Citations

Objectives Sex as a biological variable affects response to opioids. However, few reports describe the prevalence of specific adverse reactions to commonly prescribed opioids in men and women separately. A large cohort was used to investigate sex differences in type and occurrence of adverse reactions associated with use of codeine, tramadol, oxycodone and hydrocodone. Design Retrospective cohort study. Setting Participants in the Right Drug, Right Dose, Right Time (RIGHT) Study. Participants The medical records of 8457 participants in the RIGHT Study who received an opioid prescription between 1 January 2004 and 31 December 2017 were reviewed 61% women, 94% white, median age (Q1–Q3)=58 (47–66). Primary and secondary outcome measures Adverse reactions including gastrointestinal, skin, psychiatric and nervous system issues were collected from the allergy section of each patient’s medical record. Sex differences in the risk of adverse reactions due to prescribed opioids were modelled using logistic regression adjusted for age, body mass index, race and ethnicity. Results From 8457 participants (of which 449 (5.3%) reported adverse reactions), more women (6.5%) than men (3.4%) reported adverse reactions to at least one opioid (OR (95% CI)=2.3 (1.8 to 2.8), p<0.001). Women were more likely to report adverse reactions to tramadol (OR (95% CI)=2.8 (1.8 to 4.4), p<0.001) and oxycodone (OR (95% CI)=2.2 (1.7 to 2.9), p<0.001). Women were more likely to report gastrointestinal (OR (95% CI)=3.1 (2.3 to 4.3), p<0.001), skin (OR (95% CI)=2.1 (1.4 to 3.3), p=0.001) and nervous system issues (OR (95% CI)=2.3 (1.3 to 4.2), p=0.004). Conclusions These findings support the importance of sex as a biological variable to be factored into pain management studies.

Background: Prevention strategies for Alzheimer’s disease and Alzheimer’s disease related dementias (AD/ADRDs) are urgently needed for reducing incidence. Intra-patient variability in lipid levels is a potentially modifiable risk factor for incident AD/ADRD. Although regular lipid measurements are a part of common clinical practice and longitudinal data routinely available in electronic health records (EHR), research examining this association between AD/ADRD and lipid variability across multiple lipid types remains scarce. Methods: All residents living in Olmsted County, MN on 1/1/2006 age ≥60 years without an AD/ADRD diagnosis were identified using Centers for Medicare & Medicaid Services diagnostic codes. Persons with ≥3 lipid measurements (total cholesterol or triglycerides) in the 5 years prior to index date were retained. Lipid variability was quantified using variability independent of the mean (VIM). Models were adjusted for traditional risk factors. Associations between lipid variability quintiles and incident AD/ADRD were assessed using Cox proportional hazards regression. Multiple imputation was used for missing covariates. Participants were followed through 2018 for incident AD/ADRD. Results: The final analysis included data on 11,551 participants with total cholesterol and 11,502 participants with triglycerides. Participants had a mean age of 71 (range 60-99) years, and were primarily white (96%). Females (54%) were also slightly more frequent than males. Median follow up was 12.9 years (range: 0-13). Participants in the highest quintile of variability for total cholesterol and triglycerides had a 20% increased risk of incident AD/ADRD. Similar results were found in the subset with complete covariate data. Conclusion: In a large EHR derived cohort, persons in the highest quintile of lipid variation had an increased risk of incident AD/ADRD. Further studies to identify the mechanisms behind this risk factor and replication of these results across a more diverse population are needed.

Background: Larger within-patient variability of lipid levels has been associated with an increased risk of cardiovascular disease (CVD). However, measures of lipid variability are not currently used clinically. We investigated the feasibility of calculating lipid variability within a large electronic health record (EHR)-based population cohort and assessed associations with incident CVD. Methods: We identified all individuals ≥40 years of age who resided in Olmsted County, MN on 1/1/2006 (index date) without prior CVD. CVD was defined as myocardial infarction, coronary artery bypass graft surgery, percutaneous coronary intervention or stroke. Patients with ≥3 measurements of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and/or triglycerides during the 5 years before the index date were retained in the analyses. Lipid variability was calculated using variability independent of the mean (VIM). Patients were followed through 9/30/2017 for incident CVD (including CVD death). Cox regression was used to investigate the association between quintiles of lipid VIMs and incident CVD. Results: We identified 18,642 individuals (mean age 60; 55% female) who were free of CVD at baseline and VIM calculated for at least one lipid measurement. After adjustment, those in the highest VIM quintiles of total cholesterol had a 25% increased risk of CVD (Q5 vs. Q1 HR: 1.25, 95% CI: 1.08-1.45; Table). We observed similar results for LDL-C (Q5 vs. Q1 HR: 1.20, 95% CI: 1.04-1.39) and HDL-C (Q5 vs. Q1 HR: 1.25, 95% CI: 1.09-1.43). There was no association between triglyceride variability quintiles and CVD risk. Conclusion: In a large EHR-based population cohort, high variability in total cholesterol, HDL-C and LDL-C was associated with an increased risk of CVD, independently of traditional risk factors, suggesting it may be a target for intervention. Lipid variability can be calculated in the EHR environment but more research is needed to determine its clinical utility.

Abstract Background: Adrenal adenomas have been linked with cardiovascular morbidity in selected patient populations from specialized referral centers. Population-based data examining the association of adrenal adenomas with cardiometabolic outcomes are lacking. Aim: To determine the incidence of cardiometabolic outcomes in a population-based cohort of patients with adrenal adenomas. Methods: We conducted a population-based cohort study of patients diagnosed with adrenal adenomas while living in a defined community from 1995–2017. Eligible patients were retrospectively identified by a medical records linkage system and confirmed on chart review. Adenomas were classified as mild autonomous cortisol secretion (MACS) if the serum cortisol concentration was &gt;1.8 mcg/dL after 1 mg overnight dexamethasone suppression test (DST), nonfunctioning adrenal tumor (NFAT) if serum cortisol after DST was ≤1.8 mcg/dL, and adenoma with unknown cortisol secretion (AUCS) if DST was not performed. Patients with overt hormone excess were excluded. Cardiometabolic outcomes were assessed at the time of adrenal adenoma diagnosis. Patients were then followed until death, migration out of the community, or through December 31, 2017. Incident outcomes were assessed starting at 1 year following the diagnosis and excluded those with the outcome of interest at baseline, except for myocardial infarction (MI) and coronary intervention, which were adjusted for in the analysis. Results: were compared to 1:1 age and sex-matched referent subjects without adrenal adenomas from the same community. Results: Adrenal adenomas were diagnosed in 1004 patients with 141 (14%) NFAT, 81 (8%) MACS, and 782 (78%) AUCS. The median age of diagnosis was 63 years (range, 20–96), and 582 (58%) were women. The baseline data was previously presented and showed higher prevalence of hypertension, diabetes, peripheral vascular disease (PVD), and heart failure (HF) in the adenoma group, after adjusting for BMI and tobacco use. During a median follow-up of 6.8 years (range, 0–22), patients with adrenal adenomas were more likely than referent subjects to develop new-onset dyslipidemia (HR 1.31, 95% CI 1.03–1.67), diabetes (HR 1.68, 95% CI 1.28–2.22), chronic kidney disease (HR 1.77, 95% CI 1.39–2.25), atrial fibrillation (HR 1.32 (1.03–1.70), PVD (HR 1.61, 95% CI 1.24–2.09), and HF (HR 1.46, 95% CI 1.15–1.85). In addition, the adenoma group had higher risk for incident MI (HR 1.62, 95% CI 1.16–2.25) and coronary intervention (HR 1.70, 95% CI 1.25–2.31). Conclusions: Adrenal adenomas are associated with increased incidence of adverse cardiometabolic outcomes in this population-based cohort study. While these results are potentially explained by different degrees of cortisol excess, the majority of patients received suboptimal hormone evaluation, suggesting a knowledge gap in the workup of adrenal adenomas in the broader medical community.

Objectives To describe the percentile distribution of multimorbidity across age by sex, race and ethnicity, and to demonstrate the utility of multimorbidity percentiles to predict mortality. Design Population-based descriptive study and cohort study. Setting Olmsted County, Minnesota (USA). Participants We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org ) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years). Methods For each person, we obtained the count of chronic conditions (out of 20 conditions) present on each birthday by extracting all of the diagnostic codes received in the 5 years before the index birthday from the electronic indexes of the REP. To compare each person’s count to peers of same age, the counts were transformed into percentiles of the total population and displayed graphically across age by sex, race and ethnicity. In addition, quintiles 1, 2, 4 and 5 were compared with quintile 3 (reference) to predict the risk of death at 1 year, 5 years and through end of follow-up using time-to-event analyses. Follow-up was passive using the REP. Results We identified 238 010 persons who experienced a total of 1 458 094 birthdays during the study period (median of 6 birthdays per person; IQR 3–10). The percentiles of multimorbidity across age did not vary noticeably by sex, race or ethnicity. In general, there was an increased risk of mortality at 1 and 5 years for quintiles 4 and 5 of multimorbidity. The risk of mortality for quintile 5 was greater for younger age groups and for women. Conclusions The assignment of multimorbidity percentiles to persons in a population may be a simple and intuitive tool to assess relative health status, and to predict short-term mortality, especially in younger persons and in women.

Background Prior reports indicate that living in a rural area may be associated with worse health outcomes. However, data on rurality and heart failure (HF) outcomes are scarce. Methods and Results Residents from 6 southeastern Minnesota counties with a first‐ever code for HF ( International Classification of Diseases, Ninth Revision [ ICD‐9 ], code 428, and International Classification of Diseases, Tenth Revision [ ICD‐10 ] code I50) between January 1, 2013 and December 31, 2016, were identified. Resident address was classified according to the rural‐urban commuting area codes. Rurality was defined as living in a nonmetropolitan area. Cox regression was used to analyze the association between living in a rural versus urban area and death; Andersen‐Gill models were used for hospitalization and emergency department visits. Among 6003 patients with HF (mean age 74 years, 48% women), 43% lived in a rural area. Rural patients were older and had a lower educational attainment and less comorbidity compared with patients living in urban areas ( P <0.001). After a mean (SD) follow‐up of 2.8 (1.7) years, 2440 deaths, 20 506 emergency department visits, and 11 311 hospitalizations occurred. After adjustment, rurality was independently associated with an increased risk of death (hazard ratio [HR], 1.18; 95% CI, 1.09–1.29) and a reduced risk of emergency department visits (HR, 0.89; 95% CI, 0.82–0.97) and hospitalizations (HR, 0.78; 95% CI, 0.73–0.84). Conclusions Among patients with HF, living in a rural area is associated with an increased risk of death and fewer emergency department visits and hospitalizations. Further study to identify and address the mechanisms through which rural residence influences mortality and healthcare utilization in HF is needed in order to reduce disparities in rural health.

Abstract Background Bloodstream infections (BSIs) confer an increased risk of infective endocarditis (IE) in patients with a prosthetic cardiac valve. This relationship is less well established in patients undergoing valve repair. We conducted a retrospective population-based study to determine the incidence of BSIs following valve repair and identify risk factors associated with the development of IE. Methods The Rochester Epidemiology Project (REP) data linkage system was used to identify all persons who underwent valve repair in a 7-county region in Southeastern Minnesota between January 1, 2010 and December 31, 2018. Medical records were screened for the development of a BSI from time of procedure until May 15, 2020. Patients were classified as having BSI only, BSI with IE at outset, or BSI with subsequent development of new IE. IE at outset was defined as cases where IE was diagnosed at the time of initial positive blood culture. Results A total of 387 patients underwent valve repair surgery. A total of 31 (8%) patients subsequently developed a BSI, 4% within one year of surgery. Seventeen patients underwent mitral repair with annuloplasty, 9 underwent tricuspid annuloplasty, and 5 had concurrent repairs. Median time to the development of BSI was 338 days. Of the 31 patients with BSI, 4 (13%) had BSI with IE at outset. No patients developed IE subsequent to BSI, Enterococcus spp. was responsible for 3 cases of IE, and MSSA for 1. All cases occurred within one year of surgery. Given the low incidence, statistical analysis of associated risk factors for IE was not feasible. All patients with BSI and IE at outset, however, died by the end of the study period, versus 11/27 in the BSI only group. Conclusion Incidence of BSIs was higher in patients undergoing cardiac valve repair than in the general population. The incidence of IE with a BSI was 13%, which is lower than what has been previously published. It is notable that all cases of IE occurred within one year of surgery. Recognizing that endothelialization of device surfaces occurs, it is tempting to speculate that the risk of IE may be time dependent and may decline over time. Subsequent investigation of this theory is underway. Disclosures Larry M. Baddour, MD, Boston Scientific (Consultant)

Abstract Background Transcatheter aortic valve replacement (TAVR) was initially approved as an alternative to surgery for patients at high surgical risk. However, it is now being considered for patients with intermediate and low surgical risk. This will result in the expansion of patient pool for TAVR; hence it is of interest to ascertain risk of blood stream infection (BSI) and infective endocarditis (IE) following TAVR. We aim to study the incidence, epidemiology and risk factors associated with IE in patients who underwent TAVR and subsequently developed a BSI. Methods A population-based study was conducted in 7 counties in southeastern Minnesota using the expanded Rochester Epidemiology Project (E-REP) for all adult (≥18 years) patients who underwent TAVR from January 1st, 2010 to December 31st, 2018. Transcatheter procedures that included replacement of either the aortic or mitral valve were included. Medical records were screened for development of BSI from time of TAVR until May 15th, 2020. Patients were classified as having BSI only, BSI with IE at outset, or BSI with subsequent development of new IE. ‘Early’ IE was defined as that occurring &lt; 12 months following TAVR, with subsequent cases defined as ‘late’ IE. Results A total of 247 patients underwent TAVR during the study period. There were 24 patients with of BSI and 10 (42%) developed IE with an annual incidence of 5 per 1000 persons-years. Median age for patients who developed IE was 85.4 years. Male gender was affected predominantly (70%). Six developed IE at outset of BSI, while four developed IE subsequent to IE. The median time to development of IE was 791 days following TAVR. There was an equal number of early and late IE cases (n=5). The most common pathogen causing IE was viridians group streptococci (n=4) followed by enterococci and coagulase-negative staphylococci with 2 patients each. Mean Charlson comorbidity index was 6.6. Two patients with IE died before resolution of infection (20%). Conclusion The incidence of BSI and subsequent IE in patients with TAVR was low in our population. Due to the small number of BSI and IE cases, statistical analysis was not feasible. An analysis of all cases seen at Mayo Clinic is planned since the number of cases would be much higher to investigate potential risk factors associated with BSI and IE. Disclosures Larry M. Baddour, MD, Boston Scientific (Consultant) M. Rizwan Sohail, MD, Aziyo Biologics (Consultant)Medtronic Inc (Consultant, Research Grant or Support)

Abstract Background Delirium is underdiagnosed in clinical practice and is not routinely coded for billing. Manual chart review can be used to identify the occurrence of delirium; however, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) has the capability to process raw text in electronic health records (EHRs) and determine the meaning of the information. We developed and validated NLP algorithms to automatically identify the occurrence of delirium from EHRs. Methods This study used a randomly selected cohort from the population-based Mayo Clinic Biobank (N = 300, age ≥65). We adopted the standardized evidence-based framework confusion assessment method (CAM) to develop and evaluate NLP algorithms to identify the occurrence of delirium using clinical notes in EHRs. Two NLP algorithms were developed based on CAM criteria: one based on the original CAM (NLP-CAM; delirium vs no delirium) and another based on our modified CAM (NLP-mCAM; definite, possible, and no delirium). The sensitivity, specificity, and accuracy were used for concordance in delirium status between NLP algorithms and manual chart review as the gold standard. The prevalence of delirium cases was examined using International Classification of Diseases, 9th Revision (ICD-9), NLP-CAM, and NLP-mCAM. Results NLP-CAM demonstrated a sensitivity, specificity, and accuracy of 0.919, 1.000, and 0.967, respectively. NLP-mCAM demonstrated sensitivity, specificity, and accuracy of 0.827, 0.913, and 0.827, respectively. The prevalence analysis of delirium showed that the NLP-CAM algorithm identified 12 651 (9.4%) delirium patients, the NLP-mCAM algorithm identified 20 611 (15.3%) definite delirium cases, and 10 762 (8.0%) possible cases. Conclusions NLP algorithms based on the standardized evidence-based CAM framework demonstrated high performance in delineating delirium status in an expeditious and cost-effective manner.