Predictors of patients’ choices for breast-conserving therapy or mastectomy: a prospective study

A study was undertaken to describe the treatment preferences and choices of patients with breast cancer, and to identify predictors of undergoing breast-conserving therapy (BCT) or mastectomy (MT). Consecutive patients with stage I/II breast cancer were eligible. Information about predictor variables, including socio-demographics, quality of life, patients' concerns, decision style, decisional conflict and perceived preference of the surgeon was collected at baseline, before decision making and surgery. Patients received standard information (n = 88) or a decision aid (n = 92) as a supplement to support decision making. A total of 180 patients participated in the study. In all, 72% decided to have BCT (n = 123); 28% chose MT (n = 49). Multivariate analysis showed that what patients perceived to be their surgeons' preference and the patients' concerns regarding breast loss and local tumour recurrence were the strongest predictors of treatment preference. Treatment preferences in itself were highly predictive of the treatment decision. The decision aid did riot influence treatment choice. The results of this study demonstrate that patients' concerns and their perceptions of the treatment preferences of the physicians are important factors in patients' decision making. Adequate information and communication are essential to base treatment decisions on realistic concerns, and the treatment preferences of patients, (C) 2004 Cancer Research U

Do Interventions Designed to Support Shared Decision-Making Reduce Health Inequalities? : A Systematic Review and Meta-Analysis

Copyright: © 2014 Durand et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: Increasing patient engagement in healthcare has become a health policy priority. However, there has been concern that promoting supported shared decision-making could increase health inequalities. Objective: To evaluate the impact of SDM interventions on disadvantaged groups and health inequalities. Design: Systematic review and meta-analysis of randomised controlled trials and observational studies.Peer reviewe

High local substrate availability stabilizes a cooperative trait

Cooperative behavior is widely spread in microbial populations. An example is the expression of an extracellular protease by the lactic acid bacterium Lactococcus lactis, which degrades milk proteins into free utilizable peptides that are essential to allow growth to high cell densities in milk. Cheating, protease-negative strains can invade the population and drive the protease-positive strain to extinction. By using multiple experimental approaches, as well as modeling population dynamics, we demonstrate that the persistence of the proteolytic trait is determined by the fraction of the generated peptides that can be captured by the cell before diffusing away from it. The mechanism described is likely to be relevant for the evolutionary stability of many extracellular substrate-degrading enzymes

The 12-item World Health Organization Disability Assessment Schedule II (WHO-DAS II): a nonparametric item response analysis

<p>Abstract</p> <p>Background</p> <p>Previous studies have analyzed the psychometric properties of the World Health Organization Disability Assessment Schedule II (WHO-DAS II) using classical omnibus measures of scale quality. These analyses are sample dependent and do not model item responses as a function of the underlying trait level. The main objective of this study was to examine the effectiveness of the WHO-DAS II items and their options in discriminating between changes in the underlying disability level by means of item response analyses. We also explored differential item functioning (DIF) in men and women.</p> <p>Methods</p> <p>The participants were 3615 adult general practice patients from 17 regions of Spain, with a first diagnosed major depressive episode. The 12-item WHO-DAS II was administered by the general practitioners during the consultation. We used a non-parametric item response method (Kernel-Smoothing) implemented with the TestGraf software to examine the effectiveness of each item (item characteristic curves) and their options (option characteristic curves) in discriminating between changes in the underliying disability level. We examined composite DIF to know whether women had a higher probability than men of endorsing each item.</p> <p>Results</p> <p>Item response analyses indicated that the twelve items forming the WHO-DAS II perform very well. All items were determined to provide good discrimination across varying standardized levels of the trait. The items also had option characteristic curves that showed good discrimination, given that each increasing option became more likely than the previous as a function of increasing trait level. No gender-related DIF was found on any of the items.</p> <p>Conclusions</p> <p>All WHO-DAS II items were very good at assessing overall disability. Our results supported the appropriateness of the weights assigned to response option categories and showed an absence of gender differences in item functioning.</p

Discrepancies between the medical record and the reports of patients with acute coronary syndrome regarding important aspects of the medical history

<p>Abstract</p> <p>Background</p> <p>Many critical treatment decisions are based on the medical history of patients with an acute coronary syndrome (ACS). Discrepancies between the medical history documented by a health professional and the patient's own report may therefore have important health consequences.</p> <p>Methods</p> <p>Medical histories of 117 patients with an ACS were documented. A questionnaire assessing the patient's health history was then completed by 62 eligible patients. Information about 13 health conditions with relevance to ACS management was obtained from the questionnaire and the medical record. Concordance between these two sources and reasons for discordance were identified.</p> <p>Results</p> <p>There was significant variation in agreement, from very poor in angina (kappa < 0) to almost perfect in diabetes (kappa = 0.94). Agreement was substantial in cerebrovascular accident (kappa = 0.76) and hypertension (kappa = 0.73); moderate in cocaine use (kappa = 0.54), smoking (kappa = 0.46), kidney disease (kappa = 0.52) and congestive heart failure (kappa = 0.54); and fair in arrhythmia (kappa = 0.37), myocardial infarction (kappa = 0.31), other cardiovascular diseases (kappa = 0.37) and bronchitis/pneumonia (kappa = 0.31). The odds of agreement was 42% higher among individuals with at least some college education (OR = 1.42; 95% CI, 1.00 - 2.01, p = 0.053). Listing of a condition in medical record but not in the questionnaire was a common cause of discordance.</p> <p>Conclusion</p> <p>Discrepancies in aspects of the medical history may have important effects on the care of ACS patients. Future research focused on identifying the most effective and efficient means to obtain accurate health information may improve ACS patient care quality and safety.</p

Machine Learning Methods for Prediction of CDK-Inhibitors

Progression through the cell cycle involves the coordinated activities of a suite of cyclin/cyclin-dependent kinase (CDK) complexes. The activities of the complexes are regulated by CDK inhibitors (CDKIs). Apart from its role as cell cycle regulators, CDKIs are involved in apoptosis, transcriptional regulation, cell fate determination, cell migration and cytoskeletal dynamics. As the complexes perform crucial and diverse functions, these are important drug targets for tumour and stem cell therapeutic interventions. However, CDKIs are represented by proteins with considerable sequence heterogeneity and may fail to be identified by simple similarity search methods. In this work we have evaluated and developed machine learning methods for identification of CDKIs. We used different compositional features and evolutionary information in the form of PSSMs, from CDKIs and non-CDKIs for generating SVM and ANN classifiers. In the first stage, both the ANN and SVM models were evaluated using Leave-One-Out Cross-Validation and in the second stage these were tested on independent data sets. The PSSM-based SVM model emerged as the best classifier in both the stages and is publicly available through a user-friendly web interface at http://bioinfo.icgeb.res.in/cdkipred

Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect

The Warburg effect - a classical hallmark of cancer metabolism - is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do. The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids