that if we retrofit all the lighting systems in the buildings of California with dimming ballasts, then it would be possible to obtain a 450 MW of regulation, 2.5 GW of nonspinning reserve, and 380 MW of contingency reserve from participation of lighting loads in the energy market. However, in order to guarantee participation, it will be important to monitor and model lighting demand and supply in buildings. To this end, wireless sensor and actuator networks have proven to bear a great potential for personalized intelligent lighting with reduced energy use at 50%-70%. Closed-loop control of these lighting systems relies upon instantaneous and dense sensing. Such systems can be expensive to install and commission. In this paper, we present a sensor-based intelligent lighting system for future grid-integrated buildings. The system is intended to guarantee participation of lighting loads in the energy market, based on predictive models of indoor light distribution, developed using sparse sensing. We deployed ~92 % fewer sensors compared with state-of-art systems using one photosensor per luminaire. The sensor modules contained small solar panels that were powered by ambient light. Reduction in sensor deployments is achieved using piecewise linear predictive models of indoor light, discretized by clustering for sky conditions and sun positions. Day-ahead daylight is predicted from forecasts of temperature, humidity, and cloud cover. With two weeks of daylight and artificial light training data acquired at the sustainability base at NASA Ames, our model was able to predict the illuminance at seven monitored workstations with 80%-95% accuracy. Moreover, our support vector regression model was able to predict day-ahead daylight at ~92% accuracy.

Health care reform in Vermont promotes patient-centered medical homes (PCMH) and multi-disciplinary community health teams to support population health. This qualitative study describes the expansion of Vermont's health care reform efforts, initially focused on adult primary care, to pediatrics through interviews with project managers and facilitators, CHT members, pediatric practitioners and care coordinators, and community-based providers. Analyses used grounded theory, identifying themes confirmed by repeat occurrence across respondents. Respondents believed that PCMH recognition and financial and community supports would improve care for pediatric patients and families. Respondents shared three main challenges with health care reform efforts: achieving PCMH recognition, adapting community health teams for pediatric patients and families, and defining roles for care coordinators. For health care reform efforts to support pediatric patients and be family-centered, states may need additional resources to understand how pediatric and adult primary care differ and how best to support pediatrics during health care reform efforts.

Rationale, aims, and objectives Adverse drug reactions (ADRs) are a critical concern: they are costly, both in dollars and in diminishing patients' quality of life. ADRs that occur due to prolonged exposure to a pharmaceutical agent (adverse drug reactions of long latency, ADRLLs) may be easier to prevent than acute ADRs, as ADRLLs inherently require continued medication exposures. This pilot study used glucocorticoid-induced osteoporosis (GIO) as an example ADRLL. The aims were to survey health care providers' current practices in avoiding ADRLLs and the perceived utility of decisional support systems (DSS) to aid them in preventing GIO. Methods We administered an anonymous, cross-sectional survey to health care providers (fellows, doctor assistants, nurse practitioners and attending doctors) focusing on their methods to monitor for and prevent ADRLLs. The questionnaire also gauged usage of electronic medical records (EMRs) and each provider's perceived utility of specific DSS-based approaches to monitoring for GIO. Data were interpreted using descriptive statistics and histograms. Results A majority of the 33 responding providers (84.8%) reported that their primary ADRLL avoidance technique is simply remembering that a patient is on chronic glucocorticoids. The most favourably perceived DSS options included tracking medications on a flow sheet (84.8%) and digital tracking of cumulative glucocorticoid exposure with real-time prompts (83.9%). Conclusions Surveyed providers reported that additional DSS implementation may help in the avoidance of ADRLLs such as GIO. Providers ranked both digital and non-digital DSS favourably, but a computerized approach is appealing in that it may be integrated into extant EMR systems.

Long-term exposure to glucocorticoids can cause adverse drug reactions of long latency (ADRLLs), including glucocorticoid-induced diabetes mellitus (GID). Providers can monitor for GID using the glycosylated haemoglobin blood (HbA1C) test. This study examined the utility of decisional support to improve HbA1C-based screening for GID. US veterans were identified as chronic users of oral glucocorticoids (120 days of oral glucocorticoids in the last 2 years). The primary care providers caring for these patients were the target of the intervention. Providers were randomized to receive automatic HbA1C orders for their patients receiving chronic glucocorticoid or usual care.This study was a pilot two-arm, group-randomized, controlled trial (n = 12 providers, n = 38 patients). Data collection occurred from 5 May 2013 until 10 January 2014. A pharmacist generated the order for an HbA1C through the electronic medical record. The time between the intervention start date and the date on which an HbA1C order was signed were compared using Cox proportional and hierarchical linear regression.The time to sign HbA1C orders (mean 12.0 days for the intervention arm; 104.0 days for control arm) was associated with significant differences favouring the intervention [HR (Hazard Ratio) 50.2, P 0.001, confidence interval (CI) 6.3 to 398.7]. For the intervention group, 95% of orders were signed, whereas only 12% of control providers signed orders (odds ratio 150, P 0.001, CI 12.4 to 1812.9).The results of this study strongly suggest that the clinical pharmacist-triggered order intervention is effective. This method of computerized decisional support may be useful in improving screening for GID and ADRLLs.

Adverse drug reactions (ADRs) are a critical concern: they are costly, both in dollars and in diminishing patients' quality of life. ADRs that occur due to prolonged exposure to a pharmaceutical agent (adverse drug reactions of long latency, ADRLLs) may be easier to prevent than acute ADRs, as ADRLLs inherently require continued medication exposures. This pilot study used glucocorticoid-induced osteoporosis (GIO) as an example ADRLL. The aims were to survey health care providers' current practices in avoiding ADRLLs and the perceived utility of decisional support systems (DSS) to aid them in preventing GIO.We administered an anonymous, cross-sectional survey to health care providers (fellows, doctor assistants, nurse practitioners and attending doctors) focusing on their methods to monitor for and prevent ADRLLs. The questionnaire also gauged usage of electronic medical records (EMRs) and each provider's perceived utility of specific DSS-based approaches to monitoring for GIO. Data were interpreted using descriptive statistics and histograms.A majority of the 33 responding providers (84.8%) reported that their primary ADRLL avoidance technique is simply remembering that a patient is on chronic glucocorticoids. The most favourably perceived DSS options included tracking medications on a flow sheet (84.8%) and digital tracking of cumulative glucocorticoid exposure with real-time prompts (83.9%).Surveyed providers reported that additional DSS implementation may help in the avoidance of ADRLLs such as GIO. Providers ranked both digital and non-digital DSS favourably, but a computerized approach is appealing in that it may be integrated into extant EMR systems.

Objective We discuss the use of structural models for the analysis of biosurveillance related data. Methods and results Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm. Conclusions Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data.

Clinical studies have been initiated in conformal radiotherapy using a computer controlled multileaf collimator. Quantitative dosimetry and treatment planning studies comparing field shaping by lead alloy blocks and the multileaf collimator demonstrate the clinical acceptability of the multileaf collimator. Sixteen patients with tumors in multiple sites have received some part of their treatments with both blocking systems. Studies of dosimetry and field shaping show that the multileaf collimator produces clinically acceptable blocking for most field shapes and disease sites. The 80 – 20% penumbra was characterized for a wide range of shaped beams. For straight edges perpendicular to the leaf travel, the penumbra of measured dose distributions from the multileaf collimator is equal to conventional divergent blocking. When the multileaf collimator leaves approach a contour at an angle, the penumbra increases. At forty-five degrees, the maximum angle of approach, the penumbra is approximately 4 mm wider than that for divergent blocks. Three-dimensional treatment planning demonstrates that equivalent dose distributions can be obtained from the two field shaping systems. The multileaf collimator can be used effectively and efficiently to treat a variety of disease sites. Its optimal utility may be in treating complex fields—five or more shaped coplanar or non-coplanar beams. It is well suited for conformal therapy applications.

On June 6, 1989, seventy agents from the FBI and EPA, armed with a search warrant, entered the Department of Energy's Rocky Flats nuclear weapons plant in Colorado. Capping a tenmonth criminal investigation, the search exposed evidence of a wide range of environmental violations, including the secret incineration of hazardous wastes, false claims of compliance with groundwater monitoring requirements, and intentional mixing of hazardous and radioactive wastes.1 The Rocky Flats incident is a particularly dramatic but nonetheless accurate reflection of the type of environmental problems created by federal facilities. By the mid-1970s, the federal government had adopted statutes governing almost every major environmental threat, including air emissions, pesticides, and hazardous waste dumping. As the 1990s commence, one great irony is that the federal government continues to be a major violator of its own laws. From the Portsmouth Naval Shipyard in Maine to the Feed Materials Production Center in Ohio to the Hanford Reservation in Washington, almost every state in the country faces pollution problems caused by the activities of federal facilities.2 The prob