The Experts below are selected from a list of 168141 Experts worldwide ranked by ideXlab platform
Qing Lu - One of the best experts on this subject based on the ideXlab platform.
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Risk Prediction models for oral clefts allowing for phenotypic heterogeneity.
Frontiers in Genetics, 2015Co-Authors: Qing LuAbstract:Oral clefts are common birth defects that have a major impact on the affected individual, their family and society. World-wide, the incidence of oral clefts is 1/700 live births, making them the most common craniofacial birth defects. The successful Prediction of oral clefts may help identify sub-population at high Risk, and promote new diagnostic and therapeutic strategies. Nevertheless, developing a clinically useful oral clefts Risk Prediction model remains a great challenge. Compelling evidences suggest the etiologies of oral clefts are highly heterogeneous, and the development of a Risk Prediction model with consideration of phenotypic heterogeneity may potentially improve the accuracy of a Risk Prediction model. In this study, we applied a previously developed statistical method to investigate the Risk Prediction on sub-phenotypes of oral clefts. Our results suggested subtypes of cleft lip (CL) and palate have similar genetic etiologies (AUC = 0.572) with subtypes of CL only (AUC = 0.589), while the subtypes of cleft palate only (CPO) have heterogeneous underlying mechanisms (AUCs for soft CPO and hard CPO are 0.617 and 0.623, respectively). This highlighted the potential that the hard and soft forms of CPO have their own mechanisms despite sharing some of the genetic Risk factors. Comparing with conventional methods for Risk Prediction modeling, our method considers phenotypic heterogeneity of a disease, which potentially improves the accuracy for predicting each sub-phenotype of oral clefts.
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A multiclass likelihood ratio approach for genetic Risk Prediction allowing for phenotypic heterogeneity
Genetic Epidemiology, 2013Co-Authors: Qing LuAbstract:The translation of human genome discoveries into health practice is one of the major challenges in the coming decades. The use of emerging genetic knowledge for early disease Prediction, prevention, and pharmacogenetics will advance genome medicine and lead to more effective prevention/treatment strategies. For this reason, studies to assess the combined role of genetic and environmental discoveries in early disease Prediction represent high priority research projects, as manifested in the multiple Risk Prediction studies now underway. However, the Risk Prediction models formed to date lack sufficient accuracy for clinical use. Converging evidence suggests that diseases with the same or similar clinical manifestations could have different pathophysiological and etiological processes. When heterogeneous subphenotypes are treated as a single entity, the effect size of predictors can be reduced substantially, leading to a low-accuracy Risk Prediction model. The use of more refined subphenotypes facilitates the identification of new predictors and leads to improved Risk Prediction models. To account for the phenotypic heterogeneity, we have developed a multiclass likelihood-ratio approach, which simultaneously determines the optimum number of subphenotype groups and builds a Risk Prediction model for each group. Simulation results demonstrated that the new approach had more accurate and robust performance than existing approaches under various underlying disease models. The empirical study of type II diabetes (T2D) by using data from the Genes and Environment Initiatives suggested heterogeneous etiology underlying obese and nonobese T2D patients. Considering phenotypic heterogeneity in the analysis leads to improved Risk Prediction models for both obese and nonobese T2D subjects.
Donald M Lloydjones - One of the best experts on this subject based on the ideXlab platform.
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cardiovascular Risk Prediction basic concepts current status and future directions
Circulation, 2010Co-Authors: Donald M LloydjonesAbstract:Few topics have received as much attention in the cardiovascular literature over the last 5 years as Risk Prediction. The assessment of Risk has been a key element in efforts to define Risk factors for cardiovascular disease (CVD), to identify novel markers of Risk for CVD, to identify and assess potential targets of therapy, and to enhance the cost-effective implementation of therapies for both primary and secondary prevention of CVD. With the publication of the Third Report of the National Cholesterol Education Program’s Adult Treatment Panel (ATP-III)1 in 2001 and similar guidelines from other national and international bodies,2–5 Risk Prediction assumed a central role in the field of CVD prevention. Since then, numerous attempts have been made to refine and improve Risk assessment methods. A basic review of the history and principles of Risk Prediction, their statistical underpinnings, and their clinical implications is thus required to help clinicians and researchers understand the increasingly complex approaches being undertaken to refine CVD Risk Prediction. Other articles in this series address the current adoption, utility, and effect of Risk estimation algorithms in clinical prevention practice. Risk estimates can theoretically be used to raise population awareness of diseases (such as CVD) that cause a significant burden of morbidity and mortality, to communicate knowledge about that Risk to individuals and subgroups, and to motivate adherence to recommended lifestyle changes or therapies. In clinical practice, Risk Prediction algorithms have been used most directly to identify individuals at high Risk for developing CVD in the short term to select those individuals for more intensive preventive interventions. The prime example of this latter “high-Risk” prevention strategy, which relies heavily on the quantitative Prediction of CVD Risk, is the approach promulgated by the ATP-III panel.1 In this algorithm, the stated underlying assumption is that the intensity …
Nazriah Mahmud - One of the best experts on this subject based on the ideXlab platform.
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Stroke Risk Prediction Model
International Journal of Biology, 2019Co-Authors: Wan Nor Syuhada Wan Zahari, Eko Supriyanto, Nazriah MahmudAbstract:The purpose of this study is to improve the existing stroke Risk Prediction model for the next 10 years. Current existing Risk Prediction model was done based on the data obtained mostly from America and Africa. There are a few Risk Prediction models done based on the data from Asian participants. Hence, this paper will predict the Risk for stroke for the next 10 years using data collected from Asia. Data from Korean and China Risk Prediction model were obtained and sorted according to categories. The weightage of each Risk factors is then calculated. This study also used the artificial intelligence as a comparison to predict the Risk of stroke. Data from a few studies obtained and compared to estimate the probability of stroke that may occur in 10 years of time. Then, a set of data will be used to train the Artificial Neural Network (ANN) and compared to the results obtained using conventional calculation method. The usage of ANN to predict and learn about the Risk factors for stroke will give a significant benefit in the future. This study developed a personalized stroke Risk Prediction which expected to be better and relevant to Asian people compared to other Risk Prediction model.
Gary S. Collins - One of the best experts on this subject based on the ideXlab platform.
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Statistical Primer: developing and validating a Risk Prediction model.
European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery, 2018Co-Authors: Stuart W Grant, Gary S. Collins, Nashef Sam.Abstract:A Risk Prediction model is a mathematical equation that uses patient Risk factor data to estimate the probability of a patient experiencing a healthcare outcome. Risk Prediction models are widely studied in the cardiothoracic surgical literature with most developed using logistic regression. For a Risk Prediction model to be useful, it must have adequate discrimination, calibration, face validity and clinical usefulness. A basic understanding of the advantages and potential limitations of Risk Prediction models is vital before applying them in clinical practice. This article provides a brief overview for the clinician on the various issues to be considered when developing or validating a Risk Prediction model. An example of how to develop a simple model is also included.
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Risk Prediction Models in Perioperative Medicine: Methodological Considerations
Current Anesthesiology Reports, 2016Co-Authors: Gary S. Collins, Stephen Gerry, E Ohuma, L Odondi, Marialena Trivella, J De Beyer, Maria D. L. A. Vazquez-montesAbstract:Purpose of Review Risk Prediction models hold enormous potential for assessing surgical Risk in a standardized, objective manner. Despite the vast number of Risk Prediction models developed, they have not lived up to their potential. The aim of this paper is to provide an overview of the methodological issues that should be considered when developing and validating a Risk Prediction model to ensure a useful, accurate model. Recent Findings Systematic reviews examining the methodological and reporting quality of these models have found widespread deficiencies that limit their usefulness. Summary Risk Prediction modelling is a growing field that is gaining huge interest in the era of personalized medicine. Although there are no shortcuts and many challenges are faced when developing and validating accurate, useful Prediction models, these challenges are surmountable, if the abundant methodological and practical guidance available is used correctly and efficiently.
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Risk Prediction models in perioperative medicine methodological considerations
Current Anesthesiology Reports, 2016Co-Authors: Gary S. Collins, Stephen Gerry, E Ohuma, L Odondi, Marialena Trivella, J De Beyer, Vazquezmontes MdlaAbstract:Purpose of Review Risk Prediction models hold enormous potential for assessing surgical Risk in a standardized, objective manner. Despite the vast number of Risk Prediction models developed, they have not lived up to their potential. The aim of this paper is to provide an overview of the methodological issues that should be considered when developing and validating a Risk Prediction model to ensure a useful, accurate model.
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Comparing Risk Prediction models
BMJ (Clinical research ed.), 2012Co-Authors: Gary S. Collins, Karel G.m. MoonsAbstract:Should be routine when deriving a new model for the same purpose Risk Prediction models have great potential to support clinical decision making and are increasingly incorporated into clinical guidelines.1 Many Prediction models have been developed for cardiovascular disease—the Framingham Risk score, SCORE, QRisk, and the Reynolds Risk score—to mention just a few. With so many Prediction models for similar outcomes or target populations, clinicians have to decide which model should be used on their patients. To make this decision they need to know, as a minimum, how well the score predicts disease in people outside the populations used to develop the model (“what is the external validation?”) and which model performs best.2 In a linked research study (doi:10.1136/bmj.e3318), Siontis and colleagues examined the comparative performance of several prespecified cardiovascular Risk Prediction models for the general population.3 They identified 20 published studies that compared two or more models and they highlighted problems in design, analysis, and reporting. What can be inferred from the findings of this well conducted systematic review? Firstly, direct comparisons are few. A plea for more direct comparisons is increasingly heard in the field of therapeutic intervention and diagnostic research …
Wan Nor Syuhada Wan Zahari - One of the best experts on this subject based on the ideXlab platform.
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Stroke Risk Prediction Model
International Journal of Biology, 2019Co-Authors: Wan Nor Syuhada Wan Zahari, Eko Supriyanto, Nazriah MahmudAbstract:The purpose of this study is to improve the existing stroke Risk Prediction model for the next 10 years. Current existing Risk Prediction model was done based on the data obtained mostly from America and Africa. There are a few Risk Prediction models done based on the data from Asian participants. Hence, this paper will predict the Risk for stroke for the next 10 years using data collected from Asia. Data from Korean and China Risk Prediction model were obtained and sorted according to categories. The weightage of each Risk factors is then calculated. This study also used the artificial intelligence as a comparison to predict the Risk of stroke. Data from a few studies obtained and compared to estimate the probability of stroke that may occur in 10 years of time. Then, a set of data will be used to train the Artificial Neural Network (ANN) and compared to the results obtained using conventional calculation method. The usage of ANN to predict and learn about the Risk factors for stroke will give a significant benefit in the future. This study developed a personalized stroke Risk Prediction which expected to be better and relevant to Asian people compared to other Risk Prediction model.
