Nadeem Qureshi

Bio: Nadeem Qureshi is an academic researcher from University of Nottingham. The author has contributed to research in topics: Population & Family history. The author has an hindex of 33, co-authored 143 publications receiving 4095 citations. Previous affiliations of Nadeem Qureshi include Centers for Disease Control and Prevention.

Can machine-learning improve cardiovascular risk prediction using routine clinical data?

Abstract: Background Current approaches to predict cardiovascular risk fail to identify many people who would benefit from preventive treatment, while others receive unnecessary intervention. Machine-learning offers opportunity to improve accuracy by exploiting complex interactions between risk factors. We assessed whether machine-learning can improve cardiovascular risk prediction. Methods Prospective cohort study using routine clinical data of 378,256 patients from UK family practices, free from cardiovascular disease at outset. Four machine-learning algorithms (random forest, logistic regression, gradient boosting machines, neural networks) were compared to an established algorithm (American College of Cardiology guidelines) to predict first cardiovascular event over 10-years. Predictive accuracy was assessed by area under the ‘receiver operating curve’ (AUC); and sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) to predict 7.5% cardiovascular risk (threshold for initiating statins). Findings 24,970 incident cardiovascular events (6.6%) occurred. Compared to the established risk prediction algorithm (AUC 0.728, 95% CI 0.723–0.735), machine-learning algorithms improved prediction: random forest +1.7% (AUC 0.745, 95% CI 0.739–0.750), logistic regression +3.2% (AUC 0.760, 95% CI 0.755–0.766), gradient boosting +3.3% (AUC 0.761, 95% CI 0.755–0.766), neural networks +3.6% (AUC 0.764, 95% CI 0.759–0.769). The 78 highest achieving (neural networks) algorithm predicted 4,998/7,404 cases (sensitivity 79 67.5%, PPV 18.4%) and 53,458/75,585 non-cases (specificity 70.7%, NPV 95.7%), correctly predicting 355 (+7.6%) more patients who developed cardiovascular disease compared to the established algorithm. Conclusions Machine-learning significantly improves accuracy of cardiovascular risk prediction, increasing the number of patients identified who could benefit from preventive treatment, while avoiding unnecessary treatment of others.

Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews

Abstract: This study is to identify, summarise and synthesise literature on the causes of the evidence to practice gap for complex interventions in primary care. This study is a systematic review of reviews. MEDLINE, EMBASE, CINAHL, Cochrane Library and PsychINFO were searched, from inception to December 2013. Eligible reviews addressed causes of the evidence to practice gap in primary care in developed countries. Data from included reviews were extracted and synthesised using guidelines for meta-synthesis. Seventy reviews fulfilled the inclusion criteria and encompassed a wide range of topics, e.g. guideline implementation, integration of new roles, technology implementation, public health and preventative medicine. None of the included papers used the term “cause” or stated an intention to investigate causes at all. A descriptive approach was often used, and the included papers expressed “causes” in terms of “barriers and facilitators” to implementation. We developed a four-level framework covering external context, organisation, professionals and intervention. External contextual factors included policies, incentivisation structures, dominant paradigms, stakeholders’ buy-in, infrastructure and advances in technology. Organisation-related factors included culture, available resources, integration with existing processes, relationships, skill mix and staff involvement. At the level of individual professionals, professional role, underlying philosophy of care and competencies were important. Characteristics of the intervention that impacted on implementation included evidence of benefit, ease of use and adaptability to local circumstances. We postulate that the “fit” between the intervention and the context is critical in determining the success of implementation. This comprehensive review of reviews summarises current knowledge on the barriers and facilitators to implementation of diverse complex interventions in primary care. To maximise the uptake of complex interventions in primary care, health care professionals and commissioning organisations should consider the range of contextual factors, remaining aware of the dynamic nature of context. Future studies should place an emphasis on describing context and articulating the relationships between the factors identified here. PROSPERO CRD42014009410

Family history in public health practice: a genomic tool for disease prevention and health promotion.

Abstract: Family history is a risk factor for many chronic diseases, including cancer, cardiovascular disease, and diabetes. Professional guidelines usually include family history to assess health risk, initiate interventions, and motivate behavioral changes. The advantages of family history over other genomic tools include a lower cost, greater acceptability, and a reflection of shared genetic and environmental factors. However, the utility of family history in public health has been poorly explored. To establish family history as a public health tool, it needs to be evaluated within the ACCE framework (analytical validity; clinical validity; clinical utility; and ethical, legal, and social issues). Currently, private and public organizations are developing tools to collect standardized family histories of many diseases. Their goal is to create family history tools that have decision support capabilities and are compatible with electronic health records. These advances will help realize the potential of family history as a public health tool.

The 'new genetics' and primary care: GPs' views on their role and their educational needs.

Abstract: BACKGROUND Given the limited specialist resources available to cope with the rising demand for genetic services, it has been proposed that at least some of these services are provided by primary care in the future. OBJECTIVE We aimed to explore GPs' attitudes towards new developments in genetics, to establish the role they envisage for primary care and to clearly define the education, information and training needed to support them in this role. METHODS We carried out a qualitative study with GPs using four focus groups (26 GPs) and 15 individual semi-structured interviews. RESULTS GPs perceive genetics as an important and increasingly relevant topic for primary care. Views on the appropriate level of involvement for primary care are mixed. GPs currently lack the relevant knowledge and skills to manage patients concerned about their family history. Other potential barriers to increasing primary care involvement included the time and costs involved, and ethical and legal concerns. CONCLUSION If primary care is to become more involved in the delivery of genetic services in the future, then a major educational effort is required to raise awareness of the potential scope and limitations of new developments.

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

The role of behavioral science theory in development and implementation of public health interventions.

Abstract: Increasing evidence suggests that public health and health-promotion interventions that are based on social and behavioral science theories are more effective than those lacking a theoretical base. This article provides an overview of the state of the science of theory use for designing and conducting health-promotion interventions. Influential contemporary perspectives stress the multiple determinants and multiple levels of determinants of health and health behavior. We describe key types of theory and selected often-used theories and their key concepts, including the health belief model, the transtheoretical model, social cognitive theory, and the ecological model. This summary is followed by a review of the evidence about patterns and effects of theory use in health behavior intervention research. Examples of applied theories in three large public health programs illustrate the feasibility, utility, and challenges of using theory-based interventions. This review concludes by identifying cross-cutting themes and important future directions for bridging the divides between theory, practice, and research.

Global epidemiology of haemoglobin disorders and derived service indicators

Abstract: To demonstrate a method for using genetic epidemiological data to assess the needs for equitable and cost-effective services for the treatment and prevention of haemoglobin disorders. We obtained data on demographics and prevalence of gene variants responsible for haemoglobin disorders from online databases, reference resources, and published articles. A global epidemiological database for haemoglobin disorders by country was established, including five practical service indicators to express the needs for care (indicator 1) and prevention (indicators 2-5). Haemoglobin disorders present a significant health problem in 71% of 229 countries, and these 71% of countries include 89% of all births worldwide. Over 330,000 affected infants are born annually (83% sickle cell disorders, 17% thalassaemias). Haemoglobin disorders account for about 3.4% of deaths in children less than 5 years of age. Globally, around 7% of pregnant women carry b or a zero thalassaemia, or haemoglobin S, C, D Punjab or E, and over 1% of couples are at risk. Carriers and at-risk couples should be informed of their risk and the options for reducing it. Screening for haemoglobin disorders should form part of basic health services in most countries.