Our multiethnic community-based study demonstrates positive associations between diabetes and irritative LUTS and nocturia. Moreover, the association between irritative LUTS and diabetes is increased in black men. There was no strong evidence for an association between diabetes and BPH across measures more specific to BPH (i.e., prostate volume, PSA, and peak urinary flow rate). Taken together, our findings suggest that the presence of diabetes may be less related to prostate growth and more related to the dynamic components of lower urinary tract function. Further evaluations of the association between diabetes and BPH and related racial variations are warranted.

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 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.

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.

Reproducibility is an important quality criterion for the secondary use of electronic health records (EHRs). However, multiple barriers to reproducibility are embedded in the heterogeneous EHR environment. These barriers include complex processes for collecting and organizing EHR data and dynamic multi-level interactions occurring during information use (e.g., inter-personal, inter-system, and cross-institutional). To ensure reproducible use of EHRs, we investigated four information quality dimensions and examine the implications for reproducibility based on a real-world EHR study. Four types of IQ measurements suggested that barriers to reproducibility occurred for all stages of secondary use of EHR data. We discussed our recommendations and emphasized the importance of promoting transparent, high-throughput, and accessible data infrastructures and implementation best practices (e.g., data quality assessment, reporting standard).

ObjectivesSex 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.DesignRetrospective cohort study.SettingParticipants in the Right Drug, Right Dose, Right Time (RIGHT) Study.ParticipantsThe 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 measuresAdverse 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.ResultsFrom 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).ConclusionsThese findings support the importance of sex as a biological variable to be factored into pain management studies.