Pathak, Jyotishman

Abstract Background: The use of anti-estrogen (AE) therapies (selective estrogen receptor modulators [SERMs], aromatase inhibitors [AIs]) in breast cancer (BC) patients was recently associated with risk of developing rheumatoid arthritis (RA) (J Rheum 2015;42:55-9). We attempted to replicate and extend these results using electronic health record (EHR) enhanced cancer registry data that assessed specific AE therapies and also accounted for potential confounding by age, smoking status, and chemotherapy use. Methods: Using cancer registry and EHR data, we identified a cohort of BC patients who were newly diagnosed and treated at Mayo Clinic Rochester from 1998-2011 and had no previous diagnosis of RA. Smoking status at diagnosis, BC treatments and RA at diagnosis and during follow-up were identified using validated EHR-based algorithms. Hazard ratios (HRs) and 95% confidence intervals (CI) from a multivariate Cox model were used to estimate the association of AE use with risk of RA, controlling for age, smoking status at BC diagnosis, and BC chemotherapy use. Results: The analytic cohort of 9,244 newly diagnosed BC patients treated and followed at Mayo Clinic had a median age at diagnosis of 59 years (range 18-97); 32% were smokers; and 29% were treated with chemotherapy. During a median follow-up of 49 months (range 1-191), 19 patients developed RA. Compared to BC patients not receiving any AE therapy, those receiving AI monotherapy, but not SERM monotherapy or both SERMs and AIs, were at significantly increased risk of developing RA (Table). AE Therapy# of RA Cases / # patientsHR* (95% CI)P-valueNo AE therapy6/4,0711.00 (reference)SERMs only5/2,5371.20 (0.36 - 3.95)0.77AIs only5/7126.37 (1.77 - 22.92)0.005SERMs then AIs3/1,8750.90AI then SERMs0/490.91 (0.21 - 3.86)*Adjusted for age, smoking status and use of chemotherapy Conclusions: Our findings indicate that the use of AIs for BC therapy, but not SERMs or SERMs followed by AIs, is associated with increased risk for development of RA, when controlling for smoking status and use of chemotherapy. While needing replication, these findings suggest a specific role for AIs in RA development and suggest pathways that could be targeted to prevent this potential treatment complication. Citation Format: Matthew K. Breitenstein, Ming-Fen Ho, Richard M. Weinshilboum, James R. Cerhan, Jyotishman Pathak, Tim Bongartz, Liewei Wang, James N. Ingle. Association of anti-estrogen therapy in breast cancer patients and subsequent risk of rheumatoid arthritis: An electronic health record study. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-191. doi:10.1158/1538-7445.AM2015-LB-191

Abstract Objective To review and evaluate available software tools for electronic health record–driven phenotype authoring in order to identify gaps and needs for future development. Materials and Methods Candidate phenotype authoring tools were identified through (1) literature search in four publication databases (PubMed, Embase, Web of Science, and Scopus) and (2) a web search. A collection of tools was compiled and reviewed after the searches. A survey was designed and distributed to the developers of the reviewed tools to discover their functionalities and features. Results Twenty-four different phenotype authoring tools were identified and reviewed. Developers of 16 of these identified tools completed the evaluation survey (67% response rate). The surveyed tools showed commonalities but also varied in their capabilities in algorithm representation, logic functions, data support and software extensibility, search functions, user interface, and data outputs. Discussion Positive trends identified in the evaluation included: algorithms can be represented in both computable and human readable formats; and most tools offer a web interface for easy access. However, issues were also identified: many tools were lacking advanced logic functions for authoring complex algorithms; the ability to construct queries that leveraged un-structured data was not widely implemented; and many tools had limited support for plug-ins or external analytic software. Conclusions Existing phenotype authoring tools could enable clinical researchers to work with electronic health record data more efficiently, but gaps still exist in terms of the functionalities of such tools. The present work can serve as a reference point for the future development of similar tools.

Abstract Background: Literature addressing metformin exposure post diagnosis of breast cancer hints at a potential protective association on mortality outcomes, although findings have been inconclusive. In this research, we contribute to this body of evidence by using robust clinical indicators to focus on subgroups of breast cancer patients that might be most appropriate for potential metformin treatment. Methods: Robust breast cancer staging, treatment, and medication exposure attributes were compiled from Mayo Clinic Electronic Health Records and Breast Cancer Registry using a combination of structured queries and natural language processing of clinical notes. A cohort of female Breast Cancer Registry patients having type II diabetes mellitus (T2DM) at breast cancer diagnosis with a median follow-up period of 79.0 months after breast cancer diagnosis was identified (N=350). Univariate and multivariate cox proportional hazard regression analysis were used to model characteristics of BMI at diagnosis, age at diagnosis, metformin, insulin, thiazolidinedione, and sulfonylurea exposure after diagnosis, cancer grade, cancer stage, chemotherapy, hormone therapy, radiotherapy, lumpectomy, and mastectomy. Patients presenting with metastatic breast cancer at diagnosis, receiving immunotherapy for breast cancer treatment, and concurrent metformin and insulin exposure for T2DM treatments were excluded from the analysis cohort. Results: Univariate analysis independently identified metformin exposure (HR=0.243,p=0.0016), insulin exposure (HR=3.481,p=0.0210), age at breast cancer diagnosis (HR=1.075,p=0.0001), and targeted radiotherapy (HR=0.337,p=0.0082) as having a significant association with all-cause mortality. The multivariate model of metformin exposure (HR=0.274,p=0.0123), insulin exposure (HR=2.502,p=0.1160), age at breast cancer diagnosis (HR=1.043,p=0.0480), and targeted radiotherapy (HR=0.303,p=0.0044) on all-cause mortality demonstrated significant associations for all variables (Wald Chi-Square=25.2916,p<0.0001). A parallel multivariate model demonstrated metformin exposure (HR=0.101,p=0.0323), insulin exposure (HR=5.241,p=0.0265), and targeted radiotherapy (HR=0.292,p=0.0520) as having significant associations with breast cancer-specific mortality. Discussion: These findings suggest a protective association for metformin and a potential detrimental association for insulin therapy on all-cause and breast cancer-specific mortality amongst non-metastatic breast cancer patients with T2DM. The findings presented include robust clinical indicators for BMI, stage, and grade at diagnosis, post-diagnosis diabetes medication exposure, and endpoints of all cause and breast cancer specific mortality that have not been available in larger population studies. Our findings have potential pharmacogenetic implications for inhibition of localized breast cancer cell growth by targeting downstream components of the adenosine monophosphate-activated protein kinase pathway, the insulin/insulin-like growth factor-1 signaling pathway, and directly and indirectly via additional metabolic pathways with specific drug therapy combinations. Citation Format: Matthew K. Breitenstein, Leiwei Wang, Richard M. Weinshilboum, Jyotishman Pathak. Metformin exposure on survival in localized breast cancer patients with type II diabetes mellitus using electronic health records. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-278. doi:10.1158/1538-7445.AM2014-LB-278

Background/Aims: Depressive disorders (DEP) are often comorbid with other chronic conditions. Bidirectional associations between DEP and these conditions are poorly understood. In this study, we aimed to identify chronic conditions with the greatest influence on the DEP diagnosis. Methods: A validated electronic health record-based algorithm was applied to identify DEP patients receiving primary care at Mayo Clinic between 2000 and 2013. Cases were identified on the basis of having > 2 DEP-related ICD-9 diagnosis codes, > 1 antidepressant prescription, and > 3 mentions of in- or outpatient clinical notes for DEP. Controls were matched on birth year (± 2 years), sex and outpatient clinic visits at the same year. We ascertained 26 chronic condition categories, as defined by Chronic Conditions Data Warehouse, using the 5 years of medical records prior to the DEP diagnosis. For each age group at diagnosis (60 years), gradient boosting machine models were applied to estimate relative influence (RI) of the chronic conditions on the DEP diagnosis. Results: A total of 11,219 DEP cases were identified (median age at DEP diagnosis 44 years, 34% male and 89% white). The proportion of subjects with at least one co-occurring condition increased with older age and was higher in DEP cases compared to controls (9.5% vs. 6.9% for subjects 60 years). For subjects aged ≤ 45 years, diabetes mellitus (RI=19.6%) was identified as the most influential condition contributing to the risk of DEP, followed by asthma (RI=11.6%) and rheumatoid arthritis (RI=11.0%). For subjects aged 46–60 years, the most influential condition was rheumatoid arthritis (RI=13.2%) followed by diabetes mellitus (RI=12.9%). For subjects older than 60 years, dementia (RI=10.7%) and rheumatoid arthritis (RI=10.2%) showed similar relative contribution to the risk of DEP. Discussion: Among nonelderly adults who received primary care at Mayo Clinic (2000–2013), diabetes mellitus and rheumatoid arthritis were the strongest risk predictors for a diagnosis of depression among 26 chronic conditions considered. Our results suggest that certain chronic conditions may play a role in exacerbating, precipitating or increasing longitudinal risk of medically significant depression.

Abstract Background: Transfusion-related acute lung injury (TRALI) is the leading cause of transfusion-related death in the United States; however, it remains poorly characterized in surgical populations. To better inform perioperative transfusion practice, and to help mitigate perioperative TRALI, the authors aimed to better define its epidemiology before and after TRALI mitigation strategies were introduced. Methods: This retrospective cohort study examined outcomes of adult patients undergoing noncardiac surgery with general anesthesia who received intraoperative transfusions during 2004 (n = 1,817) and 2011 (n = 1,562). The demographics and clinical characteristics of transfusion recipients, blood transfusion descriptors, and combined TRALI/possible TRALI incidence rates were evaluated. Univariate analyses were used to compare associations between patient characteristics, transfusion details, and TRALI mitigation strategies with TRALI/possible TRALI incidence rates in a before-and-after study design. Results: The incidence of TRALI/possible TRALI was 1.3% (23 of 1,613) in 2004 versus 1.4% (22 of 1,562) in 2011 (P = 0.72), with comparable overall rates in males versus females (1.4% [23 of 1,613] vs. 1.2% [22 of 1,766]) (P = 0.65). Overall, thoracic (3.0% [4 of 133]), vascular (2.7% [10 of 375]), and transplant surgeries (2.2% [4 of 178]) carried the highest rates of TRALI/possible TRALI. Obstetric and gynecologic surgical patients had no TRALI episodes. TRALI/possible TRALI incidence increased with larger volumes of blood product transfused (P < 0.001). Conclusions: Perioperative TRALI/possible TRALI is more common than previously reported and its risk increases with greater volumes of blood component therapies. No significant reduction in the combined incidence of TRALI/possible TRALI occurred between 2004 and 2011, despite the introduction of TRALI mitigation strategies. Future efforts to identify specific risk factors for TRALI/possible TRALI in surgical populations may reduce the burden of this life-threatening complication.

Abstract Background: Transfusion-associated circulatory overload (TACO) is a leading cause of transfusion-related fatalities, but its incidence and associated patient and transfusion characteristics are poorly understood. To inform surgical transfusion practice and to begin mitigating perioperative TACO, the authors aimed to define its epidemiology. Methods: In this retrospective cohort study, the medical records of adult patients undergoing noncardiac surgery with general anesthesia during 2004 or 2011 and receiving intraoperative transfusions were screened using an electronic algorithm for identification of TACO. Those patients who were screened as high probability for TACO underwent rigorous manual review. Univariate and multivariate analyses evaluated associations between patient and transfusion characteristics with TACO rates in a before-and-after study design. Results: A total of 2,162 and 1,908 patients met study criteria for 2004 and 2011, respectively. The incidence of TACO was 5.5% (119 of 2,162) in 2004 versus 3.0% (57 of 1,908) in 2011 ( P < 0.001), with comparable rates for men (4.8% [98 of 2,023]) and women (3.8% [78 of 2,047]) ( P = 0.09). Overall, vascular (12.1% [60 of 497]), transplant (8.8% [17 of 193]), and thoracic surgeries (7.2% [10 of 138]) carried the highest TACO rates. Obstetric and gynecologic patients had the lowest rate (1.4% [4 of 295]). The incidence of TACO increased with volume transfused, advancing age, and total intraoperative fluid balance (all P < 0.001). Conclusions: The incidence of perioperative TACO is similar to previous estimates in nonsurgical populations. There was a reduction in TACO rate between 2004 and 2011, with incidence patterns remaining comparable in subgroup analyses. Future efforts exploring risk factors for TACO may guide preventive or therapeutic interventions, helping to further mitigate this transfusion complication.

Abstract BACKGROUND: Biobanks collect and store biological samples and data from participants for use in research by multiple investigators. The Mayo Clinic Biobank was established by the Mayo Clinic Center for Individualized Medicine with a goal to support a wide array of health-related research studies at Mayo Clinic, including cancer studies. The Biobank is not focused on any particular disease, however, through annotation by questionnaires, the Mayo electronic medical record (EMR), and the Mayo Cancer Registry, it is a useful clinical research resource for cancer related studies. METHODS: Recruitment started in April 2009. Our goal is 50,000 enrollees. Eligible subjects are adult Mayo Clinic patients, U.S residents, and able to consent. Mailed invitations were sent to patients with appointments in certain Mayo Clinic departments. Each participant completed a health history questionnaire including a wide variety of health information as well as whether they or their 1st-degree relatives had ever been diagnosed with specific cancers. For self-reported cancers, subjects were asked to provide the age they were first diagnosed. Subjects also provided a blood sample and granted research access to clinically collected specimens and data from their Mayo Clinic record, at enrollment and into the future. All research projects utilizing samples from this resource are required to return to the Biobank the data generated from their projects. RESULTS: In the first three years, 21736 subjects consented to participate (30% response rate). Participants are 58% female, 95% White, and range in age from 18 to 97 with a median age of 62. At enrollment, 74% were residents in Minnesota, 6% in Iowa, 4% in Wisconsin, and 15% in the other states. Twelve percent reported being a current smoker, while 59% were never smokers. The most common self-reported cancers at baseline were non-melanoma skin cancer (n=2950), prostate cancer (n=1107), breast cancer (n=941), melanoma (n=692), colorectal cancer (n=296), cervical cancer (n=240), sarcoma (n=233), urinary/bladder (n=211), lymphoma (n=187), thyroid cancer (n=186), endometrial cancer (n=170), kidney cancer (n=163), lung cancer (n=145). Excluding non-melanoma skin cancer, 16188 (74%) (6516 male, 9672 female) self-reported being cancer free at baseline. Data will be presented on the frequency of incident cancers diagnosed since enrollment in the Biobank. To date, the Biobank has served as a source of controls for 17 cancer related studies on genetic susceptibility, including studies of the breast, colon, myeloma, brain, lung, pancreas, endometrium, and kidneys. CONCLUSIONS: The Mayo Clinic Biobank has quickly been established as a valuable resource for cancer researchers at Mayo Clinic - primarily as a source of controls for genetic studies. As the Biobank continues to grow in sample size and length of follow-up, it will also serve as a cohort study of cancer incidence embedded within an EMR of a large health organization. Citation Format: Janet E. Olson, Euijung Ryu, Kiley J. Johnson, Karen Maschke, Jody A. Morrisette, Mark Liebow, Paul Y. Takahashi, Ruchi G. Sharma, Kari S. Anderson, Matthew A. Hathcock, Jyotishman Pathak, Noralane M. Lindor, Timothy J. Beebe, Stephen N. Thibodeau, James R. Cerhan. The Mayo Clinic Biobank - A growing resource for cancer related studies. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2515. doi:10.1158/1538-7445.AM2013-2515

AbstractWe provide a mini‐review of how biobanks can support clinical genetics in the era of personalized medicine. We discuss types of biobanks, including disease specific and general biobanks not focused on one disease. We present considerations in setting up a biobank, including consenting and governance, biospecimens, risk factor and related data, informatics, and linkage to electronic health records for phenotyping. We also discuss the uses of biobanks and ongoing considerations, including genotype‐driven recruitment, investigations of gene–environment associations, and the re‐use of data generated from studies. Finally, we present a brief discussion of some of the unresolved issues, such as return of research results and sustaining biobanks over time. In summary, carefully designed biobanks can provide critical research and infrastructure support for clinical genetics in the era of personalized medicine.