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Background Opioid analgesics are among the most commonly prescribed medications in the United States. Opioids are useful in the management of pain, but their use can also adversely affect patients. However, studies reporting patterns of adverse events due to use of opioid analgesics in men and women are still scarce. Understanding the individual characteristics affecting response to opioids may allow clinicians to better individualize treatments. In this study, we investigated sex differences in the patterns of adverse events as a result of opioid use. Methods We used data from 8,457 patients participating in the Mayo Clinic Biobank “Right Drug, Right Dose, Right Time (RIGHT protocol)” study of pre‐emptive genotyping who were prescribed at least one of four opioid medications (codeine, tramadol, hydrocodone, or oxycodone) between 01/01/2004 and 12/31/2017. Adverse events due to use of these medications were collected from the allergy section in each patient’s electronical medical record, and were organized into four Medical Dictionary for Regulatory Activities (MedDRA) categories: gastrointestinal (e.g. nausea, vomiting), skin and subcutaneous tissue (e.g. rash, itching), psychiatric (e.g. anxiety, hallucinations), and nervous system (e.g. headache, dizziness). We modeled sex differences in the risk of adverse events by each opioid, and sex differences in the risk of each adverse event due to any opioid using logistic regression adjusted for age, race, ethnicity, and body mass index (BMI). Results Participants with at least one opioid prescription were 60.8% female, mostly white (94.2%) and non‐Hispanic (98.9%). Women (relative to men) were younger (median = 61.6 vs. 69.1, respectively), had lower BMI (median = 27.4 vs. 28.9), and were prescribed more codeine (18.8% vs. 15.5%). Overall, 456 (5.4%) persons had a note in the allergy section of their medical record indicating a problem with an opioid medication. Women were more likely than men to have a note in the allergy section referring to at least one opioid (6.6% and 3.5%, respectively; p < 0.001). In addition, among participants who had at least one note related to opioid use in their allergy section, women were more likely to have notes regarding gastrointestinal complications (OR [95% CI] = 2.07 [1.3 – 3.2], p = 0.002), but less likely to have notes regarding psychiatric issues (OR [95% CI] = 0.30 [0.2 – 0.5], p < 0.001). Conclusion Women were more likely than men to experience adverse events due to use of codeine, tramadol, hydrocodone, and oxycodone. In addition, the types of adverse reactions differed between men and women. These data suggest that further studies are needed to ensure that both men and women have optimal responses to opioid medications. Support or Funding Information This work was supported by the Mayo Clinic Specialized Center of Research Excellent on Sex Differences, the Mayo Clinic Center for Individualized Medicine, the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, and the National Institutes of Health grants [U19 GM61388 (The Pharmacogenomics Research Network), R01 GM28157, U01 HG005137, R01 GM125633, R01 AG034676 (The Rochester Epidemiology Project)], and [U01 HG06379 and U01 HG06379 Supplement (The Electronic Medical Record and Genomics (eMERGE) Network)].

PURPOSEMore than 80% of patients who undergo sentinel lymph node (SLN) biopsy have no nodal metastasis. Here, we describe a model that combines clinicopathologic and molecular variables to identify patients with thin- and intermediate-thickness melanomas who may forgo the SLN biopsy procedure because of their low risk of nodal metastasis.PATIENTS AND METHODSGenes with functional roles in melanoma metastasis were discovered by analysis of next-generation sequencing data and case-control studies. We then used polymerase chain reaction to quantify gene expression in diagnostic biopsy tissue across a prospectively designed archival cohort of 754 consecutive thin- and intermediate-thickness primary cutaneous melanomas. Outcome of interest was SLN biopsy metastasis within 90 days of melanoma diagnosis. A penalized maximum likelihood estimation algorithm was used to train logistic regression models in a repeated cross-validation scheme to predict the presence of SLN metastasis from molecular, clinical, and histologic variables.RESULTSExpression of genes with roles in epithelial-to-mesenchymal transition (glia-derived nexin, growth differentiation factor 15, integrin-β3, interleukin 8, lysyl oxidase homolog 4, transforming growth factor-β receptor type 1, and tissue-type plasminogen activator) and melanosome function (melanoma antigen recognized by T cells 1) were associated with SLN metastasis. The predictive ability of a model that only considered clinicopathologic or gene expression variables was outperformed by a model that included molecular variables in combination with the clinicopathologic predictors Breslow thickness and patient age (area under the receiver operating characteristic curve, 0.82; 95% CI, 0.78 to 0.86; SLN biopsy reduction rate, 42%; negative predictive value, 96%).CONCLUSIONA combined model that included clinicopathologic and gene expression variables improved the identification of patients with melanoma who may forgo the SLN biopsy procedure because of their low risk of nodal metastasis.

Higher levels of hepatocyte growth factor (HGF) have been associated with the presence of peripheral arterial disease (PAD), but prospective associations are unknown. We examined the association of circulating HGF levels with incident PAD. Between 2000 and 2002, HGF was measured in 6742 Multi-Ethnic Study of Atherosclerosis participants without PAD. Incident clinical PAD, adjudicated on the basis of a positive history for the presence of disease-related symptoms or treatment, was ascertained through 2015. Incident low ankle-brachial index (ABI), defined as an ABI < 0.9 and a decline of ≥ 0.15, was assessed among 5736 individuals who had an ABI > 0.9 at baseline and ≥1 follow-up ABI measurement 3 to 10 years later. There were 116 clinical PAD and 197 low ABI events that occurred over a median follow-up of 14 and 9 years, respectively. After adjustment for demographic and clinical variables, a standard deviation increment of HGF (303 ng/L) was associated with an increased risk of clinical PAD (hazard ratio: 1.21; 95% confidence interval [CI]: 1.05-1.39) but not a low ABI (rate ratio: 1.03; 95% CI: 0.85-1.25). In conclusion, higher HGF levels were modestly associated with an increased risk of developing clinical PAD.

Abstract Integrins are the major family of cell adhesion receptors in humans and essential for a wide range of normal physiology, including formation and maintenance of tissue structure integrity, cell migration, proliferation, and differentiation. Integrins also play a prominent role in tumor growth and metastasis. Translational research has tried to define the contribution of integrins to the phenotypic aggressiveness of melanoma because such knowledge is clinically useful. For example, differential expression of integrins in primary cutaneous melanoma can be used to distinguish indolent from aggressive, prometastatic melanoma. Recent studies have shown that gene expression–based testing of patient‐derived melanoma tissue is feasible, and molecular tests may fully replace interventional surgical methods such as sentinel lymph node biopsies in the future. Because of their central role in mediating invasion and metastasis, integrins are likely to be useful biomarkers. Integrins are also attractive candidate targets for interventional therapy. This article focuses on the role of integrins in melanoma and highlights recent advances in the field of translational research.

Background: The effective self-management of heart failure (HF) is highly dependent on cognitive function. Dementia frequently coexists with HF, but its exact prevalence and impact on health care utilization and death is not well understood. Methods: Residents from 7 southeast Minnesota counties with a first-ever code for HF (ICD-9 428 and ICD-10 I50) between 1/1/2012 and 12/31/2017 were identified. Dementia before and after the HF diagnosis was ascertained with codes recommended by the US Department of Health and Human Services. Patients were followed through 12/31/2018 for death and hospitalizations. Cox and Andersen-Gill models were used to examine associations between dementia (prior and post HF) and death and hospitalizations, respectively. Dementia after HF was analyzed as a time-dependent variable. Results: Among 6,312 patients with HF (mean age 75 years, 49% female), 636 (10%) had a prior diagnosis of dementia. After a mean (SD) follow-up of 3.1 (1.8) years, 601 (9.5%) patients were diagnosed with dementia post HF. The median time from HF to dementia was 1.3 years, with 43% of dementia cases occurring within 1 year and 64% within 2 years post HF. During follow-up, 2,678 deaths and 15,095 hospitalizations occurred. After adjustment, patients with dementia before HF had nearly a 2-times increased risk of death and 10% increased risk of hospitalization compared to those that did not have dementia (Table). Dementia after HF was associated with a 78% increased risk of death and 63% increased risk of hospitalization compared to those that did not have dementia. Conclusions: Approximately 10% of patients had dementia prior to HF and 10% develop dementia shortly after HF diagnosis. Patients with HF and dementia have an increased risk of death and hospitalization compared to dementia-free counterparts. These data underscore the importance of recognizing the presence and impact of dementia on outcomes in HF to optimize care delivery.

Background: Current cardiovascular disease (CVD) risk scores are derived from research cohorts and are particularly inaccurate in women, older adults, and those with missing data. To overcome these limitations, we aimed to develop a cohort to capitalize on the depth and breadth of clinical data within electronic health record (EHR) systems in order to develop next-generation sex-specific risk prediction scores for incident CVD. Methods: All individuals 30 years of age or older residing in Olmsted County, Minnesota on 1/1/2006 were identified. We developed and validated algorithms to define a variety of risk factors, thus building a comprehensive risk profile for each patient. Outcomes including myocardial infarction (MI), percutaneous intervention (PCI), coronary artery bypass graft (CABG), and CVD death were ascertained through 9/30/2017. Results: We identified 73,069 individuals without CVD (Table). We retrieved a total of 14,962,762 lab results; 14,534,466 diagnoses; 17,062,601 services/procedures; 1,236,998 outpatient prescriptions; 1,079,065 heart rate measurements; and 1,320,115 blood pressure measurements. The median number of blood pressure and heart rate measurements ascertained per individuals were 11 and 9, respectively. The five most prevalent conditions were: hypertension, hyperlipidemia, arthritis, depression, and cardiac arrhythmias. During follow-up 1,455 MIs, 1,581 PCI, 652 CABG, and 2,161 CVD-related deaths occurred. Conclusions: We developed a cohort with comprehensive risk profiles and follow-up for each patient. Using sophisticated machine learning approaches, this electronic cohort will be utilized to develop next-generation sex-specific CVD risk prediction scores. These approaches will allow us to address several challenges with use of EHR data including the ability to 1) deal with missing values, 2) assess and utilize a large number of variables without over-fitting, 3) allow non-linear relationships, and 4) use time-to-event data.

Background: The focus of most existing phenotyping algorithms based on electronic health record (EHR) data has been to accurately identify cases and non-cases of specific diseases. However, a more challenging task is to accurately identify disease incidence, as identifying the first occurrence of disease is more important for efficient and valid clinical and epidemiological research. Moreover, stroke is a challenging phenotype due to diagnosis difficulty and common miscoding. This task generally requires utilization of multiple types of EHR data (e.g., diagnoses and procedure codes, unstructured clinical notes) and a more robust algorithm integrating both natural language processing and machine learning. In this study, we developed and validated an EHR-based classifier to accurately identify stroke incidence among a cohort of atrial fibrillation (AF) patients Methods: We developed a stroke phenotyping algorithm using International Classification of Diseases, Ninth Revision (ICD-9) codes, Current Procedural Terminology (CPT) codes, and expert-provided keywords as model features. Structured data was extracted from Rochester Epidemiology Project (REP) database. Natural Language Processing (NLP) was used to extract and validate keyword occurrence in clinical notes. A window of ±30 days was considered when including/excluding keywords/codes into the input vector. Frequencies of keywords/codes were used as input feature sets for model training. Multiple competing models were trained using various combinations of feature sets and two machine learning algorithms: logistic regression and random forest. Training data were provided by two nurse abstractors and included validated stroke incidences from a previously established atrial fibrillation cohort. Precision, recall, and F-score of the algorithm were calculated to assess and compare model performances. Results: Among 4,914 patients with atrial fibrillation, 1,773 patients were screened. 3,141 patients had no stroke-related codes or keywords and were presumed to be free of stroke during follow-up. Among the screened patients, 740 had validated strokes and 1,033 did not have a stroke based on review of the EHR by trained nurse abstractors. The best performing stroke incidence phenotyping classifier utilized Keywords+ICD-9+CPT features using a random forest classifier, achieving a precision of 0.942, recall of 0.943, and F-score of 0.943. Conclusion: In conclusion, we developed and validated a stroke algorithm that performed well for identifying stroke incidence in an enriched population (AF cohort), which extends beyond the typical binary case/non-case stroke identification problem. Future work will involve testing the generalizability of this algorithm in a general population.

Purpose The Mayo Clinic Biobank was established to provide a large group of patients from which comparison groups (ie, controls) could be selected for case–control studies, to create a prospective cohort with sufficient power for common outcomes and to support electronic health record (EHR) studies. Participants A total of 56 862 participants enrolled (21% response rate) into the Mayo Clinic Biobank from Rochester, Minnesota (77%, n=43 836), Jacksonville, Florida (18%, n=10 368) and La Crosse, Wisconsin (5%, n=2658). Participants were all Mayo Clinic patients, 18 years of age or older and US residents. Findings to date Overall, 43% of participants were 65 years of age or older and female participants were more frequent (59%) than males at all sites. Most participants resided in the Upper Midwest regions of the USA (Minnesota, Iowa, Illinois or Wisconsin), Florida or Georgia. Self-reported race among Biobank participants was 90% white. Here we provide examples of the types of studies that have successfully utilised the resource, including (1) investigations of the population itself, (2) provision of controls for case–control studies, (3) genotype-driven research, (4) EHR-based research and (5) prospective recruitment to other studies. Over 270 projects have been approved to date to access Biobank data and/or samples; over 200 000 sample aliquots have been approved for distribution. Future plans The data and samples in the Mayo Clinic Biobank can be used for various types of epidemiological and clinical studies, especially in the setting of case–control studies for which the Biobank samples serve as control samples. We are planning cohort studies with additional follow-up and acquisition of genetic information on a large scale.

Abstract Pharmacogenomics (PGx) clinical decision support integrated into the electronic health record (EHR) has the potential to provide relevant knowledge to clinicians to enable individualized care. However, past experience implementing PGx clinical decision support into multiple EHR platforms has identified important clinical, procedural, and technical challenges. Commercial EHRs have been widely criticized for the lack of readiness to implement precision medicine. Herein, we share our experiences and lessons learned implementing new EHR functionality charting PGx phenotypes in a unique repository, genomic indicators, instead of using the problem or allergy list. The Gen-Ind has additional features including a brief description of the clinical impact, a hyperlink to the original laboratory report, and links to additional educational resources. The automatic generation of genomic indicators from interfaced PGx test results facilitates implementation and long-term maintenance of PGx data in the EHR and can be used as criteria for synchronous and asynchronous CDS.