The ideal blood pressure target to prevent cardiovascular disease in type 2 diabetes: A neutral viewpoint

Frontoni, Simona; Solini, Anna; Fioretto, Paola; Natali, Andrea; Zuccalà, A; Cosentino, Francesco; Penno, Giuseppe

doi:10.1016/j.numecd.2014.01.004

Cited by 4 publications

(2 citation statements)

References 63 publications

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“…In contrast, another recent trial from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Study Group demonstrated that intensive blood pressure control in patients at high risk for CVD did not reduce the rate of a composite outcome of fatal and nonfatal major cardiovascular events [34, 35]. Several more studies raise concerns that blood pressure reduction below a certain threshold may pose dangers, the so-called ‘J-curve’, which affects groups of individuals who are older and with comorbidities [19, 36, 37]. All these highlight the importance of personalized management, considering individual risks and benefits in managing hypertension.…”

Section: Discussionmentioning

confidence: 99%

Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients

Lacson

Baker

Suresh

et al. 2018

Clinical Kidney Journal

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Background We re-analyzed data from the Systolic Blood Pressure Intervention Trial (SPRINT) trial to identify features of systolic blood pressure (SBP) variability that portend poor cardiovascular outcomes using a nonlinear machine-learning algorithm. Methods We included all patients who completed 1 year of the study without reaching any primary endpoint during the first year, specifically: myocardial infarction, other acute coronary syndromes, stroke, heart failure or death from a cardiovascular event ( n = 8799; 94%). In addition to clinical variables, features representing longitudinal SBP trends and variability were determined and combined in a random forest algorithm, optimized using cross-validation, using 70% of patients in the training set. Area under the curve (AUC) was measured using a 30% testing set. Finally, feature importance was determined by minimizing node impurity averaging over all trees in the forest for a specific feature. Results A total of 365 patients (4.1%) reached the combined primary outcome over 37 months of follow-up. The random forest classifier had an AUC of 0.71 on the testing set. The 10 most significant features selected in order of importance by the automated algorithm included the urine albumin/creatinine (CR) ratio, estimated glomerular filtration rate, age, serum CR, history of subclinical cardiovascular disease (CVD), cholesterol, a variable representing SBP signals using wavelet transformation, high-density lipoprotein, the 90th percentile of SBP and triglyceride level. Conclusions We successfully demonstrated use of random forest algorithm to define best prognostic longitudinal SBP representations. In addition to known risk factors for CVD, transformed variables for time series SBP measurements were found to be important in predicting poor cardiovascular outcomes and require further evaluation.

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Section: Discussionmentioning

confidence: 99%

Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients

Lacson

Baker

Suresh

et al. 2018

Clinical Kidney Journal

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“…Cardiovascular diseases are the most frequent cause of mortality in patients with diabetes while a good blood pressure and cholesterol control was proved to reduce cardiovascular risk for diabetic patients. On the other hand, a good control of lipid profile is essential for the evolution of diabetes to microvascular complications [10][11][12][13].…”

Section: Comorbidities Associated With Diabetesmentioning

confidence: 99%

Prevalence of Physiological, Pathological and Behavioral Risk Factors in Patients Treated with Antidiabetic Sulfonylureas

Confederat

Stefan

Sandra

et al. 2015

Acta Medica Marisiensis

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Diabetes mellitus is an important public health problem because of its increased incidence and its devastating complications. In addition to this, it was observed an increase in prevalence for diabetes risk factors. The aim of this study was to evaluate the frequency of the pathophysiological and behavioral risk factors among patients treated with oral hypoglycemic sulfonylureas. Material and methods: It were analyzed the observation sheets of 200 patients ambulatory treated at the "Providența" Medical Center. Results and discussions: Most of the patients were aged over 65 years, being 48.5% women and 51.5% men. The obesity was the most frequent (88%) risk factor in patients; the majority of them were included in the "overweight" and "first-degree obesity" class (72%). Regarding associated comorbidities, hypertension was found in 77% of cases, followed by dyslipidemia. In terms of physiological and behavioral aspects, age and alcohol consumption were predominant. Conclusions: A significant percentage of diabetic patients treated with hypoglycemic sulfonylureas present a high prevalence of the risk factors and even two or three factors associated.

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Thresholds and Targets for Hypertension Management in Adults With Type 2 Diabetes Should Remain at 130/80 mmHg: What's the Evidence?

Shahi

Tobe

2018

Canadian Journal of Diabetes

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The ideal blood pressure target to prevent cardiovascular disease in type 2 diabetes: A neutral viewpoint

Cited by 4 publications

References 63 publications

Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients

Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients

Prevalence of Physiological, Pathological and Behavioral Risk Factors in Patients Treated with Antidiabetic Sulfonylureas

Thresholds and Targets for Hypertension Management in Adults With Type 2 Diabetes Should Remain at 130/80 mmHg: What's the Evidence?

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