Nutrition Assessment and Education of Patients with Heart Failure by Cardiologists.

Although nutritional assessment and education are important for hospitalized patients with heart failure, the extent of their implementation in real-world clinical practice is unknown. Therefore, this study aimed to investigate the evaluation and management of nutrition during hospitalization for heart failure using a questionnaire survey for cardiologists.In this cross-sectional multicenter survey, 147 cardiologists from 32 institutions completed a web-based questionnaire (response rate, 95%).The survey showed that 78.2% of the respondents performed a nutritional assessment for hospitalized patients, whereas 38.3% used objective tools. In contrast, only 9.5% of the respondents evaluated the presence or absence of cardiac cachexia. Most respondents (89.8%) reported providing nutritional education to their patients before hospital discharge. However, compared with the number of respondents who provided information on sodium (97.0%) and water (63.6%) restrictions, a limited number of respondents provided guidance on optimal protein (20.5%) and micronutrient (9.1%) intake as part of the nutritional education. Less than 50% of the respondents provided guidance on optimal calorie intake (43.2%) and ideal body weight (34.8%) as a part of the nutritional education for patients identified as malnourished.Although nutritional assessment is widely performed for hospitalized patients with heart failure, most assessments are subjective rather than objective. Nutritional education, frequently provided before hospital discharge, is limited to information on water or salt intake restrictions. Therefore, more comprehensive and individualized nutritional assessments and counselling with a scientific basis are required.

Diagnostic and Cost Efficiency of the 0-h/1-h Rule-out and Rule-in Algorithm for Patients With Chest Pain in the Emergency Department.

BACKGROUND: This study investigated the economic impact of the European Society of Cardiology (ESC) clinical practice guideline recommendation of using the 0-h/1-h rule-out and rule-in algorithm with high-sensitivity cardiac troponin assays (0/1-h algorithm) to triage patients presenting with chest pain.Methods and Results: This post hoc cost-effectiveness evaluation (DROP-ACS; UMIN000030668) used deidentified electronic medical records from health insurance claims from 2 diagnostic centers in Japan. A cost-effectiveness analysis was conducted with 472 patients with care provided following the 0/1-h algorithm (Hospital A) and 427 patients following point-of-care testing (Hospital B). The clinical outcome of interest was all-cause mortality or subsequent myocardial infarction within 30 days of the index presentation. The sensitivity and specificity for the clinical outcome were 100% (95% confidence interval [CI] 91.1-100%) and 95.0% (95% CI 94.3-95.0%), respectively, in Hospital A and 92.9% (95% CI 69.6-98.7%) and 89.8% (95% CI 89.0-90.0%), respectively, in Hospital B. If the diagnostic accuracy of the 0/1-h algorithm was implemented in Hospital B, it is expected that the number of urgent (<24-h) coronary angiograms would decrease by 50%. Incorporating this assumption, implementing the 0/1-h algorithm could potentially reduce medical costs by JPY4,033,874 (95% CI JPY3,440,346-4,627,402) in Hospital B (JPY9,447 per patient; 95% CI JPY 8,057-10,837 per patient). CONCLUSIONS: The ESC 0/1-h algorithm was efficient for risk stratification and for reducing medical costs.

Nutritional Status in Patients Undergoing Phase II Cardiac Rehabilitation by Mini Nutritional Assessment.

Background: Malnutrition impairs quality of life and prognosis of patients with cardiovascular disease. The Mini Nutritional Assessment (MNA) is a screening tool developed for the nutritional assessment of older adults. However, usefulness of MNA for patients undergoing cardiac rehabilitation (CR) has not been fully investigated. Methods: From March 2017 to September 2019, the MNA-short form (MNA-SF) and the MNA total score in patients undergoing phase II CR at the Juntendo University Hospital were evaluated. Results: A total of 336 patients (mean age 70.1 ± 11.4 years; males: 209) were analyzed. In the MNA-SF, 157 patients (47%) were found to be malnourished or at risk of malnutrition. In MNA total score, 168 patients (50%) were found to be malnourished or at risk of malnutrition. The MNA-SF < 12 group had significantly lower body mass index (BMI), hemoglobin level, low MNA scores for protein/water intake, self-evaluation of nutrition and health, and upper arm and calf circumferences compared to the MNA-SF ≥ 12 group. Assuming BMI < 18.5 as malnutrition, the sensitivity and specificity for malnutrition were 100% and 58.9% for MNA-SF, and 96.9% and 54.9% for MNA total score, respectively. Conclusions: MNA is useful in screening for malnutrition in patients undergoing CR. Approximately 50% of them were determined to be malnourished or at risk of malnutrition, suggesting the need for detailed evaluation regarding their food intake and dietary intervention.

Relationship Between Medical Therapy, Long-Term Care Insurance, and Comorbidity in Elderly Patients With Heart Failure With Systolic Dysfunction.

BACKGROUND: Although guideline-directed medical therapy (GDMT), including ß-blockers, angiotensin-converting enzyme inhibitors (ACEi)/angiotensin receptor blockers (ARBs), and mineralocorticoid receptor antagonists (MRAs), improves survival and quality of life, most patients with heart failure with reduced (HFrEF) and mildly reduced (HFmrEF) ejection fraction are treated with inadequate medications. We investigated the prescription patterns of GDMT in elderly patients with HFrEF and HFmrEF and their characteristics, including the certification of long-term care insurance (LTCI), which represents frailty and disability.Methods and Results: This retrospective cross-sectional study analyzed 1,296 elderly patients with symptomatic HFrEF and HFmrEF with diuretic use (median age 78 years; 63.8% male; median left ventricular ejection fraction 40%). Prescription rates of GDMT were inadequate (ACEi, ARBs, ß-blockers, and MRAs: 27.0%, 30.1%, 54.1%, and 41.9%, respectively). LTCI certification was independently associated with reduced prescription of all medications (ACEi/ARB: odds ratio [OR] 0.591, 95% confidence interval [CI] 0.449-0.778, P=0.001; ß-blockers: OR 0.698, 95% CI 0.529-0.920, P<0.001; MRAs: OR 0.743, 95% CI 0.560-0.985, P=0.052). Patients with LTCI certification also had a high prevalence of polypharmacy and prescription of diuretics. CONCLUSIONS: Vulnerable patients with LTCI may be an explanation for the challenges in implementing GDMT, and communicating is required for favorable heart failure care in this population.

Validation of telemedicine-based self-assessment of vital signs for patients with COVID-19: A pilot study.

INTRODUCTION: In the ongoing COVID-19 pandemic, the development of a system that would prevent the infection of healthcare providers is in urgent demand. We sought to investigate the feasibility and validity of a telemedicine-based system in which healthcare providers remotely check the vital signs measured by patients with COVID-19. METHODS: Patients hospitalized with confirmed or suspected COVID-19 measured and uploaded their vital signs to secure cloud storage. Additionally, the respiratory rates were monitored using a mat-type sensor placed under the bed. We assessed the time until the values became available on the Cloud and the agreements between the patient-measured vital signs and simultaneous healthcare provider measurements. RESULTS: Between 26 May-23 September 2020, 3835 vital signs were measured and uploaded to the cloud storage by the patients (n=16, median 72 years old, 31% women). All patients successfully learned how to use these devices with a 10-minute lecture. The median time until the measurements were available on the cloud system was only 0.35 min, and 95.2% of the vital signs were available within 5 min of the measurement. The agreement between the patients' and healthcare providers' measurements was excellent for all parameters. Interclass coefficient correlations were as follows: systolic (0.92, p<0.001), diastolic blood pressure (0.86, p<0.001), heart rate (0.89, p<0.001), peripheral oxygen saturation (0.92, p<0.001), body temperature (0.83, p<0.001), and respiratory rates (0.90, p<0.001). CONCLUSIONS: Telemedicine-based self-assessment of vital signs in patients with COVID-19 was feasible and reliable. The system will be a useful alternative to traditional vital sign measurements by healthcare providers during the COVID-19 pandemic.

Accuracy of IVUS-Based Machine Learning Segmentation Assessment of Coronary Artery Dimensions and Balloon Sizing.

Background: Accurate intravascular ultrasound (IVUS) measurements are important in IVUS-guided percutaneous coronary intervention optimization by choosing the appropriate device size and confirming stent expansion. Objectives: The purpose of this study was to assess the accuracy of machine learning (ML) automatic segmentation of coronary artery vessel and lumen dimensions and balloon sizing. Methods: Using expert analysis as the gold standard, ML segmentation of 60 MHz IVUS images was developed using 8,076 IVUS cross-sectional images from 234 patients, which were randomly split into training (83%) and validation (17%) data sets. The performance of ML segmentation was then evaluated using an independent test data set (437 images from 92 patients). The endpoints were the agreement rate between ML vs experts' measurements for appropriate balloon size selection, and lumen and acute stent areas. Appropriate balloon size was determined by rounding down from the mean vessel diameter or rounding up from the mean lumen diameter to the next balloon size. The difference of lumen area ≥0.5 mm2 was considered as clinically significant. Results: ML model segmentation correlated well with experts' segmentation for training data set with a correlation coefficient of 0.992 and 0.993 for lumen and vessel areas, respectively. The agreement rate in lumen and acute stent areas was 85.5% and 97.0%, respectively. The agreement rate for appropriate balloon size selection was 70.6% by vessel diameter only and 92.4% by adding lumen diameter. Conclusions: ML model IVUS segmentation measurements were well-correlated with those of experts and selected an appropriate balloon size in more than 90% of images.

Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification.

BACKGROUND: Coronary computed tomography angiography examinations are increasingly becoming established as a minimally invasive method for diagnosing coronary diseases. However, although various imaging and processing methods have been developed, coronary artery calcification remains a major limitation in the evaluation of the vascular lumen. Subtraction coronary computed tomography angiography (Sub-CCTA) is a method known to be able to reduce the influence of coronary artery calcification and is therefore feasible for improving the diagnosis of significant stenosis in patients with severe calcification. However, Sub-CCTA still involves some problems, such as the increased radiation dose due to plain (mask) imaging, extended breath-holding time, and misregistration due to differences in the imaging phase. Therefore, we considered using artificial intelligence instead of Sub-CCTA to visualize the coronary lumen with high calcification. Given this background, the present study aimed to evaluate the diagnostic performance of a deep learning-based lumen extraction method (DL-LEM) to detect significant stenosis on CCTA in 99 consecutive patients (891 segments) with severe coronary calcification from November 2015 to March 2018. We also estimated the impact of DL-LEM on the medical economics in Japan. RESULTS: The DL-LEM slightly improved the per-segment diagnostic accuracy from 74.5 to 76.4%, and the area under the curve (AUC) slightly improved from 0.752 to 0.767 (p = 0.030). When analyzing the 228 segments that could not be evaluated because of severe calcification on the original CCTA images, the DL-LEM improved the accuracy from 35.5 to 42.5%, and the AUC improved from 0.500 to 0.587 (p = 0.00018). As a result, DL-LEM analysis could have avoided invasive coronary angiography in 4/99 cases (per patient). From the calculated results, it was estimated that the number of exams that can be avoided in Japan in one year is approximately 747 for invasive coronary angiography, 219 for fractional flow reserve, and 248 for nuclear exam. The total amount of medical fee that could be reduced was 225,629,368 JPY. CONCLUSIONS: These findings suggest that the DL-LEM may improve the diagnostic performance in detecting significant stenosis in patients with severe coronary calcification. In addition, the results suggest that not a small medical economic effect can be expected.

Incidence and Risk Factors of Future Need for Long-Term Care Insurance in Japanese Elderly Patients With Left Ventricular Systolic Dysfunction.

BACKGROUND: Heart failure in elderly people causes physical and cognitive dysfunction and often requires long-term care insurance (LTCI); however, among patients with left ventricular (LV) systolic dysfunction, the incidence and risk factors of future LTCI requirements need to be elucidated.Methods and Results:The study included 1,852 patients aged ≥65 years with an echocardiographic LV ejection fraction (LVEF) ≤50%; we referred to their LTCI data and those of 113,038 community-dwelling elderly people. During a mean 1.7-year period, 332 patients newly required LTCI (incidence 10.7 per 100 person-years); the incidence was significantly higher than that for the community-dwelling people (hazard ratio [HR], 1.47; 95% confidence interval [CI], 1.32-1.64). On multivariate analysis, the risk factors at the time of echocardiography leading to future LTCI requirement were atrial fibrillation (HR, 1.588; 95% CI, 1.279-1.971), history of stroke (HR, 2.02; 95% CI, 1.583-2.576), osteoporosis (HR, 1.738; 95% CI, 1.253-2.41), dementia (HR, 2.804; 95% CI, 2.075-3.789), hypnotics (HR, 1.461; 95% CI, 1.148-1.859), and diuretics (HR, 1.417; 95% CI, 1.132-1.773); however, the LVEF was not a risk factor (HR, 0.997; 95% CI, 0.983-1.011). CONCLUSIONS: In elderly patients with LV systolic dysfunction, the incidence of LTCI requirement was more common than that for community-dwelling people; its risk factors did not include LVEF, but included many other non-cardiac comorbidities and therapies, suggesting the need for interdisciplinary cooperation to prevent disabilities.