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Total 162 results found since Jan 2013.

Proteomics profiling reveals a distinct high-risk molecular subtype of hypertrophic cardiomyopathy
Conclusions Our prospective plasma proteomics study not only exhibited the presence of HCM molecular subtypes but also identified pathobiological mechanisms associated with a distinct high-risk subtype of HCM.
Source: Heart - October 28, 2022 Category: Cardiology Authors: Liang, L. W., Raita, Y., Hasegawa, K., Fifer, M. A., Maurer, M. S., Reilly, M. P., Shimada, Y. J. Tags: Heart failure and cardiomyopathies Source Type: research

Late-Breaking Data from Pivotal Phase 3 PRECISION Study Demonstrates Significant and Sustained Effect of Aprocitentan on Lowering Blood Pressure for Patients with Difficult-to-Control Hypertension
RARITAN, NJ, November 7, 2022 – The Janssen Pharmaceutical Companies of Johnson & Johnson, in collaboration with Idorsia Ltd, today announced results from the Phase 3 PRECISION study, which found aprocitentan, an investigational, novel dual endothelin receptor antagonist (ERA), significantly reduced blood pressure (BP) and maintained the effect for up to 48 weeks when added to standardized combination background antihypertensive therapy in patients with difficult-to-control hypertension (sometimes referred to as resistant hypertension). These data were presented as a Late-Breaking Science presentation during the Amer...
Source: Johnson and Johnson - November 7, 2022 Category: Pharmaceuticals Source Type: news

Sensors, Vol. 22, Pages 8615: A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions
is Kyriazis Extracting useful knowledge from proper data analysis is a very challenging task for efficient and timely decision-making. To achieve this, there exist a plethora of machine learning (ML) algorithms, while, especially in healthcare, this complexity increases due to the domain&rsquo;s requirements for analytics-based risk predictions. This manuscript proposes a data analysis mechanism experimented in diverse healthcare scenarios, towards constructing a catalogue of the most efficient ML algorithms to be used depending on the healthcare scenario&rsquo;s requirements and datasets, for efficient...
Source: Sensors - November 8, 2022 Category: Biotechnology Authors: Argyro Mavrogiorgou Athanasios Kiourtis Spyridon Kleftakis Konstantinos Mavrogiorgos Nikolaos Zafeiropoulos Dimosthenis Kyriazis Tags: Article Source Type: research

Multi-modal fusion model for predicting adverse cardiovascular outcome post percutaneous coronary intervention
Conclusion. To the best of our knowledge, this is the first study that developed a deep learning model with joint fusion architecture for the prediction of post-PCI prognosis and outperformed machine learning models developed using traditional single-source features (clinical variables or E CG features). Adding ECG data with clinical variables did not improve prediction of all-cause mortality as may be expected, but the improved performance of related cardiac outcomes shows that the fusion of ECG generates additional value.
Source: Physiological Measurement - December 22, 2022 Category: Physiology Authors: Amartya Bhattacharya, Sudarsan Sadasivuni, Chieh-Ju Chao, Pradyumna Agasthi, Chadi Ayoub, David R Holmes, Reza Arsanjani, Arindam Sanyal and Imon Banerjee Source Type: research

Sensors, Vol. 23, Pages 1161: Efficient Data-Driven Machine Learning Models for Cardiovascular Diseases Risk Prediction
In this study, a supervised ML-based methodology is presented through which we aim to design efficient prediction models for CVD manifestation, highlighting the SMOTE technique&rsquo;s superiority. Detailed analysis and understanding of risk factors are shown to explore their importance and contribution to CVD prediction. These factors are fed as input features to a plethora of ML models, which are trained and tested to identify the most appropriate for our objective under a binary classification problem with a uniform class probability distribution. Various ML models were evaluated after the use or non-use of Synt...
Source: Sensors - January 19, 2023 Category: Biotechnology Authors: Elias Dritsas Maria Trigka Tags: Article Source Type: research

Janssen Data at ASCO GU Support Ambition to Transform Treatment of Prostate and Bladder Cancer Through Precision Medicine and Early Intervention
RARITAN, N.J., February 13, 2023 – The Janssen Pharmaceutical Companies of Johnson & Johnson today announced plans to present more than 20 abstracts featuring seven oncology therapies from its robust portfolio and pipeline at the annual American Society of Clinical Oncology (ASCO) Genitourinary (GU) Cancers Symposium, taking place in San Francisco on February 16-18. Building on more than a decade of leadership in the development of medicines for people diagnosed with GU cancers, Janssen will present data demonstrating its ambition to advance patient-centered treatment through precision medicine, real-world evidence a...
Source: Johnson and Johnson - February 13, 2023 Category: Pharmaceuticals Tags: Latest News Source Type: news

Straight from the heart: Mysterious lipids may predict cardiac problems better than cholesterol
Stephanie Blendermann, 65, had good reason to worry about heart disease. Three of her sisters died in their 40s or early 50s from heart attacks, and her father needed surgery to bypass clogged arteries. She also suffered from an autoimmune disorder that results in chronic inflammation and boosts the odds of developing cardiovascular illnesses. “I have an interesting medical chart,” says Blendermann, a real estate agent in Prior Lake, Minnesota. Yet Blendermann’s routine lab results weren’t alarming. At checkups, her low-density lipoprotein (LDL), or “bad,” cholesterol hovered around the 100 milligrams-per-...
Source: Science of Aging Knowledge Environment - March 16, 2023 Category: Geriatrics Source Type: research

ERLEADA ® (apalutamide), First-and-Only Next-Generation Androgen Receptor Inhibitor with Once-Daily, Single-Tablet Option, Now Available in the U.S.
HORSHAM, Pa., April 3, 2023 – The Janssen Pharmaceutical Companies of Johnson & Johnson today announced the availability of an additional tablet strength of ERLEADA® (apalutamide) in the United States. The introduction of the 240mg tablet provides the first-and-only option for a once-daily, single-tablet Androgen Receptor Inhibitor (ARI) approved for the treatment of patients with non-metastatic castration-resistant prostate cancer (nmCRPC) and for the treatment of patients with metastatic castration-sensitive prostate cancer (mCSPC).With two strengths available, healthcare professionals will have the flexibility to...
Source: Johnson and Johnson - April 3, 2023 Category: Pharmaceuticals Tags: Latest News Source Type: news

Social bias in artificial intelligence algorithms designed to improve cardiovascular risk assessment relative to the Framingham Risk Score: a protocol for a systematic review
This study will employ an equity-lens to identify sources of bias (ie, race/ethnicity, gender and social stratum) in ML algorithms designed to improve CVD risk assessment relative to the FRS. A comprehensive literature search will be completed using MEDLINE, Embase and IEEE to answer the research question: do AI algorithms that are designed for the estimation of CVD risk and that compare performance with the FRS address the sources of bias inherent in the FRS? No study date filters will be imposed on the search, but English language filters will be applied. Studies describing a specific algorithm or ML approach that provid...
Source: BMJ Open - May 31, 2023 Category: General Medicine Authors: Garcha, I., Phillips, S. P. Tags: Open access, General practice / Family practice Source Type: research

Cardiovascular disease (CVD) outcomes and associated risk factors in a medicare population without prior CVD history: an analysis using statistical and machine learning algorithms
AbstractThere is limited information on predicting incident cardiovascular outcomes among high- to very high-risk populations such as the elderly ( ≥ 65 years) in the absence of prior cardiovascular disease and the presence of non-cardiovascular multi-morbidity. We hypothesized that statistical/machine learning modeling can improve risk prediction, thus helping inform care management strategies. We defined a population from the Medicare he alth plan, a US government-funded program mostly for the elderly and varied levels of non-cardiovascular multi-morbidity. Participants were screened for cardiovascular disease (CVD)...
Source: Internal and Emergency Medicine - June 9, 2023 Category: Emergency Medicine Source Type: research

Development and validation of a prediction model to predict major adverse cardiovascular events in elderly patients undergoing noncardiac surgery: A retrospective cohort study
CONCLUSIONS: This prediction model based on the traditional method could accurately predict the risk of MACEs after noncardiac surgery in elderly patients.PMID:37315395 | DOI:10.1016/j.atherosclerosis.2023.06.008
Source: Atherosclerosis - June 14, 2023 Category: Cardiology Authors: Kai Zhang Chang Liu Xiaoling Sha Siyi Yao Zhao Li Yao Yu Jingsheng Lou Qiang Fu Yanhong Liu Jiangbei Cao Jiaqiang Zhang Yitian Yang Weidong Mi Hao Li Source Type: research

Sensors, Vol. 23, Pages 5618: Automated Signal Quality Assessment of Single-Lead ECG Recordings for Early Detection of Silent Atrial Fibrillation
s D. Zink Atrial fibrillation (AF) is an arrhythmic cardiac disorder with a high and increasing prevalence in aging societies, which is associated with a risk for stroke and heart failure. However, early detection of onset AF can become cumbersome since it often manifests in an asymptomatic and paroxysmal nature, also known as silent AF. Large-scale screenings can help identifying silent AF and allow for early treatment to prevent more severe implications. In this work, we present a machine learning-based algorithm for assessing signal quality of hand-held diagnostic ECG devices to prevent misclassification due to insu...
Source: Sensors - June 15, 2023 Category: Biotechnology Authors: Markus Lueken Michael Gramlich Steffen Leonhardt Nikolaus Marx Matthias D. Zink Tags: Article Source Type: research

Prediction of short-term atrial fibrillation risk using primary care electronic health records
Conclusions FIND-AF, a machine learning algorithm applicable at scale in routinely collected primary care data, identifies people at higher risk of short-term AF.
Source: Heart - June 26, 2023 Category: Cardiology Authors: Nadarajah, R., Wu, J., Hogg, D., Raveendra, K., Nakao, Y. M., Nakao, K., Arbel, R., Haim, M., Zahger, D., Parry, J., Bates, C., Cowan, C., Gale, C. P. Tags: Open access, Editor's choice Arrhythmias and sudden death Source Type: research

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