Impact of Modifiable Bleeding Risk Factors on Major Bleeding in Patients With Atrial Fibrillation Anticoagulated With Rivaroxaban.

Background Reducing major bleeding events is a challenge when managing anticoagulation in patients with atrial fibrillation. This study evaluated the impact of modifiable and nonmodifiable bleeding risk factors in patients with atrial fibrillation receiving rivaroxaban and estimated the impact of risk factor modification on major bleeding events. Methods and Results Modifiable and nonmodifiable risk factors associated with major bleeding events were identified from the XANTUS (Xarelto for Prevention of Stroke in Patients With Atrial Fibrillation) prospective registry data set (6784 rivaroxaban-treated patients). Parameters showing univariate association with bleeding were used to construct a multivariable model identifying independent risk factors. Modeling was used to estimate attributed weights to risk factors. Heavy alcohol use (hazard ratio [HR]=2.37; 95% CI 1.24-4.53); uncontrolled hypertension (HR after parameter-wise shrinkage=1.79; 95% CI 1.05-3.05); and concomitant treatment with antiplatelets, nonsteroidal anti-inflammatory drugs, or paracetamol (HR=1.80; 95% CI 1.24-2.61) were identified as modifiable, independent bleeding risk factors. Increasing age (HR=1.25 [per 5-year increment]; 95% CI 1.12-1.38); heart failure (HR=1.97; 95% CI 1.36-2.86); and vascular disease (HR=1.91; 95% CI 1.32-2.77) were identified as nonmodifiable bleeding risk factors. Overall, 128 (1.9%) patients experienced major bleeding events; of these, 11% had no identified bleeding risk factors, 50% had nonmodifiable bleeding risk factors only, and 39% had modifiable bleeding risk factors (with or without nonmodifiable risk factors). The presence of 1 modifiable bleeding risk factor doubled the risk of major bleeding. Conclusions Elimination of modifiable bleeding risk factors is a potentially effective strategy to reduce bleeding risk in atrial fibrillation patients receiving rivaroxaban. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT01606995

Maternal Lifestyle and Pregnancy Complications: The Generation R Study

Adverse maternal lifestyle habits during pregnancy are important modifiable risk factors for pregnancy complications in Western countries. Most common adverse maternal lifestyle habits include smoking, alcohol consumption, and caffeine consumption. Although not directly lifestyle related, maternal age is also considered as a modifiable risk factor for adverse pregnancy outcomes

Hypertension Education and the Burlington Housing Authority

Background: • 2/3 seniors are hypertensive (\u3e140/90 mm Hg) • Hypertension is the most common risk factor for premature heart disease and stroke • Non-modifiable risk factors: race, age, sex, diabetes mellitus, and hypercholesterolemia • Modifiable risk factors: smoking, obesity, and excessive alcohol • Clinical trials show that lifestylemodification and medications can reduce the incidence of adverse outcomes associated with hypertension • Patient education is a high priorityhttps://scholarworks.uvm.edu/comphp_gallery/1026/thumbnail.jp

Reducing dementia risk by targeting modifiable risk factors in mid-life: study protocol for the Innovative midlife intervention for dementia deterrence (In-MINDD) randomised controlled feasibility trial

Background Dementia prevalence is increasing as populations live longer, with no cure and the costs of caring exceeding many other conditions. There is increasing evidence for modifiable risk factors which, if addressed in mid-life, can reduce the risk of developing dementia in later life. These include physical inactivity, low cognitive activity, mid-life obesity, high blood pressure, and high cholesterol. This study aims to assess the acceptability and feasibility and impact of giving those in mid-life, aged between 40 and 60 years, an individualised dementia risk modification score and profile and access to personalised on-line health information and goal setting in order to support the behaviour change required to reduce such dementia risk. A secondary aim is to understand participants’ and practitioners’ views of dementia prevention and explore the acceptability and integration of the Innovative Midlife Intervention for Dementia Deterrence (In-MINDD) intervention into daily life and routine practice. Methods/design In-MINDD is a multi-centre, primary care-based, single-blinded randomised controlled feasibility trial currently being conducted in four European countries (France, Ireland, the Netherlands and the UK). Participants are being recruited from participating general practices. Inclusion criteria will include age between 40 and 60 years; at least one modifiable risk factor for dementia risk (including diabetes, hypertension, obesity, renal dysfunction, current smoker, raised cholesterol, coronary heart disease, current or previous history of depression, self-reported sedentary lifestyle, and self-reported low cognitive activity) access to the Internet. Primary outcome measure will be a change in dementia risk modification score over the timescale of the trial (6 months). A qualitative process evaluation will interview a sample of participants and practitioners about their views on the acceptability and feasibility of the trial and the links between modifiable risk factors and dementia prevention. This work will be underpinned by Normalisation Process Theory. Discussion This study will explore the feasibility and acceptability of a risk profiler and on-line support environment to help individuals in mid-life assess their risk of developing dementia in later life and to take steps to alleviate that risk by tackling health-related behaviour change. Testing the intervention in a robust and theoretically informed manner will inform the development of a future, full-scale randomised controlled trial

Barriers facing people with obesity and type 2 diabetes in weight control: a systematic review

Type 2 diabetes has both non-modifiable and modifiable risk factors, such as heredity and obesity respectively. Obesity or overweight is a key modifiable risk factor for type 2 diabetes. Therefore, weight control through non-pharmacological interventions such as diet and physical exercise are some of the important measures used to reduce the potential complications and mortality associated with diabetes. Although, there are various policies and guidelines to tackle obesity in the UK, controlling weight gain in clinical practice remains a challenge. This systematic review sought to examine the evidence relating to the barriers preventing people with obesity or overweight and type 2 diabetes from making lifestyle changes, such as increased physical activity and changes to diet as a means of weight control

Vascular risk factors and diabetic neuropathy

Background: Other than glycemic control, there are no treatments for diabetic neuropathy. Thus, identifying potentially modifiable risk factors for neuropathy is crucial. We studied risk factors for the development of distal symmetric neuropathy in 1172 patients with type 1 diabetes mellitus from 31 centers participating in the European Diabetes (EURODIAB) Prospective Complications Study. Methods: Neuropathy was assessed at baseline (1989 to 1991) and at follow-up (1997 to 1999), with a mean (±SD) follow-up of 7.3±0.6 years. A standardized protocol included clinical evaluation, quantitative sensory testing, and autonomic-function tests. Serum lipids and lipoproteins, glycosylated hemoglobin, and the urinary albumin excretion rate were measured in a central laboratory. Results: At follow-up, neuropathy had developed in 276 of 1172 patients without neuropathy at baseline (23.5 percent). The cumulative incidence of neuropathy was related to the glycosylated hemoglobin value and the duration of diabetes. After adjustment for these factors, we found that higher levels of total and low-density lipoprotein cholesterol and triglycerides, a higher body-mass index, higher von Willebrand factor levels and urinary albumin excretion rate, hypertension, and smoking were all significantly associated with the cumulative incidence of neuropathy. After adjustment for other risk factors and diabetic complications, we found that duration of diabetes, current glycosylated hemoglobin value, change in glycosylated hemoglobin value during the follow-up period, body-mass index, and smoking remained independently associated with the incidence of neuropathy. Cardiovascular disease at baseline was associated with double the risk of neuropathy, independent of cardiovascular risk factors. Conclusions: This prospective study indicates that, apart from glycemic control, the incidence of neuropathy is associated with potentially modifiable cardiovascular risk factors, including a raised triglyceride level, body-mass index, smoking, and hypertension

Statins for primary and secondary prevention in the oldest old : an overview of the existing evidence

Hypercholesterolemia, although a modifiable risk factor for cardiovascular disease, is still one of the leading causes of death among older people in western countries. The use of statins among cholesterol reducing agents in both primary and secondary prevention has not been extensively studied in older patients in contrast to middle-aged patients. Despite a growing body of evidence in secondary prevention, statins are still under utilized in older patients with established vascular disease. On the other hand, the benefits of statins in primary prevention are not so clear. Therefore, the systematic use of statins in older patients with hypercholesterolemia needs to be further investigated

Vascular risk factors and diabetic neuropathy

Background: Other than glycemic control, there are no treatments for diabetic neuropathy. Thus, identifying potentially modifiable risk factors for neuropathy is crucial. We studied risk factors for the development of distal symmetric neuropathy in 1172 patients with type 1 diabetes mellitus from 31 centers participating in the European Diabetes (EURODIAB) Prospective Complications Study. Methods: Neuropathy was assessed at baseline (1989 to 1991) and at follow-up (1997 to 1999), with a mean (±SD) follow-up of 7.3±0.6 years. A standardized protocol included clinical evaluation, quantitative sensory testing, and autonomic-function tests. Serum lipids and lipoproteins, glycosylated hemoglobin, and the urinary albumin excretion rate were measured in a central laboratory. Results: At follow-up, neuropathy had developed in 276 of 1172 patients without neuropathy at baseline (23.5 percent). The cumulative incidence of neuropathy was related to the glycosylated hemoglobin value and the duration of diabetes. After adjustment for these factors, we found that higher levels of total and low-density lipoprotein cholesterol and triglycerides, a higher body-mass index, higher von Willebrand factor levels and urinary albumin excretion rate, hypertension, and smoking were all significantly associated with the cumulative incidence of neuropathy. After adjustment for other risk factors and diabetic complications, we found that duration of diabetes, current glycosylated hemoglobin value, change in glycosylated hemoglobin value during the follow-up period, body-mass index, and smoking remained independently associated with the incidence of neuropathy. Cardiovascular disease at baseline was associated with double the risk of neuropathy, independent of cardiovascular risk factors. Conclusions: This prospective study indicates that, apart from glycemic control, the incidence of neuropathy is associated with potentially modifiable cardiovascular risk factors, including a raised triglyceride level, body-mass index, smoking, and hypertension

A machine learning approach towards detecting dementia based on its modifiable risk factors

Dementia is considered one of the greatest global health and social care challenges in the 21st century. Fortunately, dementia can be delayed or possibly prevented by changes in lifestyle as dictated through known modifiable risk factors. These risk factors include low education, hypertension, obesity, hearing loss, depression, diabetes, physical inactivity, smoking, and social isolation. Other risk factors are non- modifiable and include aging and genetics. The main goal of this study is to demonstrate how machine learning methods can help predict dementia based on an individual’s modifiable risk factors profile. We use publicly available datasets for training algorithms to predict participant’ s cognitive state diagnosis, as cognitive normal or mild cognitive impairment or dementia. Several approaches were implemented using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) longitudinal study. The best classification results were obtained using both the Lancet and the Libra risk factor lists via longitudinal datasets, which outperformed cross-sectional baseline datasets. Moreover, using only data of the most recent visits provided even better results than using the complete longitudinal set. A binary classification (dementia vs non- dementia) yielded approximately 92% accuracy, while the full multi-class prediction performance yielded to a 77% accuracy using logistic regression, followed by random forest with 92% and 70% respectively. The results demonstrate the utility of machine learning in the prediction of cognitive impairment based on modifiable risk factors and may encourage interventions to reduce the prevalence or severity of the condition in large populations