Disparity in spatial distribution of pericardial calcifications in constrictive pericarditis.

Background: Pericardial calcification is seen among patients with constrictive pericarditis (CP). However, the pattern of pericardial calcium distribution and the association with clinical outcomes and imaging data are not well described. Methods: This was a retrospective study from 2007 to 2013 to evaluate the pattern of pericardial calcium distribution by CT in CP using a semiquantitative calcium scoring system to calculate total pericardial calcium burden and distribution. Calcium localisation was allocated to 20 regions named after the corresponding heart structure. Baseline clinical data, imaging data and clinical outcomes were collected and compared between the calcified pericardium and non-calcified pericardium groups. We assessed the effect of pericardial calcium on clinical outcomes and echocardiographic data between the two groups. Results: Of the 123 consecutive patients with CP (93 male; mean age 61±13 years) between 2007 and 2013, 49 had calcified pericardium and 74 had non-calcified pericardium. Distribution of calcium on the left ventricle (LV) basal anterior, mid-anterior and apical segments in addition to right ventricle (RV) apical segment was involved in <30% of the cases with the remaining segments involved in >35% of cases. A potential protective role of RV calcium on regional myocardial mechanics was noted. Conclusion: Preferential distribution of calcium in CP in a partial band-like pattern (from basal anterolateral LV going inferiorly and then encircling the heart to reach the RV outflow tract) with extension into the mitral and tricuspid annuli was noted. Pericardial calcium was not significantly associated to clinical outcomes.

Worsening of the Cardiovascular Profile in a Developing Country: The Greater Beirut Area Cardiovascular Cohort.

BACKGROUND: Lebanon has no established governmental noncommunicable diseases surveillance and monitoring system to permit reporting on noncommunicable diseases rates. The last World Health Organization-supported surveillance report showed worrying trends in cardiovascular disease (CVD) risk factors. OBJECTIVES: A cardiovascular cohort was established to permit CVD outcomes studies in an urban sample in the Lebanese capital and the study in hand presents the baseline CVD risk factors of this cohort. METHODS: A cross-sectional study was carried out including 501 Lebanese adults (64.3% women) from the Greater Beirut area using random multistage probability sampling. Interviews, physical exams, and blood withdrawal were conducted to collect information on demographic and lifestyle factors, body mass index, blood pressure, fasting blood glucose, blood lipids, as well as history of coronary artery diseases, hypertension, diabetes mellitus type 2, dyslipidemia, and stroke. Means with SD for continuous variables and frequencies and percentages for categorical variables are reported. RESULTS: The prevalence CVD risk factors including obesity, smoking, diabetes mellitus type 2, hypertension, and dyslipidemia prevalence in the Greater Beirut area was higher than that reported for the general population. Important sex and age differences were also observed, whereby older participants and women had higher rates of obesity, diabetes mellitus type 2, and dyslipidemia and younger participants and men were engaged more in cigarette smoking and alcohol consumption. Interestingly, water pipe smoking was similarly prevalent among genders. CONCLUSIONS: The overall prevalence of CVD risk factors in this urban population is higher than reported in the 2010 World Health Organization Stepwise Approach to Surveillance report on the Lebanese population, indicating that the urban population in the capital carries a higher burden of CVD risk. In addition, sex and age difference rates of CVD risk factors highlight the need for tailored public health measures to tackle the sex- and age-based CVD risk factors.

Artificial neural network-based model enhances risk stratification and reduces non-invasive cardiac stress imaging compared to Diamond-Forrester and Morise risk assessment models: A prospective study.

BACKGROUND: Coronary artery disease (CAD) accounts for more than half of all cardiovascular events. Stress testing remains the cornerstone for non-invasive assessment of patients with possible or known CAD. Clinical utilization reviews show that most patients presenting for evaluation of stable CAD by stress testing are categorized as low risk prior to the test. Attempts to enhance risk stratification of individuals who are sent for stress testing seem to be more in need today. The present study compares artificial neural networks (ANN)-based prediction models to the other risk models being used in practice (the Diamond-Forrester and the Morise models). METHODS: In our study, we prospectively recruited patients who were 19 years of age or older, and were being evaluated for coronary artery disease with imaging-based stress tests. For ANN, the network architecture employed a systematic method, where the number of neurons is changed incrementally, and bootstrapping was performed to evaluate the accuracy of the models. RESULTS: We prospectively enrolled 486 patients. The mean age of patients undergoing stress test was 55.2 ± 11.2 years, 35% were women, and 12% had a positive stress test for ischemic heart disease. When compared to Diamond-Forrester and Morise risk models, the ANN model for predicting ischemia provided higher discriminatory power (DP)(1.61), had a negative predictive value of 98%, Sensitivity 91% [81%-97%], Specificity 65% [60%-79%], positive predictive value 26%, and a potential 59% reduction of non-invasive imaging. CONCLUSION: The ANN models improved risk stratification when compared to the other risk scores (Diamond-Forrester and Morise) with a 98% negative predictive value and a significant potential reduction in non-invasive imaging tests.

Waterpipe smoking and risk of coronary artery disease.

PURPOSE OF REVIEW: Smoking tobacco using a water pipe is becoming more prevalent globally, particularly amongst younger populations. In addition to its growing popularity, more evidence is emerging regarding associated harm, and several misconceptions exist concerning the likely adverse health effects of waterpipe smoking (WPS). It is timely, therefore, to examine the body of evidence linking WPS to coronary artery disease (CAD). Here, we review the direct evidence linking WPS to CAD and examine additional, indirect evidence of associated harm. We discuss the clinical and public health implications of the current evidence and provide suggestions for further research. RECENT FINDINGS: A multicentre case-control study in Lebanon has recently demonstrated an association between WPS and CAD. There are few prior studies making this direct link. However, a large body of evidence has emerged showing close similarities between WPS and cigarette smoking with regard to the toxicity of smoke and acute inflammatory and haemodynamic effects following exposure to it. SUMMARY: There are consistent similarities between WPS and cigarette smoking in regard to association with CAD, the nature of the smoke produced, and the acute haemodynamic effects and inflammatory responses that follow exposure. These findings justify both public health and clinical interventions to reduce WPS. Further studies are warranted to confirm a causal association between WPS and CAD.

Artificial neural network modeling enhances risk stratification and can reduce downstream testing for patients with suspected acute coronary syndromes, negative cardiac biomarkers, and normal ECGs.

Despite uncertain yield, guidelines endorse routine stress myocardial perfusion imaging (MPI) for patients with suspected acute coronary syndromes, unremarkable serial electrocardiograms, and negative troponin measurements. In these patients, outcome prediction and risk stratification models could spare unnecessary testing. This study therefore investigated the use of artificial neural networks (ANN) to improve risk stratification and prediction of MPI and angiographic results. We retrospectively identified 5354 consecutive patients referred from the emergency department for rest-stress MPI after serial negative troponins and normal ECGs. Patients were risk stratified according to thrombolysis in myocardial infarction (TIMI) scores, ischemia was defined as >5 % reversible perfusion defect, and obstructive coronary artery disease was defined as >50 % angiographic obstruction. For ANN, the network architecture employed a systematic method where the number of neurons is changed incrementally, and bootstrapping was performed to evaluate the accuracy of the models. Compared to TIMI scores, ANN models provided improved discriminatory power. With regards to MPI, an ANN model could reduce testing by 59 % and maintain a 96 % negative predictive value (NPV) for ruling out ischemia. Application of an ANN model could also avoid 73 % of invasive coronary angiograms while maintaining a 98 % NPV for detecting obstructive CAD. An online calculator for clinical use was created using these models. The ANN models improved risk stratification when compared to the TIMI score. Our calculator could also reduce downstream testing while maintaining an excellent NPV, though further study is needed before the calculator can be used clinically.

Quantifying the impact of using Coronary Artery Calcium Score for risk categorization instead of Framingham Score or European Heart SCORE in lipid lowering algorithms in a Middle Eastern population.

BACKGROUND: The use of the Coronary Artery Calcium Score (CACS) for risk categorization instead of the Framingham Risk Score (FRS) or European Heart SCORE (EHS) to improve classification of individuals is well documented. However, the impact of reclassifying individuals using CACS on initiating lipid lowering therapy is not well understood. We aimed to determine the percentage of individuals not requiring lipid lowering therapy as per the FRS and EHS models but are found to require it using CACS and vice versa; and to determine the level of agreement between CACS, FRS and EHS based models. METHODS: Data was collected for 500 consecutive patients who had already undergone CACS. However, only 242 patients met the inclusion criteria and were included in the analysis. Risk stratification comparisons were conducted according to CACS, FRS, and EHS, and the agreement (Kappa) between them was calculated. RESULTS: In accordance with the models, 79.7% to 81.5% of high-risk individuals were down-classified by CACS, while 6.8% to 7.6% of individuals at intermediate risk were up-classified to high risk by CACS, with slight to moderate agreement. Moreover, CACS recommended treatment to 5.7% and 5.8% of subjects untreated according to European and Canadian guidelines, respectively; whereas 75.2% to 81.2% of those treated in line with the guidelines would not be treated based on CACS. CONCLUSION: In this simulation, using CACS for risk categorization warrants lipid lowering treatment for 5-6% and spares 70-80% from treatment in accordance with the guidelines. Current strong evidence from double randomized clinical trials is in support of guideline recommendations. Our results call for a prospective trial to explore the benefits/risks of a CACS-based approach before any recommendations can be made.

Discordant Electrocardiogram Left Ventricular Wall Thickness and Strain Findings in Influenza Myocarditis.

A 42-year-old man presented with a viral prodrome and tested positive for influenza A. He rapidly deteriorated developing cardiogenic shock, rhabdomyolysis, and acute kidney injury. Patient improved 1 week later with supportive measures including vasopressors, inotropes, and an intraaortic balloon pump. We report this case as it highlights the discordance between echocardiographic ventricular wall thickening as a result of myocardial edema, and electrocardiographic findings at presentation, with a reversal in findings at time of resolution. Additionally, there was some suggestion of a regional pattern to the reduced longitudinal strain.

Sodium intake reduction efforts in Lebanon.

Sodium intake reduction efforts in Lebanon are quite recent and have just started to take effect on the national level. Starting out from an academic institution, the Lebanese Action on Sodium and Health (LASH) campaign was established to counter the increasing prevalence of hypertension and associated adverse health effects. The campaign's strategy was based on four pillars: research, health communication, advocacy, and monitoring. The LASH group set out with determining: baseline sodium intake of the population, main sources of sodium intake, and the knowledge, attitudes, and behaviors (KAB) of the population as a situation analysis that prompts for action. This gave LASH tangible evidence of the magnitude of the problem and the need for the government, the food industry, and the consumers, to be mobilized to take part in devising a solution. Currently, Lebanon is at a stage of technically working to reduce the sodium content in the major sources of sodium, namely local bread and bread-like products. The next steps will include implementation of a plan for monitoring industry compliance, while studying other food targets, including dairy products and processed meat. Meanwhile, the health communication plan is ongoing and the Salt Awareness Week is celebrated every year with media appearances of LASH researchers to raise the issue to the public eye.

Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior.

BACKGROUND: High dietary salt intake is directly linked to hypertension and cardiovascular diseases (CVDs). Predicting behaviors regarding salt intake habits is vital to guide interventions and increase their effectiveness. We aim to compare the accuracy of an artificial neural network (ANN) based tool that predicts behavior from key knowledge questions along with clinical data in a high cardiovascular risk cohort relative to the least square models (LSM) method. METHODS: We collected knowledge, attitude and behavior data on 115 patients. A behavior score was calculated to classify patients' behavior towards reducing salt intake. Accuracy comparison between ANN and regression analysis was calculated using the bootstrap technique with 200 iterations. RESULTS: Starting from a 69-item questionnaire, a reduced model was developed and included eight knowledge items found to result in the highest accuracy of 62% CI (58-67%). The best prediction accuracy in the full and reduced models was attained by ANN at 66% and 62%, respectively, compared to full and reduced LSM at 40% and 34%, respectively. The average relative increase in accuracy over all in the full and reduced models is 82% and 102%, respectively. CONCLUSIONS: Using ANN modeling, we can predict salt reduction behaviors with 66% accuracy. The statistical model has been implemented in an online calculator and can be used in clinics to estimate the patient's behavior. This will help implementation in future research to further prove clinical utility of this tool to guide therapeutic salt reduction interventions in high cardiovascular risk individuals.

Lifetime cumulative exposure to waterpipe smoking is associated with coronary artery disease.

OBJECTIVE: Globally, waterpipe (WP) smoking is becoming a more prevalent form of tobacco consumption. Whilst research so far has demonstrated a significant link between WP use and a number of health outcomes, little is known of its association with heart disease. We examine in this study the association of WP smoking with angiographically confirmed coronary artery disease (CAD). METHODS: A total of 1210 patients, aged 40 years and over and free from smoking-associated illnesses or history of cardiovascular procedures, admitted for coronary angiography at four major hospitals in Lebanon, were included. The extent of CAD was summarized in two ways, firstly as diseased (≥ 50% and ≥ 70% occlusion in at least one main coronary artery) versus non-diseased (entirely normal coronaries), and secondly, as CAD cumulative score based on Duke CAD Prognostic Index. A score of WP-years, capturing intensity and lifetime duration of exposure, was estimated for each individual. RESULTS: Lifetime exposure exceeding 40 WP-years was associated with a threefold significant increase in the odds of having severe stenosis (≥ 70%) compared to non-smokers (OR = 2.94, 95% CI 1.04-8.33) as well as with the CAD Index (ß = 7.835, p-value = 0.027), net of the effect of socio-demographic characteristics, health behaviors and co-morbidity. A dose-response relationship between WP-years and percent stenosis was also established. WP smoking status (never, past and current) did not associate with CAD. CONCLUSIONS: Cumulative exposure to WP smoking is significantly associated with severe CAD. There is a need to monitor WP use among cardiac patients and include this information in their medical charts in the same manner cigarettes smoking is documented. This is likely to increase awareness of the hazards of WP smoking and prompt physicians to target WP tobacco control by providing advice to their patients on WP smoking cessation.

Improved accuracy of anticoagulant dose prediction using a pharmacogenetic and artificial neural network-based method.

BACKGROUND: The unpredictability of acenocoumarol dose needed to achieve target blood thinning level remains a challenge. We aimed to apply and compare a pharmacogenetic least-squares model (LSM) and artificial neural network (ANN) models for predictions of acenocoumarol dosing. METHODS: LSM and ANN models were used to analyze previously collected data on 174 participants (mean age: 67.45 SD 13.49 years) on acenocoumarol maintenance therapy. The models were based on demographics, lifestyle habits, concomitant diseases, medication intake, target INR, and genotyping results for CYP2C9 and VKORC1. LSM versus ANN performance comparisons were done by two methods: by randomly splitting the data as 50 % derivation and 50 % validation cohort followed by a bootstrap of 200 iterations, and by a 10-fold leave-one-out cross-validation technique. RESULTS: The ANN-based pharmacogenetic model provided higher accuracy and larger R value than all other LSM-based models. The accuracy percentage improvement ranged between 5 % and 24 % for the derivation cohort and between 12 % and 25 % for the validation cohort. The increase in R value ranged between 6 % and 31 % for the derivation cohort and between 2 % and 31 % for the validation cohort. ANN increased the percentage of accurately dosed subjects (mean absolute error ≤1 mg/week) by 14.1 %, reduced the percentage of mis-dosed subjects (mean absolute error 2-3 mg/week) by 7.04 %, and reduced the percentage of grossly mis-dosed subjects (mean absolute error ≥4 mg/week) by 24 %. CONCLUSIONS: ANN-based pharmacogenetic guidance of acenocoumarol dosing reduces the error in dosing to achieve target INR. These results need to be ascertained in a prospective study.