Optimizing warfarin dosing for patients with atrial fibrillation using machine learning

While novel oral anticoagulants are increasingly used to reduce risk of stroke in patients with atrial fibrillation, vitamin K antagonists such as warfarin continue to be used extensively for stroke prevention across the world. While effective in reducing the risk of strokes, the complex pharmacodynamics of warfarin make it difficult to use clinically, with many patients experiencing under- and/or over- anticoagulation. In this study we employed a novel implementation of deep reinforcement learning to provide clinical decision support to optimize time in therapeutic International Normalized Ratio (INR) range. We used a novel semi-Markov decision process formulation of the Batch-Constrained deep Q-learning algorithm to develop a reinforcement learning model to dynamically recommend optimal warfarin dosing to achieve INR of 2.0-3.0 for patients with atrial fibrillation. The model was developed using data from 22,502 patients in the warfarin treated groups of the pivotal randomized clinical trials of edoxaban (ENGAGE AF-TIMI 48), apixaban (ARISTOTLE) and rivaroxaban (ROCKET AF). The model was externally validated on data from 5730 warfarin-treated patients in a fourth trial of dabigatran (RE-LY) using multilevel regression models to estimate the relationship between center-level algorithm consistent dosing, time in therapeutic INR range (TTR), and a composite clinical outcome of stroke, systemic embolism or major hemorrhage. External validation showed a positive association between center-level algorithm-consistent dosing and TTR (R2 = 0.56). Each 10% increase in algorithm-consistent dosing at the center level independently predicted a 6.78% improvement in TTR (95% CI 6.29, 7.28; p < 0.001) and a 11% decrease in the composite clinical outcome (HR 0.89; 95% CI 0.81, 1.00; p = 0.015). These results were comparable to those of a rules-based clinical algorithm used for benchmarking, for which each 10% increase in algorithm-consistent dosing independently predicted a 6.10% increase in TTR (95% CI 5.67, 6.54, p < 0.001) and a 10% decrease in the composite outcome (HR 0.90; 95% CI 0.83, 0.98, p = 0.018). Our findings suggest that a deep reinforcement learning algorithm can optimize time in therapeutic range for patients taking warfarin. A digital clinical decision support system to promote algorithm-consistent warfarin dosing could optimize time in therapeutic range and improve clinical outcomes in atrial fibrillation globally.

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets

Identification of gene-by-environment interactions (GxE) is crucial to understand the interplay of environmental effects on complex traits. However, current methods evaluating GxE on biobank-scale datasets have limitations. We introduce MonsterLM, a multiple linear regression method that does not rely on model specification and provides unbiased estimates of variance explained by GxE. We demonstrate robustness of MonsterLM through comprehensive genome-wide simulations using real genetic data from 325,989 individuals. We estimate GxE using waist-to-hip-ratio, smoking, and exercise as the environmental variables on 13 outcomes (N = 297,529-325,989) in the UK Biobank. GxE variance is significant for 8 environment-outcome pairs, ranging from 0.009 - 0.071. The majority of GxE variance involves SNPs without strong marginal or interaction associations. We observe modest improvements in polygenic score prediction when incorporating GxE. Our results imply a significant contribution of GxE to complex trait variance and we show MonsterLM to be well-purposed to handle this with biobank-scale data.

Optimizing Patient Record Linkage in a Master Patient Index Using Machine Learning: Algorithm Development and Validation

BACKGROUND

To provide quality care, modern health care systems must match and link data about the same patient from multiple sources, a function often served by master patient index (MPI) software. Record linkage in the MPI is typically performed manually by health care providers, guided by automated matching algorithms. These matching algorithms must be configured in advance, such as by setting the weights of patient attributes, usually by someone with knowledge of both the matching algorithm and the patient population being served.

OBJECTIVE

We aimed to develop and evaluate a machine learning-based software tool, which automatically configures a patient matching algorithm by learning from pairs of patient records previously linked by humans already present in the database.

METHODS

We built a free and open-source software tool to optimize record linkage algorithm parameters based on historical record linkages. The tool uses Bayesian optimization to identify the set of configuration parameters that lead to optimal matching performance in a given patient population, by learning from prior record linkages by humans. The tool is written assuming only the existence of a minimal HTTP application programming interface (API), and so is agnostic to the choice of MPI software, record linkage algorithm, and patient population. As a proof of concept, we integrated our tool with SantéMPI, an open-source MPI. We validated the tool using several synthetic patient populations in SantéMPI by comparing the performance of the optimized configuration in held-out data to SantéMPI's default matching configuration using sensitivity and specificity.

RESULTS

The machine learning-optimized configurations correctly detect over 90% of true record linkages as definite matches in all data sets, with 100% specificity and positive predictive value in all data sets, whereas the baseline detects none. In the largest data set examined, the baseline matching configuration detects possible record linkages with a sensitivity of 90.2% (95% CI 88.4%-92.0%) and specificity of 100%. By comparison, the machine learning-optimized matching configuration attains a sensitivity of 100%, with a decreased specificity of 95.9% (95% CI 95.9%-96.0%). We report significant gains in sensitivity in all data sets examined, at the cost of only marginally decreased specificity. The configuration optimization tool, data, and data set generator have been made freely available.

CONCLUSIONS

Our machine learning software tool can be used to significantly improve the performance of existing record linkage algorithms, without knowledge of the algorithm being used or specific details of the patient population being served.

Nailfold capillaroscopy and deep learning in diabetes

OBJECTIVE

To determine whether nailfold capillary images, acquired using video capillaroscopy, can provide diagnostic information about diabetes and its complications.

RESEARCH DESIGN AND METHODS

Nailfold video capillaroscopy was performed in 120 adult patients with and without type 1 or type 2 diabetes, and with and without cardiovascular disease. Nailfold images were analyzed using convolutional neural networks, a deep learning technique. Cross-validation was used to develop and test the ability of models to predict five5 prespecified states (diabetes, high glycosylated hemoglobin, cardiovascular event, retinopathy, albuminuria, and hypertension). The performance of each model for a particular state was assessed by estimating areas under the receiver operating characteristics curves (AUROC) and precision recall curves (AUPR).

RESULTS

A total of 5236 nailfold images were acquired from 120 participants (mean 44 images per participant) and were all available for analysis. Models were able to accurately identify the presence of diabetes, with AUROC 0.84 (95% confidence interval [CI] 0.76, 0.91) and AUPR 0.84 (95% CI 0.78, 0.93), respectively. Models were also able to predict a history of cardiovascular events in patients with diabetes, with AUROC 0.65 (95% CI 0.51, 0.78) and AUPR 0.72 (95% CI 0.62, 0.88) respectively.

CONCLUSIONS

This proof-of-concept study demonstrates the potential of machine learning for identifying people with microvascular capillary changes from diabetes based on nailfold images, and for possibly identifying those most likely to have diabetes-related complications.

Machine learning for detecting centre-level irregularities in randomized controlled trials: A pilot study

Centralized statistical monitoring is sometimes employed as an alternative to onsite monitoring for randomized control trials. Current central monitoring methods have limitations, in that they are relatively resource intensive and do not necessarily generalize to studies where an irregularity pattern has not been observed before. Machine learning has been effective in detecting irregularities in industries such as finance and manufacturing, but to date none have been applied to clinical trials. We conducted a pilot study for the use of machine learning to identify center-level irregularities in data from multicenter clinical trials. We employed unsupervised machine learning methods, which do not rely on labelled data, and therefore allow for the automated discovery of previously unseen irregularity patterns while maintaining flexibility when applied to new data with different structures. This pilot study employs unsupervised machine learning to compute distance matrices between centres, which we used to produce centre-level continuous features. We then used a one-class support vector machine to learn the underlying distribution of each data set to identify data that was substantially different from these distributions. We evaluated our approach against current automatable centralized monitoring methods on two trials with known irregularities. While current approaches performed well on one trial (AUROC 0.752 for monitoring vs. 0.584 for machine learning), our techniques performed substantially better on the other (AUROC 0.140 for monitoring vs 0.728 for machine learning). The results of this pilot study suggest both the feasibility and the potential value of a machine learning-based approach to irregularity detection in RCTs.

The Toronto Postliver Transplantation Hepatocellular Carcinoma Recurrence Calculator: A Machine Learning Approach

Liver transplantation (LT) listing criteria for hepatocellular carcinoma (HCC) remain controversial. To optimize the utility of limited donor organs, this study aims to leverage machine learning to develop an accurate posttransplantation HCC recurrence prediction calculator. Patients with HCC listed for LT from 2000 to 2016 were identified, with 739 patients who underwent LT used for modeling. Data included serial imaging, alpha-fetoprotein (AFP), locoregional therapies, treatment response, and posttransplantation outcomes. We compared the CoxNet (regularized Cox regression), survival random forest, survival support vector machine, and DeepSurv machine learning algorithms via the mean cross-validated concordance index. We validated the selected CoxNet model by comparing it with other currently available recurrence risk algorithms on a held-out test set (AFP, Model of Recurrence After Liver Transplant [MORAL], and Hazard Associated with liver Transplantation for Hepatocellular Carcinoma [HALT-HCC score]). The developed CoxNet-based recurrence prediction model showed a satisfying overall concordance score of 0.75 (95% confidence interval [CI], 0.64-0.84). In comparison, the recalibrated risk algorithms' concordance scores were as follows: AFP score 0.64 (outperformed by the CoxNet model, 1-sided 95% CI, >0.01; P = 0.04) and MORAL score 0.64 (outperformed by the CoxNet model 1-sided 95% CI, >0.02; P = 0.03). The recalibrated HALT-HCC score performed well with a concordance of 0.72 (95% CI, 0.63-0.81) and was not significantly outperformed (1-sided 95% CI, ≥0.05; P = 0.29). Developing a comprehensive posttransplantation HCC recurrence risk calculator using machine learning is feasible and can yield higher accuracy than other available risk scores. Further research is needed to confirm the utility of machine learning in this setting.

GWAS and ExWAS of blood Mitochondrial DNA copy number identifies 71 loci and highlights a potential causal role in dementia

Background:

Mitochondrial DNA copy number (mtDNA-CN) is an accessible blood-based measurement believed to capture underlying mitochondrial (MT) function. The specific biological processes underpinning its regulation, and whether those processes are causative for disease, is an area of active investigation.

Methods:

We developed a novel method for array-based mtDNA-CN estimation suitable for biobank-scale studies, called ‘automatic mitochondrial copy (AutoMitoC).’ We applied AutoMitoC to 395,781 UKBiobank study participants and performed genome- and exome-wide association studies, identifying novel common and rare genetic determinants. Finally, we performed two-sample Mendelian randomization to assess whether genetically low mtDNA-CN influenced select MT phenotypes.

Results:

Overall, genetic analyses identified 71 loci for mtDNA-CN, which implicated several genes involved in rare mtDNA depletion disorders, deoxynucleoside triphosphate (dNTP) metabolism, and the MT central dogma. Rare variant analysis identified SAMHD1 mutation carriers as having higher mtDNA-CN (beta = 0.23 SDs; 95% CI, 0.18–0.29; p=2.6 × 10^-19), a potential therapeutic target for patients with mtDNA depletion disorders, but at increased risk of breast cancer (OR = 1.91; 95% CI, 1.52–2.40; p=2.7 × 10^-8). Finally, Mendelian randomization analyses suggest a causal effect of low mtDNA-CN on dementia risk (OR = 1.94 per 1 SD decrease in mtDNA-CN; 95% CI, 1.55–2.32; p=7.5 × 10^-4).

Conclusions:

Altogether, our genetic findings indicate that mtDNA-CN is a complex biomarker reflecting specific MT processes related to mtDNA regulation, and that these processes are causally related to human diseases.

Funding:

No funds supported this specific investigation. Awards and positions supporting authors include: Canadian Institutes of Health Research (CIHR) Frederick Banting and Charles Best Canada Graduate Scholarships Doctoral Award (MC, PM); CIHR Post-Doctoral Fellowship Award (RM); Wellcome Trust Grant number: 099313/B/12/A; Crasnow Travel Scholarship; Bongani Mayosi UCT-PHRI Scholarship 2019/2020 (TM); Wellcome Trust Health Research Board Irish Clinical Academic Training (ICAT) Programme Grant Number: 203930/B/16/Z (CJ); European Research Council COSIP Grant Number: 640580 (MO); E.J. Moran Campbell Internal Career Research Award (MP); CISCO Professorship in Integrated Health Systems and Canada Research Chair in Genetic and Molecular Epidemiology (GP)

Our cells are powered by small internal compartments known as mitochondria, which host several copies of their own ‘mitochondrial’ genome. Defects in these semi-autonomous structures are associated with a range of severe, and sometimes fatal conditions: easily checking the health of mitochondria through cheap, quick and non-invasive methods can therefore help to improve human health.

Measuring the concentration of mitochondrial DNA molecules in our blood cells can help to estimate the number of mitochondrial genome copies per cell, which in turn act as a proxy for the health of the compartment. In fact, having lower or higher concentration of mitochondrial DNA molecules is associated with diseases such as cancer, stroke, or cardiac conditions. However, current approaches to assess this biomarker are time and resource-intensive; they also do not work well across people with different ancestries, who have slightly different versions of mitochondrial genomes.

In response, Chong et al. developed a new method for estimating mitochondrial DNA concentration in blood samples. Called AutoMitoC, the automated pipeline is fast, easy to use, and can be used across ethnicities. Applying this method to nearly 400,000 individuals highlighted 71 genetic regions for which slight sequence differences were associated with changes in mitochondrial DNA concentration. Further investigation revealed that these regions contained genes that help to build, maintain, and organize mitochondrial DNA. In addition, the analyses yield preliminary evidence showing that lower concentration of mitochondrial DNA may be linked to a higher risk of dementia.

Overall, the work by Chong et al. demonstrates that AutoMitoC can be used to investigate how mitochondria are linked to health and disease in populations across the world, potentially paving the way for new therapeutic approaches.

Opening the black box: the promise and limitations of explainable machine learning in cardiology

Many clinicians remain wary of machine learning due to long-standing concerns about "black box" models. "Black box" is shorthand for models that are sufficiently complex that they are not straightforwardly interpretable to humans. Lack of interpretability in predictive models can undermine trust in those models, especially in health care where so many decisions are literally life and death. There has recently been an explosion of research in the field of explainable machine learning aimed at addressing these concerns. The promise of explainable machine learning is considerable, but it is important for cardiologists who may encounter these techniques in clinical decision support tools or novel research papers to have a critical understanding of both their strengths and their limitations. This paper reviews key concepts and techniques in the field of explainable machine learning as they apply to cardiology. Key concepts reviewed include interpretability versus explainability and global versus local explanations. Techniques demonstrated include permutation importance, surrogate decision trees, local interpretable model-agnostic explanations, and partial dependence plots. We discuss several limitations with explainability techniques, focusing on the how the nature of explanations as approximations may omit important information about how black box models work and why they make certain predictions. We conclude by proposing a rule of thumb about when it is appropriate to use black box models with explanations, rather than interpretable models.

Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review

Background

Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs–based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results.

Objective

This study aimed to identify, summarize, and evaluate the available research, current state of utility, and challenges with machine learning–based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings.

Methods

PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs,” “clinical deterioration,” and “machine learning.” Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines.

Results

We identified 24 peer-reviewed studies from 417 articles for inclusion; 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, intensive care units, emergency departments, step-down units, medical assessment units, postanesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods, and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97.

Conclusions

In studies that compared performance, reported results suggest that machine learning–based early warning systems can achieve greater accuracy than aggregate-weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

To Embed or Not: Network Embedding as a Paradigm in Computational Biology

Current technology is producing high throughput biomedical data at an ever-growing rate. A common approach to interpreting such data is through network-based analyses. Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks. In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly. We consider a broad variety of applications including protein network alignment, community detection, and protein function prediction. We find that in all of these domains both types of approaches are of value and their performance depends on the evaluation measures being used and the goal of the project. In particular, network embedding methods outshine direct methods according to some of those measures and are, thus, an essential tool in bioinformatics research.

Microtextured CoCrMo alloy for use in metal-on-polyethylene prosthetic joint bearings: multi-directional wear and corrosion measurements

The longevity of metal-on-polyethylene (MoP) prosthetic hip joint bearings, in which a polished CoCrMo femoral head articulates with a polyethylene liner, may be limited by mechanical instability or inflammation resulting from osteolysis caused by polyethylene wear debris. This study uses laser surface texturing to manufacture a pattern of shallow spherical microtexture features on a polished CoCrMo surface. Gravimetric wear measurements of a highly-crosslinked polyethylene pin articulating with a CoCrMo disc under multi-directional shear demonstrate that polyethylene wear is reduced by more 50% when articulating with a microtextured as opposed to a polished CoCrMo disc. Electrochemical measurements also show that laser texturing does not negatively affect the corrosion potential of CoCrMo.

High-power lasers for directed-energy applications: reply

The comment by Vorontsov and Weyrauch [Appl. Opt.55, 9950 (2016)APOPAI0003-693510.1364/AO.55.009950] is aimed at rebutting the critiques in Sprangle et al. [Appl. Opt.54, F201 (2015)APOPAI0003-693510.1364/AO.54.00F201] and Nelson et al. [Appl. Opt.55, 1757 (2016)APOPAI0003-693510.1364/AO.55.001757]. In the comment, Vorontsov and colleagues describe their experiments aimed at demonstrating the feasibility of coherent combining of lasers on a distant target, using relatively low-power lasers and a cooperative retro-reflective target. The Naval Research Laboratory has demonstrated the capability to project high power on a distant target by making use of an incoherent combining architecture. The proof-of-concept experiments were performed in a realistic environment without employing cooperative targets and without sophisticated adaptive optics instrumentation.

Pre-diabetes and serum sex steroid hormones among US men

Several studies demonstrate a link between diabetes and sex steroid hormones, but the link with pre-diabetes remains elusive. In this study, we hypothesize that pre-diabetes, which is characterised by having impaired fasting glucose and/or impaired glucose tolerance and/or impaired HbA1C, may influence circulating sex steroid hormone concentrations in men. Thus, we investigated whether serum sex steroid hormone concentrations differ between men with and without pre-diabetes. We analyzed data for 1139 men who were aged 20+ years when they participated in the Third National Health and Nutrition Examination Survey. We calculated adjusted geometric mean serum concentrations of total and estimated free testosterone, androstanediol glucuronide, total and estimated free estradiol, and sex hormone-binding globulin (SHBG) in men with and without pre-diabetes. Logistic regression was used to calculate adjusted odds ratios (OR) of lower concentrations of androgens and SHBG, and higher concentrations of estradiol by prediabetes status. Adjusting for age and race/ethnicity, total testosterone concentration was lower among men with (geometric mean: 4.68 ng/mL) than without (5.36 ng/mL, p = 0.01) pre-diabetes. SHBG concentration was also lower in men with (31.67 nmol/L) than without (36.16 nmol/L; p = 0.01) pre-diabetes. Concentrations of the other hormones did not differ between men with and without pre-diabetes. After adjusting for demographic and lifestyle factors, pre-diabetic men had a higher odds of lower testosterone (OR: 2.58; 95% CI: 1.54-4.29), higher free estradiol level (OR: 1.59; 95% CI: 1.14-2.22), and lower SHBG level (OR: 2.27; 95% CI: 1.32-3.92) compared to men without pre-diabetes. These associations were attenuated after adjusting for adiposity (testosterone OR: 1.76; 95% CI 0.95-3.27, free estradiol OR: 1.29, 95% CI: 0.88-1.88, SHBG OR: 1.71; 95% CI 0.88-3.30). Our findings suggest that men with pre-diabetes have lower circulating total testosterone and SHBG and higher free estradiol levels.

Atmospheric propagation and combining of high-power lasers: reply

We do not believe that there is any inconsistency between the findings of our paper [Appl. Opt.55, 1757 (2016)APOPAI0003-693510.1364/AO.55.001757] and the observations of Goodno and Rothenberg [Appl. Opt.55, 8335 (2016)]. APOPAI0003-69351559-128X/16/298335-03.

Reciprocity breaking during nonlinear propagation of adapted beams through random media

Adaptive optics (AO) systems rely on the principle of reciprocity, or symmetry with respect to the interchange of point sources and receivers. These systems use the light received from a low power emitter on or near a target to compensate phase aberrations acquired by a laser beam during linear propagation through random media. If, however, the laser beam propagates nonlinearly, reciprocity is broken, potentially undermining AO correction. Here we examine the consequences of this breakdown, providing the first analysis of AO applied to high peak power laser beams. While discussed for general random and nonlinear media, we consider specific examples of Kerr-nonlinear, turbulent atmosphere.

Using an incoherent target return to adaptively focus through atmospheric turbulence

A laser beam propagating to a remote target through atmospheric turbulence acquires intensity fluctuations. If the target is cooperative and provides a coherent return beam, the phase measured near the beam transmitter and adaptive optics, in principle, can correct these fluctuations. Generally, however, the target is uncooperative. In this case, we show that an incoherent return from the target can be used instead. Using the principle of reciprocity, we derive a novel relation between the field at the target and the returned field at a detector. We simulate an adaptive optics system that utilizes this relation to focus a beam through atmospheric turbulence onto a rough surface.

Atmospheric propagation and combining of high-power lasers

In this paper, we analyze beam combining and atmospheric propagation of high-power lasers for directed-energy (DE) applications. The large linewidths inherent in high-power fiber and slab lasers cause random phase and intensity fluctuations that occur on subnanosecond time scales. Coherently combining these high-power lasers would involve instruments capable of precise phase control and operation at rates greater than ∼10  GHz. To the best of our knowledge, this technology does not currently exist. This presents a challenging problem when attempting to phase lock high-power lasers that is not encountered when phase locking low-power lasers, for example, at milliwatt power levels. Regardless, we demonstrate that even if instruments are developed that can precisely control the phase of high-power lasers, coherent combining is problematic for DE applications. The dephasing effects of atmospheric turbulence typically encountered in DE applications will degrade the coherent properties of the beam before it reaches the target. Through simulations, we find that coherent beam combining in moderate turbulence and over multikilometer propagation distances has little advantage over incoherent combining. Additionally, in cases of strong turbulence and multikilometer propagation ranges, we find nearly indistinguishable intensity profiles and virtually no difference in the energy on the target between coherently and incoherently combined laser beams. Consequently, we find that coherent beam combining at the transmitter plane is ineffective under typical atmospheric conditions.

Enhanced backscatter of optical beams reflected in turbulent air

Optical beams propagating through air acquire phase distortions from turbulent fluctuations in the refractive index. While these distortions are usually deleterious to propagation, beams reflected in a turbulent medium can undergo a local recovery of spatial coherence and intensity enhancement referred to as enhanced backscatter (EBS). Here we validate the commonly used phase screen simulation with experimental results obtained from lab-scale experiments. We also verify theoretical predictions of the dependence of the turbulence strength on EBS. Finally, we present a novel algorithm called the "tilt-shift method" which allows detection of EBS in frozen turbulence, reducing the time required to detect the EBS signal.

Propagation of Bessel and Airy beams through atmospheric turbulence

We investigate, through simulation, the modifications to Bessel and Airy beams during propagation through atmospheric turbulence. We find that atmospheric turbulence disrupts the quasi-non-diffracting nature of Bessel and Airy beams when the transverse coherence length (Fried parameter) nears the initial aperture diameter or diagonal, respectively. The turbulence-induced transverse phase distortion limits the effectiveness of Bessel and Airy beams for applications requiring propagation over long distances in the turbulent atmosphere.

Factors associated with 'caregiver burden' for atrial fibrillation patients

BACKGROUND

The burden on caregivers providing support to atrial fibrillation (AF) patients has not been evaluated.

OBJECTIVE

To examine the interrelationship between unpaid caregiver, patient and thromboprophylaxis characteristics and caregiver burden in AF.

METHODS

We conducted a cross-sectional survey study of AF patient-caregiver dyads recruited from cardiology clinics at an urban teaching hospital. Eligible patients had a diagnosis of AF, received thromboprophylaxis to prevent stroke, lived in the community and had an adult, unpaid, English-speaking caregiver. Hierarchical multivariate regression was used to evaluate the association between caregiver, patient and thromboprophylaxis characteristics and caregiver burden as measured by the 'Caregiver Reaction Assessment' (CRA).

RESULTS

Eighty patient-caregiver dyads were surveyed. The mean ± standard deviation scores for each CRA domain were 'Disrupted schedule' (2.4 ± 1.0), 'Financial problems' (2.1 ± 0.8), 'Lack of family support' (1.9 ± 0.7), 'Health problems' (1.9 ± 0.7) and 'Self-esteem' (0.9 ± 0.5). Significantly greater caregiver burden due to 'Disrupted schedule' was seen in those spending > 4 h/week providing care and when caring for frail, sick or disabled patients, with higher CHADS2 scores and requiring help with their medications. 'Financial problems' burden scores were significantly associated with caring for frail patients and those requiring more frequent office follow-up. 'Lack of family support' scores were inversely associated with having somebody else to help provide care and increased as patients CHADS2 score increased. Lower 'Health problem' burden scores were associated with female gender and higher scores with the need to spend > 4 h/week providing care.

CONCLUSION

The greatest burden to caregivers of AF patients occurs due to schedule disruption.

The SQUIRE (Standards for QUality Improvement Reporting Excellence) guidelines for quality improvement reporting: explanation and elaboration

As the science of quality improvement in health care advances, the importance of sharing its accomplishments through the published literature increases. Current reporting of improvement work in health care varies widely in both content and quality. It is against this backdrop that a group of stakeholders from a variety of disciplines has created the Standards for QUality Improvement Reporting Excellence, which we refer to as the SQUIRE publication guidelines or SQUIRE statement. The SQUIRE statement consists of a checklist of 19 items that authors need to consider when writing articles that describe formal studies of quality improvement. Most of the items in the checklist are common to all scientific reporting, but virtually all of them have been modified to reflect the unique nature of medical improvement work. This "Explanation and Elaboration" document (E & E) is a companion to the SQUIRE statement. For each item in the SQUIRE guidelines the E & E document provides one or two examples from the published improvement literature, followed by an analysis of the ways in which the example expresses the intent of the guideline item. As with the E & E documents created to accompany other biomedical publication guidelines, the purpose of the SQUIRE E & E document is to assist authors along the path from completion of a quality improvement project to its publication. The SQUIRE statement itself, this E & E document, and additional information about reporting improvement work can be found at http://www.squire-statement.org.

Schedule shifts, cancer and longevity: good, bad or indifferent?

Prompted by a recent report of the possible carcinogenic effect of shiftwork focusing on the disruption of circadian rhythms, we review studies involving shifts in schedule implemented at varying intervals in unicells, insects and mammals, including humans. Results indicate the desirability to account for a broader-than-circadian view. They also suggest the possibility of optimizing schedule shifts by selecting intervals between consecutive shifts associated with potential side-effects such as an increase in cancer risk. Toward this goal, marker rhythmometry is most desirable. The monitoring of blood pressure and heart rate present the added benefit of assessing cardiovascular disease risks resulting not only from an elevated blood pressure but also from abnormal variability in blood pressure and/or heart rate of normotensive as well as hypertensive subjects.

Hyperoxaluric nephrolithiasis is a complication of Roux-en-Y gastric bypass surgery

Roux-en-Y bypass surgery is the most common bariatric procedure currently performed in the United States for medically complicated obesity. Although this leads to a marked and sustained weight loss, we have identified an increasing number of patients with episodes of nephrolithiasis afterwards. We describe a case series of 60 patients seen at Mayo Clinic-Rochester that developed nephrolithiasis after Roux-en-Y gastric bypass (RYGB), including a subset of 31 patients who had undergone metabolic evaluation in the Mayo Stone Clinic. The mean body mass index of the patients before procedure was 57 kg/m(2) with a mean decrease of 20 kg/m(2) at the time of the stone event, which averaged 2.2 years post-procedure. When analyzed, calcium oxalate stones were found in 19 and mixed calcium oxalate/uric acid stones in two patients. Hyperoxaluria was a prevalent factor even in patients without a prior history of nephrolithiasis, and usually presented more than 6 months after the procedure. Calcium oxalate supersaturation, however, was equally high in patients less than 6 months post-procedure due to lower urine volumes. In a small random sampling of patients undergoing this bypass procedure, hyperoxaluria was rare preoperatively but common 12 months after surgery. We conclude that hyperoxaluria is a potential complicating factor of RYGB surgery manifested as a risk for calcium oxalate stones.

A proposed rural healthcare ethics agenda

The unique context of the rural setting provides special challenges to furnishing ethical healthcare to its approximately 62 million inhabitants. Although rural communities are widely diverse, most have the following common features: limited economic resources, shared values, reduced health status, limited availability of and accessibility to healthcare services, overlapping professional-patient relationships and care giver stress. These rural features shape common healthcare ethical issues, including threats to confidentiality, boundary issues, professional-patient relationship and allocation of resources. To date, there exists a limited focus on rural healthcare ethics shown by the scarcity of rural healthcare ethics literature, rural ethics committees, rural focused ethics training and research on rural ethics issues. An interdisciplinary group of rural healthcare ethicists with backgrounds in medicine, nursing and philosophy was convened to explore the need for a rural healthcare ethics agenda. At the meeting, the Coalition for Rural Health Care Ethics agreed to a definition of rural healthcare ethics and a broad-ranging rural ethics agenda with the ultimate goal of enhancing the quality of patient care in rural America. The proposed agenda calls for increasing awareness and understanding of rural healthcare ethics through the development of evidence--informed, rural-attuned research, scholarship and education in collaboration with rural healthcare professionals, healthcare institutions and the diverse rural population.

Genome of Geobacter sulfurreducens: metal reduction in subsurface environments

The complete genome sequence of Geobacter sulfurreducens, a delta-proteobacterium, reveals unsuspected capabilities, including evidence of aerobic metabolism, one-carbon and complex carbon metabolism, motility, and chemotactic behavior. These characteristics, coupled with the possession of many two-component sensors and many c-type cytochromes, reveal an ability to create alternative, redundant, electron transport networks and offer insights into the process of metal ion reduction in subsurface environments. As well as playing roles in the global cycling of metals and carbon, this organism clearly has the potential for use in bioremediation of radioactive metals and in the generation of electricity.

Role of mobile DNA in the evolution of vancomycin-resistant Enterococcus faecalis

The complete genome sequence of Enterococcus faecalis V583, a vancomycin-resistant clinical isolate, revealed that more than a quarter of the genome consists of probable mobile or foreign DNA. One of the predicted mobile elements is a previously unknown vanB vancomycin-resistance conjugative transposon. Three plasmids were identified, including two pheromone-sensing conjugative plasmids, one encoding a previously undescribed pheromone inhibitor. The apparent propensity for the incorporation of mobile elements probably contributed to the rapid acquisition and dissemination of drug resistance in the enterococci.

Complete genome sequence and comparative analysis of the metabolically versatile Pseudomonas putida KT2440

Pseudomonas putida is a metabolically versatile saprophytic soil bacterium that has been certified as a biosafety host for the cloning of foreign genes. The bacterium also has considerable potential for biotechnological applications. Sequence analysis of the 6.18 Mb genome of strain KT2440 reveals diverse transport and metabolic systems. Although there is a high level of genome conservation with the pathogenic Pseudomonad Pseudomonas aeruginosa (85% of the predicted coding regions are shared), key virulence factors including exotoxin A and type III secretion systems are absent. Analysis of the genome gives insight into the non-pathogenic nature of P. putida and points to potential new applications in agriculture, biocatalysis, bioremediation and bioplastic production.

How is the liver primed or sensitized for alcoholic liver disease?

This article represents the proceedings of a symposium at the 2000 ISBRA Meeting in Yokohama, Japan. The chairs were Hidekazu Tsukamoto and Yoshiyuki Takei. The presentations were (1) Tribute to Professor Rajendar K. Chawla, by Craig J. McClain; (2) Dysregulated TNF signaling in alcoholic liver disease, by Craig J. McClain, S. Joshi-Barve, D. Hill, J Schmidt, I. Deaciuc, and S. Barve; (3) The role of mitochondria in ethanol-mediated sensitization of the liver, by Anna Colell, Carmen Garcia-Ruiz, Neil Kaplowitz, and Jose C. Fernandez-Checa; (4) A peroxisome proliferator (bezafibrate) can prevent superoxide anion release into hepatic sinusoid after acute ethanol administration, by Hirokazu Yokoyama, Yukishige Okamura, Yuji Nakamura, and Hiromasa Ishii; (5) S-adenosylmethionine affects tumor necrosis factor-alpha gene expression in macrophages, by Rajendar K. Chawla, S. Barve, S. Joshi-Barve, W. Watson, W. Nelson, and C. McClain; (6) Iron, retinoic acid and hepatic macrophage TNFalpha gene expression in ALD, by Hidekazu Tsukamoto, Min Lin, Mitsuru Ohata, and Kenta Motomura; and (7) Role of Kupffer cells and gut-derived endotoxin in alcoholic liver injury, by N. Enomoto, K. Ikejima, T. Kitamura, H. Oide, Y. Takei, M. Hirose, B. U. Bradford, C. A. Rivera, H. Kono, S. Peter, S. Yamashina, A. Konno, M. Ishikawa, H. Shimizu, N. Sato, and R. Thurman.

Risk-reduction mastectomy: clinical issues and research needs

Risk-reduction mastectomy (RRM), also known as bilateral prophylactic mastectomy, is a controversial clinical option for women who are at increased risk of breast cancer. High-risk women, including women with a strong family history of breast cancer and BRCA1/2 mutation carriers, have several clinical options: risk-reduction surgery (bilateral mastectomy and bilateral oophorectomy), surveillance (mammography, clinical breast examination, and breast self-examination), and chemoprevention (tamoxifen). We review research in a number of areas central to our understanding of RRM, including recent data on 1) the effectiveness of RRM in reducing breast cancer risk, 2) the perception of RRM among women at increased risk and health-care providers, 3) the decision-making process for follow-up care of women at high risk, and 4) satisfaction and psychological status after surgery. We suggest areas of future research to better guide high-risk women and their health-care providers in the decision-making process.

Using action research in paediatric oncology to develop an oral care algorithm

The concurrent use of an oral assessment guide and oral care algorithm proved valuable in providing a more consistent approach to the oral care of children on a particular paediatric oncology ward (Gibson et al. 1997). However, as action research was chosen to develop practice, the diagnosis of new problems was anticipated. The identification of problem areas arose from two sources-informal feedback from the practice setting and via a formal audit using a tripartite approach consisting of a structured interview, vignettes and an analysis of existing documentation. The findings firstly demonstrated that practitioners experienced problems associated with the interpretation of the information contained within the existing algorithm. Secondly, there was also strong evidence to show that the production of a second 'therapeutic' algorithm would be beneficial to patient care. Finally, to ensure clarity, minor changes were required to the oral assessment guide. By using a collaborative approach between researchers and practitioners a positive resolution to a commonly identified clinical problem was expedited.

How is the liver primed or sensitized for alcoholic liver disease?

This article represents the proceedings of a symposium at the 2000 ISBRA Meeting in Yokohama, Japan. The chairs were Hidekazu Tsukamoto and Yoshiyuki Takei. The presentations were (1) Tribute to Professor Rajendar K. Chawla, by Craig J. McClain; (2) Dysregulated TNF signaling in alcoholic liver disease, by Craig J. McClain, S. Joshi-Barve, D. Hill, J Schmidt, I. Deaciuc, and S. Barve; (3) The role of mitochondria in ethanol-mediated sensitization of the liver, by Anna Colell, Carmen Garcia-Ruiz, Neil Kaplowitz, and Jose C. Fernandez-Checa; (4) A peroxisome proliferator (bezafibrate) can prevent superoxide anion release into hepatic sinusoid after acute ethanol administration, by Hirokazu Yokoyama, Yukishige Okamura, Yuji Nakamura, and Hiromasa Ishii; (5) S-adenosylmethionine affects tumor necrosis factor-alpha gene expression in macrophages, by Rajendar K. Chawla, S. Barve, S. Joshi-Barve, W. Watson, W. Nelson, and C. McClain; (6) Iron, retinoic acid and hepatic macrophage TNFalpha gene expression in ALD, by Hidekazu Tsukamoto, Min Lin, Mitsuru Ohata, and Kenta Motomura; and (7) Role of Kupffer cells and gut-derived endotoxin in alcoholic liver injury, by N. Enomoto, K. Ikejima, T. Kitamura, H. Oide, Y. Takei, M. Hirose, B. U. Bradford, C. A. Rivera, H. Kono, S. Peter, S. Yamashina, A. Konno, M. Ishikawa, H. Shimizu, N. Sato, and R. Thurman.

Is there a tardive dysmentia?

A change in the phenomenology of schizophrenia has been observed over the past several decades; affective disturbances and phasic courses have become more evident. Although there is no obvious single explanation for these changes, several ideas have been considered. The advent and use of antipsychotic drugs over the past 30 years stands out as the most significant change. Because it is well known that chronic treatment with antipsychotic drugs can induce tardive dyskinesia and has been hypothesized to induce a supersensitivity psychosis, it is reasonable to believe that other behavioral changes may occur over time. We here describe a behavioral disorder that we have termed tardive dysmentia, involving changes in affect, activation level, and interpersonal interaction. A relationship between tardive dysmentia and tardive dyskinesia is suggested. It is our hypothesis that tardive dysmentia contributes to the changing course of schizophrenia and occurs after long-term treatment with antipsychotic drugs.

Mood states and disease activity in patients with systemic lupus erythematosus treated with bromocriptine

We tested mood states in patients with systemic lupus erythematosus (SLE) treated with the prolactin-lowering drug, bromocriptine. Bromocriptine was given to seven patients in an open-label study to test its effects on active SLE. Two independent measures of SLE activity, the SLE Activity Measure (SLAM) and the SLE Disease Activity Index (SLEDAI), were scored and the Symptom Questionnaire (SQ) mood survey was administered at entry and at 6 monthly follow-up visits. The SLAM and SLEDAI scores improved significantly during treatment. Two of the four mood scales in the SQ (Anxiety Scale and Anger-Hostility Scale) showed significant improvement compared to the entry value at least once during treatment. Significant improvement was also observed in the Total Distress Score, which is the sum of the four scales and is a more sensitive measure of distress than the score of an individual scale. Depression, anxiety, somatic complaints, and total distress correlated positively with SLAM and/or SLEDAI scores. The Anxiety Scale and the Total Distress Score improved with treatment and did correlate positively with SLE activity. In contrast, the Anger-Hostility Scale improved with treatment but did not correlate with SLE activity.

Ligand-dependent regulation of vascular endothelial growth factor and erythropoietin expression by a plasmid-based autoinducible GeneSwitch system

We investigated the ability of an improved mifepristone-dependent GeneSwitch system to regulate the expression of genes for two therapeutic proteins: vascular endothelial growth factor (VEGF) and erythropoietin. The GeneSwitch system consisted of two plasmids, one encoding the chimeric GeneSwitch protein, the other an inducible transgene. When the constitutive CMV promoter of the GeneSwitch plasmid was replaced by an autoinducible promoter consisting of four copies of GAL4 DNA binding sites linked to a minimal thymidine kinase promoter, the tightness of transgene regulation was improved by an order of magnitude. Quantitative RT-PCR analysis of GeneSwitch mRNA confirmed that the autoinducible promoter was responsive to mifepristone. We demonstrated the ability of the improved GeneSwitch system to regulate the expression of VEGF or erythropoietin in a biologically relevant manner after delivery of plasmids to the hind-limb muscle of adult mice. This ability of the autoinducible GeneSwitch system to regulate the expression of therapeutic proteins in mice indicates its potential for use in human gene therapy applications.