BACKGROUND
A polypill that includes key medications associated with improved outcomes (aspirin, angiotensin-converting-enzyme [ACE] inhibitor, and statin) has been proposed as a simple approach to the secondary prevention of cardiovascular death and complications after myocardial infarction.


METHODS
In this phase 3, randomized, controlled clinical trial, we assigned patients with myocardial infarction within the previous 6 months to a polypill-based strategy or usual care. The polypill treatment consisted of aspirin (100 mg), ramipril (2.5, 5, or 10 mg), and atorvastatin (20 or 40 mg). The primary composite outcome was cardiovascular death, nonfatal type 1 myocardial infarction, nonfatal ischemic stroke, or urgent revascularization. The key secondary end point was a composite of cardiovascular death, nonfatal type 1 myocardial infarction, or nonfatal ischemic stroke.


RESULTS
A total of 2499 patients underwent randomization and were followed for a median of 36 months. A primary-outcome event occurred in 118 of 1237 patients (9.5%) in the polypill group and in 156 of 1229 (12.7%) in the usual-care group (hazard ratio, 0.76; 95% confidence interval [CI], 0.60 to 0.96; P = 0.02). A key secondary-outcome event occurred in 101 patients (8.2%) in the polypill group and in 144 (11.7%) in the usual-care group (hazard ratio, 0.70; 95% CI, 0.54 to 0.90; P = 0.005). The results were consistent across prespecified subgroups. Medication adherence as reported by the patients was higher in the polypill group than in the usual-care group. Adverse events were similar between groups.


CONCLUSIONS
Treatment with a polypill containing aspirin, ramipril, and atorvastatin within 6 months after myocardial infarction resulted in a significantly lower risk of major adverse cardiovascular events than usual care. (Funded by the European Union Horizon 2020; SECURE ClinicalTrials.gov number, NCT02596126; EudraCT number, 2015-002868-17.).

Polypill Strategy in Secondary Cardiovascular Prevention.

A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly carry valuable information for the prediction of stroke and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high performance that is validated by various metrics, such as AUC, precision, recall, F-measure and accuracy. The experiment results showed that the stacking classification outperforms the other methods, with an AUC of 98.9%, F-measure, precision and recall of 97.4% and an accuracy of 98%.

/pdf/stroke-risk-prediction-with-machine-learning-techniques-35jz9bdz.pdf

Stroke Risk Prediction with Machine Learning Techniques

Ultrasensitive wearable strain sensor for promising application in cardiac rehabilitation

Importance
According to National Health and Nutrition Examination Survey data, 52% of surveyed US adults reported using at least 1 dietary supplement in the prior 30 days and 31% reported using a multivitamin-mineral supplement. The most commonly cited reason for using supplements is for overall health and wellness and to fill nutrient gaps in the diet. Cardiovascular disease and cancer are the 2 leading causes of death and combined account for approximately half of all deaths in the US annually. Inflammation and oxidative stress have been shown to have a role in both cardiovascular disease and cancer, and dietary supplements may have anti-inflammatory and antioxidative effects.


Objective
To update its 2014 recommendation, the US Preventive Services Task Force (USPSTF) commissioned a review of the evidence on the efficacy of supplementation with single nutrients, functionally related nutrient pairs, or multivitamins for reducing the risk of cardiovascular disease, cancer, and mortality in the general adult population, as well as the harms of supplementation.


Population
Community-dwelling, nonpregnant adults.


Evidence Assessment
The USPSTF concludes with moderate certainty that the harms of beta carotene supplementation outweigh the benefits for the prevention of cardiovascular disease or cancer. The USPSTF also concludes with moderate certainty that there is no net benefit of supplementation with vitamin E for the prevention of cardiovascular disease or cancer. The USPSTF concludes that the evidence is insufficient to determine the balance of benefits and harms of supplementation with multivitamins for the prevention of cardiovascular disease or cancer. Evidence is lacking and the balance of benefits and harms cannot be determined. The USPSTF concludes that the evidence is insufficient to determine the balance of benefits and harms of supplementation with single or paired nutrients (other than beta carotene and vitamin E) for the prevention of cardiovascular disease or cancer. Evidence is lacking and the balance of benefits and harms cannot be determined.


Recommendation
The USPSTF recommends against the use of beta carotene or vitamin E supplements for the prevention of cardiovascular disease or cancer. (D recommendation) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of the use of multivitamin supplements for the prevention of cardiovascular disease or cancer. (I statement) The USPSTF concludes that the current evidence is insufficient to assess the balance of benefits and harms of the use of single- or paired-nutrient supplements (other than beta carotene and vitamin E) for the prevention of cardiovascular disease or cancer. (I statement).

Vitamin, Mineral, and Multivitamin Supplementation to Prevent Cardiovascular Disease and Cancer: US Preventive Services Task Force Recommendation Statement.

Cardiovascular Risk Factors Mediate the Long-Term Maternal Risk Associated With Hypertensive Disorders of Pregnancy.

Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). Methods: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year’s worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year’s edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy. Results: Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. Conclusions: The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.

Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association

In this paper, a novel technique for human daily motion analysis and recognition is proposed. The technique is based on the use of inertial sensors, and integrates a longest common subsequences (LCSS) algorithm as the kernel function for support vector machines (SVM), which measures the similarity of human daily motion time-series. In our system, we use the wearable motion capture system to obtain the body posture data. The entire system is based on the quantized body posture pattern recognition, which means, we can still reconstruct the body's posture if we use the quantified posture data as the input data. At the same time, we first use LCSS as the kernel function of SVM for action recognition, which is quite different from the traditional time-based single point of information, or the windowing method. It does take advantage of the time-series information contained in the action, so can we design a method to classify the human daily motion under the posture space methods.

SVM-based approach for human daily motion recognition

Sleep apnea contributes to a variety of health threatening problems. However, there is a extremely low public and medical awareness of this disease. In order to identify sleep apnea/hyopnea, some effective features have been extracted from ECG signal, PPG signal and EEG signal. In this work, a novel combined of features characterizing physiological signals for monitoring epochs of sleep apnea is presented. They are RR interval, the amplitude of RR, TT interval, the amplitude of TT, real-time heart rate, angle of QRS, Inter-quartile range, and absolute deviation values of RR-intervals. These physiological indicators extracted from single-lead ECG signal distinguish the sleep apnea from normal events based on support vector machines with linear kernel. The aim of study was to classify a short-duration epoch of ECG data obtained from real-time detecting system. Fifteen seconds were chosen to be the epoch length. Additionally, a preprocessing method is carried out to detect QRS and T-wave from ECG signals. Associating with these techniques, a portable real-time automated monitoring system for detecting sleep apnea is designed.

Junxiu Liu

Papers

Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association

SVM-based approach for human daily motion recognition

Real-time automatic monitoring system for sleep apnea using single-lead electrocardiogram