Citations

Introduction Optimal hospital bed utilization requires innovative patient care models. We studied a novel hospitalist model utilizing telemedicine to facilitate collaboration with affiliated emergency departments (EDs) and support medical triage and care of ED patients with high likelihood of hospital admission. Methods Telehospitalists based at a tertiary care facility collaborated with four community EDs in the same healthcare network between January 1, 2022, and April 30, 2023. Telehospitalists supported ED clinicians in medical care decisions and facilitated patient disposition. Emergency department length of stay (LOS) and disposition were evaluated, as were hospital LOS, 30-day readmission, and in-hospital mortality. For patients discharged from the ED, 7-day ED readmission and subsequent hospitalization were evaluated. Results Telehospitalists discussed 550 “admit-likely” patients with ED clinicians: 105 patients (19.1%) discharged from the ED and avoided admission; 322 patients (58.5%) were admitted to local or nearby community hospitals; 123 patients (22.4%) transferred to the tertiary care facility. Emergency department LOS differed significantly among disposition groups, including patients discharged home (10.2 h), admitted to local hospitals (12.6 h), and transferred to tertiary care hospitalist services (14.9 h; p < 0.001). Hospital LOS and in-hospital mortality were not significantly different among disposition groups. Patients admitted locally had lower 30-day readmission compared to those transferred to tertiary care facility (odds ratio = 0.59 [0.36, 0.99], p = 0.048). Discussion Telehospitalists as triage clinicians is an innovative approach to support local ED clinicians and patients. Telehospitalists optimized hospital bed utilization and healthcare system resources by facilitating safe discharges to home and expediting tertiary care transfers when necessary.

Introduction: Living in an area with high Area Deprivation Index (ADI) is linked to worse cardiovascular disease (CVD) outcomes. However, the association of ADI with type 2 diabetes (T2D), a major CVD risk factor, is incompletely described. This knowledge may guide primary CVD prevention. Method: Rochester Epidemiology Project is a medical record linkage system of persons in southeast Minnesota and southwest Wisconsin. We identified 253,890 persons (aged 4–98 yrs on July 1, 2015) in 9 counties with >90% population coverage. ADI scores, from 2015 home addresses, were grouped into quartiles of low (Q1) to high (Q4) deprivation. We excluded persons with type 1 diabetes (T1D) or history of T2D and identified new T2D cases (2015-2020) using ICD codes, HbA1c and medications. We examined the association between ADI quartile and risk of incident T2D using Cox regression with persons right censored at death, T2D, T1D or last visit. Models were adjusted for age and gender, and reported adjusted hazard ratios (aHR [95% confidence interval]). Results: Age, gender, and race statistically differed across ADI quartiles (Table). Age × ADI interaction was significant ( P <.001). T2D incidence ranged from 6.3 (Q1) to 9.0 (Q4) per 1000 person-years ( P <.001). Women vs. men/non-binary persons (combined due to limited non-binary persons) had 30% lower risk of incident T2D (aHR 0.70 [0.67–0.72]). Risk of incident T2D was higher in persons from higher ADI areas. At median age of 48.6 yrs, the risk was 30% higher in Q2 (aHR 1.3 [1.22–1.38]), 38% higher in Q3 (aHR 1.38 [1.30–1.47]) and 53% higher in Q4 (aHR 1.53 [1.43–1.62]) vs. Q1. Higher ADI was associated with higher incident T2D in younger persons, with risk attenuation with increasing age (Figure). Conclusion: In this population cohort, persons, particularly of younger age, living in more deprived areas had a higher risk of incident T2D. ADI may help identify areas for targeted T2D screening, with implications for CVD screening and prevention.

Abstract Introduction Vulnerable populations experience a higher burden of cardiovascular disease.(1) However, the incidence of cardiovascular risk factors, such as diabetes, within these populations, is incompletely characterized. Recently, the United States Centers for Disease Control and Prevention (US CDC) developed the Social Vulnerability Index (SVI) to categorize counties that may be vulnerable to worse health outcomes. The SVI is composed of 4 themes: socioeconomic status (Theme 1), household composition & disability (Theme 2), minority status & language (Theme 3), and housing type & transportation (Theme 4).(2) We sought to evaluate the association of the 4 SVI themes with county incidence of diagnosed diabetes. Method We used CDC data (2004-2019; reported for 94% of US counties), on county incidence of diagnosed diabetes. The SVI themes were scored from 0 to 1 (low to high vulnerability) based on national percentile ranking. We used weighted least squares regression with restricted cubic splines to model the relationship between SVI themes and incidence of diagnosed diabetes (median data 2004−2019), adjusting for median age, sex ratio, state, percent uninsured, food environment index, healthcare providers, and rurality.(3) The partial effects of SVI themes and diabetes incidence were reported as F-statistics (P-values) and diabetes incidence rate ratios (IRR [95% confidence interval]), which were plotted for all values compared with the 50th percentile (SVI 0.5). For each theme, we also present the IRR for a 0.5-point increase when moving over the lower (SVI score 0.5 vs. 0.0) and the upper (1.0 vs. 0.5) half of the SVI score distribution. Results The 4 SVI theme rank scores were significantly associated with diabetes incidence (all, P&lt;.001). However, Theme 1 and 2 scores showed stronger associations with IRR than Theme 3 and 4 (Figure). For Theme 1, SVI score 0.5 vs. 0.0 was associated with a 15% higher incidence (IRR 1.15 [1.13–1.18]) and score 1.0 vs. 0.5 was associated with a 13% higher incidence (IRR 1.13 [1.11–1.16]). For Theme 2, SVI score 0.5 vs. 0.0 was associated with a 16% higher incidence (IRR 1.16 [1.14–1.18]), but there was no difference between rates for SVI score 1.0 vs. 0.5 (IRR 1.01 [0.99–1.03]). For Theme 3, there was no difference between SVI score 0.5 vs. 0.0 (IRR 0.98 [0.96–1.00]), but rates were 8% lower for SVI score 1.0 vs. 0.5 (IRR 0.92 [0.90–0.94]). For Theme 4, SVI score 0.5 vs. 0.0 was associated with a 3% lower incidence (IRR 0.97 [0.96–0.99]) and score 1.0 vs. 0.5 was associated with a 2% higher incidence (IRR 1.02 [1.01–1.04]). Conclusion In US counties, the strength and directionality of the association between SVI themes and diabetes incidence differed by theme and level of SVI scores. These themes, particularly socioeconomic status and household composition & disability may be leveraged to identify high-risk counties for primary prevention of diabetes, with implications for reduction in cardiovascular risk.

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.

Background: The prevalence of type 2 diabetes (T2D) is increasing in Hispanic or Latino adults. The American Diabetes Association (ADA) score to identify current risk of T2D was developed with limited Hispanic or Latino representation. Therefore, we sought to validate the ADA score in a cohort of Hispanic or Latino adults. Methods: At enrollment in the Sangre Por Salud Biobank, 3,625 Hispanic or Latino adults from Phoenix, Arizona completed a baseline questionnaire. Current risk of undiagnosed T2D was based on non-laboratory risk factors and ADA score ≥5 (Table). Participants also completed T2D screening (ie, fasting glucose, 2-hour oral glucose tolerance test, and/or hemoglobin A1c). Undiagnosed T2D was based on 'no self-reported T2D but meeting T2D diagnostic’ criteria. Multivariable logistic regression analyzed associations of ADA risk factors and undiagnosed T2D, and model performance was based on sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: At enrollment, 7.3% of participants had undiagnosed T2D (n=265/3,625). In multivariable analysis, most risk factors described for the ADA score were significantly associated with T2D, except for sex, which showed no association. ADA score ≥5 had sensitivity 50%, specificity 79%, PPV 18%, and NPV 95%. However, ADA score ≥4 had sensitivity 72%, specificity 61%, PPV 14%, and NPV 96%. ADA score ≥4 vs ≥5 identified 22% (n=59) additional cases of undiagnosed T2D. Furthermore, using the revised definition for elevated blood pressure (>120/80 mmHg) vs ≥140/90 mmHg (current ADA score) further improved the ADA score ≥4 (sensitivity 81%; specificity 52%). Other scores had lower performance using both hypertension definitions. Conclusion: In Hispanic or Latino adults, a lower ADA score than currently recommended identified additional cases of undiagnosed T2D. The ADA score requires validation and recalibration, including for the updated hypertension guidelines.

Abstract 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 &lt;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 &lt;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.

AbstractBackgroundHospital‐at‐home (HaH) is a growing model of care that has been shown to improve patient outcomes, satisfaction, and cost‐effectiveness. However, selecting appropriate patients for HaH is challenging, often requiring burdensome manual screening by clinicians. To facilitate HaH enrollment, electronic health record (EHR) tools such as best practice advisories (BPAs) can be used to alert providers of potential HaH candidates.ObjectiveTo describe the development and implementation of a BPA for identifying HaH eligible patients in Mayo Clinic's Advanced Care at Home (ACH) program, and to evaluate the provider response and the patient characteristics that triggered the BPA.Design, Setting, and ParticipantsWe conducted a retrospective multicenter study of hospitalized patients who triggered the BPA notification for ACH eligibility between March and December 2021 at Mayo Clinic in Jacksonville, FL and Mayo Clinic Health System in Eau Claire, WI. We extracted demographic and diagnosis data from the patients as well as characteristics of the providers who received the BPA notification.InterventionThe BPA was developed based on the ACH inclusion and exclusion criteria, which were derived from clinical guidelines, literature review, and expert consensus. The BPA was integrated into the EHR and displayed a pop‐up message to the provider when a patient met the criteria for ACH eligibility. The provider could choose to refer the patient to ACH, dismiss the notification, or defer the decision.Main Outcomes and MeasuresThe main outcomes were the number and proportion of BPA notifications that resulted in a referral to ACH, and the number and proportion of referrals that were accepted by the ACH clinical team and transferred to ACH. We also analyzed the factors associated with the provider's decision to refer or not refer the patient to ACH, such as the provider's role, location, and specialty.ResultsDuring the study period, 8962 notifications were triggered for 2847 patients. Providers opted to refer 711 (11.4%) of the total notifications linked to 324 unique patients. After review by the ACH clinical team, 31 of the 324 referrals (9.6%) met clinical and social criteria and were transferred to ACH. In multivariable analysis, Wisconsin nurses, physician assistants, and in‐training personnel had lower odds of referring the patients to ACH when compared to attending physicians.

Introduction: US rural areas have a disproportionate burden of CVD and hypertension. Physical inactivity is a major risk factor, with 25% prevalence in US adults. However, rural disparities in physical inactivity are incompletely described. The aim was to evaluate differences in prevalence of physical inactivity based on county-level rurality. Methods: We used CDC county-level data for adults aged ≥20 years, 2004–2019, available for 97.6% of US counties (n=3148/3226). CDC defined physical inactivity as self-reported lack of leisure time physical activity in the month preceding the survey. Rurality was based on six categories ordered from least rural (large central metro) to most rural (noncore). We used weighted least squares regression to compare prevalence by rurality level, adjusting for 13 county-level sociodemographic factors; results reported as prevalence ratios (95% CI) with partial F -tests and P -values. Results: In age and sex ratio adjusted analysis (model 1), we observed a gradient in the prevalence of physical inactivity across levels of rurality, with significantly higher rates in small metro (1.03 [1.00–1.06]), micropolitan (1.05 [1.02–1.07]), and noncore (1.05 [1.02–1.08]) compared with large central metro counties ( F -statistic=7.5; P <.001). With further adjustment for other sociodemographic factors (model 2), the relative differences were negated in noncore counties (0.99 [0.97-1.02]), attenuated in intermediate counties, and accentuated in large fringe metro counties (1.06 [1.04-1.08]) ( F -statistic=14.3; P <.001). Further adjustment for obesity (model 3) largely attenuated the association of rurality with physical inactivity, resulting in no significant differences relative to large central metro counties ( F -statistic=4.8; P <.001). Conclusions: County-level rurality was associated with prevalence of physical inactivity. These disparities were largely accounted for by obesity, which may be targeted for intervention.