Physical inactivity is one of the most prevalent major health risk factors, with 8 in 10 US adults not meeting aerobic and muscle-strengthening guidelines, and is associated with a high burden of ...cardiovascular disease. Improving and maintaining recommended levels of physical activity leads to reductions in metabolic, hemodynamic, functional, body composition, and epigenetic risk factors for noncommunicable chronic diseases. Physical activity also has a significant role, in many cases comparable or superior to drug interventions, in the prevention and management of >40 conditions such as diabetes mellitus, cancer, cardiovascular disease, obesity, depression, Alzheimer disease, and arthritis. Whereas most of the modifiable cardiovascular disease risk factors included in the American Heart Association's My Life Check - Life's Simple 7 are evaluated routinely in clinical practice (glucose and lipid profiles, blood pressure, obesity, and smoking), physical activity is typically not assessed. The purpose of this statement is to provide a comprehensive review of the evidence on the feasibility, validity, and effectiveness of assessing and promoting physical activity in healthcare settings for adult patients. It also adds concrete recommendations for healthcare systems, clinical and community care providers, fitness professionals, the technology industry, and other stakeholders in order to catalyze increased adoption of physical activity assessment and promotion in healthcare settings and to contribute to meeting the American Heart Association's 2020 Impact Goals.
Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, ...observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data. Here, we show that deep learning can extract new knowledge from retinal fundus images. Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70). We also show that the trained deep-learning models used anatomical features, such as the optic disc or blood vessels, to generate each prediction.
Morbidity from undiagnosed atrial fibrillation (AF) may be preventable with early detection. Many consumer wearables contain optical photoplethysmography (PPG) sensors to measure pulse rate. ...PPG-based software algorithms that detect irregular heart rhythms may identify undiagnosed AF in large populations using wearables, but minimizing false-positive detections is essential.
We performed a prospective remote clinical trial to examine a novel PPG-based algorithm for detecting undiagnosed AF from a range of wrist-worn devices. Adults aged ≥22 years in the United States without AF, using compatible wearable Fitbit devices and Android or iOS smartphones, were included. PPG data were analyzed using a novel algorithm that examines overlapping 5-minute pulse windows (tachograms). Eligible participants with an irregular heart rhythm detection (IHRD), defined as 11 consecutive irregular tachograms, were invited to schedule a telehealth visit and were mailed a 1-week ambulatory ECG patch monitor. The primary outcome was the positive predictive value of the first IHRD during ECG patch monitoring for concurrent AF.
A total of 455 699 participants enrolled (median age 47 years, 71% female, 73% White) between May 6 and October 1, 2020. IHRDs occurred for 4728 (1%) participants, and 2070 (4%) participants aged ≥65 years during a median of 122 (interquartile range, 110-134) days at risk for an IHRD. Among 1057 participants with an IHRD notification and subsequent analyzable ECG patch monitor, AF was present in 340 (32.2%). Of the 225 participants with another IHRD during ECG patch monitoring, 221 had concurrent AF on the ECG and 4 did not, resulting in an IHRD positive predictive value of 98.2% (95% CI, 95.5%-99.5%). For participants aged ≥65 years, the IHRD positive predictive value was 97.0% (95% CI, 91.4%-99.4%).
A novel PPG software algorithm for wearable Fitbit devices exhibited a high positive predictive value for concurrent AF and identified participants likely to have AF on subsequent ECG patch monitoring. Wearable devices may facilitate identifying individuals with undiagnosed AF.
URL: https://www.
gov; Unique identifier: NCT04380415.
Informed Consent Grady, Christine; Cummings, Steven R; Rowbotham, Michael C ...
The New England journal of medicine,
03/2017, Letnik:
376, Številka:
9
Journal Article
Each decade, the American Heart Association (AHA) develops an Impact Goal to guide its overall strategic direction and investments in its research, quality improvement, advocacy, and public health ...programs. Guided by the AHA's new Mission Statement, to be a relentless force for a world of longer, healthier lives, the 2030 Impact Goal is anchored in an understanding that to achieve cardiovascular health for all, the AHA must include a broader vision of health and well-being and emphasize health equity. In the next decade, by 2030, the AHA will strive to equitably increase healthy life expectancy beyond current projections, with global and local collaborators, from 66 years of age to at least 68 years of age across the United States and from 64 years of age to at least 67 years of age worldwide. The AHA commits to developing additional targets for equity and well-being to accompany this overarching Impact Goal. To attain the 2030 Impact Goal, we recommend a thoughtful evaluation of interventions available to the public, patients, providers, healthcare delivery systems, communities, policy makers, and legislators. This presidential advisory summarizes the task force's main considerations in determining the 2030 Impact Goal and the metrics to monitor progress. It describes the aspiration that these goals will be achieved by working with a diverse community of volunteers, patients, scientists, healthcare professionals, and partner organizations needed to ensure success.
Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs, and delayed diagnosis leads to higher ...cost and worse outcomes. Computer-aided diagnosis (CAD) algorithms have shown promise for helping radiologists detect fractures, but the image features underpinning their predictions are notoriously difficult to understand. In this study, we trained deep-learning models on 17,587 radiographs to classify fracture, 5 patient traits, and 14 hospital process variables. All 20 variables could be individually predicted from a radiograph, with the best performances on scanner model (AUC = 1.00), scanner brand (AUC = 0.98), and whether the order was marked "priority" (AUC = 0.79). Fracture was predicted moderately well from the image (AUC = 0.78) and better when combining image features with patient data (AUC = 0.86, DeLong paired AUC comparison,
= 2e-9) or patient data plus hospital process features (AUC = 0.91,
= 1e-21). Fracture prediction on a test set that balanced fracture risk across patient variables was significantly lower than a random test set (AUC = 0.67, DeLong unpaired AUC comparison,
= 0.003); and on a test set with fracture risk balanced across patient and hospital process variables, the model performed randomly (AUC = 0.52, 95% CI 0.46-0.58), indicating that these variables were the main source of the model's fracture predictions. A single model that directly combines image features, patient, and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital process data. If CAD algorithms are inexplicably leveraging patient and process variables in their predictions, it is unclear how radiologists should interpret their predictions in the context of other known patient data. Further research is needed to illuminate deep-learning decision processes so that computers and clinicians can effectively cooperate.
The Use of Smartphones for Health Research Dorsey, E Ray; Yvonne Chan, Yu-Feng; McConnell, Michael V ...
Academic medicine,
02/2017, Letnik:
92, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Because of their growing popularity and functionality, smartphones are increasingly valuable potential tools for health and medical research. Using ResearchKit, Apple's open-source platform to build ...applications ("apps") for smartphone research, collaborators have developed apps for researching asthma, breast cancer, cardiovascular disease, type 2 diabetes, and Parkinson disease. These research apps enhance widespread participation by removing geographical barriers to participation, provide novel ways to motivate healthy behaviors, facilitate high-frequency assessments, and enable more objective data collection. Although the studies have great potential, they also have notable limitations. These include selection bias, identity uncertainty, design limitations, retention, and privacy. As smartphone technology becomes increasingly available, researchers must recognize these factors to ensure that medical research is conducted appropriately. Despite these limitations, the future of smartphones in health research is bright. Their convenience grants unprecedented geographic freedom to researchers and participants alike and transforms the way clinical research can be conducted.
Identification and treatment of abdominal aortic aneurysm (AAA) remain among the most prominent challenges in vascular medicine. MicroRNAs (miRNAs) are crucial regulators of cardiovascular pathology ...and represent intriguing targets to limit AAA expansion. Here we show, by using two established murine models of AAA disease along with human aortic tissue and plasma analysis, that miR-24 is a key regulator of vascular inflammation and AAA pathology. In vivo and in vitro studies reveal chitinase 3-like 1 (Chi3l1) to be a major target and effector under the control of miR-24, regulating cytokine synthesis in macrophages as well as their survival, promoting aortic smooth muscle cell migration and cytokine production, and stimulating adhesion molecule expression in vascular endothelial cells. We further show that modulation of miR-24 alters AAA progression in animal models, and that miR-24 and CHI3L1 represent novel plasma biomarkers of AAA disease progression in humans.
Identification and treatment of abdominal aortic aneurysm (AAA) remains among the most prominent challenges in vascular medicine. MicroRNAs are crucial regulators of cardiovascular pathology and ...represent possible targets for the inhibition of AAA expansion. We identified microRNA-21 (miR-21) as a key modulator of proliferation and apoptosis of vascular wall smooth muscle cells during development of AAA in two established murine models. In both models (AAA induced by porcine pancreatic elastase or infusion of angiotensin II), miR-21 expression increased as AAA developed. Lentiviral overexpression of miR-21 induced cell proliferation and decreased apoptosis in the aortic wall, with protective effects on aneurysm expansion. miR-21 overexpression substantially decreased expression of the phosphatase and tensin homolog (PTEN) protein, leading to increased phosphorylation and activation of AKT, a component of a pro-proliferative and antiapoptotic pathway. Systemic injection of a locked nucleic acid-modified antagomir targeting miR-21 diminished the pro-proliferative impact of down-regulated PTEN, leading to a marked increase in the size of AAA. Similar results were seen in mice with AAA augmented by nicotine and in human aortic tissue samples from patients undergoing surgical repair of AAA (with more pronounced effects observed in smokers). Modulation of miR-21 expression shows potential as a new therapeutic option to limit AAA expansion and vascular disease progression.
Cardiovascular disease is the leading cause of death worldwide, mainly due to an increasing prevalence of atherosclerosis characterized by inflammatory plaques. Plaques with high levels of macrophage ...infiltration are considered "vulnerable" while those that do not have significant inflammation are considered stable; cathepsin protease activity is highly elevated in macrophages of vulnerable plaques and contributes to plaque instability. Establishing novel tools for non-invasive molecular imaging of macrophages in plaques could aid in preclinical studies and evaluation of therapeutics. Furthermore, compounds that reduce the macrophage content within plaques should ultimately impact care for this disease.
We have applied quenched fluorescent cathepsin activity-based probes (ABPs) to a murine atherosclerosis model and evaluated their use for in vivo imaging using fluorescent molecular tomography (FMT), as well as ex vivo fluorescence imaging and fluorescent microscopy. Additionally, freshly dissected human carotid plaques were treated with our potent cathepsin inhibitor and macrophage apoptosis was evaluated by fluorescent microscopy.
We demonstrate that our ABPs accurately detect murine atherosclerotic plaques non-invasively, identifying cathepsin activity within plaque macrophages. In addition, our cathepsin inhibitor selectively induced cell apoptosis of 55%±10% of the macrophage within excised human atherosclerotic plaques.
Cathepsin ABPs present a rapid diagnostic tool for macrophage detection in atherosclerotic plaque. Our inhibitor confirms cathepsin-targeting as a promising approach to treat atherosclerotic plaque inflammation.
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