[Targeted therapy:the benefit of new oncological tests].

Voor vele kankervormen komen doelgerichte behandelingen beschikbaar, waarvoor op grond van tumoreigenschappen ook rationele keuzes gemaakt kunnen worden.Er is grote behoefte aan adequate biomarkers die het effect van doelgerichte therapie bij individuele kankerpatiënten kunnen voorspellen, om daarmee de juiste oncologische behandeling voor de juiste patiënt te kunnen bepalen. Zo kunnen nutteloze behandelingen en onnodige bijwerkingen vermeden worden, en kosten worden gereduceerd.Bij borstkanker zijn de oestrogeenreceptor (ER) en de humane epidermale groeifactorreceptor 2 (HER2) voorbeelden van gestandaardiseerde, in gerandomiseerde onderzoeken gevalideerde, predictieve testen van behandeleffecten.Voor genexpressieprofielen die samenhangen met tumorgroei worden ook gerandomiseerde onderzoeksdata verwacht.Het kwantificeren van de predictieve waarde van testen op verwachte behandeleffecten in gerandomiseerde studies is kostbaar en tijdrovend. Gezien de toename van doelgerichte medicijnen en diagnostische en prognostische technieken, wordt in allerlei domeinen gezocht naar alternatieve onderzoeksopzetten die kunnen leiden tot snellere en efficiëntere bewijsvoering.An increasing number of targeted drug treatments are becoming available for many types of cancer. There is a great need for adequate biomarkers that can predict the effect of targeted therapy in individual cancer patients, in order to determine the correct oncological treatment per patient. This way, non-effective treatments can be spared, side-effects avoided, and costs reduced. Oestrogen receptor (ER) and the human epidermal growth factor receptor 2 (HER2) are examples of standardized tests for breast cancer that have been validated in randomised studies. Data from randomised studies is also expected for gene expression profiles that correlate with tumour growth. Quantifying the predictive value of tests for anticipated treatment effects is costly and time-consuming. Given the increasing availability of targeted agents and diagnostic and prognostic techniques, alternative clinical study designs that can lead to quicker and more efficient verification are being sought in many different domains.</p

Prognostic models for radiation-induced complications after radiotherapy in head and neck cancer patients

Objectives: This is a protocol for a Cochrane Review (prognosis). The objectives are as follows:. Primary objective The review question is “Which prognostic models are available to predict the risk of radiation-induced side effects after radiation exposure to patients with head and neck cancer, what is their quality, and what is their predictive performance?”. Investigation of sources of heterogeneity between studies We will assess sources of heterogeneity among the prognostic models developed in the eligible studies. The potential sources are study population (e.g. site/stage of cancer, the use of other treatment [surgery and chemotherapy]), predictors, definition and incidence of the predicted outcomes, and prediction horizons. If there are multiple validation studies for the same model, the same sources of between-study heterogeneity will be investigated

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Background and ObjectivesWe sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques.MethodsWe search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes.ResultsWe included 152 studies, 58 (38.2% [95% CI 30.8–46.1]) were diagnostic and 94 (61.8% [95% CI 53.9–69.2]) prognostic studies. Most studies reported only the development of prediction models (n = 133, 87.5% [95% CI 81.3–91.8]), focused on binary outcomes (n = 131, 86.2% [95% CI 79.8–90.8), and did not report a sample size calculation (n = 125, 82.2% [95% CI 75.4–87.5]). The most common algorithms used were support vector machine (n = 86/522, 16.5% [95% CI 13.5–19.9]) and random forest (n = 73/522, 14% [95% CI 11.3–17.2]). Values for area under the Receiver Operating Characteristic curve ranged from 0.45 to 1.00. Calibration metrics were often missed (n = 494/522, 94.6% [95% CI 92.4–96.3]).ConclusionOur review revealed that focus is required on handling of missing values, methods for internal validation, and reporting of calibration to improve the methodological conduct of studies on machine learning–based prediction models

Safety of off-label dose reduction of non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation

Aim: To investigate the effects of off-label non-vitamin K oral anticoagulant (NOAC) dose reduction compared with on-label standard dosing in atrial fibrillation (AF) patients in routine care. Methods: Population-based cohort study using data from the United Kingdom Clinical Practice Research Datalink, comparing adults with non-valvular AF receiving an off-label reduced NOAC dose to patients receiving an on-label standard dose. Outcomes were ischaemic stroke, major/non-major bleeding and mortality. Inverse probability of treatment weighting and inverse probability of censoring weighting on the propensity score were applied to adjust for confounding and informative censoring. Results: Off-label dose reduction occurred in 2466 patients (8.0%), compared with 18 108 (58.5%) on-label standard-dose users. Median age was 80 years (interquartile range [IQR] 73.0-86.0) versus 72 years (IQR 66-78), respectively. Incidence rates were higher in the off-label dose reduction group compared to the on-label standard dose group, for ischaemic stroke (0.94 vs 0.70 per 100 person years), major bleeding (1.48 vs 0.83), non-major bleeding (6.78 vs 6.16) and mortality (10.12 vs 3.72). Adjusted analyses resulted in a hazard ratio of 0.95 (95% confidence interval [CI] 0.57-1.60) for ischaemic stroke, 0.88 (95% CI 0.57-1.35) for major bleeding, 0.81 (95% CI 0.67-0.98) for non-major bleeding and 1.34 (95% CI 1.12-1.61) for mortality. Conclusion: In this large population-based study, the hazards for ischaemic stroke and major bleeding were low, and similar in AF patients receiving an off-label reduced NOAC dose compared with on-label standard dose users, while non-major bleeding risk appeared to be lower and mortality risk higher. Caution towards prescribing an off-label reduced NOAC dose is therefore required

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD):Explanation and Elaboration. Translation into Russian

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.</p

The cardiovascular risk profile of middle-aged women with polycystic ovary syndrome

Objectives: Contradictory results have been reported regarding the association between polycystic ovary syndrome (PCOS) and cardiovascular disease (CVD). We assessed the cardiometabolic phenotype and prevalence of CVD in middle-aged women with PCOS, compared with age-matched controls from the general population, and estimated 10-year CVD risk and cardiovascular health score. Design: A cross-sectional study. Participants: 200 women aged >45 with PCOS, and 200 age-matched controls. Measurements: Anthropometrics, insulin, lipid levels, prevalence of metabolic syndrome and type II diabetes. Ten-year Framingham risk score and the cardiovascular health score were calculated, and carotid intima-media thickness (cIMT) was measured. Results: Mean age was 50.5 years (SD = 5.5) in women with PCOS and 51.0 years (SD = 5.2) in controls. Increased waist circumference, body mass index and hypertension were more often observed in women with PCOS (P <.001). In women with PCOS, the prevalence of type II diabetes and metabolic syndrome was not significantly increased and lipid levels were not different from controls. cIMT was lower in women with PCOS (P <.001). Calculated cardiovascular health and 10-year CVD risk were similar in women with PCOS and controls. Conclusions: Middle-aged women with PCOS exhibit only a moderately unfavourable cardiometabolic profile compared to age-matched controls, even though they present with an increased BMI and waist circumference. Furthermore, we found no evidence for increased (10-year) CVD risk or more severe atherosclerosis compared with controls from the general population. Long-term follow-up of women with PCOS is necessary to provide a definitive answer concerning lon

Poetics and politics of destination branding: Rebranding Zimbabwe 2010

Master's thesis in International Hospitality ManagementMost destination branding literature ignore the poetics and the actual exercise of destination branding only dwelling much on the promotion of brands. Again many branding and destination branding studies fail to distil some unique challenges between place and product branding and their inspiration comes from general marketing literature. The researcher outlines a conceptual framework for developing a destination brand namely the dialogic perspective to destination branding which accentuates social multiplicity and complexity in destination branding and this will be applied on the Zimbabwean 2010 rebranding context. Using the dialogic perspective Zimbabwe`s branding process is evaluated and the analysis and evaluation of destination brand poetics was done using available destination management models and also by drawing comparisons with other African destinations brands. The goal was to highlight the politics and poetics of destination branding in Zimbabwe and to achieve this; an explorative research design was adopted. Data was gathered through key informant face to face interviews, online questionnaire surveys, literature search and content analysis of destination marketing materials. Sampling was largely non- probability convenient sampling. Major results shows that strategic and creative brand execution, funding, lack of promotional efforts, lack of co-branding and lack of collaboration to destination branding are the major changes to the success of destination branding in Zimbabwe. A major recommendation is that Zimbabwe adopts broader collaboration in destination branding to ease off these challenges. As a conclusion there is need to study destination branding in the economic and social contexts in which they exist and this paper also opens up possible areas for further research

When and how to use data from randomised trials to develop or validate prognostic models

Prediction models have become an integral part of clinical practice, providing information for patients and clinicians and providing support for their shared decision making. The development and validation of prognostic prediction models requires substantial volumes of high quality information on relevant predictors and patient health outcomes. Primary data collection dedicated to prognostic model (development or validation) research could come with substantial time and costs and can be seen as a waste of resources if suitable data are already available. Randomised clinical trials are a source of high quality clinical data with a largely untapped potential for use in further research. This article addresses when and how data from a randomised clinical trial can be used additionally for prognostic model research, and provides guidance for researchers with access to trial data to evaluate the suitability of their data for the development and validation of prognostic prediction models