Strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS): explanation and elaboration

Evaluation of polygenic risk models using multiple performance measures: a critical assessment of discordant results

Purpose

The area under the receiver operating characteristic curve (AUC) is commonly used for evaluating the improvement of polygenic risk models and increasingly assessed together with the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). We evaluated how researchers described and interpreted AUC, NRI, and IDI when simultaneously assessed.

Methods

We reviewed how researchers described definitions of AUC, NRI and IDI and how they computed each metric. Next, we reviewed how the increment in AUC, NRI and IDI were interpreted; and how the overall conclusion about the improvement of the risk model was reached.

Results

AUC, NRI and IDI were correctly defined in 63%, 70%, and 0% of the articles. All statistically significant values and almost half of the non-significant were interpreted as indicative of improvement, irrespective of the values of the metrics. Also, small, nonsignificant changes in the AUC were interpreted as indication of improvement when NRI and IDI were statistically significant.

Conclusion

Researchers have insufficient knowledge about how to interpret the various metrics for the assessment of the predictive performance of polygenic risk models and rely on the statistical significance for their interpretation. A better understanding is needed to achieve more meaningful interpretation of polygenic prediction studies.

Genetics of coronary artery disease and myocardial infarction

Atherosclerotic coronary artery disease (CAD) comprises a broad spectrum of clinical entities that include asymptomatic subclinical atherosclerosis and its clinical complications, such as angina pectoris, myocardial infarction (MI) and sudden cardiac death. CAD continues to be the leading cause of death in industrialized society. The long-recognized familial clustering of CAD suggests that genetics plays a central role in its development, with the heritability of CAD and MI estimated at approximately 50% to 60%. Understanding the genetic architecture of CAD and MI has proven to be difficult and costly due to the heterogeneity of clinical CAD and the underlying multi-decade complex pathophysiological processes that involve both genetic and environmental interactions. This review describes the clinical heterogeneity of CAD and MI to clarify the disease spectrum in genetic studies, provides a brief overview of the historical understanding and estimation of the heritability of CAD and MI, recounts major gene discoveries of potential causal mutations in familial CAD and MI, summarizes CAD and MI-associated genetic variants identified using candidate gene approaches and genome-wide association studies (GWAS), and summarizes the current status of the construction and validations of genetic risk scores for lifetime risk prediction and guidance for preventive strategies. Potential protective genetic factors against the development of CAD and MI are also discussed. Finally, GWAS have identified multiple genetic factors associated with an increased risk of in-stent restenosis following stent placement for obstructive CAD. This review will also address genetic factors associated with in-stent restenosis, which may ultimately guide clinical decision-making regarding revascularization strategies for patients with CAD and MI.

Helical tomotherapy for cancer treatment: a rapid health technology assessment

OBJECTIVES

Helical tomotherapy (HT) can be applied to treat complex malignant cancer with high-precise radiotherapy, and it can reduce the damage to normal tissues and improve treatment effects. But the procurement of HT must be approved by relevant departments of administration affairs. This study, appointed by the National Health and Family Planning Commission of China and undertook by the National Health Development Research Center and the Chinese Evidence-Based Medicine Centre, was aimed to rapidly assess the effectiveness, safety, costs, and applicability of HT, so as to provide currently available best evidence for decision-makers of health policies.

METHODS

We electronically searched databases including PubMed, EMbase, The Cochrane Library, CNKI, WanFang Data, VIP, CBM, and other professional websites. Two reviewer independently screened literature according to the inclusion and exclusion criteria, extracted data, assessed quality, and then performed descriptive analysis.

RESULTS

(i) We finally included 150 studies, encompassing 5 HTAs, 18 CCTs, and 127 observational studies. (ii) The included HTAs were published during 2006-2009, providing fairly less evidence of low quality and the results of 145 primary studies showed that: HT had been used mainly in the treatments of 14 kinds of cancer, with low total toxicity and high survival rates. Although the quality of the included studies was poor, there was much evidence about prostate cancer, head and neck cancer, nasopharynx cancer, cervical cancer, lung cancer and liver cancer, with enough sample and fairly reliable results in HT efficacy and safety. And (iii) a total of 56 clinical trials were registered in Clinicaltrials.gov, most of which were registered by the occident. Among them, 9 were completed but the results had not been published yet.

CONCLUSIONS

The evidence of this study showed that, HT is safe and effective in clinic. But the abovementioned conclusion needs to be verified by conducting more high-quality studies with long-term follow-up. The costs of HT in procurement, maintenance, and application are high; and the skills, training, and qualification of operators are required. We suggest that the procurement of HT should be reduced; it should be allocated rationally and effectively used after comprehensive assessment in China's cancer epidemiology characteristics, health resource allocation, disease burden, medical service level, etc.; and also high-quality studies with long-term follow-up should be financially supported on the basis of establishing projects, so as to provide local evidence and consistently guide and improve scientific decision-making.

Environmental risk score as a new tool to examine multi-pollutants in epidemiologic research: an example from the NHANES study using serum lipid levels

Objective

A growing body of evidence suggests that environmental pollutants, such as heavy metals, persistent organic pollutants and plasticizers play an important role in the development of chronic diseases. Most epidemiologic studies have examined environmental pollutants individually, but in real life, we are exposed to multi-pollutants and pollution mixtures, not single pollutants. Although multi-pollutant approaches have been recognized recently, challenges exist such as how to estimate the risk of adverse health responses from multi-pollutants. We propose an “Environmental Risk Score (ERS)” as a new simple tool to examine the risk of exposure to multi-pollutants in epidemiologic research.

Methods and Results

We examined 134 environmental pollutants in relation to serum lipids (total cholesterol, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL) and triglycerides) using data from the National Health and Nutrition Examination Survey between 1999 and 2006. Using a two-stage approach, stage-1 for discovery (n = 10818) and stage-2 for validation (n = 4615), we identified 13 associated pollutants for total cholesterol, 9 for HDL, 5 for LDL and 27 for triglycerides with adjustment for sociodemographic factors, body mass index and serum nutrient levels. Using the regression coefficients (weights) from joint analyses of the combined data and exposure concentrations, ERS were computed as a weighted sum of the pollutant levels. We computed ERS for multiple lipid outcomes examined individually (single-phenotype approach) or together (multi-phenotype approach). Although the contributions of ERS to overall risk predictions for lipid outcomes were modest, we found relatively stronger associations between ERS and lipid outcomes than with individual pollutants. The magnitudes of the observed associations for ERS were comparable to or stronger than those for socio-demographic factors or BMI.

Conclusions

This study suggests ERS is a promising tool for characterizing disease risk from multi-pollutant exposures. This new approach supports the need for moving from a single-pollutant to a multi-pollutant framework.

The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation

Motivation

Biomedical ontologists to date have concentrated on ontological descriptions of biomedical entities such as gene products and their attributes, phenotypes and so on. Recently, effort has diversified to descriptions of the laboratory investigations by which these entities were produced. However, much biological insight is gained from the analysis of the data produced from these investigations, and there is a lack of adequate descriptions of the wide range of software that are central to bioinformatics. We need to describe how data are analyzed for discovery, audit trails, provenance and reproducibility.

Results

The Software Ontology (SWO) is a description of software used to store, manage and analyze data. Input to the SWO has come from beyond the life sciences, but its main focus is the life sciences. We used agile techniques to gather input for the SWO and keep engagement with our users. The result is an ontology that meets the needs of a broad range of users by describing software, its information processing tasks, data inputs and outputs, data formats versions and so on. Recently, the SWO has incorporated EDAM, a vocabulary for describing data and related concepts in bioinformatics. The SWO is currently being used to describe software used in multiple biomedical applications.

Conclusion

The SWO is another element of the biomedical ontology landscape that is necessary for the description of biomedical entities and how they were discovered. An ontology of software used to analyze data produced by investigations in the life sciences can be made in such a way that it covers the important features requested and prioritized by its users. The SWO thus fits into the landscape of biomedical ontologies and is produced using techniques designed to keep it in line with user’s needs.

Availability

The Software Ontology is available under an Apache 2.0 license at http://theswo.sourceforge.net/; the Software Ontology blog can be read at http://softwareontology.wordpress.com.

Reporting guidelines: optimal use in preventive medicine and public health

Numerous reporting guidelines are available to help authors write higher-quality papers more efficiently. Almost 200 are listed on the EQUATOR (Enhancing the Quality and Transparency of Health Research) Network's website and they vary in authority, usability, and breadth, making it difficult to decide which one(s) to use. This paper provides consistent information about guidelines for preventive medicine and public health and a framework and sequential approach for selecting them. The EQUATOR guidelines were reviewed for relevance to target audiences; selected guidelines were classified as "core" (frequently recommended) or specialized, and the latter were grouped by their focus. Core and specialized guidelines were coded for indicators of authority (simultaneous publication in multiple journals, rationale, scientific background supporting each element, expertise of designers, permanent website/named group), usability (presence of checklists and examples of good reporting), and breadth (article sections covered). Discrepancies were resolved by consensus. Selected guidelines are presented in four tables arranged to facilitate selection: core guidelines, all of which pertain to major research designs; guidelines for additional study designs; topical guidelines; and guidelines for particular article sections. A flow diagram provides an overview. The framework and sequential approach will enable authors as well as editors, peer reviewers, researchers, and systematic reviewers to make optimal use of available guidelines to improve the transparency, clarity, and rigor of manuscripts and research protocols and the efficiency in conducting systematic reviews and meta-analyses.

Search filters for finding prognostic and diagnostic prediction studies in Medline to enhance systematic reviews

Background

The interest in prognostic reviews is increasing, but to properly review existing evidence an accurate search filer for finding prediction research is needed. The aim of this paper was to validate and update two previously introduced search filters for finding prediction research in Medline: the Ingui filter and the Haynes Broad filter.

Methodology/Principal Findings

Based on a hand search of 6 general journals in 2008 we constructed two sets of papers. Set 1 consisted of prediction research papers (n = 71), and set 2 consisted of the remaining papers (n = 1133). Both search filters were validated in two ways, using diagnostic accuracy measures as performance measures. First, we compared studies in set 1 (reference) with studies retrieved by the search strategies as applied in Medline. Second, we compared studies from 4 published systematic reviews (reference) with studies retrieved by the search filter as applied in Medline. Next – using word frequency methods – we constructed an additional search string for finding prediction research. Both search filters were good in identifying clinical prediction models: sensitivity ranged from 0.94 to 1.0 using our hand search as reference, and 0.78 to 0.89 using the systematic reviews as reference. This latter performance measure even increased to around 0.95 (range 0.90 to 0.97) when either search filter was combined with the additional string that we developed. Retrieval rate of explorative prediction research was poor, both using our hand search or our systematic review as reference, and even combined with our additional search string: sensitivity ranged from 0.44 to 0.85.

Conclusions/Significance

Explorative prediction research is difficult to find in Medline, using any of the currently available search filters. Yet, application of either the Ingui filter or the Haynes broad filter results in a very low number missed clinical prediction model studies.