St Sauver, Jennifer

Objective: This study determined the incidence rates for obesity among adult patients ages 20 and older empaneled in primary care practice in Midwest United States to potentially identify an optimum timeframe for initiating intervention. Background: Primary care practice patients are likely to reflect underlying community trends in overweight and obesity; however, data on overweight and obesity in primary care patients is limited. While childhood incidence rates of obesity have been well reported, there is still a paucity of data on the incidence of obesity among adult population; literature has mainly focused on its prevalence. Methods: Medical record review of identified cohort with BMI data was conducted. Population was stratified by age and sex and overweight category was subdivided into tertiles. Results: Majority of 40 390 individuals who comprised the final population and had follow-up data, consisted of adults ages 40 to 69 years (47.5%), female (59.8%) of non-Hispanic ethnicity (95.9%) with 21 379 (52.8%) falling in weight category of overweight. Incidence of obesity was 7% at 1 year and 16% at 3 years follow-up. Highest percentages of individuals who became obese at 1 and 3 years were in age category of 40 to 69 years among men and 20 to 39 years among women. In Cox regression analysis, there was statistically significant association to developing obesity among all tertile groups in the overweight category. Age and particularly gender appeared to be modifying factors to likelihood of developing obesity. Conclusion: Study results suggest that while obesity incidence is higher among certain age groups in both genders, middle-aged women, and men in all tertiles of overweight category are at highest risk and may be the optimum population to target for weight loss interventions. Findings support the initiation of population-based interventions before onset of obesity.

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 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.

AbstractThe purpose of this study was to estimate the extent of potential antidepressant overprescribing in a geographically defined U.S. population, and to determine the indications and factors that account for it. We conducted a cohort study of new antidepressant prescriptions for elderly residents of Olmsted County, Minnesota, 2005‐2012, using the Rochester Epidemiology Project medical records‐linkage system. Indications for antidepressants were abstracted from health records for all cohort members. Potential antidepressant overprescribing was defined based on regulatory approval, the level of evidence identified from a standardized drug information database, and multidisciplinary expert review. Predictors of potential antidepressant overprescribing were investigated using logistic regression models, stratified by general antidepressant indication (general medical indication, specific psychiatric diagnosis, and non‐specific psychiatric symptoms). Potential antidepressant overprescribing occurred in 24% of 3199 incident antidepressant prescriptions during the study period, and involved primarily newer antidepressants that were prescribed for non‐specific psychiatric symptoms and subthreshold diagnoses. Potential antidepressant overprescribing was associated with nursing home residence, having a higher number of comorbid medical conditions and outpatient prescribers, taking more concomitant medications, having greater use of urgent or acute care services in the year preceding the index antidepressant prescription, and being prescribed antidepressants via telephone, e‐mail, or patient portal. In conclusion, potential antidepressant overprescribing occurred in elderly persons and involved mainly newer antidepressants used for non‐specific psychiatric symptoms and subthreshold diagnoses, and was associated with indicators of higher clinical complexity or severity and with prescribing without face‐to‐face patient contact.

AbstractBackgroundTo assess the risk of cerebrovascular accidents (CVAs) and second brain tumors (SBTs) in patients with pituitary adenoma after surgery or radiotherapy.MethodsA cohort of 143 people from Olmsted County, who were diagnosed with pituitary adenoma between 1933 and 2000, was studied. Only patients from Olmsted County were included because of the unique nature of medical care in Olmsted County, which allows the ascertainment of virtually all cases of pituitary adenoma for this community's residents and comparisons to the general population in the county. Surgical resection was performed in 76 patients, 29 patients underwent radiotherapy (with 21 undergoing both surgery and radiotherapy), 5 patients were reirradiated, and 59 patients were managed conservatively and observed.ResultsMedian follow-up was 15.5 years. There was no difference in CVA-free survival between treatment groups. On univariate analysis age > 60 years (hazard ratio [HR], 11.93; 95% CI, 6.26–23.03; P < .001); male sex (HR, 3.67; 95% CI, 2.03–6.84; P < .001), and reirradiation (HR, 3.41; 95% CI, 1.05–9.68; P = .04) were associated with worse CVA-free survival. In multivariate analysis, only age > 60 years was associated with worse CVA-free survival. Compared with the general population, there was a 4-fold increase in the rate of CVAs in pituitary adenoma patients (HR, 4.2; 95% CI, 2.8–6.1). Two patients developed SBT (an irradiated patient and a surgically managed patient).ConclusionCVA is a significant risk for patients with pituitary tumors, but treatment does not seem to impact the risk. Even with long-term follow-up, SBTs are a rare event regardless of treatment modality.