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OBJECTIVE: To create a cohort for cost-effective genetic research, the Mayo Genome Consortia (MayoGC) has been assembled with participants from research studies across Mayo Clinic with high-throughput genetic data and electronic medical record (EMR) data for phenotype extraction. PARTICIPANTS AND METHODS: Eligible participants include those who gave general research consent in the contributing studies to share high-throughput genotyping data with other investigators. Herein, we describe the design of the MayoGC, including the current participating cohorts, expansion efforts, data processing, and study management and organization. A genome-wide association study to identify genetic variants associated with total bilirubin levels was conducted to test the genetic research capability of the MayoGC. RESULTS: Genome-wide significant results were observed on 2q37 (top single nucleotide polymorphism, rs4148325; P=5.0 × 10 –62) and 12p12 (top single nucleotide polymorphism, rs4363657; P=5.1 × 10 –8) corresponding to a gene cluster of uridine 5′-diphospho-glucuronosyltransferases (the UGT1A cluster) and solute carrier organic anion transporter family, member 1B1 ( SLCO1B1), respectively. CONCLUSION: Genome-wide association studies have identified genetic variants associated with numerous phenotypes but have been historically limited by inadequate sample size due to costly genotyping and phenotyping. Large consortia with harmonized genotype data have been assembled to attain sufficient statistical power, but phenotyping remains a rate-limiting factor in gene discovery research efforts.
The EMR consists of an abundance of phenotype data that can be extracted in a relatively quick and systematic manner. The MayoGC provides a model of a unique collaborative effort in the environment of a common EMR for the investigation of genetic determinants of diseases. ABI = ankle-brachial index; BORA = Biologically Oriented Repository Architectures; DUA = data use agreement; eMERGE = Electronic Medical Records and Genomics; EMR = electronic medical record; GENEVA = Gene Environment Association Studies; GWA = genome-wide association; MayoGC = Mayo Genome Consortia; PAD = peripheral arterial disease; SNP = single nucleotide polymorphism Genome-wide association (GWA) studies have identified genetic variants associated with numerous diseases and phenotypes. This agnostic approach to discovering genetic variants important to diseases has been historically limited by inadequate sample size due to costly genotyping and phenotyping. Traditionally, GWA studies have been conducted in research cohorts designed to capture and measure outcomes and phenotypes specific to one disease domain.
This has prompted the formation of large consortia comprising several research cohorts with harmonized genotype data and similar phenotype measurements to attain the statistical power required for genetic variant discovery. Despite the high initial cost of genotyping, the static nature of germline genotypes allows such data to be mined and fitted to pursue various hypotheses, including those unrelated to the original study. As such, a collection of genotype data from existing GWA studies would be a valuable cost-saving resource for genetic research. In contrast, phenotype acquisition and standardization across different studies, which has been time-consuming, laborious, and prone to error, is a rate-limiting factor in gene discovery research efforts. The electronic medical record (EMR) consists of an abundance of phenotype data that can be extracted in a relatively quick and systematic manner.
The Electronic Medical Records and Genomics (eMERGE) Network of the National Human Genome Research Institute (for more information, see ) is a national consortium formed to develop, disseminate, and apply approaches for genetic research within EMR systems., Successful use of this approach in the eMERGE Network has inspired the creation of the intramural Mayo Genome Consortia (MayoGC). The goal of the MayoGC is to assemble a large cohort of participants from research studies across Mayo Clinic with high-throughput genetic data and to use EMR for phenotype extraction for cost-effective genetic research. Herein, we describe the design of the MayoGC, including the current participating cohorts, expansion efforts, data processing, and study management and organization.
As a test of the genetic research capability of the MayoGC, we conducted a GWA study to identify genetic variants associated with total bilirubin levels. Bilirubin levels have a large variability in the population, with heritability of roughly 0.50. Two previous GWA studies identified variants from similar genomic locations with strong and moderate effects on bilirubin levels, making this phenotype an ideal candidate for testing.
The MayoGC provides a model of a unique collaborative effort in the environment of a common EMR for the investigation of genetic determinants of diseases. PARTICIPANTS AND METHODS MayoGC is a large cohort of Mayo Clinic patients with EMR and genotype data. Eligible participants include those who gave general research (ie, not disease-specific) consent in the contributing studies to share high-throughput genotyping data with other investigators. This cohort is being built in 2 phases. Phase 1, which has been completed, includes participants from 3 studies funded by the National Institutes of Health, which sought to identify genetic determinants of peripheral arterial disease (PAD), venous thromboembolism, and pancreatic cancer, respectively, with a combined total sample size of 6307 unique participants. The eMERGE study contributed genotype data for 3336 participants with PAD and control participants recruited from Mayo Clinic's noninvasive vascular and exercise stress testing laboratories, respectively.
Peripheral arterial disease was defined by documentation of at least 1 of the following: (1) an ankle-brachial index (ABI) of 0.9 or less at rest or 1 minute after exercise, (2) the presence of poorly compressible arteries, or (3) a normal ABI but history of revascularization for PAD. Control participants had a normal ABI and no history of PAD. The GENEVA (Gene Environment Association Studies) Study of Venous Thromboembolism of the National Human Genome Research Institute enrolled consecutive Mayo Clinic outpatients with objectively diagnosed deep venous thrombosis and/or pulmonary embolism who resided in the upper Midwest and had been referred by a Mayo Clinic physician to the Mayo Clinic Special Coagulation Laboratory or to the Mayo Clinic Thrombophilia Center. A deep venous thrombosis or pulmonary embolism was categorized as objectively diagnosed (1) when it was confirmed by venography or pulmonary angiography or via a pathology examination of a thrombus removed at surgery or (2) if findings on at least 1 noninvasive test (compression duplex ultrasonography, lung scan, computed tomography, magnetic resonance imaging) were positive. Persons with venous thromboembolism related to active cancer were excluded.
A control group was prospectively recruited for this study. Control participants were frequency-matched to the age group (18-29 years, 30-39 years, 40-49 years, 50-59 years, 60-69 years, 70-79 years, and ≥80 years), sex, myocardial infarction or stroke status, and state of residence distribution of the cases. The study selected clinic-based controls using a database of persons undergoing general medical examinations in the Mayo Clinic Divisions of General Internal Medicine and Primary Care Internal Medicine.
Additionally, persons undergoing evaluation at the Mayo Clinic Sports Medicine Center and the Department of Family Medicine were screened for inclusion as control participants. Genotype data for 2497 participants were contributed by the GENEVA study. The Mayo Clinic Molecular Epidemiology of Pancreatic Cancer Study contributed genotype data for 613 control participants to the MayoGC. Details of this study have been described previously., In brief, patients scheduled for a general medical examination between May 2004 and February 2007 were recruited from the Divisions of General Internal Medicine and Primary Care Internal Medicine.
Control participants were frequency-matched to pancreatic cancer cases on the basis of sex, state of residence, age at recruitment, and race and ethnicity. At the time of recruitment, control participants had no personal history of cancer except nonmelanoma skin cancer. Phase 2 is under way with the goal of expanding the MayoGC by recruiting eligible patients from other studies funded by the National Institutes of Health.
All of the MayoGC studies were approved by the Mayo Clinic Institutional Review Board, and the participants from each involved study provided written and informed consent for general research. M ayoGC O rganization The MayoGC represents a voluntary collaboration of investigators across disciplines at Mayo Clinic. The organizational structure comprises a MayoGC Research Group and a Steering Committee. The Research Group is a collaboration of investigators with expertise in epidemiology, statistical genetics, and bioinformatics who are responsible for the creation, implementation, and maintenance of the consortia.
Specifically, members of this group are responsible for study recruitment, development of policies and procedures for widespread use of MayoGC data, harmonization of genetic and phenotype data, and maintenance and storage of study data. The Steering Committee consists of a member from each of the contributing studies and members of the Research Group. This committee is responsible for approving all study policies and procedures and recommending modifications to operational policy as needed. Furthermore, the committee reviews all research proposals that use MayoGC data. Via its representative on the Steering Committee, each contributing study may formally opt out of participation in research proposals that are in conflict with its goals. M ayo C linic B iobank The MayoGC is a passive collection of existing data and, as such, does not have stored biological samples on participants in the consortia. However, 6% of the participants in phase 1 were also enrolled in the Mayo Clinic Biobank.
The Mayo Clinic Biobank (for more information, see ) has enrolled more than 18,000 participants and has the goal of reaching 20,000 participants by the end of 2011 in an effort to support a wide array of health-related research studies throughout the institution. Study participants provide a blood sample for DNA and serum/plasma research, complete a health risk questionnaire, allow access to medical records, and consent to prospective follow-up for health outcomes. Thus, for the subset of the MayoGC participants in the Biobank, biological specimens are available. Likewise, genotype data is available through the MayoGC for users of the Biobank. D ata U se A greement To protect the confidentiality and privacy of the MayoGC, investigators who are granted access to MayoGC data will operate under a data use agreement (DUA). The DUA describes the terms and conditions for the following: data use and transferability, publication, termination/expiration of the data agreement, obtaining of informed consent from study participants, and compliance by data recipients with the requirements of the institutional review board of their home institution. Further, the DUA outlines the procedures for amending a current agreement and specifies how the failure to comply with the terms of the DUA will be addressed.
B ilirubin P henotype—A GWA A pplication For phase 1 of the MayoGC, all bilirubin levels clinically ordered from January 1, 1994, through August 31, 2010, were retrieved from a structured laboratory database. Extracted data included the test code and description, the date and time of the sample, the units of results, the associated reference range and indicators for low and high results, the laboratory accession number, and the results of the test in both character and numeric format. Bilirubin measurements were available for 4195 participants. Because our hypothesis focused on identifying genetic variants that affect bilirubin levels within the normal range, we excluded 726 participants who had at least 1 abnormal bilirubin level from the primary analysis. Of the 3469 participants with normal bilirubin levels, 58% had serial measurements of bilirubin.
This analysis includes only the first-ever bilirubin level measured after age 18 years. To further explore the genetic effects on bilirubin levels, secondary analyses included a GWA study of all 4195 participants without exclusions and 2 subsets in which we excluded participants with any abnormal test results for alanine aminotransferase, alkaline phosphatase, aspirate aminotransferase, or γ-glutamyltransferase on the same day as the bilirubin measurement (n=2427) or those without any abnormal liver test results within 1 year before or after the bilirubin measurement (n=2191).
G enotype H armonization Participants in the eMERGE Network and GENEVA were genotyped using Illumina HumanHap660-Quad chips (Illumina, San Diego, CA). Participants from the Mayo Clinic Molecular Epidemiology of Pancreatic Cancer Study were typed on the HumanHap550 and the Human 610-Quad chips., PLINK files, in which genotypes were coded as the number of minor alleles, were provided by the individual studies. Of the participants in phase 1 of the MayoGC, 60 also participated in 2 of the contributing studies, and 1 was involved in all 3 studies. These duplicated samples were used to check genotype concordance across studies as well as to inform flipping of minor allele/strand as necessary. Single nucleotide polymorphisms (SNPs) that had more than 2 discordant genotypes among the duplicates or that were monomorphic in all phase 1 samples were excluded from the dataset. Other commonly used SNP-filtering criteria were not imposed because of their dependency on the samples used for a specific hypothesis (ie, the availability of EMR data). We retained 1 duplicated sample with more nonmissing genotypes from each of the 61 study participants.
PLINK was then used for sample-wise quality control on the remaining data. Relatedness was determined on the basis of identity-by-descent estimates generated from the “-Zgenome” option in PLINK. Genotype data of trios from the Centre d'Etude du Polymorphisme Humain collected in Utah, USA, with ancestry from northern and western Europe were used to define an identity-by-descent threshold for relatedness. No cryptic relatedness was detected. Because the patterns of missingness differed somewhat across studies, we excluded samples on the basis of the notion of within-study outlier. This approach eliminated 12 samples with a comparably high missing genotype rate. On the basis of the genotype data, a large majority of the participants in phase 1 of MayoGC were of European ancestry.
The 48 samples in which population admixture was evident were filtered out. Cross-checking of the sex of study participants identified and removed 5 with a mismatch between the reported and deduced sex. These quality controls left 6307 study participants in whom 583,129 SNPs were available for analysis. No SNP-wise quality control filtering based on Hardy-Weinberg equilibrium or minor allele frequency was completed at this stage because these measures may change depending on the sample with the phenotype of interest. D ata M anagement and D ata S ecurity The Bioinformatics Core at Mayo Clinic has developed a system for the storage of processed genomics data.
The Biologically Oriented Repository Architectures (BORA) system includes components to facilitate the processing and analysis of data produced by high-throughput genomics platforms. BORA features fully deployed methods for data security and access control, including user authentication and user authorization. Authentication involves validation of the institution's user ID and password and verification that the user ID is included in the BORA list of authorized users. Authorization of data use is defined on a user and user group level.
A user or user group is provided access to explicitly defined samples or sample sets. Designation of specific user or user group authorization to access the MayoGC data is approved by the MayoGC Steering Committee. The BORA system stores and provides access to MayoGC genomics data that have been quality controlled and imputed following the described procedures. RESULTS The MayoGC phase 1 population is 44% female, has a mean age of 60.3 years, and is predominately white. As a whole, the cohort is a rich source of phenotypes, with a mean medical record length of 22.4 years. Bilirubin levels (0.1-1.1 mg/dL to convert to μmol/L, multiply by 17.104) were available for 3469 participants: 1726 in the eMERGE Network, 1316 in GENEVA, and 427 with pancreatic cancer.
Of participants in this group, 47% were women, the mean ± SD age at the time of first bilirubin measurement in adulthood was 57±12.3 years, and the mean ± SD bilirubin level was 0.56±0.20 mg/dL. All studies showed significantly higher bilirubin levels in men ( P. DISCUSSION Inspired by the National Center for Biotechnology Information's Database of Genotypes and Phenotypes, the MayoGC represents a model for genetic research that capitalizes on existing genetic data in the environment of a common EMR.
Furthermore, this cost-effective approach expands the use of genetic data, derived from available data within the medical record, to phenotypes unrelated to the original study. The utility of the MayoGC is demonstrated by genome-wide significant results for clinically ordered serum bilirubin levels that replicate findings from several cohort studies. In addition to classic gene discovery, other applications of the MayoGC resource include replication of results from other studies, association of SNPs with multiple phenotypes, and pilot studies for rare diseases. Recruitment is under way for phase 2 of MayoGC, which is intended to expand the cohort and to develop the infrastructure and processes needed for wider and long-term use of the resource. Lists the cohorts that have agreed to participate.
Phase 2 is expected to be completed by the end of 2011 and will include more than 10,000 participants with harmonized and imputed high-throughput genetic data. This effort requires processing of a greater variety of platforms. Thus, a fully automated workflow for forward strand mapping and imputation of genotyping data will be implemented. The workflow will standardize the genotyping data stored in BORA to enable integrated analysis of genotypes from different platforms and from multiple studies collected at Mayo Clinic.
In addition, current genotyping-focused quality control procedures will be extended with generalized procedures for population structure validation as well as patient relationship cross-checking to monitor for duplicates and relatedness. BORA is also being interfaced with the Enterprise Data Trust, a central data warehouse in which minable clinical information on Mayo Clinic's patients will be stored and can be accessed. The interface between the 2 systems will greatly facilitate phenotype-genotype association and replication studies. Significant associations were observed on chromosome 2 corresponding to the UGT1A cluster ( P=5.0 × 10 –62) and chromosome 12 in the SLCO1B1 gene ( P=5.1 × 10 –8), confirming previous results in GWA and linkage studies., The UGT1A cluster encodes 9 transferase genes ( UGT1A.
1, 3, 4, 5, 6, 7, 8, 9, 10), of which 8 are known active gene transcripts. All of the UGT1A proteins are identical in the amino acid sequence encoded by exons 2 through 5 with unique alternate first exons.
The glucuronidation of bilirubin by UGT1A and specifically by UGT1A1 is essential for the elimination of bilirubin from the body. Three heritable forms of hyperbilirubinemia (ie, Crigler-Najjar syndrome types 1 and 2 and Gilbert syndrome) all result from mutations in UGT1A1, and candidate gene studies in multiple ethnic populations have shown that UGT1A1 is the major gene influencing total bilirubin levels. The SLCO1B1 locus encodes a protein that mediates sodium-dependent uptake of bilirubin. Although not as influential as UGT1A1, SLCO1B1 is a major contributor to total bilirubin levels and variations in it have been associated with hyperbilirubinemia. The MayoGC is a collaborative effort for data sharing and, as such, has no stored DNA for the study participants for de novo genotyping. Likewise, phenotyping is restricted to data available within the medical record.
For a subset of MayoGC participants, biological specimens are available through the Mayo Clinic Biobank. Biological specimens may also be available in the contributing studies; however, large-scale de novo measurements are likely not practical. The ability to contact and consent participants for additional measurements is possible for MayoGC participants whose contributing study consent did not prohibit recontact or for those who are enrolled in the Mayo Clinic Biobank.
Despite these limitations, which are common to genetic consortia, the MayoGC leverages the success of EMR-derived phenotyping with high-throughput genetic data obtained at considerable research costs to enhance genetic research. Furthermore, the relatively stable population in Olmsted County, Minnesota, the county in which Mayo Clinic in Rochester is located, and surrounding counties and the ability to search EMR data spanning decades are unique features of Mayo Clinic that enhance the utility of the MayoGC.
However, the MayoGC can serve as a template for other institutions with EMR-phenotyping capability. CONCLUSION Genome-wide association studies have identified genetic variants associated with numerous phenotypes but have been limited historically by inadequate sample size due to costly genotyping and phenotyping. Large consortia with harmonized genotype data have been assembled to attain sufficient statistical power, but phenotyping remains a rate-limiting factor in gene discovery research efforts. The EMR offers an abundance of phenotype data that can be extracted in a relatively quick and systematic manner. The utility of the MayoGC was successfully demonstrated by the replication of GWA results for bilirubin levels obtained from the EMR.
Thus, MayoGC provides a model of a unique collaborative effort in the environment of a common EMR for the investigation of genetic determinants of diseases. Funding for this research was provided by the Electronic Medical Records and Genomics (eMERGE) Network of the National Human Genome Research Institute (HG05499), Mayo Clinic Genome-wide Association Study of Venous Thromboembolism (HG04735) from the National Human Genome Research Institute (NHGRI; Gene Environment Association Studies GENEVA consortium), Mayo Clinic Specialized Programs of Research Excellence (SPORE) in Pancreatic Cancer (P50CA102701) from the National Cancer Institute, and Mayo Clinic Cancer Center (Genetic Epidemiology and Risk Assessment GERA Program).
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Dr Limburg served as a consultant for Genomic Health, Inc from August 12, 2008, through April 19, 2010. Mayo Clinic has licensed Dr Limburg's intellectual property to Exact Sciences, and he and Mayo Clinic have contractual rights to receive royalties through this agreement. An earlier version of this article appeared Online First.
For editorial comment, see page Supporting Online Material Figures S1-S3.
Abstract The end of life is a time in which both the intensity of cancer patients’ needs and the complexity of care increase, heightening the need for effective care coordination between oncology and primary care physicians. However, little is known about the extent to which such coordination occurs or the ways in which it is achieved. We review existing evidence on current practice patterns, patient and physician preferences regarding involvement of oncology and primary care physicians in end-of-life care, and the potential impact of care coordination on the quality of care and health outcomes. Data are lacking on the extent to which end-of-life care is coordinated between oncology and primary care physicians. Patients appear to prefer the continued involvement of both types of physicians, and preliminary evidence suggests that coordinated care improves health outcomes. However, more work needs to be done to corroborate these findings, and many unanswered questions remain.
The end of life, the final stage in the cancer care continuum, poses unique challenges for the coordination of care between oncology and primary care physicians. At this stage, perhaps more than at any other, patients’ needs for physical, emotional, and existential support become especially great, as symptoms often intensify and death becomes imminent. Communication between clinicians, patients, and family members—a vital endeavor at all stages of medical care—takes on added importance and difficulty under these circumstances. Health-care delivery also becomes more complex at the end of life as the number of involved clinicians often increases and their care responsibilities change in fundamental ways. At earlier points on the disease continuum, care is delivered primarily by one or two sets of physicians: primary care physicians providing the bulk of care related to prevention, screening, and initial diagnosis, and oncologists providing care related to curative or palliative cancer treatments. The end of life, however, often involves additional clinical personnel, including hospice and palliative medicine physicians and nurses, as well as other allied health and supportive care professionals, such as social workers and clergy.
The involvement of these various clinical professionals adds complexity to the care delivered to cancer patients and may alter the roles, responsibilities, and levels of engagement of primary care and oncology physicians. At the same time, the settings in which health care is delivered often change and become more varied at the end of life, moving outside of hospitals and clinics and into skilled nursing facilities, inpatient hospice units, and/or the family home. As both the complexity of care delivery and the intensity of patients’ and families’ needs increase at the end of life, the risks of fragmented and ineffective care become greater, and the coordination of care among various providers becomes an ever more critical task. A major aspect of this task is the coordination of care between primary care physicians and oncology specialists, who each have integral patient care responsibilities and roles. Despite this, surprisingly little is currently known about the coordination of care delivered by these two types of physicians and many unanswered questions exist. To what extent do oncology and primary care physicians assume various end-of-life care responsibilities, such as pain and symptom management, psychosocial and existential supportive care, communication regarding end-of-life goal setting and care priorities, and overall coordination of symptomatic and supportive care? How does the transition from active treatment or cancer survivorship to end-of-life care occur, and how are oncology and primary care specialists involved in these transitions?
What are patients’ and physicians’ preferences regarding the involvement of different specialists in end-of-life care? And finally, how does the presence or lack of multidisciplinary care affect the quality of care delivery, and do best practices aiming to maximize care quality exist?
In this article, we review existing literature in search of answers to these key questions, discuss issues raised by past work, and attempt to identify critical areas in which further research is needed to move the field forward. To begin to address these questions, some operational assumptions are needed.
First, we define “oncologist” as any physician who has primary responsibility for patients’ cancer treatment; this includes medical and radiation oncologists, hematologists, and various surgical subspecialists (eg, ENT and gynecologic surgeons). We chose this inclusive definition because of our main interest in issues surrounding the coordination of care with primary care physicians; however, we recognize that these issues may differ depending on the oncology subspecialists involved. Second, we define “end of life” as the stage of care that occurs in the context of progressive disease and begins when the primary goal of treatment shifts from potential cure or significant prolongation of symptom-free survival to active palliation in the setting of treatment-refractory or clinically life-threatening disease. This shift to end-of-life care has profound medical, psychological, and existential implications, and the timing of this transition for any given patient varies as a function of numerous factors. We recognize that the onset of this stage, furthermore, is often difficult to identify prospectively and may not always be explicitly acknowledged by either clinicians or patients. We also recognize that in certain clinical situations, the timeline to “end of life” may be particularly long and that with the advent of novel targeted cancer therapies, the future holds promise for further prolongation of this timeline. This can add to the difficulty of delineating transition points between active oncological and “end of life” care.
This difficulty, furthermore, makes the measurement of the quality of end-of-life care problematic. For our purposes, however, we assume it is possible to define this stage with sufficient specificity and in a sufficient proportion of patients to make an analysis of end-of-life care coordination worthwhile.
We also distinguish between end-of-life and palliative care, which refers to care directed at maximizing patients’ quality of life along multiple dimensions. The two are closely related both conceptually and practically because of the overlapping goal of maximizing quality of life through both symptom control and psychosocial support. The integration of palliative and oncological care across the cancer continuum is an important clinical and research problem in its own right and has attracted growing attention (,). Our focus in this article, however, is on end-of-life care, although we will often touch on palliative care to the extent that its goals are conceptually and practically related.
We did not restrict our review to studies from any particular country or geographic region, although studies from Canada turned out to be particularly prominent, possibly because of health-care system attributes that have made the coordination of primary and specialty care an especially salient issue in Canada, compared with countries like the United States. These include potential differences in the overall utilization of primary care physicians in different countries. Challenges at the Interface of Primary and Specialty Care: Current Practice Patterns in End-of-Life Care A major challenge confronting primary care and oncology physicians is to determine their respective care responsibilities with respect to one another, and this challenge is arguably the most problematic at the end of life. At earlier stages in the cancer care continuum (ie, screening, diagnosis, primary treatment, and adjuvant therapy administration) (Figure 1), responsibilities for patient care lie more clearly with one group of physicians or the other.
During the phase of cancer survivorship, the boundaries of care responsibilities become more difficult to delineate because the main tasks at hand, such as surveillance for recurrent disease and the late or long-term effects of cancer or its treatment, potentially fall within the expertise and interest of both oncologists and primary care physicians. Care coordination issues at this stage, the subject of the article by Grunfeld and Earle in this supplement, have thus become the subject of active research with evolving evidence regarding best practices and ongoing refinement of key conceptual issues. End-of-life care poses even more ambiguity regarding the responsibilities of primary care and oncology specialists because the clinical tasks involved at this stage—including both technical aspects of care as well as communication and care coordination—may fall legitimately within each specialty's domain of expertise and interest. As well, the heightened emotional and psychodynamic tensions at this stage in the disease process can exacerbate confusion and tensions about goals, expectations, and responsibilities of care. Exactly who assumes responsibility for what aspects of end-of-life cancer care has not been fully explored.
Most previous studies related to this question have focused on the integration of palliative and oncological care and on the transition from curative to palliative goals of care in this setting. The available evidence from this line of work suggests that palliative care is often not optimally integrated in oncological care and that the transition to end-of-life care often occurs very close to death. For example, a significant minority of cancer patients experience multiple emergency room visits and intensive care unit admissions during their last months of life.
Past studies have shown that 8%–27% of patients with metastatic cancer make one or more visits to emergency room facilities, and 5.4%–12% of these patients are admitted to intensive care units in their last months of life. This suggests that for many dying cancer patients, the transition of care goals from active treatment to end-of-life care is suboptimal.
As a result, these patients end up in medical environments poorly suited to the goals of end-of-life care, with consequent escalation of unnecessary, costly, and futile interventions. Although oncologists may provide the bulk of clinical services for cancer patients at the end of life, it is conceivable that primary care physicians may become more involved as cancers progress and the goals of care shift from active oncological care to end-of-life care. However, the role and influence of primary care physicians in this process is not clear. Curiously lacking from the literature is an examination of exactly how the services of primary care physicians are coordinated and integrated in the care of dying cancer patients. To our knowledge, few studies have explored the extent, nature, and timing of primary care physicians’ involvement in the end-of-life care of cancer patients. One study by Barnes et al.
examined cancer patients’ perceptions of the extent to which their family physicians were involved in their cancer care. In a review of 365 consecutive patients with metastatic cancer presenting for urgent palliative radiotherapy to the Rapid Response Radiotherapy Program at the Toronto Sunnybrook Hospital, 98% had a family physician on record, but only 43% felt that their primary care provider was involved in their cancer care. In multivariate analyses, several factors predicted greater perception of family physician involvement, including overall satisfaction with this provider, shorter time since last family physician visit, visiting with the family physician since the cancer diagnosis, and provision of after-hours emergency services by the family physician. Overall, the findings suggested a relatively low and variable level of involvement of primary care physicians in the end-of-life care of cancer patients. Further research is needed not only to better describe the patterns and impact of care delivered by primary care and oncology physicians but also to identify other potential reasons for variation in practice.
For example, some researchers have asserted that the transition to end-of-life care for cancer patients is often marked by confusion—on the part of both patients and physicians—regarding the roles and responsibilities of primary care, oncology, and palliative care specialists (,). Delivery of active systemic therapy toward the end of life may further serve to marginalize the role of primary care providers by focusing care on active disease management rather than symptomatic and supportive care. In a population-based study from the Ontario Cancer Registry, Barbera et al.
observed that 16% of patients dying with cancer received chemotherapy in the past 2 weeks of life. The difficulties of communicating about potential changes in the goals of care in patients receiving active systemic therapy have been reviewed and may diminish the role of primary care providers in patient care. Empirical evidence on the extent and outcomes of such role limitation and confusion in end-of-life care is scant; however, it stands to reason that it could impair care coordination and lead to poorer health outcomes.
Involvement of Primary Care and Oncology Physicians in End-of-Life Care: Patient and Physician Preferences Irrespective of current practice patterns, an important issue in the coordination of end-of-life care is the question of what patients and physicians prefer regarding the involvement, roles, and responsibilities of primary care and oncology specialists. These preferences may ultimately influence patients’ experiences with care at the end of life and thus represent a critical area of research. Much of the existing research related to this issue has focused on general patient preferences regarding the role of different physicians in providing palliative care services.
For example, a small number of studies have suggested that cancer patients value the involvement of their primary care physicians as a means of addressing needs related to quality of life. Sisler et al. surveyed patient attitudes regarding family physician involvement in cancer care in a random sample of Canadian patients within the first year of a cancer diagnosis. In this study, 38.9% of patients reported that their oncologist and family physician were involved in the care of their cancer, 44.4% reported that their oncologist cared for all cancer-related problems, whereas their family physician cared for other problems, and 10.0% reported that specialists cared for all their medical needs and that they rarely saw a family physician. Furthermore, approximately 75% of patients reported that the level of involvement of their family physicians in both treatment and follow-up care was “about right,” with the remainder expressing a desire for greater involvement of their family physicians.
Patients reporting greater involvement of their family physicians had higher health-related quality-of-life scores as measured by the Functional Assessment of Cancer Therapy-general scale (FACT-G). Specific aspects of the care seen as provided by the family physician—discussing feelings of patients and family members, helping with noncancer problems, and answering questions about cancer and cancer treatment—also were associated with higher FACT-G scores. These findings provide preliminary evidence that the continued involvement of primary care physicians in cancer care is valued by patients, may influence care experiences and outcomes, and serves identifiable functions specifically related to care at the end of life—in particular, meeting patients’ needs for communication and emotional support. These findings have been corroborated by qualitative studies in both Canada and the United Kingdom , which also suggest that cancer patients value primary care physicians for providing key information about their disease and treatment, as well as emotional support to themselves and their families. A key question raised by these findings is whether primary care physicians are willing and able to remain involved in the care process, particularly in nonintegrated health-care delivery systems, such as those in the United States, where numerous barriers—including lack of time and financial incentives—may discourage them from maintaining care continuity with their cancer patients. To the extent that communication and emotional support represent key domains of palliative care, these studies provide preliminary insight into cancer patients’ perceptions and preferences regarding physician roles at the end of life.
Preferences regarding different physicians’ roles in other specific supportive care domains, such as pain and symptom management, have not been explored. Furthermore, it is not clear whether patients actually prefer receiving information and psychosocial support from primary care physicians as opposed to oncology physicians or palliative medicine specialists or simply view primary care physicians as satisfying current unmet medical and/or situational needs. Research on patient preferences regarding the provision of palliative care services provides suggestive, but indirect, evidence on preferences for the involvement of primary care and oncology physicians in end-of-life care. One relevant line of research has focused on patient perceptions regarding continuity of care, which has been identified as a core value underlying patient preferences for physician involvement in end-of-life care. Several qualitative studies have shown that terminally ill patients, family members, and health professionals all place great value on the ideal of continuity of care at the end of life.
Michiels et al. interviewed end-stage cancer patients in Belgium and distinguished between two types of continuity that were important to patients: 1) “relational continuity”—having an ongoing relationship with the same physician over time and 2) “informational continuity”—the use by physicians of information on past events and personal circumstances of their patients. The value of both types of continuity was reflected in patients’ expressed expectations and preferences for the ongoing involvement of their primary care physicians in end-of-life care and for sharing of care responsibilities between primary care and oncology specialists.
Patient preferences for physician continuity of care at the end of life have been identified in some quantitative studies (,), but more work needs to be done to understand how general preferences for continuity of care relate to preferences concerning the specific roles and responsibilities of primary care and oncology physicians—both of whom may have meaningful longitudinal relationships with their patients. Further research also is needed to understand the factors that influence patient perceptions and preferences regarding physicians’ care responsibilities at the end of life.
De Vogel-Voogt et al. conducted a qualitative study in the Netherlands of 128 patients with incurable cancer and found that satisfaction with primary care at the end of life varied according to patient education level, with those having lower education being more satisfied with their primary care providers. These findings need to be replicated, and more work needs to be done to identify additional factors that may be influential.
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Cancer patients’ preferences regarding the role of palliative medicine specialists is another important area for future research. The recent expansion in the availability and involvement of palliative medicine specialists poses new challenges to the coordination of care between primary care and oncology physicians and may even threaten continuity of care at the end of life, depending on the extent and manner in which primary care responsibilities are transferred to palliative medicine physicians. Some cancer patients may feel abandoned during these care transitions , irrespective of whether oncologists or primary care physicians are involved.
These issues call for greater understanding of not only patients’ but physicians’ preferences and expectations regarding their respective roles in palliative and end-of-life care. Data on this issue also are limited, although Cherny and Catane surveyed 895 European medical oncologists on their attitudes toward palliative care for patients with advanced and incurable cancer. In this survey, 88.4% of oncologists agreed that medical oncologists should coordinate the care of cancer patients at all stages of disease, including the end of life.
However, only 52.8% reported receiving good training in palliative care, and approximately 35% believed that a palliative care specialist was the best person to coordinate the palliative care of advanced cancer patients and preferred having another physician look after their patients who were dying. These beliefs were reflected in self-reported practices: Only 43.2% of oncologists reported that they directly provided end-of-life care to their dying patients, and less than 40% indicated that they often collaborated with home hospice palliative care teams, palliative medicine physicians, or nurses. Similar data have not been collected in the United States, with the exception of a 1998 survey conducted by the American Society of Clinical Oncology (ASCO), in which pediatric oncologists reported using referrals to pain or palliative care specialists about half of the time, and maintaining primary responsibility for providing end-of-life care 75%–100% of the time.
These findings shed light on end-of-life care continuity between oncologists and palliative medicine specialists and suggest areas for future research. One conspicuous omission from these studies, however, were physicians’ preferences and expectations regarding the role of primary care physicians in end-of-life cancer care. This represents an important direction for future work, which might further examine the concordance of physician and patient preferences regarding the respective roles of primary care physicians, oncologists, and palliative medicine specialists in end-of-life care. Such work would allow an examination of how patient and physician preferences—and the concordance between these preferences—relate to patterns of care, care processes, and important patient-centered outcomes, including communication and patient satisfaction. Quality of End-of-Life Care for Cancer Patients: Relationship to Care Coordination Between Primary Care and Oncology Physicians In the final analysis, the coordination of end-of-life care between primary care physicians and oncologists is an important issue only insofar as it affects the quality of care that is delivered.
As evidenced by recent literature syntheses (,) and a report by the Institute of Medicine , experts widely agree that coordination of care—conceived broadly—is a key factor influencing the quality of palliative and end-of-life care for cancer patients. However, as noted by the Institute of Medicine , no widely accepted indicators or measures of care coordination currently exist, and only a small number of studies have provided weak evidence that interventions to improve care coordination improve care outcomes for cancer patients at the end of life (,). Data are particularly lacking on the nature and outcomes of care coordination occurring specifically between primary care physicians and oncologists. Limited data suggest only indirectly that care coordination affects important patient outcomes.
For example, Jones et al. observed a statistically significant lower risk of death among 329 patients with lung cancer if they had one or more primary care visits in the first 6 months after cancer diagnosis compared with those who did not use primary care. This study observed median survival times of 3.7 vs 7.5, 13.9, and 13.8 months for those with zero, one, two, and three primary care contacts, respectively ( P. COMMENTARY Cancer care in the nursing home setting is an informational black hole. No good data exist.
Quality of care measures in traditional care and nursing home care don’t match. Many cancers are diagnosed after admittance to a nursing home. Everything is different in the nursing home setting: no primary care providers, no multidisciplinary care teams. Care is provided by the nursing home staff, and 50 percent of nursing homes currently have unfilled medical director positions. From a Supplement Author Some important unanswered questions, however, are whether these same potential effects apply equally to continuity of care provided by oncologists and primary care physicians in other settings, and how “discontinuity” posed by concurrent visits to both oncologists and primary care physicians in other settings might also influence the use of various health services.
A few other small studies have compared primary care and oncology physicians in terms of specific end-of-life care practices, such as pain management. Older studies of care delivered in home hospice settings have suggested that oncologists are more aggressive than primary care physicians in prescribing opioids for pain control (,). Corroborating the generalizability of these findings, a survey of French oncology and primary care physicians found that primary care physicians were less satisfied with their ability to manage pain and more reluctant to prescribe morphine for pain control. A more recent survey , however, found that although French primary care physicians were more likely than oncologists to equate high-dose morphine therapy with euthanasia, they did not report lower morphine prescribing in general.
These types of studies have not been replicated in Canada or the United States, where the rate of opioid prescribing in general and by primary care physicians has been increasing recently , but they raise the possibility that involvement of oncology physicians may foster greater use of opioids for pain control in cancer patients at the end of life. These trends further heighten the need for care coordination and communication because opioids are legally controlled substances requiring careful monitoring and management and for which individual physicians typically assume prescribing responsibility. Clearly, much remains unknown about the determinants and outcomes of care coordination and continuity in end-of-life care. Further research examining best practices, communication tools, patient experiences with care, cost effectiveness, and health-related quality of life in relation to the integration of primary and oncology care will be required to meet escalating demands for quality end-of-life care for patients with advanced cancer. Future Needs and Potential Solutions Expert consensus and emerging empirical evidence suggests that the effective coordination of health care at the end of life is a major need for cancer patients and that the interaction between oncology and primary care physicians is a critical aspect of this coordination. The end of life is a period characterized by added diversity and intensity of patient and caregiver needs, and potentially dramatic, existentially challenging transitions in care goals, providers, and settings. Under these circumstances, oncologists and primary care physicians may assume a variety of roles and care responsibilities, but these have not been fully characterized.
We also lack data about whether these roles and responsibilities vary when different oncology subspecialists are involved in patient care. Patients appear to prefer the continued involvement of both oncology and primary care physicians in end-of-life care, but these preferences have only begun to be defined, and it is unclear whether physicians share those preferences.
Similarly, we know little about how the concurrent involvement and interaction of primary care and oncology physicians affects patient outcomes and care quality at the end of life, although preliminary evidence suggests that shared care leads to improved outcomes. The primary need going forward is for more empirical research to address these many incompletely answered questions. Several specific issues require further attention:. Defining the time period at which end-of-life care begins. Assessing the patterns and specific components of end-of-life care as provided by different types of oncologists and primary care physicians. Understanding patient and caregiver preferences for the relative involvement of oncology vs primary care medical specialists at the end of life.
Designing appropriate and validated assessment tools to further understand how end-of-life care should best be managed. Evaluating the impact, in terms of quality of care and other endpoints, of different models of end-of-life care and understanding how these different models might be implemented in different health-care delivery systems. Ascertaining best practices and methodologies for integrating primary and oncology specialty care in patients dying of advanced cancer.
Adequately addressing these questions is a particular challenge, given the general lack of research support that continues to exist in palliative medicine and that limits research on the role of the primary care medical team in caring for dying cancer patients. The field also may benefit from further theoretical work to conceptualize the problem of care coordination as it applies to the involvement of primary care and oncology physicians in end-of-life care. Implicit in research examining the coordination of services by these different physicians are questions related to the relative value of different models of care and the assessment of best practices and barriers to shared coordinated care. An underlying assumption is that some sort of shared care or “collaborative” model may be preferable to patients and more effective in achieving desired outcomes, in contrast to models in which care occurs in a sequential (involving the transfer of primary care responsibilities to different providers over time) or parallel (involving the provision of care by two or more physicians acting independently) fashion. Further theoretical work examining these alternative models and concepts and their meaning for end-of-life care would be useful to orient future empirical research. The ultimate goal of research is to inform the design of interventions to improve the coordination—and thus the quality—of end-of-life care. One promising approach toward this goal may be to apply interventions that have been developed to improve communication between primary care and oncology specialists at earlier stages of the cancer care continuum.
For example, Jefford and Moore conducted a small randomized controlled trial evaluating the outcomes of providing primary care physicians with tailored specific information regarding the treatment regimens of their cancer patients receiving chemotherapy. This trial demonstrated a statistically significant improvement in confidence and satisfaction with care delivery.
Although the study did not specifically focus on end-of-life care, it does suggest a promising strategy for enhancing communication between oncology and primary care medical specialties at all stages of the cancer care continuum, including end-of-life care. Further work to develop and evaluate interventions to coordinate cancer care at the end of life are clearly needed to further knowledge and move the integration of primary and oncology care from anecdote and accident to best practice and to improve the quality end-of-life care for all cancer patients.