We found that a fixed combination of perindopril/amlodipine is effective in controlling BP in patients with essential hypertension, with older age, male gender, and diabetes mellitus being independent risk factors for less BP control.
The onset of type 2 diabetes increases the risk of vascular complications and death. We know now that that this risk begins long before the diabetes diagnosis. Prediabetes and type 2 diabetes are not separate entities in practice and exist within a continuum of dysglycaemia and vascular risk that increases in severity over time. This excess risk requires early intervention with lifestyle therapy supported with pharmacologic antidiabetic therapy, intensified promptly where necessary throughout the duration of the diabetes continuum. Metformin is an evidence-based treatment for preventing prediabetes and improves cardiovascular outcomes in people with type 2 diabetes from diagnosis onwards. Newer agents (SGLT2 inhibitors and GLP-1 agonists) are appropriate for people presenting with type 2 diabetes and significant cardiovascular comorbidity. Additional therapies should be used without delay to achieve patients’ individualised HbA1c goals and to minimise cardiovascular risk.
Opioid addiction causes high degree of morbidity and mortality. Preemptive 1 identification of patients at risk of opioid dependence and developing intelligent clinical 2 decisions to deprescribe opioids to the vulnerable patient population may help in 3 reducing the burden. Identifying patients susceptible to mortality due to opioid-induced 4 side effects and understanding the landscape of drug-drug interaction pairs aggravating 5 opioid usage are significant, yet, unexplored research questions. In this study, we 6 present a collection of predictive models to identify patients at risk of opioid abuse, 7 mortality and drug-drug interactions in the context of opioid usage. Using publicly 8 available dataset from MIMIC-III, we developed predictive models (opioid abuse models 9 a=Logistic Regression; b=Extreme Gradient Boosting and mortality model= Extreme 10 Gradient Boosting) and identified potential drug-drug interaction patterns. To enable 11 the translational value of our work, the predictive model and all associated software 12 code is provided. This repository could be used to build clinical decision aids and thus 13 improve the optimization of prescription rates for vulnerable population. 14 Introduction 15 Drug overdose is the leading cause of accidental deaths in the US with 52,404 lethal 16 drug overdoses in 2015. Opioid addiction is driving this epidemic with 20,101 overdose 17 deaths related to prescription pain relievers and 12,990 overdose deaths related to heroin 18 in 2015. The overdose death rate in 2008 was nearly four times that in 1999 and the 19 sales of prescription pain relievers in 2010 were four times those in 1999 ( [1]-[2]). Also, 20 a study done by Jeffery et al., ( [6]) highlights the fact that despite all the increased 21 attention to opioid abuse and awareness of risks, the opioid use and average daily dose 22 have not substantially decreased from the peaks. Drug overdose continues to be an 23 alarming public health problem and thus, it needs immediate attention. However, a part 24 of this problem could be addressed if we can precociously identify those subjects who 25 are the most susceptible to adverse events when given opioids. We provide a solution to 26 this by using simple yet robust machine learning techniques involving classification
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