Use of Smart Devices to Track Cardiovascular Health Goals in the United States.

BACKGROUND: Smartphone-based health applications are increasingly popular, but their real-world use for cardiovascular risk management remains poorly understood. OBJECTIVES: The purpose of this study was to investigate the patterns of tracking health goals using smart devices, including smartphones and/or tablets, in the United States. METHODS: Using the nationally representative Health Information National Trends Survey for 2017 to 2020, we examined self-reported tracking of health-related goals (optimizing body weight, increasing physical activity, and/or quitting smoking) using smart devices among those with cardiovascular disease (CVD) or cardiovascular risk factors of hypertension, diabetes, obesity, and/or smoking. Survey analyses were used to obtain national estimates of use patterns and identify features associated with the use of these devices for tracking health goals. RESULTS: Of 16,092 Health Information National Trends Survey participants, 10,660 had CVD or cardiovascular risk factors, representing 154.2 million (95% CI: 149.2-159.3 million) U.S. adults. Among the general U.S. adult population, 46% (95% CI: 44%-47%) tracked their health goals using their smart devices, compared with 42% (95% CI: 40%-43%) of those with or at risk of CVD. Younger age, female, Black race, higher educational attainment, and greater income were independently associated with tracking of health goals using smart devices. CONCLUSIONS: Two in 5 U.S. adults with or at risk of CVD use their smart devices to track health goals. While representing a potential avenue to improve care, the lower use of smart devices among older and low-income individuals, who are at higher risk of adverse cardiovascular outcomes, requires that digital health interventions are designed so as not to exacerbate existing disparities.

Automated Identification of Heart Failure with Reduced Ejection Fraction using Deep Learning-based Natural Language Processing.

Background: The lack of automated tools for measuring care quality has limited the implementation of a national program to assess and improve guideline-directed care in heart failure with reduced ejection fraction (HFrEF). A key challenge for constructing such a tool has been an accurate, accessible approach for identifying patients with HFrEF at hospital discharge, an opportunity to evaluate and improve the quality of care. Methods: We developed a novel deep learning-based language model for identifying patients with HFrEF from discharge summaries using a semi-supervised learning framework. For this purpose, hospitalizations with heart failure at Yale New Haven Hospital (YNHH) between 2015 to 2019 were labeled as HFrEF if the left ventricular ejection fraction was under 40% on antecedent echocardiography. The model was internally validated with model-based net reclassification improvement (NRI) assessed against chart-based diagnosis codes. We externally validated the model on discharge summaries from hospitalizations with heart failure at Northwestern Medicine, community hospitals of Yale New Haven Health in Connecticut and Rhode Island, and the publicly accessible MIMIC-III database, confirmed with chart abstraction. Results: A total of 13,251 notes from 5,392 unique individuals (mean age 73 ± 14 years, 48% female), including 2,487 patients with HFrEF (46.1%), were used for model development (train/held-out test: 70/30%). The deep learning model achieved an area under receiving operating characteristic (AUROC) of 0.97 and an area under precision-recall curve (AUPRC) of 0.97 in detecting HFrEF on the held-out set. In external validation, the model had high performance in identifying HFrEF from discharge summaries with AUROC 0.94 and AUPRC 0.91 on 19,242 notes from Northwestern Medicine, AUROC 0.95 and AUPRC 0.96 on 139 manually abstracted notes from Yale community hospitals, and AUROC 0.91 and AUPRC 0.92 on 146 manually reviewed notes at MIMIC-III. Model-based prediction of HFrEF corresponded to an overall NRI of 60.2 ± 1.9% compared with the chart diagnosis codes (p-value < 0.001) and an increase in AUROC from 0.61 [95% CI: 060-0.63] to 0.91 [95% CI 0.90-0.92]. Conclusions: We developed and externally validated a deep learning language model that automatically identifies HFrEF from clinical notes with high precision and accuracy, representing a key element in automating quality assessment and improvement for individuals with HFrEF.

Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images.

BACKGROUND: Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction. METHODS: Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction <40%. The model was validated within clinical settings at Yale New Haven Hospital and externally on ECG images from Cedars Sinai Medical Center in Los Angeles, CA; Lake Regional Hospital in Osage Beach, MO; Memorial Hermann Southeast Hospital in Houston, TX; and Methodist Cardiology Clinic of San Antonio, TX. In addition, it was validated in the prospective Brazilian Longitudinal Study of Adult Health. Gradient-weighted class activation mapping was used to localize class-discriminating signals on ECG images. RESULTS: Overall, 385 601 ECGs with paired echocardiograms were used for model development. The model demonstrated high discrimination across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROCs], 0.91; area under precision-recall curve [AUPRC], 0.55); and external sets of ECG images from Cedars Sinai (AUROC, 0.90 and AUPRC, 0.53), outpatient Yale New Haven Hospital clinics (AUROC, 0.94 and AUPRC, 0.77), Lake Regional Hospital (AUROC, 0.90 and AUPRC, 0.88), Memorial Hermann Southeast Hospital (AUROC, 0.91 and AUPRC 0.88), Methodist Cardiology Clinic (AUROC, 0.90 and AUPRC, 0.74), and Brazilian Longitudinal Study of Adult Health cohort (AUROC, 0.95 and AUPRC, 0.45). An ECG suggestive of LV systolic dysfunction portended >27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3-33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V2 and V3), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3-4.7]; median follow-up, 3.2 years). CONCLUSIONS: We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings.

Persistence on Novel Cardioprotective Antihyperglycemic Therapies in the United States.

Selected glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT-2is) have cardioprotective effects in patients with type 2 diabetes mellitus (T2D) and elevated cardiovascular risk. Prescription and consistent use of these medications are essential to realizing their benefits. In a nationwide deidentified United States administrative claims database of adults with T2D, the prescription practices of GLP-1RAs and SGLT-2i were evaluated across guideline-directed co-morbidity indications from 2018 to 2020. The monthly fill rates were assessed for 12 months after the initiation of therapy by calculating the proportion of days with consistent medication use. Of 587,657 subjects with T2D, 80,196 (13.6%) were prescribed GLP-1RAs and 68,149 (11.5%) SGLT-2i from 2018 to 2020, representing 12.9% and 11.6% of patients with indications for each medication, respectively. In new initiators, 1-year fill rate was 52.5% for GLP-1RAs and 52.9% for SGLT-2i, which was higher for patients with commercial insurance than those with Medicare Advantage plans for both GLP-1RAs (59.3% vs 51.0%, p <0.001) and SGLT-2i (63.4% vs 50.3%, p <0.001). After adjusting for co-morbidities, there were higher rates of prescription fills for patients with commercial insurance (odds ratio 1.17, 95% confidence interval 1.06 to 1.29 for GLP-1RAs, and 1.59 [1.42 to 1.77] for SGLT-2i); and higher income (odds ratio 1.09 [1.06 to 1.12] for GLP-1RAs, and 1.06 [1.03 to 1.1] for SGLT-2i). From 2018 to 2020, the use of GLP-1RAs and SGLT-2i remained limited to fewer than 1 in 8 patients with T2D and indications, with 1-year fill rates around 50%. The low and inconsistent use of these medications compromises their longitudinal health outcome benefits in a period of expanding indications for their use.

County-level variation in cardioprotective antihyperglycemic prescribing among medicare beneficiaries.

Background: Cardioprotective antihyperglycemic agents, SGLT2 inhibitors (SGLT2i) and GLP-1 receptor agonists (GLP1RA), improve outcomes of patients with type 2 diabetes, but adoption has been limited. Differences across individuals have been noted but area-level variation is unknown. Objectives: Given healthcare access and sociodemographic differences, we evaluated whether SGLT2i and GLP-1RA utilization varies across US counties. Methods: We linked 2019 Medicare Part D national prescription data with county-level demographic measures from the Agency for Health Quality and Research. We compared the number of beneficiaries receiving prescriptions for any cardioprotective antihyperglycemic to the number receiving metformin prescriptions across US counties. In multivariable linear regression with SGLT2i-to-metformin and GLP1RA-to-metformin prescriptions as outcomes, we evaluated county factors associated with use of cardioprotective agents while adjusting for sociodemographic measures, region, and cardiometabolic risk factor prevalence. Results: In 3066 US counties, there were a median 2,416 (IQR, 1681-3190) metformin-receiving beneficiaries per 100,000 population. A median 6.2% of beneficiaries receiving metformin received SGLT2i therapy, varying across counties (IQR, 3.4%-9.2%). A median 9.4% (IQR, 5.0%-13.0%) of beneficiaries receiving metformin received GLP-1RA. In adjusted analyses, higher percentage of Black population was associated with lower use at the county level of people on SGLT2i or GLP-1RA relative to metformin (a SD higher proportion of Black individuals with 0.4% [95% CI, -0.6% to -0.1%] and 0.5% [-0.8% to -0.2%] lower SGLT2i and GLP-1RA prescribing relative to metformin, respectively; P < 0.01). A higher median age of county residents, rural location, and lower prevalence of diabetes were associated with lower SGLT2i prescribing. Similarly, more advanced age of county residents, rural location, proportion of Hispanic individuals, and household income and lower education levels were associated with lower GLP-1RA prescribing. Prescribing was higher in the Northeast and lower in the West as compared with the Midwest for both classes. Conclusion: There was large variation by county in cardioprotective antihyperglycemic prescribing, with a pattern of lower use in Black-predominant and rural counties, highlighting the critical need to investigate equity in uptake of novel therapeutic agents.

Contemporary National Patterns of Eligibility and Use of Novel Cardioprotective Antihyperglycemic Agents in Type 2 Diabetes Mellitus.

Background SGLT-2 (sodium glucose transporter-2) inhibitors and GLP-1RAs (glucagon-like peptide-1 receptor agonists) effectively lowered cardiovascular risk in large clinical trials for patients with type 2 diabetes mellitus at high risk for these complications, and have been recommended by guidelines. To evaluate the contemporary landscape in which these recommendations would be implemented, we examined the use of these medications according to clinical guideline practice. Methods and Results In the National Health and Nutrition Examination Survey for 2017 to 2018, we defined compelling indications for SGLT-2 inhibitors by the presence of atherosclerotic cardiovascular disease, heart failure, or chronic kidney disease, and for GLP-1RAs by the presence of established or high-risk atherosclerotic cardiovascular disease, based on large clinical trials that have been incorporated in guideline recommendations of the American College of Cardiology and American Diabetes Association. We then evaluated use of these medications among patients with physician-diagnosed type 2 diabetes mellitus. All analyses incorporated complex survey design to produce nationally representative estimates. A total 1104 of 9254 sampled individuals had type 2 diabetes mellitus, representing 10.6% (95% CI, 9.7%-11.6%) of the US population or 33.2 million adults nationally. Of these, 52.6% (95% CI, 47.7%-57.5%) had an indication for SGLT-2 inhibitors, 32.8% (95% CI, 28.8%-37.2%) for GLP-1RAs, and 26.6% (95% CI, 22.2%-31.7%) for both medications. During 2017 to 2018, 4.5% (95% CI, 2.4%-8.2%) were treated with SGLT-2 inhibitors and 1.5% (95% CI, 0.7%-3.2%) with GLP-1RAs. Atherosclerotic cardiovascular disease, heart failure, or chronic kidney disease were not independently associated with SGLT-2 inhibitor or GLP-1RA use in patients with diabetes mellitus. Conclusions Despite a large number of patients being eligible for guideline-recommended cardiorenal protective therapies, there are substantial gaps in the use of SGLT-2 inhibitors and GLP-1RAs, limiting their public health benefits.

Nonlinear relation between pulse pressure and coronary heart disease in patients with type 2 diabetes or hypertension.

OBJECTIVES: Wide pulse pressure (PP) is an independent predictor of coronary heart disease (CHD). However, the linearity of the relation between PP and CHD was not examined before. We aimed to examine this relation in patients with type 2 diabetes and/or hypertension. METHODS: A total of 3120 patients (including 2607 with diabetes and 1586 with hypertension) were followed for 7.8 years. Physician-adjudicated first hard CHD event was the primary outcome. Cox regression analysis was used to investigate the association between PP and incident CHD. Restricted cubic splines were used to investigate nonlinear relations. RESULTS: Four spline covariates were defined between 30, 40, 50, 60, and 70 mmHg. The null-hypothesis of the linearity of the relation between PP and CHD was rejected (P value = 0.004). The second, third, and fourth spline covariates were significant (P value = 0.02, P value = 0.02 and P value = 0.01, respectively), supporting a nonlinear relation in corresponding PP intervals. Patients with PP less than 45 mmHg and PP more than 55 mmHg had increased risk of future CHD event, compared with those with PP between 45 and 55 mmHg [hazard ratio (HR) = 1.33 (1.00-1.77) and HR = 1.67 (1.23-2.27), respectively]. This remained significant after controlling for systolic, diastolic, or mean arterial pressures in wide PP group. However, adjustment for traditional cardiovascular risk factors (especially age) attenuated the relation in those with wide PP. CONCLUSION: The relation between PP and CHD is nonlinear in patients with type 2 diabetes and hypertension. Both narrow and wide PPs increase risk of future hard CHD events. The association between wide PP and CHD is independent from SBP, DBP, or mean arterial pressure.

Oxidized Low-Density Lipoprotein (ox-LDL) to LDL Ratio (ox-LDL/LDL) and ox-LDL to High-Density Lipoprotein Ratio (ox-LDL/HDL).

BACKGROUND: Oxidized low-density lipoprotein (ox-LDL) plays a principal role in diabetes complications, though ox-LDL concentrations and its functions are dependents of other oxidative and anti-oxidative particles. The oxLDL to low-density lipoprotein ratio (ox-LDL/LDL) and ox-LDL to high-density lipoprotein ratio (ox-LDL/HDL) as new lipid biomarkers may serve as a good estimation of oxidation and anti-oxidation in type 2 diabetes mellitus. The aim of this study was first to examine ox-LDL/LDL and ox-LDL/HDL levels in patients with type 2 diabetes mellitus compared to a control group and evaluate their diagnostic accuracies, and, second, to investigate the correlation of these two ratios with triglyceride and HDL levels. METHODS: This study was included 144 patients admitted to the diabetes clinic of a University general hospital and 41 healthy individuals as controls. Levels of LDL, HDL, triglyceride (TG) ox-LDL, and malondialdehyde (MDA) were measured for all patients. Levels of ox-LDL/LDL and ox-LDL/HDL were calculated. Diagnostic accuracies were determined by receiver-operating characteristic curve analysis by measuring the area under the curve. RESULTS: Ox-LDL, ox-LDL/LDL, and ox-LDL/HDL were significantly higher in patients compared to the control group after adjustment for age, gender, and BMI (p = 0.000). The area under the curve for diagnosing diabetes was 0.946 for ox-LDL/HDL, 0.918 for ox-LDL/LDL, and 0.832 for ox-LDL. Ox-LDL/HDL was positively correlated with MDA (r = 0.210, p = 0.011). Ox-LDL/LDL was negatively correlated with HDL (r = -0.2 p = 0.016) and positively correlated with TG (r = 0.45 p = 0.000). Ox-LDL/HDL and TG had significant positive correlation (r = 0.173 p = 0.037). The ox-LDL/LDL level was significantly higher in patients with coronary heart disease (CHD) or hypertension (HTN) compared to those without (p = 0.042). CONCLUSIONS: Our findings revealed that ox-LDL/LDL and ox-LDL/HDL are potent biomarkers of type 2 diabetes, which apparently reflect the association between lipids in the state of oxidative stress.

Manual or automated sphygmomanometer? A historical cohort to quantify measurement bias in blood pressure recording.

The authors aimed to quantify end-digit and threshold biases in blood pressure (BP) measurement with manual and digital sphygmomanometers. In a 3-year follow-up, end-digit and threshold biases were investigated and a new index, called the deviation index, was used to quantify measurement bias. The distribution of systolic and diastolic BPs became close to normal after implementation of digital sphygmomanometers. The appearance of zero end digits decreased from 97% to 30% (P<.0001). The deviation index decreased from 97% to 20% (P<.0001). Mean systolic and diastolic BPs increased immediately after implementation of automated sphygmomanometers (124.22±0.83 vs 132.90±0.78 and 74.38±0.50 vs 80.43±0.51, respectively; P<.0001 for both) but showed a linear decreasing trend during follow-up (systolic -3.59 mm Hg per year; 95% confidence interval, -5.57 to -1.61 [P<.0001]; diastolic: -2.52 mm Hg per year; 95% confidence interval, -3.78 to -1.26 [P<.0001]). Threshold bias decreased from 12.94% to 6.68% (P<.0001). Replacing manual sphygmomanometers with digital devices decreased end-digit and threshold biases in BP measurement. The deviation index can be used to quantify the magnitude of measurement bias.