Progression of subclinical atherosclerosis in ankylosing spondylitis: a 10-year prospective study.

Chronic systemic inflammation contributes to increased CVD burden in Ankylosing Spondylitis (AS). Since long-term follow-up data on subclinical atherosclerosis acceleration are lacking, we examined its progression in contemporary AS patients during 10 years. Fifty-three (89% male, aged 50.4 (36.3-55.9) years,) non-diabetic, CVD-free AS patients and 53 age-sex-matched non-diabetic, control individuals were re-evaluated after 9.2-10.2 years by ultrasonography for carotid/femoral atheromatosis, pulse wave velocity (PWV) and intima-media thickness (IMT), performed by the same operator/protocol. New atheromatic plaque formation, PWV deterioration, and IMT increase were associated only with classical CVD risk factors, as reflected by the heartSCORE (age, gender, smoking status, blood pressure and cholesterol levels) by multivariate analysis, rather than disease presence. However, among AS patients, despite remission/low disease activity at follow-up end in 79%, atheromatosis progression was associated by multivariate analysis with higher BASDAI scores (p = 0.028), independently of biologic therapies administered in 2/3 of them. Moreover, in AS patients, but not in controls, PWV values at baseline were associated with plaque progression during the 10-year follow-up after taking into account baseline heartSCORE and plaque burden status (p = 0.033). Despite comparable prevalence of both hypertension and hypercholesterolemia at baseline between patients and controls, a lower percentage of AS patients had achieved "adequate" CVD risk factor control at follow-up end (11% vs 25% respectively, p = 0.076). Classical CVD risk factors and residual disease activity account for the progression of subclinical atherosclerosis in AS, pointing to the unmet needs in the contemporary management of these patients.

Blood pressure variability: methodological aspects, clinical relevance and practical indications for management - a European Society of Hypertension position paper ∗.

Blood pressure is not a static parameter, but rather undergoes continuous fluctuations over time, as a result of the interaction between environmental and behavioural factors on one side and intrinsic cardiovascular regulatory mechanisms on the other side. Increased blood pressure variability (BPV) may indicate an impaired cardiovascular regulation and may represent a cardiovascular risk factor itself, having been associated with increased all-cause and cardiovascular mortality, stroke, coronary artery disease, heart failure, end-stage renal disease, and dementia incidence. Nonetheless, BPV was considered only a research issue in previous hypertension management guidelines, because the available evidence on its clinical relevance presents several gaps and is based on heterogeneous studies with limited standardization of methods for BPV assessment. The aim of this position paper, with contributions from members of the European Society of Hypertension Working Group on Blood Pressure Monitoring and Cardiovascular Variability and from a number of international experts, is to summarize the available evidence in the field of BPV assessment methodology and clinical applications and to provide practical indications on how to measure and interpret BPV in research and clinical settings based on currently available data. Pending issues and clinical and methodological recommendations supported by available evidence are also reported. The information provided by this paper should contribute to a better standardization of future studies on BPV, but should also provide clinicians with some indications on how BPV can be managed based on currently available data.

Time of blood pressure in target range in acute ischemic stroke.

OBJECTIVE: The purpose of this study was to investigate the association of blood pressure (BP) time-in-target range (TTR) derived from 24-h ambulatory BP monitoring (ABPM) during the acute phase of ischemic stroke (AIS), with the severity of stroke and its predictive value for the 3 months outcome. METHODS: A total of 228 AIS patients (prospective multicenter follow-up study) underwent ABPM every 20 min within 48 h from stroke onset using an automated oscillometric device. Clinical and laboratory findings were recorded. Mean BP parameters, BP variability and TTR for SBP (90-140 mmHg), DBP (60-90 mmHg), and mean arterial pressure (MAP) were calculated. Endpoints were death and disability/death at 3 months. RESULTS: A total of 14 942 BP measurements were recorded (∼66 per AIS patient) within 72 h of stroke onset. Patient's 24-h TTR was 34.7 ±â€Š29.9, 64.3 ±â€Š24.2, and 55.3 ±â€Š29.4% for SBP, DBP and MAP, respectively. In patients without prior hypertension, TTR was lower as stroke severity increased for both DBP (P = 0.031) and MAP (P = 0.016). In 175 patients without prior disability, increase in TTR of DBP and MAP associated significantly with a decreased risk of disability/death (hazard ratio 0.96, 95% CI 0.95-0.99, P = 0.007 and hazard ratio 0.97, 95% CI 0.96-0.99, P = 0.007). TTR of SBP in 130-180 mmHg and 110-160 mmHg ranges seems to be related with mortality and disability outcomes, respectively. CONCLUSION: TTR can be included for a more detailed description of BP course, according to stroke severity, and for the evaluation of BP predictive role, in addition to mean BP values, derived from ABPM during the acute phase of AIS. CLINICAL TRIAL REGISTRATIONURL: http://www.clinicaltrials.gov. Unique identifier: NCT01915862.

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.

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.

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.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans.

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the "COVLIAS 2.0-cXAI" system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans.

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models­namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet­were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests­namely, the Mann−Whitney test, paired t-test, and Wilcoxon test­demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

A clinical score for prediction of elevated aortic stiffness: derivation and validation in 3943 hypertensive patients.

OBJECTIVE: Aortic stiffness assessed by carotid-femoral pulse wave velocity (PWV) is an important predictor to gauge the overall risk of hypertensive patients; nonetheless, it is underutilized in everyday practice. We propose a simple scoring system based on clinical variables that can identify patients with a priority for measurement of PWV, that is, those with elevated PWV (≥10 m/s) and, consequently, at higher risk for events. METHODS: Patient data from three outpatient clinics (n = 3943) were used to form a derivation, internal and external validation cohort. For derivation, independent predictors of high PWV from a binary logistic regression model were split in subcategories and implemented in a simple clinical prediction scoring system with the acronym SAGE (office systolic blood pressure, age, glycemia and eGFR categories). RESULTS: Its performance was validated at the internal and external validation cohorts with c-statistics being 0.83 (95% CI 0.81-0.86) and 0.77 (95% CI 0.73-0.80), respectively. A cut-off of eight points to identify patients with high PWV in the internal/external validation cohorts yielded a positive-predictive value, negative-predictive value, sensitivity and specificity of 52/36%, 88/81%, 56/70% and 88/65%, respectively. CONCLUSION: The SAGE score that takes into account easily measured clinical variables (office SBP, age, fasting glucose and eGFR categories) can be used to accurately predict elevated levels of PWV and prioritize its measurement in specific hypertensive patients. Its use will result in greater acknowledgement of the role of aortic stiffness and aid physicians in implementing it in clinical practice.

Systemic Inflammatory Response and Atherosclerosis: The Paradigm of Chronic Inflammatory Rheumatic Diseases.

Patients with Chronic Inflammatory Rheumatic diseases (CIRD) are at increased risk of cardiovascular disease (CVD), ascribed not only to classical risk factors, but also to the presence of chronic systemic inflammatory response. Αtherosclerosis, the cornerstone of CVD, is known to be accelerated in CIRD; rheumatoid arthritis promotes atheromatosis and associates with preclinical atherosclerosis equivalent to Diabetes Mellitus, which also seems to apply for systemic lupus erythematosus. Data on ankylosing spondylitis and psoriatic arthritis, albeit more limited, also support an increased CV risk in these patients. The association between inflammation and atherosclerosis, has been thoroughly investigated in the last three decades and the role of inflammation in the pathogenesis and progression of atherogenesis has been well established. Endothelial dysfunction, oxidative stress in vascular endothelial cells and macrophage accumulation, toll-like receptor signaling, NLPR-3 formation and subsequent pro-inflammatory cytokine production, such as TNFa, IL-1ß, IL-6, and TNF-like cytokine 1A, are few of the mechanisms implicated in the atherogenic process. Moreover, there is evidence that anti-inflammatory biologic drugs, such as anti-TNF and anti-IL1ß agents, can decelerate the atherogenic process, thus setting new therapeutic targets for early and effective disease control and suppression of inflammation, in addition to aggressive management of classical CV risk factors.

Pulse pressure amplification and cardiac autonomic dysfunction in patients with type 2 diabetes mellitus.

The main aim of this cross-sectional study was to investigate the association between pulse pressure amplification (PPA) and cardiac autonomic activity (baroreflex sensitivity (BRS) and heart rate variability (HRV)) in patients with type 2 diabetes mellitus (T2DM). In addition, we examined the association between cardiac autonomic activity and central hemodynamic parameters that may affect PPA such as augmentation index (AIx), aortic stiffness (pulse wave velocity (PWV)), and common carotid artery stiffness distensibility coefficient (DC). A total of 142 patients with T2DM were included in the study. In multivariate linear regression analysis-after controlling for age, diabetes duration, height, waist circumference, aortic PWV, use of ß-blockers, and BRS-PPA was associated significantly and independently with male gender (standardized regression coefficient (ß) = 0.156, p = 0.007), aortic systolic blood pressure (ß = -0.221, p < 0.001), heart rate (ß = 0.521, p < 0.001), AΙx (ß = -0.443, p < 0.001), and parameters of HRV, such as total power of HRV (ß = -0.157, p = 0.005). No significant associations were found between BRS or parameters of HRV with aortic PWV, AIx, or DC. In patients with T2DM, cardiac autonomic dysfunction was associated with enhanced PPA. This association was independent from the well-described effect of resting heart rate, as well as from traditional cardiovascular risk factors or diabetes-related factors. Moreover, it was not mediated by effects of the autonomic dysfunction on arterial stiffness or on pressure wave reflections. These findings suggest that cardiac autonomic dysfunction affects PPA by mechanisms other than resting tachycardia and arterial properties.

The effect of raisins on biomarkers of endothelial function and oxidant damage; an open-label and randomized controlled intervention.

Based on the existing data in grapes and wine, the aim of the present study was to investigate the probability that raisins improve clinical features and markers of oxidative stress, inflammation and arterial function in healthy smokers. Thirty-six apparently healthy smokers were recruited to an open-label and randomized, controlled, 4-week prospective intervention. All participants were reported to consume less than the recommended amount of five servings fruits and vegetables daily. Participants in the intervention were instructed to consume raisins equal to five fruit servings (90g/d). Anthropometric and blood pressure (BP) measurements, assessment of dietary intake, and fasting blood draws were conducted at baseline and at week 4. Biochemical (glucose, lipids, liver enzymes), inflammation [C-reactive protein (CRP), leptin], oxidative stress [Malondialdehyde (MDA), Advanced oxidation protein products (AOPPs)] and arterial function markers [Flow-mediated dilatation (FMD), Pulse wave velocity (PWV), Intercellular adhesion molecule-1 (ICAM-1), Nitric oxide (NO)] were assessed pre- and post-intervention. Baseline characteristics did not differ between the intervention and control arm. No effect of daily raisin consumption was observed on markers assessed between baseline and week 4 in either arm. Regarding vegetable consumption, no difference was observed in either group between baseline and post-intervention; however, as expected, a significant increase was reported in the intervention arm in fruit consumption between baseline and end point (p<0.001) and between two arms post-intervention (p<0.001). When analyzing according to age, ICAM-1 levels significantly decreased in subjects >30years (n=8) in intervention arm (390.1±17.6 to 302.2±11ng/mL, p=0.004). After analysis of the data for sex, women in intervention (n=5) decreased significantly diastolic BP (74.6±4.2 to 67.4±2.6mg/dL, p=0.043), total cholesterol (175.8±7 to 166.6±6.6mg/dL, p<0.001) and LDL-cholesterol (96.2±9.6 to 89±10.5mg/dL, p=0.012). However, due to the small sample size in the above, no safe conclusions can be exported.

Atherosclerosis is not accelerated in rheumatoid arthritis of low activity or remission, regardless of antirheumatic treatment modalities.

Objectives: RA associates with increased cardiovascular disease (CVD) morbidity and mortality due to accelerated atherosclerosis, attributed to both classical risk factors and chronic inflammation. The aim of this study was to test the hypothesis that effective disease control over 3 years modifies acceleration of atherosclerosis in RA. Methods: Consecutive, non-diabetic RA patients previously examined by ultrasonography for subclinical atherosclerosis were re-evaluated after 3.2 (0.2) years, provided that they were in remission/low disease activity (DAS28 <3.2) for at least 75% of this period. Patients (n = 139) were demographically matched with 139 non-diabetic, non-RA control individuals studied in parallel. Results: Patients and controls (mean age of 56 years at baseline) had a comparable burden of classical CVD risk factors. Patients' pulse wave velocity (reflecting arterial stiffness) changed by 0.07 m/s/year and left carotid intima-media thickness (reflecting wall hypertrophy) increased by 0.009 mm/year; formation of new atheromatic plaques in carotid and/or femoral arterial beds occurred in 22%. Multivariate analysis after correcting for all classical CVD risk factors and anti-hypertensive/lipid-lowering therapies demonstrated no significant differences between patients and controls in any of the subclinical atherosclerosis indices. Changes in all atherosclerosis indices from baseline to end of follow-up were comparable between those 56 patients treated with biologic DMARDs and their demographically matched patients treated with synthetic DMARDs. Conclusion: Effective disease control may abrogate any RA-specific effect on the progression of atherosclerosis regardless of treatment. Whether early and sustained RA control translates to the CVD outcomes expected in the general population should be examined in prospective studies.

Impact of non-steroidal anti-inflammatory drugs on cardiovascular risk: Is it the same in osteoarthritis and rheumatoid arthritis?

Although large-scale population studies have shown that non-steroidal anti-inflammatory drugs (NSAIDs) increase the risk of myocardial infarction, this is not confirmed in patients with rheumatoid arthritis (RA). Herein, we review the litterature on the differential effects of NSAIDs on cardiovascular risk in osteoarthritis (OA) versus RA and discuss possible explanations for this discrepancy. To assess a potential additive effect of age in non-RA populations, we compared weighted mean age between RA patients and unselected NSAID users included in cohort and case-control studies that estimate the cardiovascular risk of NSAIDs, assuming that the main indication for NSAID usage in elderly populations is OA. Our hypothesis that advanced age in osteoarthtitis compared to RA patients confounds the effect of NSAIDs on cardiovasular risk was not confirmed. Several other hypotheses that can be proposed to explain this counterintuitive effect of NSAIDs on the cardiovascular risk of RA patients are discussed. We conclude that patients with RA have a lower cardiovascular disease risk associated with the use of NSAIDs, probably due to the nature of their disease per se, until further research indicates differently.

Blood Pressure and All-Cause Mortality by Level of Cognitive Function in the Elderly: Results From a Population-Based Study in Rural Greece.

This study aimed to investigate whether the effect of blood pressure (BP) on mortality differs by levels of cognitive function. The associations of brachial systolic BP, diastolic BP, mean arterial pressure (MAP), and pulse pressure with all-cause mortality were prospectively explored (follow-up 7.0±2.2 years) in 660 community-dwelling individuals (≥60 years) using adjusted Cox models, stratified by cognitive impairment (Mini-Mental State Examination [MMSE] <24). No association between brachial BP variables and mortality was shown for the total sample in quartiles analysis; however, MAP in the highest quartile, compared with the second, was associated with mortality (hazard ratio, 1.85; 95% confidence intervals, 1.09-3.12) among cognitively impaired individuals. The fractional-polynomials approach for BP confirmed this finding and further showed, solely in the MMSE <24 subcohort, U-shaped trends of MAP and systolic BP, with increased mortality risk in extremely low or high values; no such pattern was evident for patients with MMSE ≥24. Elderly individuals with cognitive impairment might be more susceptible to the detrimental effects of low and elevated MAP and systolic BP.

Comorbidity of Cognitive Impairment and Late-Life Depression Increase Mortality: Results From a Cohort of Community-Dwelling Elderly Individuals in Rural Greece.

OBJECTIVE: To investigate the association of cognitive impairment (COGI) and depression with all-cause mortality and cardiovascular-specific mortality among community-dwelling elderly individuals in rural Greece. METHODS: Cognition and depressive symptomatology of 676 Velestino town residents aged ≥60 years were assessed using Mini-Mental State Examination (MMSE) and Geriatric Depression Scale (GDS), respectively. Eight-year all-cause mortality and cardiovascular mortality were explored by multivariate Cox regression models controlling for major confounders. RESULTS: Two hundred and one patients died during follow-up. Cognitive impairment (MMSE ≤ 23) was independently associated with all-cause mortality (hazard ratio [HR]: 1.57, 95% confidence interval [CI]: 1.13-2.18) and cardiovascular mortality (HR: 1.57, 95%CI: 1.03-2.41). Moderate to severe depression (GDS > 10) was significantly associated only with a 51% increase in all-cause mortality. A male-specific association was noted for moderate to severe depression, whereas the effect of COGI was limited to females. Noteworthy, COGI and depression comorbidity, rather than their sole presence, increased all-cause mortality and cardiovascular mortality by 66% and 72%, respectively. The mortality effect of COGI was augmented among patients with depression and of depression among patients with COGI. CONCLUSION: COGI and depression, 2 entities often coexisting among elderly individuals, appear to increase all-cause mortality and cardiovascular mortality. Gender-specific modes may prevail but their comorbidity should be carefully assessed, as it seems to represent an independent index of increased frailty, which eventually shortens life expectancy.