Development and validation of a clinical and computerised Decision Support System for Management of Hypertension (DSS-HTN) at a Primary Health Care (PHC) setting

Background: Hypertension remains the top global cause of disease burden. Decision support systems (DSS) could provide an adequate and cost-effective m

Markers of inflammation and risk of coronary heart disease.

Cardiovascular disease is the leading cause of global mortality, with coronary heart disease (CHD) its major manifestation. Although inflammation, the body's response to noxious stimuli, is implicated in several stages of CHD development, the relevance of circulating levels of markers of inflammation to CHD risk remains uncertain. This review summarizes available epidemiological evidence for four emerging inflammatory markers implicated in CHD (fibrinogen, C-reactive protein, lipoprotein-associated phospholipase A2 and interleukin-6); considers their likely utility in cardiovascular risk prediction; and outlines areas of outstanding uncertainty.Peer Reviewe

Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.

BACKGROUND: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. We tested (1) whether ML techniques based on a state-of-the-art automated ML framework (AutoPrognosis) could improve CVD risk prediction compared to traditional approaches, and (2) whether considering non-traditional variables could increase the accuracy of CVD risk predictions. METHODS AND FINDINGS: Using data on 423,604 participants without CVD at baseline in UK Biobank, we developed a ML-based model for predicting CVD risk based on 473 available variables. Our ML-based model was derived using AutoPrognosis, an algorithmic tool that automatically selects and tunes ensembles of ML modeling pipelines (comprising data imputation, feature processing, classification and calibration algorithms). We compared our model with a well-established risk prediction algorithm based on conventional CVD risk factors (Framingham score), a Cox proportional hazards (PH) model based on familiar risk factors (i.e, age, gender, smoking status, systolic blood pressure, history of diabetes, reception of treatments for hypertension and body mass index), and a Cox PH model based on all of the 473 available variables. Predictive performances were assessed using area under the receiver operating characteristic curve (AUC-ROC). Overall, our AutoPrognosis model improved risk prediction (AUC-ROC: 0.774, 95% CI: 0.768-0.780) compared to Framingham score (AUC-ROC: 0.724, 95% CI: 0.720-0.728, p < 0.001), Cox PH model with conventional risk factors (AUC-ROC: 0.734, 95% CI: 0.729-0.739, p < 0.001), and Cox PH model with all UK Biobank variables (AUC-ROC: 0.758, 95% CI: 0.753-0.763, p < 0.001). Out of 4,801 CVD cases recorded within 5 years of baseline, AutoPrognosis was able to correctly predict 368 more cases compared to the Framingham score. Our AutoPrognosis model included predictors that are not usually considered in existing risk prediction models, such as the individuals' usual walking pace and their self-reported overall health rating. Furthermore, our model improved risk prediction in potentially relevant sub-populations, such as in individuals with history of diabetes. We also highlight the relative benefits accrued from including more information into a predictive model (information gain) as compared to the benefits of using more complex models (modeling gain). CONCLUSIONS: Our AutoPrognosis model improves the accuracy of CVD risk prediction in the UK Biobank population. This approach performs well in traditionally poorly served patient subgroups. Additionally, AutoPrognosis uncovered novel predictors for CVD disease that may now be tested in prospective studies. We found that the "information gain" achieved by considering more risk factors in the predictive model was significantly higher than the "modeling gain" achieved by adopting complex predictive models

Chocolate consumption and cardiometabolic disorders: systematic review and meta-analysis

Objective To evaluate the association of chocolate consumption with the risk of developing cardiometabolic disorders

Prevalence of Depression and Depressive Symptoms Among Resident Physicians: A Systematic Review and Meta-analysis.

IMPORTANCE: Physicians in training are at high risk for depression. However, the estimated prevalence of this disorder varies substantially between studies. OBJECTIVE: To provide a summary estimate of depression or depressive symptom prevalence among resident physicians. DATA SOURCES AND STUDY SELECTION: Systematic search of EMBASE, ERIC, MEDLINE, and PsycINFO for studies with information on the prevalence of depression or depressive symptoms among resident physicians published between January 1963 and September 2015. Studies were eligible for inclusion if they were published in the peer-reviewed literature and used a validated method to assess for depression or depressive symptoms. DATA EXTRACTION AND SYNTHESIS: Information on study characteristics and depression or depressive symptom prevalence was extracted independently by 2 trained investigators. Estimates were pooled using random-effects meta-analysis. Differences by study-level characteristics were estimated using meta-regression. MAIN OUTCOMES AND MEASURES: Point or period prevalence of depression or depressive symptoms as assessed by structured interview or validated questionnaire. RESULTS: Data were extracted from 31 cross-sectional studies (9447 individuals) and 23 longitudinal studies (8113 individuals). Three studies used clinical interviews and 51 used self-report instruments. The overall pooled prevalence of depression or depressive symptoms was 28.8% (4969/17,560 individuals, 95% CI, 25.3%-32.5%), with high between-study heterogeneity (Q = 1247, τ2 = 0.39, I2 = 95.8%, P < .001). Prevalence estimates ranged from 20.9% for the 9-item Patient Health Questionnaire with a cutoff of 10 or more (741/3577 individuals, 95% CI, 17.5%-24.7%, Q = 14.4, τ2 = 0.04, I2 = 79.2%) to 43.2% for the 2-item PRIME-MD (1349/2891 individuals, 95% CI, 37.6%-49.0%, Q = 45.6, τ2 = 0.09, I2 = 84.6%). There was an increased prevalence with increasing calendar year (slope = 0.5% increase per year, adjusted for assessment modality; 95% CI, 0.03%-0.9%, P = .04). In a secondary analysis of 7 longitudinal studies, the median absolute increase in depressive symptoms with the onset of residency training was 15.8% (range, 0.3%-26.3%; relative risk, 4.5). No statistically significant differences were observed between cross-sectional vs longitudinal studies, studies of only interns vs only upper-level residents, or studies of nonsurgical vs both nonsurgical and surgical residents. CONCLUSIONS AND RELEVANCE: In this systematic review, the summary estimate of the prevalence of depression or depressive symptoms among resident physicians was 28.8%, ranging from 20.9% to 43.2% depending on the instrument used, and increased with calendar year. Further research is needed to identify effective strategies for preventing and treating depression among physicians in training

Developing Non-Laboratory Cardiovascular Risk Assessment Charts and Validating Laboratory and Non-Laboratory-Based Models.

BACKGROUND: Developing simplified risk assessment model based on non-laboratory risk factors that could determine cardiovascular risk as accurately as laboratory-based one can be valuable, particularly in developing countries where there are limited resources. OBJECTIVE: To develop a simplified non-laboratory cardiovascular disease risk assessment chart based on previously reported laboratory-based chart and evaluate internal and external validation, and recalibration of both risk models to assess the performance of risk scoring tools in other population. METHODS: A 10-year non-laboratory-based risk prediction chart was developed for fatal and non-fatal CVD using Cox Proportional Hazard regression. Data from the Isfahan Cohort Study (ICS), a population-based study among 6504 adults aged ≥ 35 years, followed-up for at least ten years was used for the non-laboratory-based model derivation. Participants were followed up until the occurrence of CVD events. Tehran Lipid and Glucose Study (TLGS) data was used to evaluate the external validity of both non-laboratory and laboratory risk assessment models in other populations rather than one used in the model derivation. RESULTS: The discrimination and calibration analysis of the non-laboratory model showed the following values of Harrell's C: 0.73 (95% CI 0.71-0.74), and Nam-D'Agostino χ2:11.01 (p = 0.27), respectively. The non-laboratory model was in agreement and classified high risk and low risk patients as accurately as the laboratory one. Both non-laboratory and laboratory risk prediction models showed good discrimination in the external validation, with Harrell's C of 0.77 (95% CI 0.75-0.78) and 0.78 (95% CI 0.76-0.79), respectively. CONCLUSIONS: Our simplified risk assessment model based on non-laboratory risk factors could determine cardiovascular risk as accurately as laboratory-based one. This approach can provide simple risk assessment tool where laboratory testing is unavailable, inconvenient, and costly

High-Sensitivity Cardiac Troponin and New-Onset Heart Failure: A Systematic Review and Meta-Analysis of 67,063 Patients With 4,165 Incident Heart Failure Events.

OBJECTIVES: The aim of this study was to systematically collate and appraise the available evidence regarding the association between high-sensitivity cardiac troponin (hs-cTn) and incident heart failure (HF) and the added value of hs-cTn in HF prediction. BACKGROUND: Identification of subjects at high risk for HF and early risk factor modification with medications such as angiotensin-converting enzyme inhibitors may delay the onset of HF. Hs-cTn has been suggested as a prognostic marker for the incidence of first-ever HF in asymptomatic subjects. METHODS: PubMed, Embase, and Web of Science were systematically searched for prospective cohort studies published before January 2017 that reported associations between hs-cTn and incident HF in subjects without baseline HF. Study-specific multivariate-adjusted hazard ratios (HRs) were pooled using random-effects meta-analysis. RESULTS: Data were collated from 16 studies with a total of 67,063 subjects and 4,165 incident HF events. The average age was 57 years, and 47% were women. Study quality was high (Newcastle-Ottawa score 8.2 of 9). In a comparison of participants in the top third with those in the bottom third of baseline values of hs-cTn, the pooled multivariate-adjusted HR for incident HF was 2.09 (95% confidence interval [CI]: 1.76 to 2.48; p < 0.001). Between-study heterogeneity was high, with an I2 value of 80%. HRs were similar in men and women (2.29 [95% CI: 1.64 to 3.21] vs. 2.18 [95% CI: 1.68 to 2.81]) and for hs-cTnI and hs-cTnT (2.09 [95% CI: 1.53 to 2.85] vs. 2.11 [95% CI: 1.69 to 2.63]) and across other study-level characteristics. Further adjustment for B-type natriuretic peptide yielded a similar HR of 2.08 (95% CI: 1.64 to 2.65). Assay of hs-cTn in addition to conventional risk factors provided improvements in the C index of 1% to 3%. CONCLUSIONS: Available prospective studies indicate a strong association of hs-cTn with the risk of first-ever HF and significant improvements in HF prediction

Atrial Natriuretic Peptide Gene Polymorphisms and Risk of Ischemic Stroke in Humans

Background and Purpose— A precise definition of genetic factors responsible for common forms of stroke is still lacking. The purpose of the present study was to investigate the contributory role of the genes encoding atrial natriuretic peptide ( ANP ) and type A natriuretic peptide receptor ( NPRA ) in humans' susceptibility to develop ischemic stroke. Methods— Allele and genotype frequencies of ANP and NPRA were characterized in an Italian case-control study with patients affected by vascular disease or risk factors. Subjects were recruited from the island of Sardinia (206 cases, 236 controls). Results— A significant association between the ANP /TC2238 polymorphic site and stroke occurrence was found when a recessive model of inheritance was assumed. The risk conferred by this mutant genotype, when estimated by multivariate logistic regression analysis, was 3.8 (95% confidence interval, 1.4 to 10.9). A significantly increased risk of stroke recurrence was observed among cases carrying the ANP /CC2238 genotype compared with cases carrying the ANP /TT2238 genotype ( P =0.04). No direct association of NPRA with stroke occurrence was detected. However, a significant epistatic interaction between the ANP /CC2238 genotype and an allelic variant of NPRA led to a 5.5-fold increased risk of stroke (95% confidence interval, 1.5 to 19.4). Conclusions— Our findings support a direct contributory role of ANP to stroke in humans. A significant interaction between ANP and NPRA on stroke occurrence was found

Genetically Predicted Type 2 Diabetes Mellitus Liability, Glycated Hemoglobin and Cardiovascular Diseases: A Wide-Angled Mendelian Randomization Study

(1) Aim: To investigate the causal effects of T2DM liability and glycated haemoglobin (HbA1c) levels on various cardiovascular disease outcomes, both in the general population and in non-diabetic individuals specifically. (2) Methods: We selected 243 variants as genetic instruments for T2DM liability and 536 variants for HbA1c. Linear Mendelian randomization analyses were performed to estimate the associations of genetically-predicted T2DM liability and HbA1c with 12 cardiovascular disease outcomes in 367,703 unrelated UK Biobank participants of European ancestries. We performed secondary analyses in participants without diabetes (HbA1c 6.5% with no diagnosed diabetes), and in participants without diabetes or pre-diabetes (HbA1c 5.7% with no diagnosed diabetes). (3) Results: Genetically-predicted T2DM liability was positively associated (p 0.004, 0.05/12) with peripheral vascular disease, aortic valve stenosis, coronary artery disease, heart failure, ischaemic stroke, and any stroke. Genetically-predicted HbA1c was positively associated with coronary artery disease and any stroke. Mendelian randomization estimates generally shifted towards the null when excluding diabetic and pre-diabetic participants from analyses. (4) Conclusions: This genetic evidence supports causal effects of T2DM liability and HbA1c on a range of cardiovascular diseases, suggesting that improving glycaemic control could reduce cardiovascular risk in a general population, with greatest benefit in individuals with diabetes

Cardiovascular disease risk by assigned treatment using the 2013 and 1998 obesity guidelines: Evaluation of Obesity Guidelines

The 1998 and the 2013 guidelines on management of overweight and obesity in adults provided algorithms for identification of patients to be treated with weight loss. To date the cardiovascular disease (CVD) risk in the groups recommended or not recommended for weight loss treatment have not been estimated and compared