Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p

Gene signatures of breast cancer progression and metastasis

Breast cancer is a heterogeneous disease. Patient outcome varies significantly, depending on prognostic features of patients and their tumors, including patient age, menopausal status, tumor size and histology, nodal status, and so on. Response to treatment also depends on a series of predictive factors, such as hormone receptor and HER2 status. Current treatment guidelines use these features to determine treatment. However, these guidelines are imperfect, and do not always predict response to treatment or survival. Evolving technologies are permitting increasingly large amounts of molecular data to be obtained from tumors, which may enable more personalized treatment decisions to be made. The challenge is to learn what information leads to improved prognostic accuracy and treatment outcome for individual patients

The performance of the World Rugby Head Injury Assessment Screening Tool: a diagnostic accuracy study

Abstract Background Off-field screening tools, such as the Sports Concussion Assessment Tool (SCAT), have been recommended to identify possible concussion following a head impact where the consequences are unclear. However, real-life performance, and diagnostic accuracy of constituent sub-tests, have not been well characterized. Methods A retrospective cohort study was performed in elite Rugby Union competitions between September 2015 and June 2018. The study population comprised consecutive players identified with a head impact event undergoing off-field assessments with the World Rugby Head Injury Assessment (HIA01) screening tool, an abridged version of the SCAT3. Off-field screening performance was investigated by evaluating real-life removal-from-play outcomes and determining the theoretical diagnostic accuracy of the HIA01 tool, and individual sub-tests, if player-specific baseline or normative sub-test thresholds were strictly applied. The reference standard was clinically diagnosed concussion determined by serial medical assessments. Results One thousand one hundred eighteen head impacts events requiring off-field assessments were identified, resulting in 448 concussions. Real-life removal-from-play decisions demonstrated a sensitivity of 76.8% (95% CI 72.6–80.6) and a specificity of 86.6% (95% CI 83.7–89.1) for concussion (AUROC 0.82, 95% CI 0.79–0.84). Theoretical HIA01 tool performance worsened if pre-season baseline values (sensitivity 89.6%, specificity 33.9%, AUROC 0.62, p < 0.01) or normative thresholds (sensitivity 80.4%, specificity 69.0%, AUROC 0.75, p < 0.01) were strictly applied. Symptoms and clinical signs were the HIA01 screening tool sub-tests most predictive for concussion; with immediate memory and tandem gait providing little additional diagnostic value. Conclusions These findings support expert recommendations that clinical judgement should be used in the assessment of athletes following head impact events. Substitution of the tandem gait and 5-word immediate memory sub-tests with alternative modes could potentially improve screening tool performance

The gut microbiota: a major player in the toxicity of environmental pollutants?

Exposure to environmental chemicals has been linked to various health disorders, including obesity, type 2 diabetes, cancer and dysregulation of the immune and reproductive systems, whereas the gastrointestinal microbiota critically contributes to a variety of host metabolic and immune functions. We aimed to evaluate the bidirectional relationship between gut bacteria and environmental pollutants and to assess the toxicological relevance of the bacteria–xenobiotic interplay for the host. We examined studies using isolated bacteria, faecal or caecal suspensions—germ-free or antibiotic-treated animals—as well as animals reassociated with a microbiota exposed to environmental chemicals. The literature indicates that gut microbes have an extensive capacity to metabolise environmental chemicals that can be classified in five core enzymatic families (azoreductases, nitroreductases, β-glucuronidases, sulfatases and β-lyases) unequivocally involved in the metabolism of >30 environmental contaminants. There is clear evidence that bacteria-dependent metabolism of pollutants modulates the toxicity for the host. Conversely, environmental contaminants from various chemical families have been shown to alter the composition and/or the metabolic activity of the gastrointestinal bacteria, which may be an important factor contributing to shape an individual’s microbiotype. The physiological consequences of these alterations have not been studied in details but pollutant-induced alterations of the gut bacteria are likely to contribute to their toxicity. In conclusion, there is a body of evidence suggesting that gut microbiota are a major, yet underestimated element that must be considered to fully evaluate the toxicity of environmental contaminants

Nurse-patient interaction and communication: a systematic literature review

Aim: The purpose of this review is to describe the use and definitions of the concepts of nurse-patient interaction and nurse-patient communication in nursing literature. Furthermore, empirical findings of nurse-patient communication research will be presented, and applied theories will be shown. Method: An integrative literature search was executed. The total number of relevant citations found was 97. The search results were reviewed, and key points were extracted in a standardized form. Extracts were then qualitatively summarized according to relevant aspects and categories for the review. Results: The relation of interaction and communication is not clearly defined in nursing literature. Often the terms are used interchangeably or synonymously, and a clear theoretical definition is avoided or rather implicit. Symbolic interactionism and classic sender-receiver models were by far the most referred to models. Compared to the use of theories of adjacent sciences, the use of original nursing theories related to communication is rather infrequent. The articles that try to clarify the relation of both concepts see communication as a special or subtype of interaction. Conclusion: The included citations all conclude that communication skills can be learned to a certain degree. Involvement of patients and their role in communication often is neglected by authors. Considering the mutual nature of communication, patients’ share in conversation should be taken more into consideration than it has been until now. Nursing science has to integrate its own theories of nursing care with theories of communication and interaction from other scientific disciplines like sociology

Gut microbiota functions: metabolism of nutrients and other food components

The diverse microbial community that inhabits the human gut has an extensive metabolic repertoire that is distinct from, but complements the activity of mammalian enzymes in the liver and gut mucosa and includes functions essential for host digestion. As such, the gut microbiota is a key factor in shaping the biochemical profile of the diet and, therefore, its impact on host health and disease. The important role that the gut microbiota appears to play in human metabolism and health has stimulated research into the identification of specific microorganisms involved in different processes, and the elucidation of metabolic pathways, particularly those associated with metabolism of dietary components and some host-generated substances. In the first part of the review, we discuss the main gut microorganisms, particularly bacteria, and microbial pathways associated with the metabolism of dietary carbohydrates (to short chain fatty acids and gases), proteins, plant polyphenols, bile acids, and vitamins. The second part of the review focuses on the methodologies, existing and novel, that can be employed to explore gut microbial pathways of metabolism. These include mathematical models, omics techniques, isolated microbes, and enzyme assays