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Clinical Decision Support for Personalized Medicine 14

CHAPTER

14.1 IntroductionWe include this chapter in this section on sources of knowledge because it rep-resents another dimension of knowledge development processes – translational science, in which discoveries are integrated into the process of care. While transla-tional science relies on experimentation, databases, human expertise, and the litera-ture – topics we have covered in previous chapters, it also brings together a unique perspective that is forcing a focus on the n-of-1 model, which combines popula-tion perspectives, personal perspectives, and many practical issues not otherwise discussed.

Genetics in medicine began in the early twentieth century, when physicians observed certain metabolic disorders reoccurring in families which could be explained by Mendel’s laws of inheritance (Garrod, 1902). Over most of the next century, as the scientific understanding of genetics increased, its application in medicine saw modest growth, mostly in rare, single-gene disorders. However, with the more recent rapid growth of genetics research, fueled by the Human Genome Project, the genetic contribution to human disease is rapidly becoming more central to the practice of medicine (Gordijn and Dekkers, 2006).

The genetic contribution to disease etiology and individualized drug therapy has given rise to the pursuit of “personalized” medicine (sometimes also called “pre-cision” medicine or “individualized” medicine), which is defined as the tailoring of medical treatment to the individual characteristics of each patient (President’s Council of Advisors on Science and Technology, 2008). The ability to personalize care to the individual patient is an exciting prospect to many in health care, since it has the potential to reduce costs and improve the quality of care (Abrahams et al., 2005). Personalized care involves providing health care, not just based on the dis-ease being treated, but also on the individual characteristics, including the geno-type, of the individual. Personalizing characteristics can include personal health history, genotype, biomarkers, environmental and social influences, and other attributes that can be used to classify or subtype an individual for personalized care.

Brandon M. Welch, Kensaku Kawamoto, Brian Drohan and Kevin S. Hughes

Clinical Decision Support.Copyright © 2014 Elsevier Inc. All rights reserved.2014

384 CHAPTER 14 Clinical Decision Support for Personalized Medicine

Indeed, there is inconsistency as to which characteristics constitute personalized care. However, for this chapter, we restrict the definition of personalized medicine to health care that includes the consideration of an individual’s genetic information.

The impact of genes on disease spans the whole spectrum of medical conditions and specialties. Indeed, Dr. Francis Collins, the current Director of the National Institutes of Health, has stated that “virtually every disease has some genetic component.”(Collins, 2003) A large number of disease-causing germ line mutations (mutations that are inherited from previous generations) have been identified. For example, there are more than 6,000 known single-gene disorders, and many more common multifactorial diseases, such as heart disease and diabetes, which are known to have a genetic component (US Department of Energy Genome Programs, 2013). Furthermore, particular genes are also known to influence drug response, a field known as pharmacogenomics.

Other frontiers of personalized medicine research, which will not be covered in this chapter, include the role of genetics in infectious diseases and nutrition. Also, we will not cover proteomics, metabolomics, and epigenomics. Although these are important research areas, CDS applications of these topics are not typi-cally found in a clinical setting at this time. Furthermore, although genetic analysis is becoming increasingly available directly to the general public through services like 23andMe®, this chapter will not be covering decision support for direct-to- consumer genetic testing. This raises a host of ethical as well as practical issues that will need to receive attention as the practice becomes more widespread. Some of the issues relate to consumer-oriented decision support and shared decision mak-ing, which are explored in Chapter 27. A number of important practical, economic and ethical issues also relate to the prospect of whole genome mapping in screening settings. The uncovering of incidental findings in such settings, where true positives are expected to be relatively rare and where the combinatoric explosion of likeli-hood of at least some false positives is enormous (and with the potential necessity to do further testing to determine whether they represent true positives), can give rise to major anxiety, concern, and expense. An article about this topic by Kohane et  al. (2006) describes the magnitude of this potential problem. There are also major questions that are debated regarding the role of the testing lab in reporting incidental but clinically important genetic findings uncovered in the course of their work, particularly if such findings were not part of the stated indication for obtain-ing the test. The above issues, although important, will not be considered further here, because they are beyond the scope of this book.

Some specialties have made greater headway with personalized care than oth-ers. For example, oncology is relatively further advanced than other specialties in its active work on cancer predisposition syndromes. In the majority of these dis-eases, a single dominant gene mutation occurs in a tumor suppressor gene. The individual does not manifest a cancer diagnosis early, since each cell has a second normal gene that prevents cancer development until a mutation in that gene occurs (Knudson, 1971). When the second mutation occurs, that cell has lost its only

38514.1 Introduction

functioning gene, and the cell goes on to accumulate additional mutations leading to cancer development. A list of a portion of cancer syndromes and associated can-cer-causing genes is depicted in Table 14.1.

In addition to the large number of germ line mutations in oncology, a large number of somatic mutations (mutations that are not passed to subsequent genera-tions) and gene expression profiles can impact how a disease will respond to medi-cations. For example, HER2-positive cancers have amplified the HER2 gene and thus respond to the biologic agent traztuzamab (Herceptin), while HER2-negative tumors do not respond to this agent. The presence or absence of the HER2 receptor has to do with somatic mutations in the tumor tissue. For the purposes of this chap-ter, however, we will focus primarily on germ-line mutations rather than somatic mutations because only limited work in CDS has been applied to the latter and, at the same time, the topic is too broad.

Table 14.1 A partial list of hereditary cancer syndromes and their associated genes

Syndrome Gene

Alagille syndrome JAG1Ataxia telangiectasia ATMBirt-Hogg-Dube syndrome FLCLBreast Cancer, Type 1 BRCA1Breast Cancer, Type 2 BRCA2Cowden Syndrome PTENDiamond-Blackfan Anemia S19Familial Adenomatous Polyposis APCHereditary Leiomyomatosis and Renal Cell Cancer FHHereditary Medullary Thyroid Carcinoma NTRK1, RETHereditary nonpolyposis colon cancer MLH1, MSH2, MSH6, PMS1, PMS2Hereditary Prostate Cancer RNASELJuvenile intestinal Polyposis MPSH, BMPR1A, SMAD4/DPC4Li-Fraumeni Syndrome TP53, CHK2Multiple Endocrine Neoplasia, Type 2a RETMultiple Endocrine Neoplasia, Type 2b RETNevoid Basal Cell Carcinoma Syndrome PTCHPapillary Renal Cell Carcinoma METPeutz-Jeghers Syndrome STK11Prostate Cancer/Brain Cancer Susceptibility EPHB2Tuberous Sclerosis TSC1, TSC2Turcot Syndrome APCVon Hippel-Lindau Syndrome VHLXL Hereditary Prostate Cancer HPCX

386 CHAPTER 14 Clinical Decision Support for Personalized Medicine

14.2 Challenges to adoption of genomic and personalized medicine

While the use of genetics holds great promise to improve health outcomes and reduce unnecessary expenses, significant barriers exist which inhibit its adoption (Suther and Goodson, 2003). These barriers have contributed to the slow and incon-sistent adoption of genetics on a widespread scale in the clinic. Although a number of genetics uses and applications are available to clinicians today, they are often not used due to a number of reasons, as discussed below.

14.2.1 Complexity of geneticsThere has been a tremendous explosion of information regarding genetics in dis-ease and an increase in the appreciation of the complexity of genetic interactions. There can be multiple possible genetic variations within a specific gene or gene pathway that contribute to a disease etiology. Point mutations, deletions, insertions, tandem repeats, and splice-site mutations can all impact the protein produced and thus affect the normal function of that protein, contributing to disease. Moreover, gene regulatory regions, copy number variations, and epigenetic influences can fur-ther complicate genetic influences on disease. Furthermore, genetic mutations can-not be considered in isolation, since there are significant gene-gene interactions, gene-environment interactions, and gene pathways which can influence the clinical phenotype of genetic mutations (Jorde et al., 2009). In some cases a genetic vari-ant consistently causes a particular genetic phenotype, while in other cases disease-causing genes have variable penetrance, such that a disease-associated genotype does not always result in the expected disease phenotype.

To add further complication, there is still a significant lack of knowledge about the human genome. Genes often contain variants of unknown significance (VUSs) which are rare or uncommon variants yet to be associated with a phenotype or ruled out as benign (Lupski et al., 2010). VUSs are particularly challenging to deal with, when they occur within disease-causing genes because the variant could be benign or pathogenic, and doctors must take a “wait and see” approach (Domchek and Weber, 2008). Computer algorithms have been developed that predict the antic-ipated effect of the VUS based on the location of the mutation and the potential impact on the protein product; however, these algorithms are error-prone and still need further refinement before they can be fully relied upon for clinical decisions (Flanagan et al., 2010).

14.2.2 Inadequate physician training in geneticsThe contribution of genetics to disease is still a relatively new field compared to more established specialties in medicine. Thus, many clinicians have received little to no formal training on the application of genetics to clinical practice (Thurston

38714.2 Challenges to adoption of genomic and personalized medicine

et al., 2007). Any training they did receive was relatively basic, primarily focused on monogenetic diseases with simple inheritance patterns. The training required to analyze complex genetics (as described above) is beyond the scope of most medical school curricula, which are already burdened with numerous competing demands (Greb et al., 2009; McInerney, 2008). As a result, physicians rate their knowledge of genetics as fair to poor, with a number of studies confirming their poor knowl-edge and interpretation of genetics (Hunter et  al., 1998; Giardiello et  al., 1997; Menasha et al., 2000).

There are currently over 2,900 diseases for which a clinical genetic test is avail-able (Genetests medical genetics information resource (database online), 2013). Expecting a physician to know all possible genetic contributions, available genetic tests, pathogenic variants, relevant family history constellations, and appropriate treatments for every disease he or she manages is unrealistic – such capability is beyond what humans can effectively manage (Masys, 2002). To illustrate, there are 188 separate adult hereditary syndromes, and a primary care provider should be aware of how to identify them, who should be tested, and how they should be managed (Scheuner et al., 2004). The primary care provider would therefore need to know the significance of every syndrome, the familial history pattern that might suggest a person has a particular syndrome, how to best counsel patients regard-ing the syndromes, what genetic tests are available, when he/she should test for them him/herself, and when to refer the patient to a genetics specialist. The prob-lem becomes much greater when one considers the many other diseases in which there are multifactorial genetic influences that affect manifestations of the disease and response to treatments (Genetests medical genetics information resource (data-base online), 2013).

Because the complexity of genetics exceeds the genetic training of most cli-nicians, many appropriate care guidelines involving genetics are overlooked by them (Wideroff et al., 2005). To illustrate, women with mutations in the BRCA1 or BRCA2 gene, which are responsible for the majority of cases of hereditary breast and ovarian cancer syndromes, have a 50 to 80% chance of developing breast cancer and a 15 to 60% chance of developing ovarian cancer in their lifetime (Lalloo and Evans, 2012). Identification of patients appropriate for genetic testing requires collecting a complete family history and then recognizing patterns such as multiple relatives with breast or ovarian cancer, young age at diagnosis, multi-ple cancers in single family members and/or male breast cancer, all of which may suggest a high risk of BRCA mutations. At this point in time, it is estimated that 95% of the BRCA1 and BRCA2 mutation carriers who have not yet developed cancer remain unidentified (Drohan et al., 2012). In other words, after 15 years in which genetic testing has been available, the great majority of women with muta-tions are not being managed in a way that might reduce their risk of cancer or death. The possible combinations that indicate risk are so complex, and physicians are so unprepared, that most patients are not identified until they have cancer, if even then.

388 CHAPTER 14 Clinical Decision Support for Personalized Medicine

14.2.3 Limited number of genetics expertsAs with most specialties in medicine, when an area requires in-depth knowledge to manage its complexity, a specialty is developed to fill the need. Genetics is no different. Medical genetics and genetic counseling are two specialties that have emerged in recent decades to fill this need. Medical geneticists are physicians trained to evaluate patients and to assess and manage the genetic contribution to diseases (American board of medical specialties, 2013). Most medical geneti-cists are specialists in other areas as well, often pediatrics. Genetic counselors are masters-level health professionals that specialize in communicating genetic infor-mation to patients and their families. They typically work closely with medical geneticists and other physicians to convey genetic test results (National Society of Genetic Counselors Inc, 2008) These specialties attempt to fill the need; how-ever, just considering the United States, there are only approximately 1,200 medical geneticists and 3,000 genetic counselors in the nation (American Board of Genetic Counseling Inc, 2013; Carroll, 2009). This equates to roughly 1 medical geneticist per 262,000 and 1 genetic counselor per 105,000 US citizens, which is clearly not sufficient to fill the need of genetics today, let alone in the future when the demand for genetics grows (Cooksey et al., 2005). To meet the growing demand from physi-cians in various specialties, particularly oncology, some midlevel providers such as nurse practitioners are obtaining advanced training in genetics for a particular set of diseases. However, these experts often lack comprehensive genetic knowledge, or knowledge of conditions that occur on the edges of their specialty.

14.3 Clinical decision support as a solution to achieving genomic and personalized medicine

As genetics becomes more mainstream and part of “everyday” medicine, having a medical geneticist or genetic counselor available to assist in the use and interpreta-tion of genetic information is neither feasible nor sustainable. Treating physicians must be able to leverage their patients’ genetic information in real time at the point of care to support accurate, personalized, clinical decision making. Given the limi-tations of most physicians in being able to master complex genetics on their own, many prominent investigators and thought leaders have identified health IT, and in particular clinical decision support (CDS), as a solution to overcome these barriers and the key to achieving personalized health care (Abrahams et  al., 2005; Glaser et al., 2008; Downing et al., 2009; Kawamoto et al., 2009; Hoffman and Williams, 2011; Snyderman and Yoediono, 2006; Al Mallah et al., 2010).

Personalized health care represents an ideal use case that must be managed effectively to be realized. Although the capacity of physicians to manage their patient’s genetic information themselves is limited, it can be facilitated with the assistance of information technology solutions such as CDS. To illustrate this, Figure 14.1 represents the barriers described above that hinder the realization of

38914.3 Clinical decision support as a solution

personalized health care, with CDS being a bridge capable of overcoming such barriers. In Chapter  2 we cited the study by Balas and Boren (Balas and Boren, 2000) that showed that it takes an average of 17 years for established medical knowledge to be translated from research findings into everyday clinical practice. Nonetheless, we believe that, given the substantial barriers described above, the translation of genetic knowledge to practice will take substantially longer (if at all) without the assistance of health IT and CDS (Balas and Boren, 2000).

14.3.1 History of CDS for personalized medicineGiven the great need for CDS in personalized medicine, Welch and Kawamoto conducted a systematic review of the published literature in this field (Welch and Kawamoto, 2012). Between 1990 and 2011, they identified only 38 primary research articles on the design, implementation, use, and evaluation of CDS sys-tems to support genetically guided patient care (see Figure 14.2). This amounts to less than two articles per year. Even in the year with the most publications on this topic (2011), only six primary research articles were identified. The study identi-fied 22 articles on CDS for genetically guided cancer management, six on CDS for pharmacogenomics, and ten on CDS for other personalized health care use cases. Recent years have seen an increasing shift in focus from CDS for family history (n = 23) to genotype-driven CDS (n = 16), and from stand-alone CDS (n = 30) to integrated CDS (n = 7). Of note, the review identified nine randomized controlled trials (RCTs) of the impact of CDS systems for personalized medicine, but most CDS interventions for personalized medicine have not yet been rigorously assessed for their clinical impact.

FIGURE 14.1

CDS as a bridge for realizing the promise of personalized medicine.(Reprinted with permission.)

390 CHAPTER 14 Clinical Decision Support for Personalized Medicine

14.4 Focal areas of CDS for personalized medicineHere we review several focal areas in which CDS has significant potential for sup-porting personalized health care, including family health history, genetic testing, pharmacogenomics, and whole genome sequencing. As could be expected, early CDS systems were primarily focused on family health history, since genetic testing had yet to be used on a widespread scale outside of genetic specialties.

14.4.1 Family health historyA complete family health history has long been identified as one of the most impor-tant tools for assessing disease risks (Rich et al., 2004). The collection and inter-pretation of family health history provides a cost-effective glimpse of the potential genetic risks possessed by a patient. Unlike genomic information, the family health history is able to capture how the genome interacts with social and environmen-tal factors to determine the phenotypes of family members. As germ line genetic mutations are passed from one generation to the next, so too are the phenotypic characteristics of such mutations. Indeed, a positive family history can be a stronger predictor than available genetic tests for several multifactorial diseases, such as dia-betes, heart disease, and cancer (Yoon et al., 2002).

While some might believe that the utility of the family health history is becom-ing less important with the advent of high-throughput genetic testing and the

16

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1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

# of publications

FIGURE 14.2

Publications on CDS for genetically guided personalized medicine.

(Reprinted with permission.)

39114.4 Focal areas of CDS for personalized medicine

imminent clinical adoption of whole genome sequencing, family health history will continue to remain relevant for a long time and will likely become more important in the future (Guttmacher et al., 2004). The complexities of genetics – in particu-lar, variable penetrance and variants of unknown significance – will require one’s genome to be analyzed in the context of the family history to increase the accuracy of assessment (Scheuner et al., 1997). Health IT and CDS solutions can be lever-aged to support the collection and interpretation of family health history in support of personalized medicine (Crane and Raymond, 2003).

Ideally, a clinician should collect a complete, three-generational family history from every patient to determine who is at increased risk for disease and who may be appropriate for genetic testing, referral to an appropriate specialist or genetic counselor, and/or personalized preventative measures. Unfortunately, in practice, family health history is poorly collected. Physicians typically collect family history from half of new patients and less than a quarter of returning patients (Acheson et al., 2000). The primary barriers identified include lack of time, lack of reimburse-ment, and lack of expertise (Rich et al., 2004; Acton et al., 2000; Bernhardt et al., 1987). Most family history discussions are limited to a few minutes – not enough time to collect a thorough family health history from the patient (Acheson et  al., 2000). Moreover, even when clinicians attempt to collect a family health history, patients are unprepared and fail to report important information accurately (Love et al., 1985).

In addition, family health history has been traditionally collected on paper, ren-dering it unusable for automated CDS. Recently, pedigree drawing software has become available; however, these software packages are seldom used at the pri-mary care level due to the large amount of data entry required by the clinician or staff and the limited time available. A proposed solution to obtain widespread elec-tronic collection of a complete family history may be to electronically gather the information directly from the patient (Guttmacher et al., 2004; Rich et al., 2004). A number of patient-centered family history collection tools such as the Surgeon General’s My Family Health Portrait, (Online version of “My Family Health Portrait” available in English and Spanish, 2006) Hughes Risk Apps, (Ozanne et al., 2009) Health Heritage (Cohn et al., 2010), and ItRunsInMyFamily.com have been developed to allow data entry by the patient, at home or in the clinician’s waiting room. Figure 14.3 provides an example of one such system. The goals of these solutions are to provide a complete family health history in a computable format, decrease the required staff workload, and support clinicians’ decision-making abili-ties through CDS.

14.4.1.1 Assessing risk based on family historyFamily health history is most often used as a preliminary risk assessment tool. As with genetic information in general, the interpretation of a family health his-tory is not a trivial task. While simple single-gene Mendelian disorders follow known inheritance patterns, such as autosomal dominant, autosomal recessive, and X-linked, many adult-onset conditions do not follow strict inheritance patterns,

392 CHAPTER 14 Clinical Decision Support for Personalized Medicine

including diabetes, heart disease, and cancer. Each disease will have its own unique risk profile based on the constellation of affected family members (Taylor et  al., 2010). Typically, the greater the number, the more closely related, and the younger the age of onset of affected relatives in the family history, the greater the risk of disease to the proband Scheuner et al., 1997. However, the magnitude of risk var-ies greatly among diseases. Furthermore, certain genetic mutations, particularly in cancer, are known to produce variable phenotypes among family members. For example, a Jewish individual with breast cancer at age 46 who also has melanoma and pancreatic cancer in the family is at high risk of having a BRCA2 mutation and thus ovarian cancer as well. Clinicians must be aware of such subtleties in genetic syndromes, as they may otherwise miss the clues in the family history indicative of a particular genetic contribution.

There is a significant need for CDS to support the complexities of interpret-ing family health history. CDS can be used to assess family history to help deter-mine who needs genetic counseling or testing, to help determine the significance

FIGURE 14.3

Screenshot of the Surgeon General’s My Family Health Portrait.(Permission granted.)

39314.4 Focal areas of CDS for personalized medicine

of different mutations, and to help determine the best management strategy. When it comes to the interpretation of the family history, there are a number of guide-lines and models available for many genetic conditions. An area where models and guidelines are quite advanced is in mutations of the BRCA1 and BRCA2 genes. Several models have been developed to estimate the risk of having a BRCA muta-tion, including but not limited to BRCAPRO, the Myriad model, the Tyrer-Cuzick model, and BODICEA (Berry et  al., 1997; Frank et  al., 1998; Tyrer et  al., 2004; Antoniou et al., 2004). These models require the use of computer software, since the algorithms utilized are complex. See Figure 14.4.

In addition, a number of organizations have developed guidelines to help deter-mine which patients are eligible for testing, including the National Comprehensive Cancer Network (NCCN), the US Preventive Services Task Force (USPSTF), and the American College of Medical Genetics (ACMG) (National Comprehensive Cancer Network, 2013; U.S. Preventive Services Task Force, 2012; American College of Medical Genetics, 1999). Although the guidelines are meant to be used from memory or by using paper reminders without a computer, in reality they are so detailed and complex that they are seldom remembered or acted upon in the midst of a busy clinic. Computerization of the guidelines through CDS is therefore required. To this end, it is suggested that organizations publish guide-lines not only as prose, but also in a machine-readable format ready for use in CDS (Dufour et  al., 2006). Ideally, these machine-readable guidelines should be

Risk of Cancer By Model

Model Five Year Risk Lifetime Risk

BRCAPRO

Gail

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Claus

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1%

4.9%

1.4%

26.1%

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23.1%

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k of

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ast C

ance

r

Age

FIGURE 14.4

Risk of breast cancer over time using various risk models as displayed in Hughes Risk Apps.(Permission granted.)

394 CHAPTER 14 Clinical Decision Support for Personalized Medicine

available via a central web-accessible service, as will be described in further detail later in the chapter.

Ideally, family history and other risk factors could be submitted to various risk modeling and guideline services via a single software interface, with patient-specific assessments and recommendations returned in a common format. By tak-ing a structured family history data and running it through established risk models and guidelines, the CDS software can help primary care clinicians identify which patients require testing or consultation by the specialist. Once the patient gets to the specialist, the genetics professional or specialist has more time to collect a more thorough and complete family history with additional details, thereby increasing the accuracy of the models and providing the patient with the best chance of having the right tests ordered and correct diagnosis made.

Emery and colleagues undertook some of the earliest and most comprehen-sive research on CDS tools for family history collection and risk assessment in the United Kingdom in the late 1990s through the early 2000s (Emery 1999; Emery et  al., 2000; Glasspool et  al., 2001; Coulson et  al., 2001; Emery 2005; Emery et al., 2007). The earlier version of the system they developed is known as RAGs, and the later version is known as GRAIDS. Their system was shown in an RCT to significantly increase the number of appropriate referrals for genetic counseling according to evidence-based practice guidelines (Emery 2005; Emery et al., 2007). More recently, two software packages have been developed to help analyze the risk of being a BRCA mutation carrier: CancerGene and HughesRiskApps (HRA) (Ozanne et al., 2009; UTSW Medical Center, 2004). Both run the major risk mod-els, and recent development in HRA has begun to explore translating several guide-lines (NCCN, etc.) into machine-readable format and developing the algorithms needed to interpret the family history in the context of the guidelines.

As an example of how HRA makes use of clinical guidelines, the prenatal mod-ule of HRA looks at the ethnicity and family history of a pregnant woman to help determine what prenatal genetic tests might be appropriate. Prose guidelines were translated into machine-readable format and the patient's family history and ethnic-ity (also entered by the patient) were then compared to these guidelines in order to suggest management and testing. For example, Ashkenazi Jews are routinely identi-fied for possible Tay-Sachs testing, while women with a family history of mental retardation in male relatives are flagged for fragile X testing.

14.4.1.2 Family history standardsAlthough a number of solutions have been available to collect family health his-tory and provide initial risk assessment, until just a few years ago there were no standards available to collect information in a consistent way that would support interoperability and reusability of the family history data and the interpretative algorithms. Normally, the family health history would be represented as free text within the electronic health record (EHR) or using generic terms such as “family history of cancer” with no granularity as to which relatives had the disease and their age of onset, rendering the information inadequate for use in constructing a

39514.4 Focal areas of CDS for personalized medicine

complete pedigree necessary for accurate risk assessment. Central to developing a scalable approach to CDS for family health history is the definition and widespread adoption of a set of standards for the consistent collection and storage of this infor-mation. To address this need, the American Health Information Community (AHIC) convened a workgroup to define the core data set needed for family health history, published in 2008 (Family Health History Multi-Stakeholder Workgroup, 2008). In parallel, the Health Level 7 (HL7) Clinical Genomics Special Interest Group has developed a standard pedigree model that facilitates the exchange of family health history information in a way that supports pedigree construction and CDS. The standard was subsequently accepted by the American National Standards Institute (ANSI) and the Healthcare Information Technology Standards Panel (HITSP). This standardization facilitated the development of a number of patient-centric family health history collection tools adhering to the core data set. Several family health history software packages have adopted the HL7 pedigree standard, including the My Family Health Portrait tool and HughesRiskApps.

Although the AHIC core data set, HL7 family pedigree model, and subsequent family history collection tools have made it easier to collect family history infor-mation from patients, no EHR vendor, to date, has adopted these standards. In the US, Stage 2 Meaningful Use requirements are beginning to mandate the collec-tion of a structured family health history; however, only first-degree relatives are required (Centers for Medicare & Medicaid Services, 2012). Moreover, the native EHR interfaces are typically not user-friendly and are time-consuming to use. Furthermore, the efforts of the clinician in filling out the family history section is somewhat wasted, since there is typically no CDS available to analyze the data, nor is a pedigree drawn. A modular approach to EHR integration might abrogate this problem (Drohan et al., 2012). As one example of an effort in this area, the Health Heritage family history platform allows patient-entered family history and has been integrated with the Epic EHR system. Unfortunately, this system pushes data into Epic as a free text note rather than as structured data set that can populate the fam-ily history section of the EHR (Cohn et al., 2010).

14.4.2 Genetic testingGenetic testing is a laboratory technique used to assess the genetic sequence or gen-otype of a patient. It might assess a single gene or multiple genes. The genetic test-ing of patients has only been around since the 1980s but has grown significantly, with the number of diseases for which genetic testing is available more than tripling in the past ten years. As of 2012, there are over 2,900 genetic tests available for clin-ical use (Genetests medical genetics information resource (database online), 2013). The cost of genetic testing typically ranges from hundreds to thousands of dollars.

14.4.2.1 Appropriate use of genetic testingGenetic testing is primarily used to confirm a disease diagnosis or to assess genetic disease risks (Collins, 2003). As a clinician performs a differential diagnosis or disease

396 CHAPTER 14 Clinical Decision Support for Personalized Medicine

risk assessment on a patient, a genetic test is used for confirmation or to mitigate uncertainty. The current relatively high cost of genetic testing typically leads clinicians to make a reasonable assessment based on phenotypic and family history information prior to using it. For example, in the case of risk assessment for breast cancer, if a woman has a significant family history of breast and ovarian cancer, the clinician has reason to believe she has a high risk of carrying a BRCA mutation. A BRCA test can be ordered to confirm the presence or absence of the mutation. The test currently costs several thousand dollars; as a result, the test is typically ordered after a women pre-sents to the clinic with breast or ovarian cancer and/or with an extensive family history of breast and ovarian cancer. Furthermore, a number of barriers exist that hinder the consistent use of genetic testing in the clinic (Weldon et al., 2012).

Genetic testing is only valuable when ordered appropriately and interpreted accurately. Clinicians must accurately identify patients who might benefit from genetic testing. At this time it is not economically feasible to provide population-wide genetic testing for adult onset disorders for both technical and social rea-sons. There are a number of considerations that must be taken into account when ordering a genetic test (Evans et al., 2001). For example, once a patient is appro-priately identified for genetic testing, the clinician must either provide counseling and test those individuals him/herself or refer the patient to a specialist for coun-seling and testing. The clinician must determine who in the family to test, and what gene to test for. In certain cases, it makes most sense to test affected relatives for the presence of a genetic variant before testing the proband (Petersen et al., 1999). Furthermore, to illustrate the complexity of appropriately ordering genetic tests, a patient with a strong family history of breast cancer should be tested for BRCA1 and BRCA2, but if she has macrocephaly, she should also be tested for PTEN – the gene that causes Cowden's syndrome. If breast cancer has occurred under age 30 in the family, she should also be tested for p53 – the gene that causes Li-Fraumeni Syndrome. If the breast cancers were lobular and there is gastric cancer in the fam-ily, she should be tested for CDH1 – the hereditary diffuse gastric cancer syndrome. Failing to order the appropriate genetic test could result in a false sense of security regarding disease risk. These testing nuances are quite complex and present an ideal situation for CDS to support clinical decision making.

14.4.2.2 Result reportingWhen the test is ordered appropriately, a tissue sample (most often blood) is sent to the laboratory for genetic testing. The laboratory will analyze the genetic sequence and produce a report (typically paper or PDF) with interpretation based on the available literature and genetic mutation knowledge bases. The variant could fall into a number of possible categories: normal sequence of the gene, known disease-causing variant, likely cause of disorder, unlikely to cause disorder, known benign variant, and variant of uncertain significance. Furthermore, different mutations within the same gene can produce different risks or phenotypes. As a result, the potential results of genetic testing are so complex that many doctors misinterpret the results, to the detriment of their patients’ health (Giardiello et al., 1997).

39714.4 Focal areas of CDS for personalized medicine

Furthermore, the laboratory interpretation of a genetic variant represents a point-in-time assessment of the known knowledge base about the variant. However, if new information regarding a variant becomes available after the report has been sent out, it is the laboratory’s responsibility to contact the treating physician and report a change in variant significance (Richards et  al., 2008). This amounts to a challenging task for laboratories to manage, particularly when they are not being reimbursed for these subsequent reporting efforts. As the number of tests and labo-ratories grow exponentially, it will become unfeasible to report and update genetic test results under the current paradigm.

A group at Partners Healthcare in Boston has developed a laboratory reporting tool called GeneInsight that allows a lab to change variant significance when new knowledge is available, and the changes go out to all providers with patients with the particular variant (Figure 14.5) (Aronson et al., 2011; Neri et al., 2012). Such a tool will become a necessity, particularly when whole genomes will be analyzed and reported and the pace of genetic knowledge discovery accelerates. However, the manual gene review process performed by each lab will become unsustainable and ineffective. Ideally, a centralized variant knowledge base, similar to Online Mendelian Inheritance in Man (OMIM), (McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, 2013) Human Gene Mutation Database (HGMD), (Cooper et  al., 2012) and ClinVar (National Center for Biotechnology Information, 2012), should be set up to manage the genome knowledge and “push out” variant updates automatically to subscribing laboratories and clinics.

14.4.2.3 Management of patients’ genetic risksAs patients are tested and results reported, a proper disease management strategy based on the information needs to be in place. There are often multiple treatment options available for particular genotypes. For example, patients with BRCA1 or BRCA2 can be managed by chemoprevention, intensive screening, or by prophy-lactic surgery. Which treatment option is best for an individual patient is a complex

FIGURE 14.5

Screenshot of the GeneInsight Clinic interface.(Reprinted with permission.)

398 CHAPTER 14 Clinical Decision Support for Personalized Medicine

decision, and CDS can significantly facilitate this decision-making process. Even the seemingly straightforward result of a deleterious mutation brings up a multitude of treatment questions. How is it best to manage this patient? Should she have her breast removed to prevent cancer or will chemoprevention and screening suffice? What are the risks and benefits of chemoprevention in her situation? What is the patient’s preference? In addition, since this deleterious mutation places her family at risk, testing of family members (cascade testing) is essential. But then, which family members should be tested? At what age? How does one track who has and has not been tested? There is a role for CDS to support the decision-making process at every step in this process. A number of CDS tools for breast cancer are available to assess some of these questions (Schwartz et al., 2009; Glasspool et al., 2007).

Although several professional societies and government agencies have pub-lished guidance as to how to manage women at high risk, these publications are uniformly prose-based and do not address well the nuances of individual patient management. For example, the NCCN recommendation to consider prophylactic oophorectomy in BRCA1 carriers who are over age 35 and have completed child-bearing makes sense, unless the patient is 90 years old, has stage-4 triple-negative breast cancer or has significant comorbidities. There is a substantial need to make the available guidelines machine-readable and to increase the number of factors considered in making the recommendation.

An attempt to do this has been made by HRA, which takes into account muta-tion risk, genetic test results in the patient and family, and other factors such as smoking history to come to a preliminary suggestion as to the best management for the individual (Figure 14.6). In addition, HRA attempts to monitor the uptake of cascade testing in the family, a crucial step in the eradication or amelioration of genetic diseases. Although the CDS is based on an interpretation of the published guidelines and literature by the developer of HRA, it would be preferable if the guideline-developing entities took on this effort. None have stepped forward as of this writing.

14.4.2.4 Case study: HughesRiskAppsAt the Newton Wellesley Hospital (NWH), in Massachusetts, every woman having a mammogram has her family history and breast cancer risk factors entered into HRA's Breast Imaging Module as structured data. HRA sends that data using an HL7 message to the Risk Web Service housed at the Dana Farber Cancer Center campus, where breast cancer risk models (BRCAPRO and Tyrer-Cuzick) and colon cancer risk models (MMRPRO) are run, producing an estimate of the risk of carry-ing a mutation in one of the respective genes. The results are packaged into an HL7 message and returned to HRA at NWH, where the patient data and the results of the risk calculations are printed out for use by the radiologist.

Patients with a risk of mutation of 10% or greater are flagged, and once each week, letters suggesting to patients at risk that they make an appointment at the Risk Clinic to discuss their risk are automatically generated by HRA. In addition, the primary care providers receive letters letting them know about their specific

FIGURE 14.6

HRA displaying the likelihood of mutation for every family member as estimated by BRCAPRO.(Permission granted.)

400 CHAPTER 14 Clinical Decision Support for Personalized Medicine

patients at risk. In the first year of the program, 32,966 risk calculations were run on 25,763 patients, and 915 patients were identified and informed of their elevated risk (Ozanne et al., 2009). The system tracks which patients have been flagged as having elevated risk but have yet to be seen in the Risk Clinic.

Patients who respond to the letter and make an appointment are seen at the Risk Clinic, where a more thorough family history is collected into HRA's Risk Clinic Module, enhancing the data collected in breast imaging. HRA sends those data using an HL7 message to the Risk Web Service housed at the Dana Farber Cancer Center campus, where breast cancer and colon cancer risk models are run on the enhanced data set, producing an estimate of the risk of carrying a mutation in one of the respective genes. The results are packaged into an HL7 message and returned to HRA at NWH, where they are displayed for the nurse practitioner or genetic counselor with suggestions of the best management for the individual based on her breast and ovarian cancer risk. See Figure 14.7.

14.4.3 PharmacogenomicsPharmacogenomics is the ability to personalize drug selection and dosing to the unique genetic characteristics of each patient (Licinio and Wong, 2002). Genetic variants in drug-metabolizing proteins can affect how quickly or slowly a drug is activated (or deactivated) within the body. Slow-metabolizing gene variants can result in elevated levels of the therapeutic agent persisting in the body longer than expected, such that subsequent normal doses can yield toxic levels of the drug and associated complications. Conversely, rapidly-metabolizing gene variants can deac-tivate drugs so fast that they are unable to provide therapeutic benefit. In such cases, higher doses of the drug are necessary to reach the therapeutic window (the range between no therapeutic effect and overdose). By assessing for such gene variants prior to drug therapy, the clinician can reach an optimal therapeutic dose faster. Moreover, adverse events and ineffective treatment regimens can be avoided, saving lives and unnecessary costs.

One well-studied pharmacogenomics example is warfarin (Coumadin) and its association with the CYP2C9 gene. Warfarin, a highly prescribed blood thinner, has a narrow therapeutic window and causes thousands of adverse drug events every year due to inadvertent overdosing in individuals with slow metabolizing enzymes, in par-ticular the CYP2C9⁎3 variant (Aithal et  al., 1999). Beyond genetics, age, weight, medications, environmental influences, ethnicity, and many other factors influence appropriate dosing levels. As a result, accurate warfarin dosing is often a trial-and-error exercise, sometimes leading to serious complications or prolonged suboptimal therapy. Nevertheless, models are available to help clinicians accurately dose warfa-rin for a patient. However, these models are often too complex for clinicians to apply by hand. To meet this need, a number of pharmacogenomics CDS dosing calculators are available. One such example is the website WarfarinDosing.org, which allows users to enter pertinent patient information, including genotype, and provides a rec-ommended dose based on the information provided. See Figure 14.8.

FIGURE 14.7

Personalized recommendations based on family health history and genetic test results in HRA.(Permission granted.)

FIGURE 14.8

Screenshot of WarfarinDosing.org including input fields (left) and results (right).(Permission granted.)

40314.4 Focal areas of CDS for personalized medicine

This CDS tool is a valuable resource to clinicians. However, the current set up requires clinicians to step out of their clinical workflow and manually reenter infor-mation already available within the EHR. This is a barrier to widespread adoption (Kawamoto et al., 2005). Ideally, this CDS logic should interface and automatically leverage data within the primary clinical information system. Such an approach was demonstrated in a prototype developed by Kawamoto and colleagues that uses a service-oriented architecture (SOA). See Figure 14.9. This approach leverages patient information already in the EHR including the genetic test results, and sends the data as a message to WarfarinDosing.org, which then provides recommended dosing back for use within the EHR. This solution allows the constantly evolving pharmacogenomic knowledge to be maintained by a third party.

Another significant barrier to the adoption of pharmacogenomics is the lack of availability of the patient’s genetic information. When a drug is ordered, clini-cians typically cannot afford to wait three to four days for a several hundred dollar genetic test result to return with pertinent genotype information. As a result, the use of pharmacogenomics today is largely limited. However, as part of the eMERGE Network, a group at Vanderbilt is prospectively genotyping patients with a multi-plex array of pharmacogenomics genetic markers and storing the information in the EHR (Pulley et al., 2012). See Figure 14.10. When the clinician prescribes the antiplatelet drug clopidogrel, a CDS system assesses the patient’s genotype and alerts the clinician if the patient has a variant genotype associated with adverse drug effects. Alternative therapy recommendations are also provided.

FIGURE 14.9

Pharmacogenomics CDS integrated into the EHR workflow.(Permission granted.)

404 CHAPTER 14 Clinical Decision Support for Personalized Medicine

In summary, for pharmacogenomics to be effectively used on a widespread scale, genetic information must be readily available and CDS needs to be inte-grated within the clinical workflow. Furthermore, due to the evolving nature of pharmacogenomics dosing logic, it would be ideal for the CDS knowledge to be maintained by a third party. These examples illustrate that efforts are being taken to realize pharmacogenomics in the clinic; however, much more work in this space still needs to be done (Welch and Kawamoto, 2012).

14.4.4 Whole genome sequencingWhile genetic testing is a powerful tool used for diagnosis and risk assessment, as previously described, a number of limitations and barriers to consistent and proper use of genetic tests still exist in health care today (Weldon et al., 2012). The “holy grail” of genetics is the ability to leverage one’s full genome sequence for clinical care. This approach has the potential to overcome many traditional barriers and limi-tations of current genetic testing, such as the difficulty of obtaining reimbursement for individual genetic tests, the time lag from when a test is ordered to when a result is

FIGURE 14.10

Screenshot of a pharmacogenomics CDS alert in the EHR for clopidogrel at Vanderbilt.(Permission granted.)

40514.5 Complexities and considerations

available for the decision-making process, and the potential for clinicians to not know or to forget that a specific genetic test is available and relevant to a particular case.

Until very recently, the clinical application of one’s full genome informa-tion was beyond reach for most, due to prohibitive financial expense and time. However, with innovative technology advances over the past decade, the cost and time to sequence an entire genome is quickly reaching the point where a genome can feasibly be sequenced and used for clinical diagnosis (Wetterstrand, 2013). In fact, recent examples have illustrated how full genome sequences can be used for clinical diagnosis (Rope et  al., 2011; Talkowski et  al., 2012; Roach et  al., 2010). While these examples represent rare, complex cases, the availability of the full genome sequence for the “average” patient is rapidly approaching. In fact, the cost of sequencing a person’s entire genome will likely soon drop below the cost of a single genetic test, potentially motivating labs to do a full genome sequence analy-sis instead of the single gene test (McGowan).

While the ability to have full genome data available for the average patient is an exciting prospect, it represents a new set of challenges that must be overcome in order to be effectively used. For example, there are roughly three million vari-ants in every individual’s genome, most of which are benign, but some of which are clinically relevant (McKernan et al., 2009). This is an insurmountable amount of data for a clinician to work through without the aid of computer support. Several genome interpretation services are becoming available which leverage public and proprietary genome knowledge bases to filter out benign variants and identify clini-cally important variants (Bio, 2013; Omicia, 2013; Knome, 2013; GenomeQuest, 2013). These services provide a computer printout or an online portal that allows clinicians to review a patient’s relevant variants.

Although these genome interpretation services help clinicians distill the important variants within the genome, they still represent a process that requires a clinician to manually assess a patient’s genome through proprietary, stand-alone software. Ideally, this genome information should be structured to support auto-matic queries during the clinician workflow within the EHR. For example, when a clinician orders the drug warfarin, a CDS system could automatically query the patient’s genome in real time to check for variants that could produce adverse drug events and alert the clinician immediately within the clinical workflow. Such a capability is ultimately where health IT needs to be for the genome to be used effectively for personalized care. CDS for whole genome sequencing is still very nascent in its development but is clearly an important area for future research (Welch and Kawamoto, 2012).

14.5 Complexities and considerationsWe now present several important considerations regarding CDS for genomic and personalized health care.

406 CHAPTER 14 Clinical Decision Support for Personalized Medicine

14.5.1 Genomics knowledge baseAs described above, the knowledge of genetics is constantly evolving. It is impos-sible for a clinician to keep track of every gene, every variant, the classification of each variant, and changes to the classification of each variant. It is beyond the capacity for any one EHR or CDS solution to solve all the needs for genomics. Because the knowledge base is very broad and constantly evolving, to be most effective and accurate, curation of the knowledge base will require significant efforts by large groups of domain experts. Public and proprietary genomic variant knowledge bases such as OMIM, HGMD, and ClinVar, and laboratory-developed knowledge bases must be leveraged in an automated fashion to help clinicians accu-rately and efficiently interpret genetic variant information in real time.

Likewise, CDS knowledge bases need to be developed to help clinicians use genetic information to provide appropriate personalized health care through models and guidelines. These knowledge bases will need to replace what are currently only human-readable guidelines. Furthermore, most guidelines are written in a simpli-fied format, limiting the number of data points to what a human can manage. This has been shown to be an important limitation when deciding on how to optimally improve care quality while decreasing cost (Shaya and Chirikov, 2011). We need to develop a complete set of trusted, high-quality CDS knowledge bases in machine-interpretable formats.

Ideally, every guideline-developing organization (e.g. the American College of Surgeons, the American Society of Clinical Oncology, the American College of Medical Genetics) will develop standards-based, machine-readable knowledge bases to replace the prose clinical guidelines they generally distribute currently. Several potential candidates exist for representing computer-interpretable clinical guidelines (see Chapter 16) and for representing other aspects of medical knowl-edge in a standardized form (see Chapter 18). These machine-readable knowledge bases should then be available for any external system to access. CDS systems need to assess the clinical situation and compare it automatically against the avail-able machine-readable knowledge base to determine the best diagnosis or the best course of action. Furthermore, third-party model developers and knowledge engi-neers such as WarfarinDosing.org should expose their knowledge in a standard way to support service-oriented architecture capabilities.

14.5.2 Software architectures for scalable knowledge dissemination

As relevant knowledge is being curated into centrally managed repositories, scal-able approaches will be needed for disseminating knowledge into various EHR systems to enable CDS (Kawamoto et al., 2009). Currently, there are two leading potential approaches for enabling such scalable knowledge dissemination: (a) the encoding of the knowledge into a standardized, structured knowledge artifact that is then disseminated to various systems and consistently leveraged, and (b) the

40714.5 Complexities and considerations

provision of centrally managed patient guidance services that obtain patient data as the input and return patient-specific conclusions as the output (see Chapter 29 on integration of knowledge resources). An example of the former would be if geneti-cally guided breast cancer management algorithms were encapsulated in a com-monly agreed-upon knowledge representation format such as Arden Syntax, and then distributed to various EHR systems to implement.

As an example of the latter approach, an EHR or software package serves as the interface to the user, who enters risk factors and family history as part of his or her workflow. The information is then packaged as an HL7 pedigree model message and sent to the Risk Web Service, where several major risk models are run on the data (Parmigiani et al., 2012). The outputs of models are packaged up into an HL7 pedigree model message and returned to the originating software, where they are displayed to the user. This approach has the advantage that all models are centrally curated, so the latest version of each model is always used. In addition, this allows the EHR or invoking software package to determine the user interface. The service is agnostic to how the data are collected or displayed, as a result of which various packages can take different approaches to the collection and visualization of data. As discussed in the Knowledge Integration chapter, there is active work ongoing within the CDS community to advance both of these approaches to scalable knowl-edge dissemination.

14.5.3 StandardsWith the number of disparate health IT systems in health care today, standards are necessary to achieve the goals previously described. Genetic information is no different, and efforts are underway within HL7 and related standards develop-ment organizations to enable standardized representation of clinical and genetic data, as well as the standardized use of those data to provide patient-specific care advice. While a discussion of these standards in general is covered in depth in the Standards chapters, we note here that clinical genomics has been a particularly active area of standardization in recent years.

14.5.4 EHR systemsEHR vendors must begin to take the genomic age seriously. No current EHR sys-tem has incorporated a standards-based representation of family health histo-ries and genetic sequences. Every EHR system should move towards capturing standards-based, structured family histories and genotype data suitable for CDS. Moreover, each EHR system vendor cannot possibly develop and manage its own CDS for genetics, nor expect its health care organization clients to be able to do so. Therefore, EHR systems will need to leverage external CDS resources through the incorporation of modular components and service-oriented architecture (Drohan et al., 2009).

408 CHAPTER 14 Clinical Decision Support for Personalized Medicine

14.6 ConclusionsGenomic and personalized health care offers great potential to increase the quality of care and reduce costs. The complexities of genomic information are beyond the capacity of clinicians to manage without the aid of health IT. CDS offers a powerful solution to overcome the barriers to genomic and personalized health care. However, CDS and health IT solutions for this domain are relatively nascent and limited. For CDS to become valuable for personalized health care, much effort needs to be made to develop the infrastructure and relevant applications to converge towards common, standards-based approaches to enable CDS for personalized medicine across various health care organizations and health information systems. For this to happen, the health IT industry must adopt common data standards, supporting genomic knowledge bases must be developed and maintained, and guideline development organizations need to author their guidelines in a computable, machine-readable format. There is still much work that needs to be done to make this a reality. Nevertheless, the impor-tance of such efforts cannot be over-emphasized, since the realization of personal-ized health care depends vitally upon CDS.

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