The association of waterpipe smoking with arterial stiffness and wave reflection in a community-based sample.

PURPOSE: The evidence linking waterpipe smoking to cardiovascular disease is limited. We evaluated the association of waterpipe smoking (WPS) with arterial stiffness and wave reflection measured by augmentation pressure (AP), augmentation index (AIx), and carotid-femoral pulse wave velocity (CFPWV), which are validated predictors of cardiovascular disease. MATERIALS AND METHODS: Community-based, cross-sectional study including 205 exclusive waterpipe smokers and 199 matched never-smokers aged 35 years or older (mean age 51.7 ± 8.9 years, 36% females). Smoking and its extent were assessed using a validated questionnaire and urine cotinine levels. CFPWV, AP, AIx (AP/aortic pulse pressure) and heart rate adjusted AIx (AIx@75) were determined using tonometry and compared between smokers and non-smokers, and the association of WPS with tonometry measures was assessed using linear regression adjusting for possible confounders. RESULTS: Waterpipe smokers and non-smokers had similar mean age and sex distribution. Compared to non-smokers, waterpipe smokers had significantly higher adjusted AP (10.5 ± 3.9 vs. 9.4 ± 3.9 mmHg respectively; p = 0.01), AIx (28.1 ± 8.4 vs. 25.7 ± 8.5% respectively; p = 0.01) and AIx@75 (24.2 ± 8.7 vs. 21.8 ± 8.9% respectively; p = 0.01). AIx was significantly associated with WPS extent, measured by a number of waterpipe smoked/day (ß = 1.04/waterpipe, 95%CI:[0.50-1.58]), duration of waterpipe smoking (ß = 0.77/10-years, 95%CI:[0.16-1.38]), their products in waterpipe-years (ß = 0.30/10-waterpipe-year, 95%CI:[0.12-0.47]) and plasma cotinine (ß = 0.56/100 ng/ml, 95%CI:[0.14-0.98]), adjusting for possible confounders, and so were AP and AIx@75. CFPWV however, was not associated with waterpipe smoking. CONCLUSION: In a community-based sample, exclusive WPS and its extent were associated with a dose-dependent increase in AIx and AP, accounting for other risk factors, suggesting that waterpipe smokers are at increased risk of cardiovascular disease.

A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

Objectives. In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predictive value of echocardiographic strain features alone and in combination with other features to differentiate ischaemic from non-ischaemic cardiomyopathy, using artificial neural network (ANN) and logistic regression modelling. Design. We retrospectively identified 204 consecutive patients with an ejection fraction <50% and a diagnostic angiogram. Patients were categorized as either ischaemic (n = 146) or non-ischaemic cardiomyopathy (n = 58). For each patient, left ventricular strain parameters were obtained. Additionally, regional wall motion abnormality, 13 electrocardiographic (ECG) features and six demographic features were retrieved for analysis. The entire cohort was randomly divided into a derivation and a validation cohort. Using the parameters retrieved, logistic regression and ANN models were developed in the derivation cohort to differentiate ischaemic from non-ischaemic cardiomyopathy, the models were then tested in the validation cohort. Results. A final strain-based ANN model, full feature ANN model and full feature logistic regression model were developed and validated, F1 scores were 0.82, 0.79 and 0.63, respectively. Conclusions. Both ANN models were more accurate at predicting cardiomyopathy type than the logistic regression model. The strain-based ANN model should be validated in other cohorts. This model or similar models could be used to aid the diagnosis of underlying heart failure aetiology in the form of the online calculator (https://cimti.usj.edu.lb/strain/index.html) or built into echocardiogram software.

Sleepless in Beirut: sleep duration and associated subjective sleep insufficiency, daytime fatigue, and sleep debt in an urban environment.

PURPOSE: Insufficient sleep is not well studied in developing countries. We assessed sleep duration among adults in Lebanon and examined its potential predictors and relationship with subjective sleep insufficiency, daytime fatigue, and weekday sleep debt. METHODS: This cross-sectional study included 501 adults (mean age 45.2 (SD15.2) years, 64% females) from the community in Beirut and Mount Lebanon. Socio-demographic, lifestyle and health characteristics, subjective sleep insufficiency, daytime fatigue, and weekday sleep debt (weekend vs. weekdays sleep duration) were compared between individuals who reported sleeping < 6:00, 6-7:59(reference), or ≥ 8:00 h/night. Symptoms and predictors of sleep duration were assessed using logistic regression. RESULTS: Thirty-nine percent of participants reported sleeping < 6 h/night while 15% reported sleeping ≥ 8:00 h/night. Age (OR = 1.16/year, 95% CI [1.02-1.33]) and female sex (OR = 1.71, 95% CI [1.14-2.58]) were significant predictors of short sleep (< 6:00 h/night) in multivariable adjusted analyses. Compared to referent (6:00-7:59 h/night) and long sleepers (≥ 8:00 h/night), short sleepers were significantly more likely to report subjective sleep insufficiency (OR = 3.00, 95% CI [2:00-4.48], and OR = 4.52, 95% CI [2.41-8.51]; respectively) and daytime fatigue (OR = 1.53, 95% CI [1.04-2.24], and OR = 1.83, 95% CI [1.06-2.04]; respectively). Compared to long weekdays sleepers, short and referent weekdays sleepers were more likely to sleep longer on weekend (OR = 2.47, 95% CI [1.18-5.15], and OR = 4.16, 95% CI [2.03-8.5]; respectively). CONCLUSIONS: Short sleep is highly prevalent in this urban cohort from a low- to medium-income country especially among women and older adults, and is associated with subjective sleep insufficiency, daytime fatigue, and weekday sleep debt. The socio-cultural determinants of sleep duration need to be studied across different populations to better evaluate the causes and implications of short sleep.

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.

A minimally processed dietary pattern is associated with lower odds of metabolic syndrome among Lebanese adults.

OBJECTIVE: To (i) estimate the consumption of minimally processed, processed and ultra-processed foods in a sample of Lebanese adults; (ii) explore patterns of intakes of these food groups; and (iii) investigate the association of the derived patterns with cardiometabolic risk. DESIGN: Cross-sectional survey. Data collection included dietary assessment using an FFQ and biochemical, anthropometric and blood pressure measurements. Food items were categorized into twenty-five groups based on the NOVA food classification. The contribution of each food group to total energy intake (TEI) was estimated. Patterns of intakes of these food groups were examined using exploratory factor analysis. Multivariate logistic regression analysis was used to evaluate the associations of derived patterns with cardiometabolic risk factors. SETTING: Greater Beirut area, Lebanon. SUBJECTS: Adults ≥18 years (n 302) with no prior history of chronic diseases. RESULTS: Of TEI, 36·53 and 27·10 % were contributed by ultra-processed and minimally processed foods, respectively. Two dietary patterns were identified: the 'ultra-processed' and the 'minimally processed/processed'. The 'ultra-processed' consisted mainly of fast foods, snacks, meat, nuts, sweets and liquor, while the 'minimally processed/processed' consisted mostly of fruits, vegetables, legumes, breads, cheeses, sugar and fats. Participants in the highest quartile of the 'minimally processed/processed' pattern had significantly lower odds for metabolic syndrome (OR=0·18, 95 % CI 0·04, 0·77), hyperglycaemia (OR=0·25, 95 % CI 0·07, 0·98) and low HDL cholesterol (OR=0·17, 95 % CI 0·05, 0·60). CONCLUSIONS: The study findings may be used for the development of evidence-based interventions aimed at encouraging the consumption of minimally processed foods.

An Outflow Boundary Condition Model for Noninvasive Prediction of Fractional Flow Reserve in Diseased Coronary Arteries.

This paper reports on a new boundary condition formulation to model the total coronary myocardial flow and resistance characteristics of the myocardial vascular bed for any specific patient when considered for noninvasive diagnosis of ischemia. The developed boundary condition model gives an implicit representation of the downstream truncated coronary bed. Further, it is based on incorporating patient-specific physiological parameters that can be noninvasively extracted to account for blood flow demand to the myocardium at rest and hyperemic conditions. The model is coupled to a steady three-dimensional (3D) collocated pressure-based finite volume flow solver and used to characterize the "functional significance" of a patient diseased coronary artery segment without the need for predicting the hemodynamics of the entire arterial system. Predictions generated with this boundary condition provide a deep understanding of the inherent challenges behind noninvasive image-based diagnostic techniques when applied to human diseased coronary arteries. The overall numerical method and formulated boundary condition model are validated via two computational-based procedures and benchmarked with available measured data. The newly developed boundary condition is used via a designed computational methodology to (a) confirm the need for incorporating patient-specific physiological parameters when modeling the downstream coronary resistance, (b) explain the discrepancies presented in the literature between measured and computed fractional flow reserve (FFRCT), and

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.

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.

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.

Central and Peripheral Hemodynamics in Young Adults Who Use Water Pipes and the Acute Effects of Water-Pipe Use.

BACKGROUND: Tobacco use via water pipe (commonly referred to as water-pipe smoking [WPS]) is popular among young adults globally and exposes those who smoke to toxicants. RESEARCH QUESTION: Is WPS associated with impaired measures of arterial function and does WPS acutely impair these measures in young adults? STUDY DESIGN AND METHODS: We assessed heart rate (HR), brachial and aortic BP, HR-adjusted augmentation index (AI), and carotid-femoral pulse wave velocity (CFPWV) in 62 individuals who use water pipes and 34 individuals who have never used a water pipe recruited from the community (mean age, 22.5 ± 3.0 years; 48% female). Measurements were obtained before and after an outdoor session of WPS among participants who use water pipes and among the control group of participants who have never used a water pipe. Measurements were compared after vs before exposure and between those who use and those who do not use water pipes, adjusting for possible confounders using linear regression. RESULTS: Participants who use water pipes and control participants had similar demographic characteristics. BP and HR increased acutely after WPS (brachial systolic BP by 4.13 mm Hg [95% CI, 1.91-6.36 mm Hg]; aortic systolic BP by 2.31 mm Hg [95% CI, 0.28-4.33 mm Hg]; brachial diastolic BP by 3.69 mm Hg [95% CI, 1.62-5.77 mm Hg]; aortic diastolic BP by 3.03 mm Hg [95% CI, 0.74-5.33 mm Hg]; and HR by 7.75 beats/min [95% CI, 5.46-10.04 beats/min]), but not in the control group. AI was significantly higher in participants who use water pipes compared with those who do not (9.02% vs 3.06%; P = .03), including after adjusting for BMI and family history of cardiovascular disease (ß = 6.12; 95% CI, 0.55-11.69; P = .03) and when assessing habitual tobacco use via water-pipe extent (water pipes used/day × water-pipe use duration) in water-pipe-years (ß = 2.51/water-pipe-year; 95% CI, 0.10-4.92/water-pipe-year; P = .04). However, CFPWV was similar in those who use water pipes and those who do not, and AI and CFPWV did not change acutely after WPS. INTERPRETATION: In apparently healthy young individuals from the community, habitual WPS was associated with increased AI, a predictor of cardiovascular risk, and one WPS session acutely increased HR and brachial and aortic BP.

Advances in telemedicine for the management of the elderly cardiac patient.

Telemedicine is the use of information and communication technology to deliver healthcare at a distance. It has been resorted to during the COVID-19 pandemic to lessen the need for in-person patient care decreasing the risk of transmission, and it can be of benefit afterward in the management of cardiac disease. The elderly population has unique challenges concerning the use of telehealth technologies. We thus review the advances in telemedicine technologies in treating elderly cardiac patients including in our discussion only studies with a mean age of participants above 60. Remote monitoring of blood pressure, weight, and symptoms, along with home ECG recording has been found to be superior to usual in-clinic follow up. Combining remote monitoring with video conferencing with physicians, patient education websites, and applications is also of benefit. Remote monitoring of Implantable Cardioverter Defibrillators (ICD) and Cardiac Resynchronization Therapy Defibrillators (CRT-D) is also beneficial but can be at the cost of an increase in both appropriate and inappropriate interventions. Implantable sensing devices compatible with remote monitoring have been developed and have been shown to improve care and cost-effectiveness. New smartphone software can detect arrhythmias using home ECG recordings and can detect atrial fibrillation using smartphone cameras. Remote monitoring of implanted pacemakers has shown non-inferiority to in clinic follow up. On the other hand, small-scale questionnaire-based studies demonstrated the willingness of the elderly cardiac patients to use such technologies, and their satisfaction with their use and ease of use. Large-scale studies should further investigate useability in samples more representative of the general elderly population with more diverse socioeconomic and educational backgrounds. Accordingly, it seems that studying integrating multiple technologies into telehealth programs is of great value. Further efforts should also be put in validating the technologies for specific diseases along with the legal and reimbursement aspects of the use of telehealth.

The diagnostic accuracy of 64-detector cardiac computed tomography compared with stress nuclear imaging in patients undergoing invasive cardiac catheterization.

OBJECTIVE: The aim of this study was to evaluate the diagnostic accuracy of nuclear stress imaging as compared with noninvasive coronary artery imaging using 64-detector row cardiac computed tomography. BACKGROUND: Compared with invasive coronary angiography, multidetector row cardiac computed tomographic angiography (CTA) has shown promise in the accurate detection of coronary stenosis. Myocardial perfusion imaging (MPI) using single photon emission computed tomography is an established method for noninvasively assessing the functional significance of coronary stenosis. This study compared the accuracy of CTA and that of MPI in the detection of relevant lesions of coronary arteries. METHODS: One hundred twenty-two symptomatic patients (77% males) with cardiac catheterization who also underwent MPI and CTA evaluations within 6 months at 2 centers were included. Comparison of CTA for lesions causing greater than 50% and greater than 70% coronary narrowing versus respective lesions on invasive cardiac catheterization (IC) was performed. Similarly, comparison of MPI findings with greater than 50% and greater than 70% lesions on IC was done. RESULTS: The per-patient sensitivity, specificity, and positive and negative predictive values in detecting greater than 50% coronary lesions on IC for CTA were 98.9%, 74.2%, 91.8%, and 95.8%, respectively; and for MPI, 56%, 38.7%, 72.9%, and 23%, respectively. The sensitivity, the specificity, and the positive and negative predictive values in detecting greater than 70% coronary lesions on IC for CTA were 89.7%, 86.4%, 92.1%, and 82.6%, respectively; and for MPI, 57.7%, 43.2%, 64.3%, and 36.5%, respectively. The prevalence of significant coronary artery disease on cardiac catheterization was 74.6% for greater than 50% stenosis and 63.9% for greater than 70% stenosis. CONCLUSIONS: Compared with MPI, CTA provided important information and identified significant lesions in symptomatic intermediate- to high-risk patients. Cost-effective and prospective multicentered studies, currently underway, are needed to further establish the best use of these diagnostic tests in the evaluation of coronary artery disease.

Dietary Glycemic Index and Glycemic Load Are Not Associated with the Metabolic Syndrome in Lebanese Healthy Adults: A Cross-Sectional Study.

High dietary glycemic index (GI) and glycemic load (GL) were suggested to increase the risk of metabolic syndrome (MetS). This study aims to estimate dietary GI and GL in a sample of healthy Lebanese adults and examine their association with MetS and its individual abnormalities. The study uses data from a community-based survey of 501 Lebanese urban adults. Dietary intake was assessed using a food frequency questionnaire. Biochemical, anthropometric, and blood pressure measurements were obtained. Subjects with previous diagnosis of chronic disease, metabolic abnormalities, or with incomplete data or implausible energy intakes were excluded, yielding a sample of 283. Participants were grouped into quartiles of GI and GL. Multivariate logistic regression analyses were performed. Average dietary GI and GL were estimated at 59.9 ± 8 and 209.7 ± 100.3. Participants belonging to the highest GI quartile were at increased risk of having MetS (odds ratio (OR) = 2.251, 95% CI:1.120-4.525) but this association lost significance with further adjustments. Those belonging to the second quartile of GI had significantly lower odds of having hyperglycemia (OR: 0.380, 95% CI:0.174-0.833). No associations were detected between GL and MetS. The study contributes to the body of evidence discussing the relationship between GI, GL, and MetS, in a nutrition transition context.

A comparison of artificial intelligence-based algorithms for the identification of patients with depressed right ventricular function from 2-dimentional echocardiography parameters and clinical features.

BACKGROUND: Recognizing low right ventricular (RV) function from 2-dimentiontial echocardiography (2D-ECHO) is challenging when parameters are contradictory. We aim to develop a model to predict low RV function integrating the various 2D-ECHO parameters in reference to cardiac magnetic resonance (CMR)-the gold standard. METHODS: We retrospectively identified patients who underwent a 2D-ECHO and a CMR within 3 months of each other at our institution (American University of Beirut Medical Center). We extracted three parameters (TAPSE, S' and FACRV) that are classically used to assess RV function. We have assessed the ability of 2D-ECHO derived parameters and clinical features to predict RV function measured by the gold standard CMR. We compared outcomes from four machine learning algorithms, widely used in the biomedical community to solve classification problems. RESULTS: One hundred fifty-five patients were identified and included in our study. Average age was 43±17.1 years old and 52/156 (33.3%) were females. According to CMR, 21 patients were identified to have RV dysfunction, with an RVEF of 34.7%±6.4%, as opposed to 54.7%±6.7% in the normal RV population (P<0.0001). The Random Forest model was able to detect low RV function with an AUC =0.80, while general linear regression performed poorly in our population with an AUC of 0.62. CONCLUSIONS: In this study, we trained and validated an ML-based algorithm that could detect low RV function from clinical and 2D-ECHO parameters. The algorithm has two advantages: first, it performed better than general linear regression, and second, it integrated the various 2D-ECHO parameters.

Potentially inappropriate medications in elderly patients with heart failure: Beers Criteria-based study.

OBJECTIVES: Primary objectives were to evaluate the use of potentially inappropriate medication (PIM) use in elderly heart failure (HF) patients and the factors associated with the number of PIMs. Secondary objectives were to test for the correlation between PIMs and quality of life (QOL) and depression. METHODS: A cross-sectional study was conducted among 125 elderly Lebanese HF patients with left ventricular ejection fraction <50%. Data on socio-demographics, clinical data and medications were collected. Patients were interviewed with Minnesota living with Heart Failure Questionnaire (MLHFQ) and Patient Health Questionnaire-9 (PHQ-9). Medication profile per patient was evaluated for PIMs using Beers Criteria. The correlations between the number of PIMs and the MLHFQ, PHQ-9 and the number of medications were tested using Pearson's correlation. Linear regression was done to predict the factors associated with the number of PIMs. KEY FINDINGS: Patients were taking a total of 1035 medications with a mean of 8.28 ± 3.14 medications. In total, 80.0% of patients were taking at least one PIM. Diuretics (55.2%) and proton pump inhibitors (41.6%) were most commonly prescribed PIMs. The number of PIMs was moderately correlated with the number of medications and the MLHFQ score. Linear regression showed that the number of medications, age ≥85 years, chronic kidney disease and HF with New York Heart Association III were associated with more PIMs. CONCLUSIONS: A high percentage of PIMs was found among a sample of elderly Lebanese HF patients. HF multidisciplinary team is needed to control the prescription of PIMs in this vulnerable population.

Validation of the Arabic Version of the Minnesota Living with Heart Failure Questionnaire.

BACKGROUND: The Minnesota Living with Heart Failure Questionnaire (MLHFQ) is commonly used to measure quality of life (QOL) in patients with heart failure (HF). We examined the psychometric properties and cultural validity of an Arabic version of the MLHFQ. METHODS: An observational cross-sectional study was conducted with 210 adult HF outpatients. Patients were interviewed with the Arabic MLHFQ and the Patient Health Questionnaire (PHQ-9). Cronbach's alpha coefficient and confirmatory factor analysis were conducted. Patients with different NYHA classes and HF-hospitalization histories were compared on QOL to test known-group validity. RESULTS: The confirmatory factor analysis yielded 3 factors: physical, emotional, and social. Three items (4, 8, and 15) had low loadings. The overall Cronbach's alpha coefficient was 0.92. There were significant differences in MLHFQ by PHQ-9 categories, NYHA class, and HF-hospitalization history. CONCLUSIONS: This Arabic version of MLHFQ is valid and reliable and can be used in Arabic-speaking Lebanese HF populations.

Sleepless in Beirut: Sleep Difficulties in an Urban Environment With Chronic Psychosocial Stress.

STUDY OBJECTIVES: The prevalence of sleep disorders in the Lebanese population is unknown. We assessed the prevalence of insomnia and sleep apnea risk and examined their relationship with sociodemographic, lifestyle, and health characteristics in a sample from Greater Beirut. METHODS: This cross-sectional pilot survey included 501 adults from the community (age 45.2 ± 15.2 years, 64% females). Insomnia symptoms, insomnia disorder, and sleep apnea risk were assessed using the Sleep Heart Health Study and Berlin Questionnaire. Characteristics were compared between individuals with and without insomnia symptoms, insomnia disorder, and sleep apnea. Correlates were assessed using multivariate regression. RESULTS: A total of 44.5% of participants reported insomnia symptoms > 15 nights/mo and 34.5% reported insomnia. Predictors of insomnia symptoms and disorder included female sex (odds ratio [OR] 1.51, 95% confidence interval [CI] 1.01-2.27 and OR 1.76, 95% CI 1.13-2.75, respectively), education level below high school (OR 1.96, 95% CI 1.31-2.95 and OR 2.40, 95% CI 1.52-3.77, respectively) and medical comorbidities (OR 2.27, 95% CI 1.30-3.95 and OR 3.02, 95% CI 1.3-5.27, respectively). Although 31% of participants were at high risk for sleep apnea, only 5% received the diagnosis from a physician. Increased sleep apnea risk was associated with unemployment (OR 1.96, 95% CI 1.11-3.49), high body mass index (OR 1.17, 95% CI 1.11-1.24), snoring (OR 16.7, 95% CI 9.0-31.0), hypertension (OR 4.33, 95% CI 2.28-8.22), arthritis (OR 2.00, 95% CI 1.01-4.01), and other medical comorbidities (OR 2.65, 95% CI 1.24-5.68). CONCLUSIONS: Insomnia and sleep apnea are highly prevalent and likely underdiagnosed in this cohort from Lebanon and are associated with disadvantaged socioeconomic status and medical comorbidities. This alarming prevalence of sleep difficulties and disorders calls for future research exploring the causes including the potential effect of social, economic, and political instability, mental and psychological stress, local customs, and environmental factors.

High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning.

INTRODUCTION: The electrocardiogram (ECG) plays an important role in the diagnosis of heart diseases. However, most patterns of diseases are based on old datasets and stepwise algorithms that provide limited accuracy. Improving diagnostic accuracy of the ECG can be done by applying machine learning algorithms. This requires taking existing scanned or printed ECGs of old cohorts and transforming the ECG signal to the raw digital (time (milliseconds), voltage (millivolts)) form. OBJECTIVES: We present a MATLAB-based tool and algorithm that converts a printed or scanned format of the ECG into a digitized ECG signal. METHODS: 30 ECG scanned curves are utilized in our study. An image processing method is first implemented for detecting the ECG regions of interest and extracting the ECG signals. It is followed by serial steps that digitize and validate the results. RESULTS: The validation demonstrates very high correlation values of several standard ECG parameters: PR interval 0.984 +/-0.021 (p-value < 0.001), QRS interval 1+/- SD (p-value < 0.001), QT interval 0.981 +/- 0.023 p-value < 0.001, and RR interval 1 +/- 0.001 p-value < 0.001. CONCLUSION: Digitized ECG signals from existing paper or scanned ECGs can be obtained with more than 95% of precision. This makes it possible to utilize historic ECG signals in machine learning algorithms to identify patterns of heart diseases and aid in the diagnostic and prognostic evaluation of patients with cardiovascular disease.

The Association of Water-Pipe Smoking and Coronary Artery Calcium in a Community-Based Sample.

BACKGROUND: Water-pipe smoking is increasing in popularity, driven partly by a perception of reduced harm compared with cigarette smoking. This study evaluates the association of water-pipe smoking with coronary artery calcium (CAC), a marker of coronary heart disease (CHD) risk, in a community-based sample. METHODS: A total of 175 exclusive water-pipe smokers and 170 nonsmokers, ≥ 35 years of age, were recruited from the community in Lebanon and Qatar. Water-pipe smoking was assessed using a validated questionnaire. CAC score was assessed using multidetector CT scan. The association of water-pipe smoking with the presence and extent of CAC was evaluated using regression analyses adjusted for CHD risk factors. RESULTS: CAC was present in 41% of water-pipe smokers vs 28% of nonsmokers (P = .01), with an average CAC score ± SD of 90.6 ± 400.3 Agatston units (AUs) in water-pipe smokers and 52.4 ± 218.6 AUs in nonsmokers. In adjusted analyses, water-pipe smokers had significantly higher adjusted odds of having CAC (OR = 2.20; 95% CI, 1.20-4.01; P = .01) and being in the high CHD risk category defined by CAC > 300 AUs (OR = 3.41; 95% CI, 1.08-10.77; P = .04) or CAC > 75% of age, sex, and race-predicted (OR = 3.11; 95% CI, 1.55-6.24; P = .001) than nonsmokers. CAC extent was significantly associated with water-pipe smoking extent measured by smoking duration (ß = 0.17/year; 95% CI, 0.05-0.29; P = .004) or the product of smoking duration and the number of water pipes smoked daily (ß = 0.04/water-pipe-year; 95% CI, 0.003-0.07; P = .03). CONCLUSIONS: Exclusive water-pipe smoking was associated with the presence and extent of CAC, with twice the risk of having CAC and three times the risk of being in the high CHD risk category while accounting for other risk factors.