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.

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.

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.

Weighing the Anti-Ischemic Benefits and Bleeding Risks from Aspirin Therapy: a Rational Approach.

PURPOSE OF REVIEW: The role of aspirin in secondary cardiovascular prevention is well understood; however, the role in primary prevention is less clear, and requires careful balancing of potential benefits with risks. Here, we summarize the evidence base on the benefits and risks of aspirin therapy, discuss clinical practice guidelines and decision support tools to assist in initiating aspirin therapy, and highlight ongoing trials that may clarify the role of aspirin in cardiovascular disease prevention. RECENT FINDINGS: In 2016, the USPSTF released guidelines on the use of aspirin for primary prevention. Based on 11 trials (n = 118,445), aspirin significantly reduced all-cause mortality and nonfatal myocardial infarction, and in 7 trials that evaluated aspirin ≤ 100 mg/day, there was significant reduction in nonfatal stroke. The USPSTF recommends individualized use of aspirin based on factors including age, 10-year atherosclerotic cardiovascular disease risk score, and bleeding risk. Several ongoing trials are evaluating the role of aspirin in primary prevention, secondary prevention, and in combination therapy for atrial fibrillation. Evidence-based approaches to aspirin use should consider the anti-ischemic benefits and bleeding risks from aspirin. In this era of precision medicine, tools that provide the personalized benefit to risk assessment, such as the freely available clinical decision support tool (Aspirin-Guide), can be easily incorporated into the electronic health record and facilitate more informed decisions about initiating aspirin therapy for primary prevention. Aspirin has a complex matrix of benefits and risks, and its use in primary prevention requires individualized decision-making. Results from ongoing trials may guide healthcare providers in identifying appropriate candidates for aspirin therapy.

25 by 25: Achieving Global Reduction in Cardiovascular Mortality.

Four non-communicable diseases-cardiovascular disease, chronic respiratory disease, diabetes mellitus, and cancer-account for over 60 % of all deaths globally. In recognition of this significant epidemic, the United Nations set forth a target of reducing the four major NCDs by 25 % by 2025. Cardiovascular disease alone represents half of these deaths and is the leading cause of death globally, representing as much as 60 % of all deaths in regions such as Eastern Europe. In response, the WHO set specific targets on conditions and risk factors and changes in the health systems structure in order to achieve the goals. The focus was set on lifestyle risk factors-physical activity, salt-intake, and tobacco-and established conditions-obesity, hypertension, and diabetes mellitus. Health system efforts to improve medical treatment of high risk are encouraged. Efforts to achieve the goal are being promoted by leading international CVD organizations.

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 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.

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.

Rurality and patients' hospital experience: A multisite analysis from a US healthcare system.

BACKGROUND: The association between rurality of patients' residence and hospital experience is incompletely described. The objective of the study was to compare hospital experience by rurality of patients' residence. METHODS: From a US Midwest institution's 17 hospitals, we included 56,685 patients who returned a post-hospital Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. We defined rurality using rural-urban commuting area codes (metropolitan, micropolitan, small town, rural). We evaluated the association of patient characteristics with top-box score (favorable response) for 10 HCAHPS items (six composite, two individual, two global). We obtained adjusted odds ratios (aOR [95% CI]) from logistic regression models including patient characteristics. We used key driver analysis to identify associations between HCAHPS items and global rating (combined overall rating of hospital and recommend hospital). RESULTS: Of all items, overall rating of hospital had lower odds of favorable response for patients from metropolitan (0.88 [0.81-0.94]), micropolitan (0.86 [0.79-0.94]), and small towns (0.90 [0.82-0.98]) compared with rural areas (global test, P = .003). For five items, lower odds of favorable response was observed for select areas compared with rural; for example, recommend hospital for patients from micropolitan (0.88 [0.81-0.97]) but not metropolitan (0.97 [0.89-1.05]) or small towns (0.93 [0.85-1.02]). For four items, rurality showed no association. In metropolitan, micropolitan, and small towns, men vs. women had higher odds of favorable response to most items, whereas in rural areas, sex-based differences were largely absent. Key driver analysis identified care transition, communication about medicines and environment as drivers of global rating, independent of rurality. CONCLUSIONS: Rural patients reported similar or modestly more favorable hospital experience. Determinants of favorable experience across rurality categories may inform system-wide and targeted improvement.

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.

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.

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.

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.