Personalized medicine: a perspective on the patient, disease and causal diagnostics

Company Profile: Strategic Medicine, Inc. and Strategic Medicine, BV

Strategic Medicine, BV (The Hague, The Netherlands) and Strategic Medicine, Inc. (PA, USA) deliver products and services to therapeutic and diagnostic companies, healthcare providers and payers and the investment community based on unique methodologies for modeling disease processes, from predisease through diagnosis, disease and patient stratification and outcome. Strategic Medicine, Inc. focuses on the development of disease models that design and incorporate a personalized health record to model the progression of a patient from genetic risk, through interaction with lifestyle and environment, to early disease detection, disease and patient stratification and treatment decision support for enhanced outcomes. This model involves the integration of disparate data and databases, evaluation of data quality and completeness, data simulation (when necessary), systems modeling and quantitative risk/opportunity evaluation. Strategic Medicine, Inc. has been involved in applying these approaches in oncology, cardiology, women's health and pediatric conditions and rare diseases, and has focused on the development of both disease- and data-agnostic infrastructures and analytics.

The application of observational data in translational medicine: analyzing tobacco-use behaviors of adolescents

Background

Translational Medicine focuses on “bench to bedside”, converting experimental results into clinical use. The “bedside to bench” transition remains challenging, requiring clinicians to define true clinical need for laboratory study. In this study, we show how observational data (an eleven-year data survey program on adolescent smoking behaviours), can identify knowledge gaps and research questions leading directly to clinical implementation and improved health care. We studied gender-specific trends (2000–2010) in Italian students to evaluate the specific impact of various anti-smoking programs, including evaluation of perceptions of access to cigarettes and health risk.

Methods

The study used, ESPAD-Italia® (European School Survey Project on Alcohol and other Drugs), is a nationally representative sample of high-school students. The permutation test for joinpoint regression was used to calculate the annual percent change in smoking. Changes in smoking habits by age, perceived availability and risk over a 11-year period were tested using a gender-specific logistic model and a multinomial model.

Results

Gender-stratified analysis showed 1) decrease of lifetime prevalence, then stabilization (both genders); 2) decrease in last month and occasional use (both genders); 3) reduction of moderate use (females); 4) no significant change in moderate use (males) and in heavy use (both genders). Perceived availability positively associates with prevalence, while perceived risk negatively associates, but interact with different effects depending on smoking patterns. In addition, government implementation of public policies concerning access to tobacco products in this age group during this period presented a unique background to examine their specific impact on behaviours.

Conclusion

Large observational databases are a rich resource in support of translational research. From these observations, key clinically relevant issues can be identified and form the basis for further clinical studies. The ability to identify patterns of behaviour and gaps in available data translates into new experiments, but also impacts development of public policy and reveals patterns of clinical reality. The observed global decrease in use is countered by stabilization in number of heavy smokers. Increased cigarette cost has not reduced use. While perceived risk of smoking may prevent initial experimentation, how government policies impact the perception of risk is not easily quantifiable.

Anonymization of longitudinal electronic medical records

Electronic medical record (EMR) systems have enabled healthcare providers to collect detailed patient information from the primary care domain. At the same time, longitudinal data from EMRs are increasingly combined with biorepositories to generate personalized clinical decision support protocols. Emerging policies encourage investigators to disseminate such data in a deidentified form for reuse and collaboration, but organizations are hesitant to do so because they fear such actions will jeopardize patient privacy. In particular, there are concerns that residual demographic and clinical features could be exploited for reidentification purposes. Various approaches have been developed to anonymize clinical data, but they neglect temporal information and are, thus, insufficient for emerging biomedical research paradigms. This paper proposes a novel approach to share patient-specific longitudinal data that offers robust privacy guarantees, while preserving data utility for many biomedical investigations. Our approach aggregates temporal and diagnostic information using heuristics inspired from sequence alignment and clustering methods. We demonstrate that the proposed approach can generate anonymized data that permit effective biomedical analysis using several patient cohorts derived from the EMR system of the Vanderbilt University Medical Center.