Urban-rural disparities in diabetes-related mortality in the USA 1999-2019.

AIMS/HYPOTHESIS: Our study aimed to examine the trends in diabetes-related mortality in urban and rural areas in the USA over the past two decades. METHODS: We examined the trends in diabetes-related mortality (as the underlying or a contributing cause of death) in urban and rural areas in the USA between 1999 and 2019, using the CDC WONDER Multiple Cause of Death database. We estimated the 20 year trends of the age-adjusted mortality rate (AAMR) per 100,000 population in urban vs rural counties. RESULTS: The AAMR of diabetes was higher in rural than urban areas across all subgroups. In urban areas, there was a significant decrease in the AAMR of diabetes as the underlying (-16.7%) and contributing (-13.5%) cause of death (ptrend<0.001), which was not observed in rural areas (+2.6%, +8.9%, respectively). AAMRs of diabetes decreased more significantly in female compared with male individuals, both in rural and urban areas. Among people younger than 55 years old, there was a temporal increase in diabetes-related AAMR (+13.8% to +65.2%). While the diabetes-related AAMRs of American Indian patients decreased in all areas (-19.8% to -40.5%, all ptrend<0.001), diabetes-related AAMRs of Black and White patients decreased significantly in urban (-26.6% to -28.3% and -10.7% to -15.4%, respectively, all ptrend<0.001) but not rural areas (-6.5% to +1.8%, +2.4% to +10.6%, respectively, ptrend NS, NS, NS and <0.001). CONCLUSIONS/INTERPRETATION: The temporal decrease in diabetes-related mortality in the USA has been observed only in urban areas, and mainly among female and older patients. A synchronised effort is needed to improve cardiovascular health indices and healthcare access in rural areas and to decrease diabetes-related mortality

Barriers and facilitators of the uptake of digital health technology in cardiovascular care: a systematic scoping review.

Digital health technology (DHT) has the potential to revolutionize healthcare delivery but its uptake has been low in clinical and research settings. The factors that contribute to the limited adoption of DHT, particularly in cardiovascular settings, are unclear. The objective of this review was to determine the barriers and facilitators of DHT uptake from the perspective of patients, clinicians, and researchers. We searched MEDLINE, EMBASE, and CINAHL databases for studies published from inception to May 2020 that reported barriers and/or facilitators of DHT adoption in cardiovascular care. We extracted data on study design, setting, cardiovascular condition, and type of DHT. We conducted a thematic analysis to identify barriers and facilitators of DHT uptake. The search identified 3075 unique studies, of which 29 studies met eligibility criteria. Studies employed: qualitative methods (n = 13), which included interviews and focus groups; quantitative methods (n = 5), which included surveys; or a combination of qualitative and quantitative methods (n = 11). Twenty-five studies reported patient-level barriers, most common of which were difficult-to-use technology (n=7) and a poor internet connection (n=7). Six studies reported clinician-level barriers, which included increased workload (n=4) and a lack of integration with electronic medical records (n=3).Twenty-four studies reported patient-level facilitators, which included improved communication with clinicians (n=10) and personalized technology (n=6). Four studies reported clinician-level facilitators, which included approval and organizational support from cardiology departments and/or hospitals (n=3) and technologies that improved efficiency (n=3). No studies reported researcher-level barriers or facilitators. In summary, internet access, user-friendliness, organizational support, workflow efficiency, and data integration were reported as important factors in the uptake of DHT by patients and clinicians. These factors can be considered when selecting and implementing DHTs in cardiovascular clinical settings

Sex differences in the etiology and burden of heart failure across country income level: analysis of 204 countries and territories 1990–2019

Background Heart failure (HF) is a global epidemic. Objective To assess global sex differences in HF epidemiology across country income levels. Methods and results Using Global Burden of Disease (GBD) data from 204 countries and territories 1990–2019, we assessed sex differences in HF prevalence, etiology, morbidity, and temporal trends across country sociodemographic index or gross national income. We derived age-standardized rates. Of 56.2 million (95% uncertainty interval [UI] 46.4–67.8 million) people with HF in 2019, 50.3% were females and 69.2% lived in low- and middle-income countries; age-standardized prevalence was greater in males and in high-income countries. Ischaemic and hypertensive heart disease were top causes of HF in males and females, respectively. There were 5.1 million (95% UI 3.3–7.3 million) years lived with disability, distributed equally between sexes. Between 1990 and 2019, there was an increase in HF cases, but a decrease in age-standardized rates per 100 000 in males (9.1%, from 864.2 to 785.7) and females (5.8%, from 686.0 to 646.1). High-income regions experienced a 16.0% decrease in age-standardized rates (from 877.5 to 736.8), while low-income regions experienced a 3.9% increase (from 612.1 to 636.0), largely consistent across sexes. There was a temporal increase in age-standardized HF from hypertensive, rheumatic, and calcific aortic valvular heart disease, and a decrease from ischaemic heart disease, with regional and sex differences. Conclusion Age-standardized HF rates have decreased over time, with larger decreases in males than females; and with large decreases in high-income and small increases in low-income regions. Sex and regional differences offer targets for intervention

Derivation and validation of a two‐variable index to predict 30‐day outcomes following heart failure hospitalization

Background The LACE index—length of stay (L), acuity (A), Charlson co-morbidities (C), and emergent visits (E)—predicts 30-day outcomes following heart failure (HF) hospitalization but is complex to score. A simpler LE index (length of stay and emergent visits) could offer a practical advantage in point-of-care risk prediction. Methods and results This was a sub-study of the patient-centred care transitions in HF (PACT-HF) multicentre trial. The derivation cohort comprised patients hospitalized for HF, enrolled in the trial, and followed prospectively. External validation was performed retrospectively in a cohort of patients hospitalized for HF. We used log-binomial regression models with LACE or LE as the predictor and either 30-day composite all-cause readmission or death or 30-day all-cause readmission as the outcomes, adjusting only for post-discharge services. There were 1985 patients (mean [SD] age 78.1 [12.1] years) in the derivation cohort and 378 (mean [SD] age 73.1 [13.2] years) in the validation cohort. Increments in the LACE and LE indices were associated with 17% (RR 1.17; 95% CI 1.12, 1.21; C-statistic 0.64) and 21% (RR 1.21; 95% CI 1.15, 1.26; C-statistic 0.63) increases, respectively, in 30-day composite all-cause readmission or death; and 16% (RR 1.16; 95% CI 1.11, 1.20; C-statistic 0.64) and 18% (RR 1.18; 95% CI 1.13, 1.24; C-statistic 0.62) increases, respectively, in 30-day all-cause readmission. The LE index provided better risk discrimination for the 30-day outcomes than did the LACE index in the external validation cohort. Conclusions The LE index predicts 30-day outcomes following HF hospitalization with similar or better performance than the more complex LACE index

Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes

Aims Heart failure (HF) guidelines place patients into 3 discrete groups according to left ventricular ejection fraction (LVEF): reduced (<40%), mid-range (40–49%), and preserved LVEF (≥50%). We assessed whether clinical phenogroups offer better prognostication than LVEF. Methods and results This was a sub-study of the Patient-Centered Care Transitions in HF trial. We analysed baseline characteristics of hospitalized patients in whom LVEF was recorded. We used unsupervised machine learning to identify clinical phenogroups and, thereafter, determined associations between phenogroups and outcomes. Primary outcome was the composite of all-cause death or rehospitalization at 6 and 12 months. Secondary outcome was the composite cardiovascular death or HF rehospitalization at 6 and 12 months. Cluster analysis of 1693 patients revealed six discrete phenogroups, each characterized by a predominant comorbidity: coronary heart disease, valvular heart disease, atrial fibrillation (AF), sleep apnoea, chronic obstructive pulmonary disease (COPD), or few comorbidities. Phenogroups were LVEF independent, with each phenogroup encompassing a wide range of LVEFs. For the primary composite outcome at 6 months, the hazard ratios (HRs) for phenogroups ranged from 1.25 [95% confidence interval (CI) 1.00–1.58 for AF] to 2.04 (95% CI 1.62–2.57 for COPD) (log-rank P < 0.001); and at 12 months, the HRs for phenogroups ranged from 1.15 (95% CI 0.94–1.41 for AF) to 1.87 (95% 1.52–3.20 for COPD) (P < 0.002). LVEF-based classifications did not separate patients into different risk categories for the primary outcomes at 6 months (P = 0.69) and 12 months (P = 0.30). Phenogroups also stratified risk of the secondary composite outcome at 6 and 12 months more effectively than LVEF. Conclusion Among patients hospitalized for HF, clinical phenotypes generated by unsupervised machine learning provided greater prognostic information for a composite of clinical endpoints at 6 and 12 months compared with LVEF-based categories. Trial Registration: ClinicalTrials.gov Identifier: NCT0211222

Readmission and processes of care across weekend and weekday hospitalisation for acute myocardial infarction, heart failure or stroke: an observational study of the National Readmission Database.

OBJECTIVES: Variation in hospital resource allocations across weekdays and weekends have led to studies of the 'weekend effect' for ST elevation myocardial infarction (STEMI), non-ST elevation myocardial infarction (NSTEMI), heart failure (HF) and stroke. However, few studies have explored the 'weekend effect' on unplanned readmission. We aimed to investigate 30-day unplanned readmissions and processes of care across weekend and weekday hospitalisations for STEMI, NSTEMI, HF and stroke. DESIGN: We grouped hospitalisations for STEMI, NSTEMI, HF or stroke into weekday or weekend admissions. Multivariable adjusted ORs for binary outcomes across weekend versus weekday (reference) groups were estimated using logistic regression. SETTING: We included all non-elective hospitalisations for STEMI, NSTEMI, HF or stroke, which were recorded in the US Nationwide Readmissions Database between 2010 and 2014. PARTICIPANTS: The analysis sample included 659 906 hospitalisations for STEMI, 1 420 600 hospitalisations for NSTEMI, 3 027 699 hospitalisations for HF, and 2 574 168 hospitalisations for stroke. MAIN OUTCOME MEASURES: The primary outcome was unplanned 30-day readmission. As secondary outcomes, we considered length of stay and the following processes of care: coronary angiography, primary percutaneous coronary intervention, coronary artery bypass graft, thrombolysis, brain scan/imaging, thrombectomy, echocardiography and cardiac resynchronisation therapy/implantable cardioverter-defibrillator. RESULTS: Unplanned 30-day readmission rates were 11.0%, 15.1%, 23.0% and 10.9% for STEMI, NSTEMI, HF and stroke, respectively. Weekend hospitalisations for HF were associated with a statistically significant but modest increase in 30-day readmissions (OR of 1.045, 95% CI 1.033 to 1.058). Weekend hospitalisation for STEMI, NSTEMI or stroke was not associated with increased risk of 30-day readmission. CONCLUSION: There was no clinically meaningful evidence against the supposition that weekend and weekday hospitalisations have the same 30-day unplanned readmissions. Thirty-day readmission rates were high, especially for HF, which has implications for service provision. Strategies to reduce readmission rates should be explored, regardless of day of hospitalisation