09. Individual Risk Stratification

Milo Puhan, MD, PhD
Professor of Epidemiology & Public Health University of Zurich, Switzerland Johns Hopkins University, USA

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Individual Risk Stratification

Milo Puhan MD, PhD.
Professor of Epidemiology & Public Health University of Zurich, Switzerland Johns Hopkins University, USA.


Risk-stratified treatment aims at providing personalized medicine by maximizing benefits and minimizing harm. To develop risk-stratified treatment strategies, well-defined translational paths need to be developed that closely connect lab-based, clinical and epidemiological research with evidence synthesis and multidimensional benefit harm analysis. Thereby, the three key factors that determine the benefit harm balance of interventions, i.e. (1) risk factors for outcome risks (from the molecular to composite level), (2) treatment effectiveness and (3) importance of outcomes, can be identified and combined in an optimal way. For effective functioning of the translational paths, mutual understanding and communication across clinicians, basic scientists, clinical trialists, epidemiologists and specialists in information & communication technology is crucial. Well developed risk-stratified treatment strategies have the potential to provide evidence-based health care thatis personalized through individual characteristics and sensitive to the individual’s preferences for treatment goals.

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Milo Puhan obtained an MD degree from the University of Zurich and a PhD degree in Epidemiology from the University of Amsterdam. He served as Associate Professor (tenure track) at the Department of Epidemiology of the Bloomberg School of Public Health. In 2013, he was appointed as Professor of Epidemiology and Public Health and Director of the Epidemiology, Biostatistics and Prevention Institute of the University of Zurich. Milo Puhan’s research focuses on tests to diagnose, predict and monitor chronic disease and to develop individualized prevention and disease management strategies. It includes observational studies in patients with single or multiple chronic diseases (in particular lung and cardiovascular diseases) to develop and validate statistical models that predict the risk for health outcomes such as mortality, hospital admissions and quality of life. His experimental studies (randomized trials and network meta-analyses) focus on non-pharmacological and pharmacological interventions for prevention and treatment of patients with chronic disease. A substantial part of his work is methodological and includes the development of methods for benefit harm assessment and for risk-stratified prevention and management strategies.

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