Dugani, Sagar B

In July 2020, Mayo Clinic introduced a hospital-at-home program, known as Advanced Care at Home (ACH) as an alternate option for clinically stable medical patients requiring hospital-level care. This retrospective cohort study evaluates the impact of the addition of a dedicated ACH patient acquisition Advanced Practice Provider (APP) on average length of stay (ALOS) and the number of patients admitted into the program between in Florida and Wisconsin between 6 July 2020 and 31 January 2022. Patient volumes and ALOS of 755 patients were analyzed between the two sites both before and after a dedicated acquisition APP was added to the Florida site on 1 June 2021. The addition of a dedicated acquisition APP did not affect the length of time a patient was in the emergency department or hospital ward prior to ACH transition (2.91 days [Florida] vs. 2.59 days [Wisconsin], p = 0.22), the transition time between initiation of the ACH consult to patient transfer home (0.85 days [Florida] vs. 1.16 days [Wisconsin], p = 0.28), or the total ALOS (6.63 days [Florida] vs. 6.34 days [Wisconsin], p = 0.47). The average number of patients acquired monthly was significantly increased in Florida (38.3 patients per month) compared with Wisconsin (21.6 patients per month) (p < 0.01). The addition of a dedicated patient acquisition APP resulted in significantly higher patient volumes but did not affect transition time or ALOS. Other hospital-at-home programs may consider the addition of an acquisition APP to maximize patient volumes.

Abstract BACKGROUND Uncertainty remains over the relationship between blood pressure (BP) variability (BPV), measured in hospital settings, and clinical outcomes following acute ischemic stroke (AIS). We examined the association between within-person systolic blood pressure (SBP) variability (SBPV) during hospitalization and readmission-free survival, all-cause readmission, or all-cause mortality 1 year after AIS. METHODS In a cohort of 862 consecutive patients (age [mean ± SD] 75 ± 15 years, 55% women) with AIS (2005–2018, follow-up through 2019), we measured SBPV as quartiles of standard deviations (SD) and coefficient of variation (CV) from a median of 16 SBP readings obtained throughout hospitalization. RESULTS In the cumulative cohort, the measured SD and CV of SBP in mmHg were 16 ± 6 and 10 ± 5, respectively. The hazard ratios (HR) for readmission-free survival between the highest vs. lowest quartiles were 1.44 (95% confidence interval [CI] 1.04–1.81) for SD and 1.29 (95% CI 0.94–1.78) for CV after adjustment for demographics and comorbidities. Similarly, incident readmission or mortality remained consistent between the highest vs. lowest quartiles of SD and CV (readmission: HR 1.29 [95% CI 0.90–1.78] for SD, HR 1.29 [95% CI 0.94–1.78] for CV; mortality: HR 1.15 [95% CI 0.71–1.87] for SD, HR 0.86 [95% CI 0.55–1.36] for CV). CONCULSIONS In patients with first AIS, SBPV measured as quartiles of SD or CV based on multiple readings throughout hospitalization has no independent prognostic implications for the readmission-free survival, readmission, or mortality. This underscores the importance of overall patient care rather than a specific focus on BP parameters during hospitalization for AIS.

Importance US rural vs nonrural populations have striking disparities in diabetes care. Whether rurality contributes to disparities in diabetes mortality is unknown. Objective To examine rates and trends in diabetes mortality based on county urbanization. Design, Setting, and Participants In this observational, cross-sectional study, the US Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database was searched from January 1, 1999, to December 31, 2018, for diabetes as a multiple cause and the underlying cause of death among residents aged 25 years or older in US counties. County urbanization was categorized as metro, medium-small, and rural. Weighted multiple linear regression models and jackknife resampling, with a 3-segment time component, were used. The models included exposures with up to 3-way interactions and were age standardized to the 2009-2010 population. The analyses were conducted from July 1, 2020, to February 1, 2022. Exposures County urbanization (metro, medium-small, or rural), gender (men or women), age group (25-54, 55-74, or ≥75 years), and region (Midwest, Northeast, South, or West). Main Outcomes and Measures Annual diabetes mortality rate per 100 000 people. Results From 1999-2018, based on 4 022 238 309 person-years, diabetes was a multiple cause of death for 4 735 849 adults aged 25 years or older. As a multiple cause, diabetes mortality rates in 2017-2018 vs 1999-2000 were highest and unchanged in rural counties (157.2 [95% CI, 150.7-163.7] vs 154.1 [95% CI, 148.2-160.1]; P = .49) but lower in medium-small counties (123.6 [95% CI, 119.6-127.6] vs 133.6 [95% CI, 128.4-138.8]; P = .003) and urban counties (92.9 [95% CI, 90.5-95.3] vs 109.7 [95% CI, 105.2-114.1]; P &lt; .001). In 2017-2018 vs 1999-2000, mortality rates were higher in rural men (+18.2; 95% CI, 14.3-22.1) but lower in rural women (−14.0; 95% CI, −17.7 to −10.3) ( P &lt; .001 for both). In the 25- to 54-year age group, mortality rates in 2017-2018 vs 1999-2000 showed a greater increase in rural counties (+9.4; 95% CI, 8.6-10.2) compared with medium-small counties (+4.5; 95% CI, 4.0-5.0) and metro counties (+0.9; 95% CI, 0.4-1.4) ( P &lt; .001 for all). Of all regions and urbanization levels, the mortality rate in 2017-2018 vs 1999-2000 was higher only in the rural South (+13.8; 95% CI, 7.6-20.0; P &lt; .001). Conclusions and Relevance In this cross-sectional study, US rural counties had the highest overall diabetes mortality rate. The determinants of persistent rural disparities, in particular for rural men and for adults in the rural South, require investigation.

AbstractBackgroundThe early phase of the coronavirus disease 2019 (COVID‐19) pandemic had a negative impact on the wellness of hospitalists and hospital medicine advanced practice providers (APPs). However, the burden of the pandemic has evolved and the change in hospitalist and hospital medicine APP wellness is unknown.ObjectiveTo evaluate the longitudinal trend in wellness of hospitalists and hospital medicine APPs during the COVID‐19 pandemic and guide wellness interventions.Design, Setting and ParticipantsBetween May 4, 2020, and June 6, 2021, we administered three surveys to Internal Medicine hospitalists (physicians) and hospital medicine APPs (nurse practitioners and physician assistants) at 16 Mayo Clinic hospitals in four U.S. states.MeasurementsWe evaluated the association of hospitalist and hospital medicine APP characteristics with PROMIS® measures of global wellbeing‐mental health, global wellbeing‐social activities and relationships, anxiety, social isolation, and emotional support, using logistic and linear regression models.ResultsThe response rates were 52.2% (n=154/295; May 2020), 37.1% (n=111/299; October 2020) and 35.5% (n=114/321; May 2021). In mixed models that included hospitalist and hospital medicine APP characteristics and survey period, APPs, compared with physicians, had lower odds of top global wellbeing‐social activities and relationships (adjusted odds ratio 0.42 [0.22–0.82]; p = .01), whereas survey period showed no association. The survey period showed an independent association with higher anxiety (May 2020 vs. others) and higher social isolation (October 2020 vs. others), whereas profession showed no association. Concern about contracting COVID‐19 at work was significantly associated with lower odds of top global wellbeing‐mental health and global wellbeing‐social activities and relationships, and with higher anxiety and social isolation. Hospitalist and hospital medicine APP characteristics showed no association with levels of emotional support.ConclusionsIn this longitudinal assessment of hospitalists and hospital medicine APPs, concern about contracting COVID‐19 at work remained a determinant of wellness. The trend for global wellbeing, anxiety, and social isolation may guide wellness interventions.

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.