The primary goals of personalized medicine are to optimize diagnostic and treatment strategies by tailoring them to the specific characteristics of an individual patient. In this Review, we summarize basic concepts and methods of personalizing cardiovascular medicine. In-depth characterization of study participants and patients in general practice using standardized methods is a pivotal component of study design in personalized medicine. Standardization and quality assurance of clinical data are similarly important, but in daily practice imprecise definitions of clinical variables can reduce power and introduce bias, which limits the validity of the data obtained as well as their potential clinical applicability. Changes in statistical methods with personalized medicine include a shift from dichotomous outcomes towards continuously measured variables, predictive modelling, and individualized medical decisions, subgroup analyses, and data-mining strategies. A variety of approaches to personalized medicine exist in cardiovascular research and clinical practice that might have the potential to individualize diagnostic and therapeutic procedures. For some of the emerging methods, such as data mining, the most-efficient way to use these tools is not yet fully understood. In addition, the predictive models—although promising—are far from mature, and are likely to be greatly improved by using available large-scale data sets.
The primary goals of personalized medicine are to optimize diagnostic and treatment strategies by tailoring them to the specific characteristics of an individual patient
With some exceptions in antiplatelet and anticoagulation therapy, knowledge of genetic markers currently has little practical application in personalizing cardiovascular medicine
Large, comprehensive, and standardized studies will uncover as yet unknown subgroups from apparently clinically homogeneous populations, but clinical applicability critically depends on standardization of diagnostic procedures in clinical practice
To enter clinical practice, novel risk markers generated from '-omics' or imaging technologies have to provide additional predictive value beyond established markers
Statistical methods are available to identify subgroups of patients characterized by specific combinations of predictors, but these methods are not commonly applied in cardiovascular research
The most-efficient way to use some emerging methods, such as data mining, is not yet fully understood, and the predicative models—although promising—are far from mature
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This work is part of the research project Greifswald Approach to Individualized Medicine (GANI_MED). The GANI_MED consortium is funded by the Federal Ministry of Education and Research (03IS2061A/C) and the Ministry of Cultural Affairs of the Federal State of Mecklenburg–West Pomerania, Germany. Matthias Schwab is supported by the Deutsche Forschungsgemeinschaft (Grant SCHW 858/1-1). Henry Völzke, Marcus Dörr, and Stephan B. Felix are also members of the German Center for Cardiovascular Research at the partner site in Greifswald, Germany.
The authors declare no competing financial interests.
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Völzke, H., Schmidt, C., Baumeister, S. et al. Personalized cardiovascular medicine: concepts and methodological considerations. Nat Rev Cardiol 10, 308–316 (2013). https://doi.org/10.1038/nrcardio.2013.35
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