Plasma Biomarker Profiles for Premature and Nonpremature Coronary Heart Disease in Women.

BACKGROUND: Premature coronary heart disease (CHD) is a major cause of death in women. We aimed to characterize biomarker profiles of women who developed CHD before and after age 65 years. METHODS: In the Women's Health Study (median follow-up 21.5 years), women were grouped by age and timing of incident CHD: baseline age <65 years with premature CHD by age 65 years (25 042 women; 447 events) and baseline age ≥65 years with nonpremature CHD (2982 women; 351 events). Associations of 44 baseline plasma biomarkers measured using standard assays and a nuclear magnetic resonance (NMR)-metabolomics assay were analyzed using Cox models adjusted for clinical risk factors. RESULTS: Twelve biomarkers showed associations only with premature CHD and included lipoprotein(a), which was associated with premature CHD [adjusted hazard ratio (HR) per SD: 1.29 (95% CI 1.17-1.42)] but not with nonpremature CHD [1.09(0.98-1.22)](Pinteraction = 0.02). NMR-measured lipoprotein insulin resistance was associated with the highest risk of premature CHD [1.92 (1.52-2.42)] but was not associated with nonpremature CHD (Pinteraction <0.001). Eleven biomarkers showed stronger associations with premature vs nonpremature CHD, including apolipoprotein B. Nine NMR biomarkers showed no association with premature or nonpremature CHD, whereas 12 biomarkers showed similar significant associations with premature and nonpremature CHD, respectively, including low-density lipoprotein (LDL) cholesterol [1.30(1.20-1.45) and 1.22(1.10-1.35)] and C-reactive protein [1.34(1.19-1.50) and 1.25(1.08-1.44)]. CONCLUSIONS: In women, a profile of 12 biomarkers was selectively associated with premature CHD, driven by lipoprotein(a) and insulin-resistant atherogenic dyslipoproteinemia. This has implications for the development of biomarker panels to screen for premature CHD.

Avoiding Escalation to the Emergency Department by Activating an In-Home Rapid Response Team in the 30 Days After Hospital-at-Home Discharge.

BACKGROUND: Mayo Clinic's virtual hybrid hospital-at-home program, Advanced Care at Home (ACH) monitors acute and post-acute patients for signs of deterioration and institutes a rapid response (RR) system if detected. OBJECTIVE: This study aimed to describe Mayo Clinic's ACH RR team and its effect on emergency department (ED) use and readmission rates. METHODS: This was a retrospective review of all post-inpatient (restorative phase) ACH patients admitted from July 6, 2020 through June 30, 2021. If the restorative patient had a clinical decompensation, an RR was activated. All RR activations were analyzed for patient demographic characteristics, admitting and escalation diagnosis, time spent by virtual team on the RR, and whether the RR resulted in transport to the ED or hospital readmission. RESULTS: Three hundred and twenty patients were admitted to ACH during the study interval; 230 received restorative care. Seventy-two patients (31.3%) had events that triggered an RR. Fifty (69.4%) of the RR events were related to the admission diagnosis (p < 0.001; 95% CI 0.59-0.80). Twelve patients (16.7%) required transport to an ED for further treatment and were readmitted and 60 patients (83.3%) were able to be treated successfully in the home by the RR team (p < 0.001; 95% CI 0.08-0.25). CONCLUSIONS: The use of an ACH RR team was effective at limiting both escalations back to an ED and hospital readmissions, as 83% of deteriorating patients were successfully stabilized and managed in their homes. Implementing a hospital-at-home RR team can reduce the need for ED use by providing critical resources and carrying out required interventions to stabilize the patient's condition.

Psychosis Management in Lewy Body Dementia: A Comprehensive Clinical Approach.

Lewy body dementia (LBD) is asynucleinopathy that results in clinical manifestation of motor and neuropsychiatric symptoms. The disease burden associated with psychosis in LBD patients is significantly higher compared to other types of dementia or even to LBD without psychosis. Effective care management processes should include consideration of de-prescribing any offending agents including anticholinergics and dopaminergic agents, followed by nonpharmacological and low risk pharmacological approach. If addition of pharmacological agents is required, consideration should be given to acetylcholinesterase inhibitors, pimavanserin and atypical antipsychotics such as quetiapine or clozapine. Side effects of these medications should be considered prior to selection and initiation of a medication regimen. Goals of care and functional assessment are a crucial part of the optimized care plan, given overall guarded prognosis, in the context of numerous complications observed in this population. Palliative care consultation could facilitate symptom control and timely enrollment into hospice if consistent with patient's goals.

Psychologic wellness of PA, NP, and physician hospitalists during the COVID-19 pandemic.

OBJECTIVES: Hospitalists, comprising PAs, NPs, and physicians, manage patients hospitalized with COVID-19. To guide the development of support programs, this study compared the psychologic wellness of hospitalist PAs, NPs, and physicians during the COVID-19 pandemic. METHODS: We surveyed hospitalists in 16 hospitals at Mayo Clinic, from May 4 to 25, 2020. We used PROMIS surveys for self-reported global well-being (two single-item measures), anxiety, social isolation, and emotional support, before and during the pandemic. Linear and logistic regression models were adjusted for personal and professional factors. RESULTS: The response rate was 52.2% (N = 154/295). In adjusted linear regression models, the change in scores (before minus during pandemic) for anxiety, social isolation, and emotional support was similar for PAs and NPs compared with physicians. In adjusted logistic regression models, physicians, compared with PAs and NPs, had a higher odds of top global well-being for mental health (adjusted odds ratio [95% confidence interval]: 2.82 [1.12, 7.13]; P = .03) and top global well-being for social activities and relationships (adjusted odds ratio 4.08 [1.38, 12.08]; P = .01). CONCLUSIONS: During the COVID-19 pandemic, global well-being was lower for PAs and NPs compared with physician hospitalists. These results can guide support programs for hospitalists.

Association of obesity indices with in-hospital and 1-year mortality following acute coronary syndrome.

BACKGROUND/OBJECTIVES: According to the "obesity paradox", adults with obesity have a survival advantage following acute coronary syndrome, compared with those without obesity. Previous studies focused on peripheral obesity and whether this advantage is conferred by central obesity is unknown. The objective of this study was to describe the association of peripheral and central obesity indices with risk of in-hospital and 1-year mortality following acute coronary syndrome (ACS). SUBJECTS/METHODS: Gulf COAST is a prospective ACS registry that enrolled 4044 patients age ≥18 years from January 2012 through January 2013, across 29 hospitals in four Middle Eastern countries. Associations of indices of peripheral obesity (body-mass index, [BMI]) and central obesity (waist circumference [WC] and waist-to-height ratio [WHtR]) with mortality following ACS were analyzed in logistic regression models (odds ratio, 95% CI) with and without adjustment for Global Registry of Acute Coronary Events risk score. RESULTS: Of 3882 patients analyzed (mean age: 60 years; 33.3% women [n = 1294]), the prevalence of obesity was 34.5% (BMI ≥ 30.0 kg/m2), 72.2% (WC ≥ 94.0 cm [men] or ≥80.0 cm [women]) and 90.0% (WHtR ≥ 0.5). In adjusted models, deciles of obesity indices showed higher risk of mortality at extreme versus intermediate deciles (U-shaped). When defined by conventional cut-offs, peripheral obesity (BMI ≥ 30.0 versus 18.5-29.9 kg/m2) showed inverse association with risk of in-hospital mortality (0.64; 95% CI, 0.42-0.99; P = 0.04; central obesity showed trend toward reduced mortality). In contrast, for risk of 1-year mortality, all indices showed inverse association. Obesity, defined by presence of all three indices, versus nonobesity showed inverse association with risk of 1-year mortality (0.52; 95% CI, 0.35-0.75; P = 0.001). Results were similar among men and women. CONCLUSION: The degree of obesity paradox following ACS depends on the obesity index and follow-up time. Obesity indices may aid in risk stratification of mortality following ACS.

Burden and management of type 2 diabetes in rural United States.

In the United States, rural areas have a higher burden of type 2 diabetes (T2DM) compared to urban areas. However, there is limited information on risk factors and interventions that improve the primary prevention and management of T2DM in rural areas. To synthesize current knowledge on T2DM in rural areas and to guide healthcare providers and policy makers, we reviewed five scientific databases and the grey literature over the last decade (2010-2020). We described classification systems for rurality and the T2DM burden based on rurality and region (West, South, Midwest, and Northeast). We highlighted risk factors for T2DM in rural compared to urban areas, and summarized interventions to screen and manage T2DM based on opportunistic screening, T2DM self-management, community-based initiatives, as well as interventions targeting comorbidities and T2DM. Several studies identified the co-existence of T2DM and depression/psychological symptoms, which could reduce adherence to non-pharmacologic and pharmacologic management of T2DM. We highlighted the role of technology in education and counselling of patients with geographic and financial barriers to accessing care, which is exacerbated by the SARS-CoV-2 coronavirus disease-19 pandemic. We identified knowledge gaps and next steps in improving T2DM care in rural areas. There is an urgent need for interventions tailored to rural areas given that rural Americans currently experience a disproportionate burden of T2DM and are encumbered by its associated morbidity, mortality, and loss in economic productivity.

County Rurality and Incidence and Prevalence of Diagnosed Diabetes in the United States.

OBJECTIVE: To examine differences in the incidence and prevalence of diagnosed diabetes by county rurality. PATIENTS AND METHODS: This observational, cross-sectional study used US Centers for Disease Control and Prevention data from 2004 through 2019 for county estimates of incidence and prevalence of diagnosed diabetes. County rurality was based on 6 levels (large central metro counties [most urban] to noncore counties [most rural]). Weighted least squares regression was used to relate rurality with diabetes incidence rates (IRs; per 1000 adults) and prevalence (percentage) in adults aged 20 years or older after adjusting for county-level sociodemographic factors (eg, food environment, health care professionals, inactivity, obesity). RESULTS: Overall, in 3148 counties and county equivalents, the crude IR and prevalence of diabetes were highest in noncore counties. In age and sex ratio-adjusted models, the IR of diabetes increased monotonically with increasing rurality (P<.001), whereas prevalence had a weak, nonmonotonic but statistically significant increase (P=.002). Further adjustment for sociodemographic factors including food environment, health care professionals, inactivity, and obesity attenuated differences in incidence across rurality levels, and reversed the pattern for prevalence (prevalence ratios [vs large central metro] ranged from 0.98 [95% CI, 0.97 to 0.99] for large fringe metro to 0.94 [95% CI, 0.93 to 0.96] for noncore). In region-stratified analyses adjusted for sociodemographic factors including inactivity and obesity, increasing rurality was inversely associated with incidence in the Midwest and West only and inversely associated with prevalence in all regions. CONCLUSION: The crude incidence and prevalence of diagnosed diabetes increased with increasing county rurality. After accounting for sociodemographic factors including food environment, health care professionals, inactivity, and obesity, county rurality showed no association with incidence and an inverse association with prevalence. Therefore, interventions targeting modifiable sociodemographic factors may reduce diabetes disparities by region and rurality.

Writing Groups for Healthcare Professionals in Academic Medicine.

Within the Department of Medicine (DOM) in a large tertiary academic health care facility in midwestern United States, we have developed an educational offering that incorporates an academic writing program (AWP) blending the approaches of a writing accountability work group, a writing workshop, and didactic writing courses. The purpose of this AWP was to assist healthcare professionals (HCP) with their manuscript writing skills to enhance academic productivity. We report our evolving journey and experiences with this AWP. To date, it has been offered 3 times to 25 HCP over the course of 3 years. Among those responding to a post program follow up survey (N = 11), 8 (73%) indicated that they completed the project that they were working on during the AWP and went on to publish the manuscript (N = 5) or were in the process of submission (N = 2). Some indicated they has also gone on to present posters (N = 2) or were in the process of presenting posters (N = 2) or had received grants (N = 1) or were awaiting grant notice (N = 1). A number of attendees have continued to use and share the tools presented during the AWP. Based on input from attendees and increased requests for this AWP, this educational program has been deemed a success and expansion of this program is currently underway.

Development and internal validation of machine learning-based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke.

Aims: We developed new machine learning (ML) models and externally validated existing statistical models [ischaemic stroke predictive risk score (iScore) and totalled health risks in vascular events (THRIVE) scores] for predicting the composite of recurrent stroke or all-cause mortality at 90 days and at 3 years after hospitalization for first acute ischaemic stroke (AIS). Methods and results: In adults hospitalized with AIS from January 2005 to November 2016, with follow-up until November 2019, we developed three ML models [random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBOOST)] and externally validated the iScore and THRIVE scores for predicting the composite outcomes after AIS hospitalization, using data from 721 patients and 90 potential predictor variables. At 90 days and 3 years, 11 and 34% of patients, respectively, reached the composite outcome. For the 90-day prediction, the area under the receiver operating characteristic curve (AUC) was 0.779 for RF, 0.771 for SVM, 0.772 for XGBOOST, 0.720 for iScore, and 0.664 for THRIVE. For 3-year prediction, the AUC was 0.743 for RF, 0.777 for SVM, 0.773 for XGBOOST, 0.710 for iScore, and 0.675 for THRIVE. Conclusion: The study provided three ML-based predictive models that achieved good discrimination and clinical usefulness in outcome prediction after AIS and broadened the application of the iScore and THRIVE scoring system for long-term outcome prediction. Our findings warrant comparative analyses of ML and existing statistical method-based risk prediction tools for outcome prediction after AIS in new data sets.