Endovascular Treatment of Acute Ischemic Stroke With the Penumbra System in Routine Practice: COMPLETE Registry Results

Background and Purpose: The purpose of the COMPLETE (International Acute Ischemic Stroke Registry With the Penumbra System Aspiration Including the 3D Revascularization Device) registry was to evaluate the generalizability of the safety and efficacy of the Penumbra System (Penumbra, Inc, Alameda) in a real-world setting. Methods: COMPLETE was a global, prospective, postmarket, multicenter registry. Patients with large vessel occlusion–acute ischemic stroke who underwent mechanical thrombectomy using the Penumbra System with or without the 3D Revascularization Device as frontline approach were enrolled at 42 centers (29 United States, 13 Europe) from July 2018 to October 2019. Primary efficacy end points were successful postprocedure angiographic revascularization (modified Thrombolysis in Cerebral Infarction ≥2b) and 90-day functional outcome (modified Rankin Scale score 0–2). The primary safety end point was 90-day all-cause mortality. An imaging core lab determined modified Thrombolysis in Cerebral Infarction scores, Alberta Stroke Program Early CT Scores, clot location, and occurrence of intracranial hemorrhage at 24 hours. Independent medical reviewers adjudicated safety end points. Results: Six hundred fifty patients were enrolled (median age 70 years, 54.0% female, 49.2% given intravenous recombinant tissue plasminogen activator before thrombectomy). Rate of modified Thrombolysis in Cerebral Infarction 2b to 3 postprocedure was 87.8% (95% CI, 85.3%–90.4%). First pass and postprocedure rates of modified Thrombolysis in Cerebral Infarction 2c to 3 were 41.5% and 66.2%, respectively. At 90 days, 55.8% (95% CI, 51.9%–59.7%) had modified Rankin Scale score 0 to 2, and all-cause mortality was 15.5% (95% CI, 12.8%–18.3%). Conclusions: Using Penumbra System for frontline mechanical thrombectomy treatment of patients with large vessel occlusion–acute ischemic stroke in a real-world setting was associated with angiographic, clinical, and safety outcomes that were comparable to prior randomized clinical trials with stringent site and operator selection criteria. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT0346456

Phosphite deoxygenation of steroidal[3,4-c] and [16,17-c] furazan N-oxides

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Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals. © 2022 by the authors

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients

Phase 3, Randomized, 20-Month Study of the Efficacy and Safety of Bimatoprost Implant in Patients with Open-Angle Glaucoma and Ocular Hypertension (ARTEMIS 2)

Objective- To evaluate the intraocular pressure (IOP)-lowering efficacy and safety of 10 and 15 µg bimatoprost implant in patients with open-angle glaucoma (OAG) or ocular hypertension (OHT). Methods- This randomized, 20-month, multicenter, masked, parallel-group, phase 3 trial enrolled 528 patients with OAG or OHT and an open iridocorneal angle inferiorly in the study eye. Study eyes were administered 10 or 15 µg bimatoprost implant on day 1, week 16, and week 32, or twice-daily topical timolol maleate 0.5%. Primary endpoints were IOP and IOP change from baseline through week 12. Safety measures included treatment-emergent adverse events (TEAEs) and corneal endothelial cell density (CECD). Results- Both 10 and 15 µg bimatoprost implant met the primary endpoint of noninferiority to timolol in IOP lowering through 12 weeks. Mean IOP reductions from baseline ranged from 6.2–7.4, 6.5–7.8, and 6.1–6.7 mmHg through week 12 in the 10 µg implant, 15 µg implant, and timolol groups, respectively. IOP lowering was similar after the second and third implant administrations. Probabilities of requiring no IOP-lowering treatment for 1 year after the third administration were 77.5% (10 µg implant) and 79.0% (15 µg implant). The most common TEAE was conjunctival hyperemia, typically temporally associated with the administration procedure. Corneal TEAEs of interest (primarily corneal endothelial cell loss, corneal edema, and corneal touch) were more frequent with the 15 than the 10 µg implant and generally were reported after repeated administrations. Loss in mean CECD from baseline to month 20 was ~ 5% in 10 µg implant-treated eyes and ~ 1% in topical timolol-treated eyes. Visual field progression (change in the mean deviation from baseline) was reduced in the 10 µg implant group compared with the timolol group. Conclusions- The results corroborated the previous phase 3 study of the bimatoprost implant. The bimatoprost implant met the primary endpoint and effectively lowered IOP. The majority of patients required no additional treatment for 12 months after the third administration. The benefit-risk assessment favored the 10 over the 15 µg implant. Studies evaluating other administration regimens with reduced risk of corneal events are ongoing. The bimatoprost implant has the potential to improve adherence and reduce treatment burden in glaucoma

Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTIC‐HF: baseline characteristics and comparison with contemporary clinical trials

Aims: The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTIC‐HF) trial. Here we describe the baseline characteristics of participants in GALACTIC‐HF and how these compare with other contemporary trials. Methods and Results: Adults with established HFrEF, New York Heart Association functional class (NYHA) ≥ II, EF ≤35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokinetic‐guided dosing: 25, 37.5 or 50 mg bid). 8256 patients [male (79%), non‐white (22%), mean age 65 years] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NT‐proBNP 1971 pg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTIC‐HF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressure < 100 mmHg (n = 1127), estimated glomerular filtration rate < 30 mL/min/1.73 m2 (n = 528), and treated with sacubitril‐valsartan at baseline (n = 1594). Conclusions: GALACTIC‐HF enrolled a well‐treated, high‐risk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation

Not Available

Not AvailableThe presence of genotype-environment interactions (GEI) necessitates the developments of varieties or breeds suited for different agro-environments based on their stability and adaptability characteristics. In many situations, the assumptions about the normality and independence of observations as well as homogeneity of error variances are not fulfilled. Therefore, there is a need to investigate the performance of different parametric methods for stability measures when the basic data is not normally distributed. This important aspect is taken up in the present investigation. Using simulation technique,power of the test has been computed for different sample sizes when the underlying data set is normal as well as non-normal like gamma, beta, t, weibull, log normal etc. In most of the cases it is found that Eberhart and Russell parametric stability measure gives better performance.Not Availabl