An inequality for bi-orthogonal pairs

We use Salem's method to prove that there is a lower bound for partial sums of series of bi-orthogonal vectors in a Hilbert space, or the dual vectors. This is applied to some lower bounds on $L^{1}$ norms for orthogonal expansions. There is also an application concerning linear algebra

Improving chronic disease prevention and screening in primary care: results of the BETTER pragmatic cluster randomized controlled trial.

BackgroundPrimary care provides most of the evidence-based chronic disease prevention and screening services offered by the healthcare system. However, there remains a gap between recommended preventive services and actual practice. This trial (the BETTER Trial) aimed to improve preventive care of heart disease, diabetes, colorectal, breast and cervical cancers, and relevant lifestyle factors through a practice facilitation intervention set in primary care.MethodsPragmatic two-way factorial cluster RCT with Primary Care Physicians' practices as the unit of allocation and individual patients as the unit of analysis. The setting was urban Primary Care Team practices in two Canadian provinces. Eight Primary Care Team practices were randomly assigned to receive the practice-level intervention or wait-list control; 4 physicians in each team (32 physicians) were randomly assigned to receive the patient-level intervention or wait-list control. Patients randomly selected from physicians' rosters were stratified into two groups: 1) general and 2) moderate mental illness. The interventions involved a multifaceted, evidence-based, tailored practice-level intervention with a Practice Facilitator, and a patient-level intervention involving a one-hour visit with a Prevention Practitioner where patients received a tailored 'prevention prescription'. The primary outcome was a composite Summary Quality Index of 28 evidence-based chronic disease prevention and screening actions with pre-defined targets, expressed as the ratio of eligible actions at baseline that were met at follow-up. A cost-effectiveness analysis was conducted.Results789 of 1,260 (63%) eligible patients participated. On average, patients were eligible for 8.96 (SD 3.2) actions at baseline. In the adjusted analysis, control patients met 23.1% (95% CI: 19.2% to 27.1%) of target actions, compared to 28.5% (95% CI: 20.9% to 36.0%) receiving the practice-level intervention, 55.6% (95% CI: 49.0% to 62.1%) receiving the patient-level intervention, and 58.9% (95% CI: 54.7% to 63.1%) receiving both practice- and patient-level interventions (patient-level intervention versus control, P < 0.001). The benefit of the patient-level intervention was seen in both strata. The extra cost of the intervention was $26.43CAN (95$16 to $44) per additional action met.ConclusionsA Prevention Practitioner can improve the implementation of clinically important prevention and screening for chronic diseases in a cost-effective manner

Would you like to add a weight after this blood pressure, doctor? Discovery of potentially actionable associations between the provision of multiple screens in primary care

The CPCSSN was funded through a contribution agreement with the Public Health Agency of Canada.Rationale, aims, and objective:  Guidelines recommend screening for risk factors associated with chronic diseases but current electronic prompts have limited effects. Our objective was to discover and rank associations between the presence of screens to plan more efficient prompts in primary care. Methods:  Risk factors with the greatest impact on chronic diseases are associated with blood pressure, body mass index, waist circumference, glycaemic and lipid levels, smoking, alcohol use, diet, and exercise. We looked for associations between the presence of screens for these in electronic medical records. We used association rule mining to describe relationships among items, factor analysis to find latent categories, and Cronbach α to quantify consistency within latent categories. Results:  Data from 92 140 patients in or around Toronto, Ontario, were included. We found positive correlations (lift >1) between the presence of all screens. The presence of any screen was associated with confidence greater than 80% that other data on items with high prevalence (blood pressure, glycaemic and lipid levels, or smoking) would also be present. A cluster of rules predicting the presence of blood pressure were ranked highest using measures of interestingness such as standardized lift. We found 3 latent categories using factor analysis; these were laboratory tests, vital signs, and lifestyle factors; Cronbach α ranged between .58 for lifestyle factors and .88 for laboratory tests. Conclusions:  Associations between the provision of important screens can be discovered and ranked. Rules with promising combinations of associated screens could be used to implement data driven alerts.Publisher PDFPeer reviewe

Palliative care services in families of males with muscular dystrophy: Data from MD STARnet

Introduction: Information on use of palliative care services among individuals with Duchenne and Becker muscular dystrophy is scant despite the clearly documented need. Methods: We examined associations between uptake of palliative care services by 233 males with Duchenne and Becker muscular dystrophy aged 12 and older for both caregiver and affected male characteristics using the Muscular Dystrophy Surveillance Tracking and Research Network baseline interview. Results: Ninety-one percent of caregivers (213/233) used at least one palliative care service. Case management had the highest frequency of use (59%). Use of palliative care was more frequently associated with the characteristics of affected males, as were some individual palliative care services. Utilization of six individual services differed among Muscular Dystrophy Surveillance Tracking and Research Network sites. While research suggests that pain is a frequent problem in Duchenne and Becker muscular dystrophy, only 12.5% reported use of pain management services. Discussion: Although palliative care use among families of males with Duchenne and Becker muscular dystrophy is high overall, there is much variability in use of individual services. Use of palliative care is driven by disease experience in the affected male. Many of the care recommendations for these individuals highlight the importance for early involvement of palliative care professionals.Centers for Disease Control and Prevention [DD000187, DD000189, DD000190, DD000191]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Developing clinical decision tools to implement chronic disease prevention and screening in primary care: the BETTER 2 program (building on existing tools to improve chronic disease prevention and screening in primary care).

BackgroundThe Building on Existing Tools to Improve Chronic Disease Prevention and Screening in Family Practice (BETTER) trial demonstrated the effectiveness of an approach to chronic disease prevention and screening (CDPS) through a new skilled role of a 'prevention practitioner'(PP). The PP has appointments with patients 40-65 years of age that focus on primary prevention activities and screening of cancer (breast, colorectal, cervical), diabetes and cardiovascular disease and associated lifestyle factors. There are numerous and occasionally conflicting evidence-based guidelines for CDPS, and the majority of these guidelines are focused on specific diseases or conditions; however, primary care providers often attend to patients with multiple conditions. To ensure that high-level evidence guidelines were used, existing clinical practice guidelines and tools were reviewed and integrated into blended BETTER tool kits. Building on the results of the BETTER trial, the BETTER tools were updated for implementation of the BETTER 2 program into participating urban, rural and remote communities across Canada.MethodsA clinical working group consisting of PPs, clinicians and researchers with support from the Centre for Effective Practice reviewed the literature to update, revise and adapt the integrated evidence algorithms and tool kits used in the BETTER trial. These resources are nuanced, based on individual patient risk, values and preferences and are designed to facilitate decision-making between providers across the target diseases and lifestyle factors included in the BETTER 2 program. Using the updated BETTER 2 toolkit, clinicians 1) determine which CDPS actions patients are eligible to receive and 2) develop individualized 'prevention prescriptions' with patients through shared decision-making and motivational interviewing.ResultsThe tools identify the patients' risks and eligible primary CDPS activities: the patient survey captures the patient's health history; the prevention visit form and integrated CDPS care map identify eligible CDPS activities and facilitate decisions when certain conditions are met; and the 'bubble diagram' and 'prevention prescription' promote shared decision-making.ConclusionThe integrated clinical decision-making tools of BETTER 2 provide resources for clinicians and policymakers that address patients' complex care needs beyond single disease approaches and can be adapted to facilitate CDPS in the urban, rural and remote clinical setting.Trial registrationThe registration number of the original RCT BETTER trial was ISRCTN07170460

A Multiscale Approach to Blast Neurotrauma Modeling: Part II: Methodology for Inducing Blast Injury to in vitro Models

Due to the prominent role of improvised explosive devices (IEDs) in wounding patterns of U.S. war-fighters in Iraq and Afghanistan, blast injury has risen to a new level of importance and is recognized to be a major cause of injuries to the brain. However, an injury risk-function for microscopic, macroscopic, behavioral, and neurological deficits has yet to be defined. While operational blast injuries can be very complex and thus difficult to analyze, a simplified blast injury model would facilitate studies correlating biological outcomes with blast biomechanics to define tolerance criteria. Blast-induced traumatic brain injury (bTBI) results from the translation of a shock wave in-air, such as that produced by an IED, into a pressure wave within the skull–brain complex. Our blast injury methodology recapitulates this phenomenon in vitro, allowing for control of the injury biomechanics via a compressed-gas shock tube used in conjunction with a custom-designed, fluid-filled receiver that contains the living culture. The receiver converts the air shock wave into a fast-rising pressure transient with minimal reflections, mimicking the intracranial pressure history in blast. We have developed an organotypic hippocampal slice culture model that exhibits cell death when exposed to a 530 ± 17.7-kPa peak overpressure with a 1.026 ± 0.017-ms duration and 190 ± 10.7 kPa-ms impulse in-air. We have also injured a simplified in vitro model of the blood–brain barrier, which exhibits disrupted integrity immediately following exposure to 581 ± 10.0 kPa peak overpressure with a 1.067 ± 0.006-ms duration and 222 ± 6.9 kPa-ms impulse in-air. To better prevent and treat bTBI, both the initiating biomechanics and the ensuing pathobiology must be understood in greater detail. A well-characterized, in vitro model of bTBI, in conjunction with animal models, will be a powerful tool for developing strategies to mitigate the risks of bTBI

Surgical site infection after caesarean section? There is an app for that: results from a feasibility study on costs and benefits

Surgical site infections (SSIs) are one of the most common and, yet, preventable healthcare associated infections. In Ireland, the rate of Caesarean section (CS) is increasing, while postpartum hospital stay is decreasing, adversely affecting SSI among women. There is much need to develop post-discharge surveillance which can effectively monitor, detect, and arrange treatment for affected women. The use of modern technology to survey SSI following discharge from hospital remains unexplored. We report the results of a feasibility study which investigates whether an integrated mobile application (hereafter, app) is more cost-beneficial than a stand-alone app or telephone helpline at surveying SSI following CS. We find women prefer the integrated app (47.5%; n=116/244) over the stand-alone app (8.2%; n=20/244) and telephone helpline (18.0%; 44/244), although there is no significant difference in women's valuation of these services using willingness to pay techniques. The stand-alone app is the only cost-beneficial service due to low labour costs. Future research should employ alternative measures when evaluating the benefits of the health technology. The use of a mobile app as a mechanism for postpartum care could represent a considerable advancement towards technological health care

Using Biomedical Text as Data and Representation Learning for Identifying Patients with an Osteoarthritis Phenotype in the Electronic Medical Record

Introduction Electronic medical records (EMRs) are increasingly used in health services research. Accurate/efficient identification of a target population with a specific disease phenotype is a necessary precursor to studying the health of these individuals. Objectives and Approach We explored the use of biomedical text as inputs to supervised phenotype identification algorithms. We employed a two-stage classification approach to map the discrete, sparse high-dimensional biomedical text data to a dense low dimensional vector space using methods from unsupervised machine learning. Next we used these learned vectors as inputs to supervised machine learning algorithms for phenotype identification. We were able to demonstrate the applicability of the approach to identifying patients with an osteoarthritis (OA) phenotype using primary care data from the Electronic Medical Record Administrative data Linked Database (EMRALD) held at ICES. Results EMRALD contains approximately 20Gb of biomedical text data on approximately 500,000 patients. The unit of analysis for this study is the patient. We were interested in identifying OA patients using solely text data as features. Labelled outcome information wass available from a random sample of 7,500 patients. We divided patients into training (N=6000), validation (N=750) and test (N=750) cohorts. We learned low dimensional representations of the input text data on the entire EMRALD corpus (N=500,000). We used learned numeric vectors as inputs to supervised machine learning models for OA classification (N=6,000 training set patients). We compared models in terms of accuracy, sensitivity, specificity, PPV and NPV. The best learned models achieved approximately 90\% sensitivity and 80\% specificity. Classification accuracy varied as a function of learned inputs. Conclusion/Implications We developed an approach to phenotype identification using solely biomedical text as an input. Preliminary results suggest our two-stage ML approach has improved operating characteristics compared to existing clinically derived decision rules for OA classification. Future work will explore the generalizability of this methodology to other disease phenotypes

Learning Unsupervised Representations from Biomedical Text

Introduction Healthcare settings are becoming increasingly technological. Interactions/events involving healthcare providers and the patients they service are captured as digital text. Healthcare organizations are amassing increasingly large/complex collections of biomedical text data. Researchers and policy makers are beginning to explore these text data holdings for structure, patterns, and meaning. Objectives and Approach EMRALD is a primary care electronic medical record (EMR) database, comprised of over 40 family medicine clinics, nearly 400 primary care physicians and over 500,000 patients. EMRALD includes full-chart extractions, including all clinical narrative information/data in a variety of fields. The input data (raw text strings) are discrete, sparse and high dimensional. We assessed scalable statistical models for high dimensional discrete data, including fitting, assessing and exploring models from three broad statistical areas: i) matrix factorization/decomposition models ii) probabilistic topic models and iii) word-vector embedding models. Results EMRALD is comprised of 12 text data streams. EMRALD text data is structured into 84 million clinical notes (3.5 billion word/language tokens) and is approximately 18Gb in storage size. We employ a “text as data” pipeline, i) mapping raw strings to sequences of word/language tokens, ii) mapping token sequences to numeric arrays, and finally iii) using numeric arrays as inputs to statistical models. Fitted topic models yield useful thematic summaries of the EMRALD corpora. Topics discovered reflect core responsibilities of primary care physicians (e.g. women’s health, pain management, nutrition/diet, etc.). Fitted vector embedding models capture structure of discourse/syntax. Related words are mapped to similar locations of vector spaces. Analogical reasoning is possible in the embedding space. Conclusion/Implications “Text as data” requires an understanding of statistical models for discrete, sparse, high dimensional data. We fit a variety of unsupervised statistical models to biomedical text data. Preliminary results suggest that the learned low dimensional representations of the biomedical text data are effective at uncovering meaningful patterns/structure