Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 3: An Evaluation of SNP Incorporated National Health Information System of Turkey for Prostate Cancer

Abstract

BackgroundA personalized medicine approach provides opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, the tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world, including Turkey.ObjectiveAs an initial attempt to develop a sophisticated infrastructure, we have concentrated on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluated the performance of various predictive models for prostate cancer cases. We present our work as a three part miniseries: (1) an overview of requirements, (2) the incorporation of SNP data into the NHIS-T, and (3) an evaluation of SNP data incorporated into the NHIS-T for prostate cancer.MethodsIn the third article of this miniseries, we have evaluated the proposed complementary capabilities (ie, knowledge base and end-user application) with real data. Before the evaluation phase, clinicogenomic associations about increased prostate cancer risk were extracted from knowledge sources, and published predictive genomic models assessing individual prostate cancer risk were collected. To evaluate complementary capabilities, we also gathered personal SNP data of four prostate cancer cases and fifteen controls. Using these data files, we compared various independent and model-based, prostate cancer risk assessment approaches.ResultsThrough the extraction and selection processes of SNP-prostate cancer risk associations, we collected 209 independent associations for increased risk of prostate cancer from the studied knowledge sources. Also, we gathered six cumulative models and two probabilistic models. Cumulative models and assessment of independent associations did not have impressive results. There was one of the probabilistic, model-based interpretation that was successful compared to the others. In envirobehavioral and clinical evaluations, we found that some of the comorbidities, especially, would be useful to evaluate disease risk. Even though we had a very limited dataset, a comparison of performances of different disease models and their implementation with real data as use case scenarios helped us to gain deeper insight into the proposed architecture.ConclusionsIn order to benefit from genomic variation data, existing EHR/EMR systems must be constructed with the capability of tracking and monitoring all aspects of personal health status (genomic, clinical, environmental, etc) in 24/7 situations, and also with the capability of suggesting evidence-based recommendations. A national-level, accredited knowledge base is a top requirement for improved end-user systems interpreting these par...

Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 2: The Incorporation of SNP into the National Health Information System of Turkey

“…Most of the clinically relevant SNPs have minor effects (odds ratio <1.50-2.00), and there are only limited numbers of different examples. For example, in our study, we extracted more than 209 prostate cancer-associated SNPs from the literature, and many of these SNPs have minor effects for predicting the prostate-cancer risk [ 9 ]. Additionally, in many cases, SNPs do not show their effects directly for a given disease, but do so in combination with other SNP variations and clinical and environmental factors, which require a much higher level of probability calculations.…”

“…In a probabilistic model, SNP profiles with increased disease risk are determined with an evidence-based approach during development of the model, and the patient’s risk is determined through the patient’s genotyping profiles. Detailed examples of these models will be explained in the next section of the miniseries [ 9 ]. Examples of the cumulative model of prostate cancer and their reference tables are given in Multimedia Appendices 1 and 2 , and examples of the probabilistic model with the list of the possible SNP profiles are provided in Multimedia Appendices 3 and 4 .…”

“…The requirements of SNP data integrated with EMRs/EHRs from scientific literature have been reviewed in the previous part of this miniseries [ 8 ], and here we will focus on extending the capabilities of the NHIS-T via incorporating SNP and clinicogenomic data for disease risk assessment. In the final part of the miniseries, we will evaluate the proposed complementary capabilities with real data from prostate cancer cases [ 9 ].…”

Incorporation of Personal Single Nucleotide Polymorphism (SNP) Data into a National Level Electronic Health Record for Disease Risk Assessment, Part 1: An Overview of Requirements

“…In this work, as an initial attempt to develop a sophisticated infrastructure, we focused to incorporate the personal SNP data into NHIS-T for disease risk assessment, and evaluated the performance of various predictive models for prostate cancer cases. We presented our work as three parts: (1) a literature review for requirements, (2) the incorporation of SNP into the NHIS-T [ 18 ], and (3) an evaluation of SNP incorporated into NHIS-T for prostate cancer [ 19 ]. In this part, the scientific literature was reviewed, and the requirements were extracted regarding SNP data integrated EMRs/EHRs.…”