Background And Aims: Given the potential benefit of medical therapy in patients with non-obstructive coronary artery disease (CAD), there is a need for risk stratification and treatment strategy for these patients. We aimed to develop a risk prediction model for non-obstructive CAD patients for risk stratification and guidance of statin and aspirin therapy.
Methods: From a cohort of consecutive patients who underwent coronary computed tomography angiography (CCTA) (n = 25,087), we identified patients with non-obstructive CAD of 1-49% diameter-stenosis (n = 6243) and developed a risk prediction model for 5-year occurrence of a composite of all-cause mortality, myocardial infarction, and late coronary revascularization using a derivation cohort (n = 4391).
Results: Age, sex, hypertension, diabetes, anemia, C-reactive protein, and the extent of non-obstructive CAD were incorporated in the prediction model (risk score 0-13, C-index = 0.716). Patients were categorized into 4 groups; risk score of 0-3 (low-risk), 4-6 (intermediate-risk), 7-9 (high-risk), and ≥10 (very high-risk). Patients with very high-risk demonstrated unfavorable outcome comparable to patients with obstructive CAD. The low-risk group exhibited favorable outcome similar to those with no CAD. While statin therapy was associated with better outcomes in high- or very high-risk group (hazard ratio, 0.62; 95% confidence interval, 0.39-0.96; p = 0.033), aspirin use was associated with an increased risk in low-risk group (hazard ratio, 2.57; 95% confidence interval, 1.34-4.90; p = 0.004).
Conclusions: A dedicated risk scoring system for non-obstructive CAD using clinical factors and CCTA findings accurately predicted prognosis. According to our risk prediction model, statin therapy can be beneficial for high-risk patients, whereas aspirin can be harmful for low-risk patients.
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http://dx.doi.org/10.1016/j.atherosclerosis.2019.09.018 | DOI Listing |
Alzheimers Dement
December 2024
Case Western Reserve University, Cleveland, OH, USA.
Background: Traumatic Brain Injury (TBI) is one of the most common nonheritable causes of Alzheimer's disease (AD). However, there is lack of effective treatment for both AD and TBI. We posit that network-based integration of multi-omics and endophenotype disease module coupled with large real-world patient data analysis of electronic health records (EHR) can help identify repurposable drug candidates for the treatment of TBI and AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, Patiala, India.
Background: Neuroinflammation plays an important role in progression of Alzheimer's disease (AD). Interlukin-6 (IL-6) is well identified marker in initiating and regulating inflammation, and formation of senile plaques in brain. Therefore, simultaneous inhibition of both IL-6 and acetylcholinesterase (AChE) may be an effective strategy for AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Xuanwu Hospital, Capital Medical University, Beijing, Beijing, China.
Background: Effective early intervention of mild cognitive impairment (MCI) is the key for preventing dementia. However, there is currently no drug for MCI. As a multi-targeted neuroprotective agent, butylphthalide has been demonstrated to repair cognition in patients with vascular cognitive impairment, and has the potential to treat MCI due to Alzheimer's disease (AD).
View Article and Find Full Text PDFBackground: While a number of recent anti-amyloid antibodies demonstrated a robust reduction of amyloid biomarkers in clinical trials, the impact on functional improvement is much more variable. We hypothesize that this larger variability is driven by comedications, common genotype variants and underlying tau pathology.
Method: In a previously calibrated computational neuroscience model of ADAS-Cog, we implemented the effect of soluble amyloid monomers and oligomers on glutamate and nicotinic AChR neurotransmission and the effect of intracellular tau oligomers on voltage-gated Na and K+ channels and synaptic density.
Background: Pivotal Alzheimer's Disease (AD) trials typically require thousands of participants, resulting in long enrollment timelines and substantial costs. We leverage deep learning predictive models to create prognostic scores (forecasted control outcome) of trial participants and in combination with a linear statistical model to increase statistical power in randomized clinical trials (RCT). This is a straightforward extension of the traditional RCT analysis, allowing for ease of use in any clinical program.
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