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Introduction: Personalized medicine-based treatments in advanced cancer hold the promise to offer substantial health benefits to genetic subgroups,but require efficient biomarker-based patient stratification to match the right treatment and may be expensive. Standard molecular diagnostics are currently very heterogeneous, and tests are often performed sequentially. The alternative to whole genome sequencing (WGS) i.e. simultaneously testing for all relevant DNA-based biomarkers thereby allowing immediate selection of the most optimal therapy, is more costly than current techniques. In the current implementation stage, it is important to explore the added value and cost-effectiveness of using WGS on a patient level and to assess optimal introduction of WGS on the level of the healthcare system. Areas covered: First, an overview of current worldwide initiatives concerning the use of WGS in clinical practice for cancer diagnostics is given. Second, a comprehensive, early health technology assessment (HTA) approach of evaluating WGS in the Netherlands is described, relating to the following aspects: diagnostic value, WGS-based treatment decisions, assessment of long-term health benefits and harms, early cost-effectiveness modeling, nation-wide organization, and Ethical, Legal and Societal Implications. Expert opinion: This study provides evidence to guide further development and implementation of WGS in clinical practice and the healthcare system.

Presentation of all work packages at the TANGO project mini symposium in 2018, includes milestone achievements and preliminary results., The "Technology Assessment of Next Generation Sequencing in Personalized Oncology" (TANGO) project is funded by the ZonMw Personalised Medicine Programme under Dossier number: 846001002 in collaboration with KWF and Zilveren Kruis. The project period is 31 December 2016 – 31 May 2021.

OBJECTIVES: Whole genome sequencing (WGS) is a promising but complex technology. WGS is not yet widely used as a clinical diagnostic in oncology due to several barriers, such as the required infrastructure and expertise, costs, and unknown clinical utility. This study aimed to investigate possible future developments facilitating or impeding the implementation and adoption of WGS in this context. Exploring WGS development scenarios can inform strategic choices by assessing their likelihood of occurring and their consequences for the healthcare system. METHODS: Scenario drafting is an iterative process. First, a literature review was performed on potential barriers and facilitators related to the implementation of disruptive health technologies and WGS in particular. Second, the knowledge and opinions of national and international experts were used to prioritize these barriers and facilitators. Third, several of barriers or facilitators were combined into a set of coherent scenarios that each describe a possible future development. Fourth, these scenarios were validated internally and checked for plausibility by our research consortium. Fifth, expert opinion was elicited from a group of international experts. They were asked with which likelihood each scenario would take place in the next five years. Sixth, the data were pooled to create probabilities distributions for the likelihood of each scenario. RESULTS: Five domains were identified that are relevant to the implementation of WGS: technical, market access, clinical utility, and evidence generation, social, and reimbursement. Preliminary results indicate that experts consider the price of WGS, its clinical utility, and the turnaround time of WGS important aspects that can affect the implementation of WGS. Scenarios that describe a future development related to these aspects were created and probabilities were elicited. CONCLUSION: Possible future developments have been assessed on likelihood of occurring and these assessments can be used in strategic decision-making regarding the the implementation of WGS.

Preeclampsia is associated with an increased risk of coronary artery disease (CAD), although evidence on premature CAD development after preeclampsia is limited.1 A cross-sectional study among 491 postmenopausal women with a mean age of 67 years showed an increased prevalence of coronary artery calcification (CAC) in participants with self-reported high blood pressure during any previous pregnancy in comparison with women without such a history.2 However, the association between hypertensive pregnancy, CAC, and coronary plaque formation has not been reconfirmed in prospective studies, and there are no data on the timeline by which atherosclerosis develops in women with previous preeclampsia. The aim of this study is to compare the prevalence of coronary artery atherosclerosis of asymptomatic women aged 45 to 55 years who have a history of preeclampsia with a population-based reference cohort. The rationale and design of the CREW-IMAGO study (Cardiovascular Risk Profile: Imaging and Gender-Specific Disorders) have been published previously (URL: http://www.trialregister.nl/trialreg/index.asp. Unique identifier: NTR5531).3 Asymptomatic women, aged 45 to 55 years, with a history of preeclampsia 10 to 20 years earlier were included in this multicenter, prospective cohort study. Medical records, including pregnancy characteristics and hospital admission, were available for all women. Outcomes were compared with women of similar age and ethnicity who participated …

Background Although health economic evaluations (HEEs) are increasingly common for therapeutic interventions, they appear to be rare for the use of risk prediction models (PMs). Objectives To evaluate the current state of HEEs of PMs by performing a comprehensive systematic review. Methods Four databases were searched for HEEs of PM-based strategies. Two reviewers independently selected eligible articles. A checklist was compiled to score items focusing on general characteristics of HEEs of PMs, model characteristics and quality of HEEs, evidence on PMs typically used in the HEEs, and the specific challenges in performing HEEs of PMs. Results After screening 791 abstracts, 171 full texts, and reference checking, 40 eligible HEEs evaluating 60 PMs were identified. In these HEEs, PM strategies were compared with current practice (n = 32; 80%), to other stratification methods for patient management (n = 19; 48%), to an extended PM (n = 9; 23%), or to alternative PMs (n = 5; 13%). The PMs guided decisions on treatment (n = 42; 70%), further testing (n = 18; 30%), or treatment prioritization (n = 4; 7%). For 36 (60%) PMs, only a single decision threshold was evaluated. Costs of risk prediction were ignored for 28 (46%) PMs. Uncertainty in outcomes was assessed using probabilistic sensitivity analyses in 22 (55%) HEEs. Conclusions Despite the huge number of PMs in the medical literature, HEE of PMs remains rare. In addition, we observed great variety in their quality and methodology, which may complicate interpretation of HEE results and implementation of PMs in practice. Guidance on HEE of PMs could encourage and standardize their application and enhance methodological quality, thereby improving adequate use of PM strategies.

Objectives: More information is often thought to improve medical decision making, which may lead to overuse of relatively cheap tests. For diagnosing the underlying cause of aneamia in general practice, up to 14 different laboratory tests may be ordered by general practitioners (GPs), according to the Dutch guideline. This study assessed which of these 14 tests actually contribute to diagnosing an underlying cause of anaemia, and which subset of tests is optimal for establishing a correct diagnosis. Methods: Data from a previous questionnaire presenting GPs (n=139) with three varying real-world anaemia cases, and all 14 corresponding test results, was used. GPs selected the expected underlying cause (anaemia of chronic disease, iron deficiency anaemia, renal anaemia, 'unknown or other'). The correct underlying cause was established by an expert panel (i.e. GP, internist, clinical chemist). In this study, multinomial regression was used to determine the value of each test for establishing a particular underlying cause. Logistic regression was used to determine the value of each test for establishing the correct underlying cause. Stepwise backward selection using the Akaike Information Criterion was applied to determine the optimal subset of tests. Results: Only 9 (60%) of the laboratory tests, and patient age, contributed to diagnosing an underlying cause of anaemia (CRP, ESR, ferritin, folic acid, haemoglobin, leukocytes, MDRD, reticulocytes and serum iron). Diagnosing the correct underlying cause required just five (33%) tests (CRP, ferritin, folic acid, MCV and transferrin), and patient age. Conclusions: Of the full set of 14 laboratory tests recommended by the Dutch guideline, only a subset enhances the ability of the GP to diagnose an underlying cause of anaemia, from a statistical perspective. A subset of five tests has most added value. Extending this set with more tests may lead to lead to a decrease rather than increase in correct diagnoses.