Inclusion of Rurality and Social Determinants of Health in Documents for the Primary Prevention of Type 2 Diabetes: A Systematic Review.

Purpose: The type 2 diabetes (T2D) burden is disproportionately concentrated in low- and middle-income economies, particularly among rural populations. The purpose of the systematic review was to evaluate the inclusion of rurality and social determinants of health (SDOH) in documents for T2D primary prevention. Methods: This systematic review is reported following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We searched 19 databases, from 2017-2023, for documents on rurality and T2D primary prevention. Furthermore, we searched online for documents from the 216 World Bank economies, categorized by high, upper-middle, lower-middle, and low income status. We extracted data on rurality and the ten World Health Organization SDOH. Two authors independently screened documents and extracted data. Findings: Based on 3318 documents (19 databases and online search), we selected 15 documents for data extraction. The 15 documents applied to 32 economies; 12 of 15 documents were from nongovernment sources, none was from low-income economies, and 10 of 15 documents did not define or describe rurality. Among the SDOH, income and social protection (SDOH 1) and social inclusion and nondiscrimination (SDOH 8) were mentioned in documents for 25 of 29 high-income economies, while food insecurity (SDOH 5) and housing, basic amenities, and the environment (SDOH 6) were mentioned in documents for 1 of 2 lower-middle-income economies. For U.S. documents, none of the authors was from institutions in noncore (most rural) counties. Conclusions: Overall, documents on T2D primary prevention had sparse inclusion of rurality and SDOH, with additional disparity based on economic status. Inclusion of rurality and/or SDOH may improve T2D primary prevention in rural populations.

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.

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.

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.

Development and evaluation of best practice advisory alert for patient eligibility in a hospital-at-home program: A multicenter retrospective study.

BACKGROUND: Hospital-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. OBJECTIVE: To 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 PARTICIPANTS: We 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. INTERVENTION: The 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 MEASURES: The 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. RESULTS: During 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.

Risk Prediction Models for Hospital Mortality in General Medical Patients: A Systematic Review.

Objective: To systematically review contemporary prediction models for hospital mortality developed or validated in general medical patients. Methods: We screened articles in five databases, from January 1, 2010, through April 7, 2022, and the bibliography of articles selected for final inclusion. We assessed the quality for risk of bias and applicability using the Prediction Model Risk of Bias Assessment Tool (PROBAST) and extracted data using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist. Two investigators independently screened each article, assessed quality, and extracted data. Results: From 20,424 unique articles, we identified 15 models in 8 studies across 10 countries. The studies included 280,793 general medical patients and 19,923 hospital deaths. Models included 7 early warning scores, 2 comorbidities indices, and 6 combination models. Ten models were studied in all general medical patients (general models) and 7 in general medical patients with infection (infection models). Of the 15 models, 13 were developed using logistic or Poisson regression and 2 using machine learning methods. Also, 4 of 15 models reported on handling of missing values. None of the infection models had high discrimination, whereas 4 of 10 general models had high discrimination (area under curve >0.8). Only 1 model appropriately assessed calibration. All models had high risk of bias; 4 of 10 general models and 5 of 7 infection models had low concern for applicability for general medical patients. Conclusion: Mortality prediction models for general medical patients were sparse and differed in quality, applicability, and discrimination. These models require hospital-level validation and/or recalibration in general medical patients to guide mortality reduction interventions.

Digital Care Horizon: A Framework for Extending Health Care Through Digital Transformation.

The population needing health care services grows faster than the management capabilities of our current health care delivery models. Patients journeying through our current health care systems receive a spectrum of services, often imperfectly matched to medical needs. We describe a framework of the Digital Care Horizon to accelerate digital transformation from the perspective of a health care delivery system. We describe service delivery models across the horizon, discuss potential challenges and partnerships to facilitate the digital extension of health care, and mention concepts beyond the current horizon.

Postdischarge Video Visits for Adherence to Hospital Discharge Recommendations: A Randomized Clinical Trial.

Objective: To determine whether a postdischarge video visit with patients, conducted by hospital medicine advanced practice providers, improves adherence to hospital discharge recommendations. Patients and Methods: We conducted a single-institution 2-site randomized clinical trial with 1:1 assignment to intervention vs control, with enrollment from August 10, 2020, to June 23, 2022. Hospital medicine patients discharged home or to an assisted living facility were randomized to a video visit 2-5 days postdischarge in addition to usual care (intervention) vs usual care (control). During the video visit, advanced practice providers reviewed discharge recommendations. Both intervention and control groups received telephone follow-up 3-6 days postdischarge to ascertain the primary outcome of adherence to all discharge recommendations for new and chronic medication management, self-management and action plan, and home support. Results: Among 1190 participants (594 intervention; 596 control), the primary outcome was ascertained in 768 participants (314 intervention; 454 control). In intervention vs control, there was no difference in the proportion of participants with the primary outcome (76.7% vs 72.5%; P=.19) or in the individual domains of the primary outcome: new and chronic medication management (94.1% vs 92.8%; P=.50), self-management and action plan (76.5% vs 71.5%; P=.18), and home support (94.1% vs 94.3%; P=.94). Women receiving intervention vs control had higher adherence to recommendations (odds ratio, 1.77; 95% CI, 1.08-2.91). Conclusion: In hospital medicine patients, a postdischarge video visit did not improve adherence to discharge recommendations. Potential gender differences in adherence require further investigation.Clinicaltrials.gov number, NCT04547803.

Global well-being, anxiety, social isolation, and emotional support among hospitalists during COVID-19 and Mpox outbreaks.

OBJECTIVE: The Coronavirus Disease-19 (COVID-19) pandemic caused a decline in hospitalist wellness. The COVID-19 pandemic has evolved, and new outbreaks (i.e. Mpox) have challenged healthcare systems. The objective of the study was to assess changes in hospitalist wellness and guide interventions. METHODS: We surveyed hospitalists (physicians and advanced practice providers [APPs]), in May 2021 and September 2022, at a healthcare system's 16 hospitals in four US states using PROMIS® measures for global well-being, anxiety, social isolation, and emotional support. We compared wellness score between survey periods; in the September 2022 survey, we compared wellness scores between APPs and physicians and evaluated the associations of demographic and hospital characteristics with wellness using logistic (global well-being) and linear (anxiety, social isolation, emotional support) regression models. RESULTS: In May 2021 vs. September 2022, respondents showed no statistical difference in top global well-being for mental health (68.4% vs. 57.4%) and social activities and relationships (43.8% vs. 44.3%), anxiety (mean difference: +0.8), social isolation (mean difference: +0.5), and emotional support (mean difference: -1.0) (all, p ≥ 0.05). In September 2022, in logistic regression models, APPs, compared with physicians, had lower odds for top (excellent or very good) global well-being mental health (odds ratio [95% CI], 0.31 [0.13-0.76]; p < 0.05). In linear regression models, age <40 vs. ≥40 years was associated with higher anxiety (estimate ± standard error, 2.43 ± 1.05; p < 0.05), and concern about contracting COVID-19 at work was associated with higher anxiety (3.74 ± 1.10; p < 0.01) and social isolation (3.82 ± 1.21; p < 0.01). None of the characteristics showed association with change in emotional support. In September 2022, there was low concern for contracting Mpox in the community (4.6%) or at work (10.0%). CONCLUSION: In hospitalists, concern about contracting COVID-19 at work was associated with higher anxiety and social isolation. The unchanged wellness scores between survey periods identified opportunities for intervention. Mpox had apparently minor impact on wellness.

Guidelines, position statements, and advisories for the primary prevention of type 2 diabetes, hypertension, and cardiovascular disease in rural populations: A systematic review protocol.

INTRODUCTION: Globally, noncommunicable diseases (NCDs), which include type 2 diabetes (T2D), hypertension, and cardiovascular disease (CVD), are associated with a high burden of morbidity and mortality. Health disparities exacerbate the burden of NCDs. Notably, rural, compared with urban, populations face greater disparities in access to preventive care, management, and treatment of NCDs. However, there is sparse information and no known literature synthesis on the inclusion of rural populations in documents (i.e., guidelines, position statements, and advisories) pertaining to the prevention of T2D, hypertension, and CVD. To address this gap, we are conducting a systematic review to assess the inclusion of rural populations in documents on the primary prevention of T2D, hypertension, and CVD. METHODS AND ANALYSIS: This protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched 19 databases including EMBASE, MEDLINE, and Scopus, from January 2017 through October 2022, on the primary prevention of T2D, hypertension, and CVD. We conducted separate Google® searches for each of the 216 World Bank economies. For primary screening, titles and/or abstracts were screened independently by two authors (databases) or one author (Google®). Documents meeting selection criteria will undergo full-text review (secondary screening) using predetermined criteria, and data extraction using a standardized form. The definition of rurality varies, and we will report the description provided in each document. We will also describe the social determinants of health (based on the World Health Organization) that may be associated with rurality. ETHICS AND DISSEMINATION: To our knowledge, this will be the first systematic review on the inclusion of rurality in documents on the primary prevention of T2D, hypertension, and CVD. Ethics approval is not required since we are not using patient-level data. Patients are not involved in the study design or analysis. We will present the results at conferences and in peer-reviewed publication(s). TRIAL REGISTRATION: PROSPERO Registration Number: CRD42022369815.

Social Determinants of Health and Mortality After Premature and Non-premature Acute Coronary Syndrome.

Objective: To describe and compare the determinants of 1-year mortality after premature vs non-premature acute coronary syndrome (ACS). Patients and Methods: Participants presenting with ACS were enrolled in a prospective registry of 29 hospitals in 4 countries, from January 22, 2012 to January 22, 2013, with 1-year of follow-up data. The primary outcome was all-cause 1-year mortality after premature ACS (men aged <55 years and women aged <65 years) and non-premature ACS (men aged ≥55 years and women aged ≥65 years). The associations between the baseline patient characteristics and 1-year mortality were analyzed in models adjusting for the Global Registry of Acute Coronary Events (GRACE) score and reported as adjusted odds ratio (aOR) (95% CI). Results: Of the 3868 patients, 43.3% presented with premature ACS that was associated with lower 1-year mortality (5.7%) than those with non-premature ACS. In adjusted models, women experienced higher mortality than men after premature (aOR, 2.14 [1.37-3.41]) vs non-premature ACS (aOR, 1.28 [0.99-1.65]) (P interaction=.047). Patients lacking formal education vs any education had higher mortality after both premature (aOR, 2.92 [1.87-4.61]) and non-premature ACS (aOR, 1.78 [1.36-2.34]) (P interaction=.06). Lack of employment vs any employment was associated with approximately 3-fold higher mortality after premature and non-premature ACS (P interaction=.72). Using stepwise logistic regression to predict 1-year mortality, a model with GRACE risk score and 4 characteristics (education, employment, body mass index [kg/m2], and statin use within 24 hours after admission) had higher discrimination than the GRACE risk score alone (area under the curve, 0.800 vs 0.773; P comparison=.003). Conclusion: In this study, women, compared with men, had higher 1-year mortality after premature ACS. The social determinants of health (no formal education or employment) were strongly associated with higher 1-year mortality after premature and non-premature ACS, improved mortality prediction, and should be routinely considered in risk assessment after ACS.

Diagnosis Related Groups of Patients Admitted from an Urban Academic Medical Center to a Virtual Hybrid Hospital-at-Home Program.

Background: The diagnosis related group (DRG) is used as an economic patient classification system based on clinical characteristics, hospital stay, and treatment costs. Mayo Clinic's virtual hybrid hospital-at-home program, advanced care at home (ACH), offers high-acuity home inpatient care for a variety of diagnosis. This study aimed to determine the DRGs admitted to the ACH program at an urban academic center. Methods: A retrospective study was performed on all patients discharged from the ACH program at Mayo Clinic Florida from July 6, 2020, to February 1, 2022. DRG data were extracted from the Electronic Health Record (EHR). Categorization of DRG was done by systems. Results: The ACH program discharged 451 patients with DRGs. Categorization of the DRG demonstrated that the most frequent code assigned corresponded to respiratory infections (20.2%), followed by septicemia (12.9%), heart failure (8.9%), renal failure (4.9%), and cellulitis (4.0%). Conclusion: The ACH program covers a wide range of high-acuity diagnosis across multiple medical specialties at its urban academic medical campus, including respiratory infections, severe sepsis, congestive heart failure, and renal failure, all with major complications or comorbidities. The ACH model of care may be useful in taking care of patients with similar diagnosis at other urban academic medical institutions.

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.

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.

BACKGROUND: We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS: This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS: Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION: The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

Impact of an Acquisition Advanced Practice Provider on Home Hospital Patient Volumes and Length of Stay.

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.

Characteristics, Treatment Patterns, and Clinical Outcomes After Heart Failure Hospitalizations During the COVID-19 Pandemic, March to October 2020.

OBJECTIVE: To compare clinical characteristics, treatment patterns, and 30-day all-cause readmission and mortality between patients hospitalized for heart failure (HF) before and during the coronavirus disease 2019 (COVID-19) pandemic. PATIENTS AND METHODS: The study was conducted at 16 hospitals across 3 geographically dispersed US states. The study included 6769 adults (mean age, 74 years; 56% [5033 of 8989] men) with cumulative 8989 HF hospitalizations: 2341 hospitalizations during the COVID-19 pandemic (March 1 through October 30, 2020) and 6648 in the pre-COVID-19 (October 1, 2018, through February 28, 2020) comparator group. We used Poisson regression, Kaplan-Meier estimates, multivariable logistic, and Cox regression analysis to determine whether prespecified study outcomes varied by time frames. RESULTS: The adjusted 30-day readmission rate decreased from 13.1% (872 of 6648) in the pre-COVID-19 period to 10.0% (234 of 2341) in the COVID-19 pandemic period (relative risk reduction, 23%; hazard ratio, 0.77; 95% CI, 0.66 to 0.89). Conversely, all-cause mortality increased from 9.7% (645 of 6648) in the pre-COVID-19 period to 11.3% (264 of 2341) in the COVID-19 pandemic period (relative risk increase, 16%; number of admissions needed for one additional death, 62.5; hazard ratio, 1.19; 95% CI, 1.02 to 1.39). Despite significant differences in rates of index hospitalization, readmission, and mortality across the study time frames, the disease severity, HF subtypes, and treatment patterns remained unchanged (P>0.05). CONCLUSION: The findings of this large tristate multicenter cohort study of HF hospitalizations suggest lower rates of index hospitalizations and 30-day readmissions but higher incidence of 30-day mortality with broadly similar use of HF medication, surgical interventions, and devices during the COVID-19 pandemic compared with the pre-COVID-19 time frame.

Multisite analysis of patient experience scores and risk of hospital admission: a retrospective cohort study.

BACKGROUND: Routinely collected patient experience scores may inform risk of patient outcomes. The objective of the study was to evaluate the risk of hospital admission within 30-days following third-party receipt of the patient experience survey and guide interventions. METHODS: In this retrospective cohort study, we analyzed Hospital Consumer Assessment of Healthcare Providers and Systems surveys, January 2016-July 2019, from an institution's 20 hospitals in four U.S. states. Surveys were routinely sent to patients using census sampling. We analyzed surveys received ≤60 days following discharge from patients living ≤60 miles of any of the institution's hospitals. The exposures were 19 survey items. The outcome was hospital admission within 30 days after third-party receipt of the survey. We evaluated the association of favorable (top-box) vs unfavorable (non-top-box) score for survey items with risk of 30-day hospital admission in models including patient and hospitalization characteristics and reported adjusted odds ratios (aOR [95% confidence interval]). RESULTS: Among 40,162 respondents (mean age ± standard deviation: 68.1 ± 14.0 years), 49.8% were women and 4.3% had 30-day hospital admission. Patients with 30-day hospital admission, compared to those not admitted, were more likely to be discharged from a medical service line (62.9% vs 42.3%; P < 0.001) and have a higher Elixhauser index. Favorable vs unfavorable score for hospital rating was associated with lower odds of 30-day hospital admission in the overall cohort (0.88 [0.77-0.99]; P = 0.04), medical service line (0.81 [0.70-0.94]; P = 0.007), and upper tertile of Elixhauser index (0.79 [0.67-0.92]; P = 0.003). Favorable score for recommend hospital was associated with lower odds of 30-day hospital admission in the medical service line (0.83 [0.71-0.97]; P = 0.02) but for others (e.g. cleanliness of hospital environment) showed no association. CONCLUSION: In routinely collected patient experience scores, favorable hospital rating was associated with lower odds of 30-day hospital admission and may inform risk stratification and interventions. Evidence-based survey items linked to patient outcomes may also inform future surveys.

Within-Person Blood Pressure Variability During Hospitalization and Clinical Outcomes Following First-Ever Acute Ischemic Stroke.

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.

Post-discharge early assessment with remote video link (PEARL) initiative for patients discharged from hospital medicine services.

OBJECTIVES: The COVID-19 pandemic impacted the availability and accessibility of outpatient care following hospital discharge. Hospitalists (physicians) and hospital medicine advanced practice providers (HM-APPs) coordinate discharge care of hospitalized patients; however, it is unknown if they can deliver post-discharge virtual care and overcome barriers to outpatient care. The objective was to develop and provide post-discharge virtual care for patients discharged from hospital medicine services. METHODS: We developed the Post-discharge Early Assessment with Remote video Link (PEARL) initiative for HM-APPs to conduct a post-discharge video visit (to review recommendations) and telephone follow-up (to evaluate adherence) with patients 2-6 days following hospital discharge. Participants included patients discharged from hospital medicine services at an institution's hospitals in Rochester (May 2020-August 2020) and Austin (November 2020-February 2021) in Minnesota, US. HM-APPs also interviewed patients about their experience with the video visit and completed a survey on their experience with PEARL. RESULTS: Of 386 eligible patients, 61.4% were enrolled (n = 237/386) including 48.1% women (n = 114/237). In patients with complete video visit and telephone follow-up (n = 141/237), most were prescribed new medications (83.7%) and took them as prescribed (93.2%). Among five classes of chronic medications, patient-reported adherence ranged from 59.2% (narcotics) to 91.5% (anti-hypertensives). Patient-reported self-management of 12 discharge recommendations ranged from 40% (smoking cessation) to 100% (checking rashes). Patients reported benefit from the video visit (agree: 77.3%) with an equivocal preference for video visits over clinic visits. Among HM-APPs who responded to the survey (88.2%; n = 15/17), 73.3% reported benefit from visual contact with patients but were uncertain if video visits would reduce emergency department visits. CONCLUSION: In this novel initiative, HM-APPs used video visits to provide care beyond their hospital role, reinforce discharge recommendations for patients, and reduce barriers to outpatient care. The effect of this initiative is under evaluation in a randomized controlled trial.