Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio as Biomarkers in Axial Spondyloarthritis: Observational Studies From the Program to Understand the Longterm Outcomes in Spondyloarthritis Registry.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Tumor necrosis factor inhibitor (TNFi) persistence and reasons for discontinuation in a predominantly male cohort with axial spondyloarthritis.

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.

A Comparison of Two Regimens for Managing Sickle Cell Pain and Reducing Readmissions.

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% (n = 35) in the IVP group and 49% (n = 33) in the PCA group. The majority of patients were on 90 or more daily MME prior to admission. The average total daily MME was significantly higher in patients on PCA compared to IVP on the first three days of hospitalization (289 vs 146, p < 0.01). Length of hospitalization was not different between patients on IVP and PCA (7.14 vs. 6.39 days, p = 0.53). There was no difference in 30-day readmissions, average pain scores on days 1-3 of hospitalization and adverse side effects between the groups. This study showed patients on IVP had significantly lower total daily MME requirements compared to PCA within the first three calendar days of admission.

Mechanical Shock During Shipping of Medications: The Roles of Packaging and Transportation Vendors.

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.

Fully Automatic Left Atrium Segmentation From Late Gadolinium Enhanced Magnetic Resonance Imaging Using a Dual Fully Convolutional Neural Network.

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 ×0.625 mm ×1.25 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 <0.001. The estimated LA diameter and volume computed from the automatic segmentations were accurate to within 1.59 mm and 4.01 cm3 of the ground truths. Our proposed CNN was tested on the largest known data set for LA segmentation, and to the best of our knowledge, it is the most robust approach that has ever been developed for segmenting LGE-MRIs. The increased accuracy of atrial reconstruction and analysis could potentially improve the understanding and treatment of AF.

ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network.

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 8528 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 3658 testing data and obtained an F 1 accuracy of 82% for classifying sinus rhythm, AF, and other arrhythmias. RhythmNet was also ranked 5th 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.

Decisional support to prevent adverse drug reactions of long latency: pilot randomized controlled intervention for glucocorticoid-induced diabetes.

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 < 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). CONCLUSIONS: 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.

Factors affecting medication adherence: patient perspectives from five veterans affairs facilities.

BACKGROUND: In the United States, more than 25 million people have diabetes. Medication adherence is known to be important for disease control. However, factors that consistently predict medication adherence are unclear and the literature lacks patient perspectives on how health care systems affect adherence to oral hypoglycemic agents (OHAs). This study explored facilitators and barriers to OHA adherence by obtaining the perspectives of Veterans Affairs (VA) patients with OHA prescriptions. METHODS: A total of 45 patients participated in 12 focus groups that explored a wide range of issues that might affect medication adherence. Participants were patients at clinics in Seattle, Washington; San Antonio, Texas; Portland, Oregon; Salem, Oregon, and Warrenton, Oregon. RESULTS: Key system-level facilitators of OHA adherence included good overall pharmacy service and several specific mechanisms for ordering and delivering medications (automated phone refill service, Web-based prescription ordering), as well as providing pillboxes and printed lists of current medications to patients. Barriers mirrored many of the facilitators. Poor pharmacy service quality and difficulty coordinating multiple prescriptions emerged as key barriers. CONCLUSIONS: VA patient focus groups provided insights on how care delivery systems can encourage diabetes medication adherence by minimizing the barriers and enhancing the facilitators at both the patient and system levels. Major system-level factors that facilitated adherence were overall pharmacy service quality, availability of multiple systems for reordering medications, having a person to call when questions arose, counseling about the importance of adherence and providing tools such as pillboxes and updated medication lists.

Practices and preferences for detecting chronic medication toxicity: a pilot cross-sectional survey of health care providers focusing on decision support systems.

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.