Cardiovascular disease (CVD) outcomes and associated risk factors in a medicare population without prior CVD history: an analysis using statistical and machine learning algorithms.

There 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 health plan, a US government-funded program mostly for the elderly and varied levels of non-cardiovascular multi-morbidity.

Transient ischemic attack events and incident cardiovascular and non-cardiovascular complications: Observations from a large diversified multimorbid cohort.

Background: Transient ischemic attack (TIA) is a strong signal prompting the incidence of future cardiovascular and non-cardiovascular complications, in light of recent debate on the so-called "stroke-heart syndrome." We aimed to investigate the relation of TIAs to incident clinical events.

Methods: Patients were drawn from three health plans with a wide spectrum of age groups and a wide mix of socio-economic/disability status.

Medication non-adherence patterns and profiles for patients with incident myocardial infarction: Observations from a large multi-morbid US population.

Background: Consistent adherence levels to multiple long-term medications for patients with cardiovascular conditions are typically advocated in the range of 50% or higher, although very likely to be much lower in some populations. We investigated this issue in a large cohort covering a broad age and geographical spectrum, with a wide range of socio-economic disability status.

Methods: The patients were drawn from three different health plans with a varied mix of socio-economic/disability levels.

Adherence levels and patterns for multiple cardiac medications prescribed to patients with incident atrial fibrillation events.

Aims: Using advanced longitudinal analyses, this real-world investigation examined medication adherence levels and patterns for incident atrial fibrillation (AF) patients with significant cardiovascular and noncardiovascular multimorbid conditions for each of 5 medication classes (β-blockers, calcium channel blockers/digoxin, antiarrhythmics, anticoagulants, antiplatelets). The population was derived from a large cohort covering a wide age spectrum/diversified US geographical areas/wide range of socioeconomic-disability status.

Methods: The patients were drawn from 3 different health plans.

Incidence and Complications of Atrial Fibrillation in a Low Socioeconomic and High Disability United States (US) Population: A Combined Statistical and Machine Learning Approach.

Background: Poor socioeconomic status coupled with individual disability is significantly associated with incident atrial fibrillation (AF) and AF-related adverse outcomes, with the information currently lacking for US cohorts. We examined AF incidence/complications and the dynamic nature of associated risk factors in a large socially disadvantaged US population.

Methods: A large population representing a combined poor socioeconomic status/disability (Medicaid program) was examined from diverse geographical regions across the US continent.

Incident and recurrent myocardial infarction (MI) in relation to comorbidities: Prediction of outcomes using machine-learning algorithms.

Background: To date, incident and recurrent MI remains a major health issue worldwide, and efforts to improve risk prediction in population health studies are needed. This may help the scalability of prevention strategies and management in terms of healthcare cost savings and improved quality of care.

Methods: We studied a large-scale population of 4.

Effects of multimorbidity on incident COVID-19 events and its interplay with COVID-19 event status on subsequent incident myocardial infarction (MI).

Background: With the spread of COVID-19 pandemic, there have been reports on its impact on incident myocardial infarction (MI) emanating from studies with small to modest sample sizes. We therefore examined the incidence of MI in a very large population health cohort with COVID-19 using a methodology which integrates the dynamicity of prior comorbid history. We used two approaches, i.

Revisiting the dynamic risk profile of cardiovascular/non-cardiovascular multimorbidity in incident atrial fibrillation patients and five cardiovascular/non-cardiovascular outcomes: A machine-learning approach.

Background: Patients with atrial fibrillation (AF) usually have a heterogeneous co-morbid history, with dynamic changes in risk factors impacting on multiple adverse outcomes. We investigated a large prospective cohort of patients with multimorbidity, using a machine-learning approach, accounting for the dynamic nature of comorbidity risks and incident AF.

Methods: Using machine-learning, we studied a prospective US cohort using medical/pharmacy databases of 1 091 911 patients, with an incident AF cohort of 14 078 and non-AF cohort of 1 077 833 enrolled in the 4-year study.

Incident atrial fibrillation and its risk prediction in patients developing COVID-19: A machine learning based algorithm approach.

Background: The elderly multi-morbid patient is at high risk of adverse outcomes with COVID-19 complications, and in the general population, the development of incident AF is associated with worse outcomes in such patients. There is therefore the need to identify those patients with COVID-19 who are at highest risk of developing incident AF. We therefore investigated incident AF risks in a large prospective population of elderly patients with/without incident COVID-19 cases and baseline cardiovascular/non-cardiovascular multi-morbidities.

Improving dynamic stroke risk prediction in non-anticoagulated patients with and without atrial fibrillation: comparing common clinical risk scores and machine learning algorithms.

Aims: Diversified cardiovascular/non-cardiovascular multi-morbid risk and efficient machine learning algorithms may facilitate improvements in stroke risk prediction, especially in newly diagnosed non-anticoagulated atrial fibrillation (AF) patients where initial decision-making on stroke prevention is needed. Therefore the aims of this article are to study common clinical risk assessment for stroke risk prediction in AF/non-AF cohorts together with cardiovascular/ non-cardiovascular multi-morbid conditions; to improve stroke risk prediction using machine learning approaches; and to compare the improved clinical prediction rules for multi-morbid conditions using machine learning algorithms.

Methods And Results: We used cohort data from two health plans with 6 457 412 males/females contributing 14,188,679 person-years of data.

Improving Stroke Risk Prediction in the General Population: A Comparative Assessment of Common Clinical Rules, a New Multimorbid Index, and Machine-Learning-Based Algorithms.

Background: There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular comorbidity relationships with stroke. We investigated stroke risks in a very large prospective cohort of patients with multimorbidity, using two common clinical rules, a clinical multimorbid index and a machine-learning (ML) approach, accounting for the complex relationships among variables, including the dynamic nature of changing risk factors.

Methods: We studied a prospective U.

Prevalence/incidence of atrial fibrillation based on integrated medical/pharmacy claims, and association with co-morbidity profiles/multi-morbidity in a large US adult cohort.

Background: Identification of published data on prevalent/incidence of atrial fibrillation/flutter (AF) often relies on inpatient/outpatient claims, without consideration to other types of healthcare services and pharmacy claims. Accurate, population-level data that can enable the ongoing monitoring of AF epidemiology, quality of care at affordable cost, and complications are needed. We hypothesised that prevalent/incidence data would vary via the use of integrated medical/pharmacy claims, and associated comorbidities would vary accordingly.

Analysis of metallic and metal oxide nanomaterial environmental emissions.

The current study presents evidence on metallic and metal oxide engineered nanomaterial (ENM) emissions into the environment and an analytic perspective of the outcomes of evaluated studies with respect to different individual end points along the lifecycle trajectory. The key findings suggest that 1) the published literature on emissions of metallic ENMs is limited in both the number and information available on the characteristics of emitted ENMs; 2) the studies are classified as experimental and computational studies focused on predicting ENM emissions; 3) the majority of studies investigated ENM emissions during nanomaterial use and waste management, followed by raw material manufacturing, and finally, nano-enabled product manufacturing; 4) the studies primarily reported the concentration/quantity of emitted ENMs, whereas the physical-chemical characteristics of emitted ENMs were rarely measured or reported; and 5) the published literature primarily focused on emissions of silver and titanium dioxide ENMs and lacked similar information on other surging metallic and metal oxide ENMs such as nano-zero valent iron (nZVI), aluminum (Al), and aluminum oxide (AlO) ENMs. The evidence suggests that emitted nanoparticles into the air cover a wide range of concentrations below and above the allowable occupational exposure limits.

The effects of metallic engineered nanoparticles upon plant systems: An analytic examination of scientific evidence.

Recent evidence for the effects of metallic engineered nanoparticles (ENPs) on plants and plant systems was examined together with its implications for other constituents of the Society-Environment-Economy (SEE) system. In this study, we were particularly interested to determine whether or not metallic ENPs have both stimulatory and inhibitory effects upon plant performance. An emphasis was made to analyze the scientific evidence on investigations examining both types of effects in the same studies.

The path towards healthier societies, environments, and economies: a broader perspective for sustainable engineered nanomaterials.

Economic value is no longer adequate by itself as a proxy for the value-added benefits (VAB) assumed to be generated by emerging technologies such as engineered nanomaterials (ENMs). This study was conducted to explore the potential to establish an integrated sociotechnical framework with the end goal to assess whether or not ENMs and nano-enabled products contribute VAB. Based on the research in this study, it is suggested that all stakeholders in the larger society-environment-economy (SEE) system should develop an understanding of the multiple interrelationships within and between the diverse constituents along the particle lifecycle trajectory to capture their influence on the system benefit and risk outcomes.

An integrated science-based methodology to assess potential risks and implications of engineered nanomaterials.

There is an urgent need for broad and integrated studies that address the risks of engineered nanomaterials (ENMs) along the different endpoints of the society, environment, and economy (SEE) complex adaptive system. This article presents an integrated science-based methodology to assess the potential risks of engineered nanomaterials. To achieve the study objective, two major tasks are accomplished, knowledge synthesis and algorithmic computational methodology.

A system-of-systems approach as a broad and integrated paradigm for sustainable engineered nanomaterials.

There is an urgent need for a trans-disciplinary approach for the collective evaluation of engineered nanomaterial (ENM) benefits and risks. Currently, research studies are mostly focused on examining effects at individual endpoints with emphasis on ENM risk effects. Less research work is pursuing the integration needed to advance the science of sustainable ENMs.

Characterization and potential environmental implications of select Cu-based fungicides and bactericides employed in U.S. markets.

This exploratory study aimed to examine the extent and mineral speciation of nanosized Cu in two fungicide products (A and B) available in the U.S. markets.

A person-focused model of care for the twenty-first century: a system-of-systems perspective.

The US health care system is challenged to provide high-quality care and is burdened with unsustainable expenditures, making it difficult for health care participants (patients, payers, providers, caregivers) to create value. This communication presents the theoretical foundation for a person-focused model of care that addresses a number of these challenges. The model integrates aspects of prior models of chronic care with new empiric findings and complex adaptive system (CAS) theory.

Patterns and expenditures of multi-morbidity in an insured working population in the United States: insights for a sustainable health care system and building healthier lives.

The U.S. health care system is currently heading toward unsustainable health care expenditures and increased dissatisfaction with health outcomes.

Integrating science and business models of sustainability for environmentally-challenging industries such as secondary lead smelters: a systematic review and analysis of findings.

Secondary lead smelters (SLS) represent an environmentally-challenging industry as they deal with toxic substances posing potential threats to both human and environmental health, consequently, they operate under strict government regulations. Such challenges have resulted in the significant reduction of SLS plants in the last three decades. In addition, the domestic recycling of lead has been on a steep decline in the past 10 years as the amount of lead recovered has remained virtually unchanged while consumption has increased.

The role of human-at-work systems in business sustainability: perspectives based on expert and qualified production workers in a manufacturing enterprise.

A community of highly qualified employees is desirable for the workforce to become a competitive business advantage, improving and sustaining corporate health. Currently, the scientific literature is limited on information comparing the assessment of expert and qualified workers for the employee-work environment interface. Such information would be valuable for industrial managers to obtain and act on the different perspectives of its workers for business improvement and survivability.

An evidence-based environmental perspective of manufactured silver nanoparticle in syntheses and applications: a systematic review and critical appraisal of peer-reviewed scientific papers.

Background: Most recently, renewed interest has arisen in manufactured silver nanomaterials because of their unusually enhanced physicochemical properties and biological activities compared to the bulk parent materials. A wide range of applications has emerged in consumer products ranging from disinfecting medical devices and home appliances to water treatment. Because the hypothesized mechanisms that govern the fate and transport of bulk materials may not directly apply to materials at the nanoscale, there are great concerns in the regulatory and research communities about potential environmental impacts associated with the use of silver nanoparticles.

Risk analysis and protection measures in a carbon nanofiber manufacturing enterprise: an exploratory investigation.

The emerging US carbon nano-manufacturing sector accounts for 40% of nanotechnology product marketplace, thus, there is a significant potential for increased risks arising from workers' exposure to carbon nanofibers (CNF). This research aims at developing a low-cost/evidence-based tool, thereby, increasing the sustainability of CNF manufacturing firms. The following specific aims achieve the study objective: Aim 1 - To present a technical discussion of the proposed concept for risk analysis and protection measures; Aim 2 - To describe the manufacturing process utilized for the CNF production; Aim 3 - To describe the hazards typically encountered in a CNF manufacturing facility; and, Aim 4 - To document the application of the proposed tool for risk analysis and intervention strategy development.