Amjad M. Ahmed

Cardiorespiratory fitness and incident heart failure: The Henry Ford ExercIse Testing (FIT) Project

Background Prior studies have demonstrated cardiorespiratory fitness (CRF) to be a strong marker of cardiovascular health. However, there are limited data investigating the association between CRF and risk of progression to heart failure (HF). The purpose of this study was to determine the relationship between CRF and incident HF. Methods We included 66,329 patients (53.8% men, mean age 55 years) free of HF who underwent exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009. Incident HF was determined using International Classification of Diseases, Ninth Revision codes from electronic medical records or administrative claim files. Cox proportional hazards models were performed to determine the association between CRF and incident HF. Results A total of 4,652 patients developed HF after a median follow‐up duration of 6.8 (±3) years. Patients with incident HF were older (63 vs 54 years, P < .001) and had higher prevalence of known coronary artery disease (42.3% vs 11%, P < .001). Peak metabolic equivalents (METs) of task were 6.3 (±2.9) and 9.1 (±3) in the HF and non‐HF groups, respectively. After adjustment for potential confounders, patients able to achieve ≥12 METs had an 81% lower risk of incident HF compared with those achieving <6 METs (hazard ratio 0.19 [95% CI 0.14‐0.29], P for trend < .001). Each 1 MET achieved was associated with a 16% lower risk (hazard ratio 0.84 [95% CI 0.82‐0.86], P < .001) of incident HF. Conclusions Our analysis demonstrates that higher level of fitness is associated with a lower incidence of HF independent of HF risk factors.

The role of myocardial viability in contemporary cardiac practice

Ischemic heart disease (IHD) remains the single most common cause of death worldwide. Ischemic cardiomyopathy is a major sequel of coronary artery disease. The economic health burden of IHD is substantial. In patients with old myocardial infarction (OMI), the extent of viable myocardium (VM) directly affects the short- and long-term outcome. There is a considerable collection of observational data showing substantial improvement in patients with significant left ventricular dysfunction when the need for revascularization is guided by preoperative assessment of viability and hibernation. However, a major challenge for present cardiovascular imaging is to identify better ways to assess viable but inadequately perfused myocardium and thus optimize selection of patients for coronary revascularization. Several non-invasive techniques have been developed to detect signs of viability. Hence, our aim is to provide the reader a state-of-the art review for the assessment of myocardial viability.

Using Machine Learning to Define the Association between Cardiorespiratory Fitness and All-Cause Mortality (from the Henry Ford Exercise Testing Project)

Previous studies have demonstrated that cardiorespiratory fitness is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of the analysis is to compare the prediction of 10 years of all-cause mortality (ACM) using statistical logistic regression (LR) and ML approaches in a cohort of patients who underwent exercise stress testing. We included 34,212 patients (55% males, mean age 54 ± 13 years) free of coronary artery disease or heart failure who underwent exercise treadmill stress testing between 1991 and 2009 and had complete 10-year follow-up. The primary outcome of this analysis was ACM at 10 years. The probability of 10-years ACM was calculated using statistical LR and ML, and the accuracy of these methods was calculated and compared. A total of 3,921 patients died at 10 years. Using statistical LR, the sensitivity to predict ACM was 44.9% (95% confidence interval [CI] 43.3% to 46.5%), whereas the specificity was 93.4% (95% CI 93.1% to 93.7%). The sensitivity of ML to predict ACM was 87.4% (95% CI 86.3% to 88.4%), whereas the specificity was 97.2% (95% CI 97.0% to 97.4%). The ML approach was associated with improved model discrimination (area under the curve for ML [0.923 (95% CI 0.917 to 0.928)]) compared with statistical LR (0.836 [95% CI 0.829 to 0.846], p<0.0001). In conclusion, our analysis demonstrates that ML provides better accuracy and discrimination of the prediction of ACM among patients undergoing stress testing.

The impact of digoxin on mortality in patients with chronic systolic heart failure: A propensity-matched cohort study

BACKGROUND Prior Studies showed mixed results in association of digoxin use with all-cause mortality (ACM). The aim of this analysis is to identify the impact of digoxin use on ACM in a contemporary heart failure (HF) cohort treated with guideline based therapy. METHODS We included 2298 consecutive patients seen in an HF clinic between 2000 and 2015. Patients were considered to be a digoxin user if he/she received digoxin at any point during the enrollment period in the HF clinic. Patients were matched based on digoxin utility using propensity matching in 2-3:1 fashion. The primary outcome was ACM. RESULT Of 2298 patients, 325 digoxin users were matched with 750 non-digoxin users. The Matched cohort did not have differences among demographics and clinical variables except for worse HF symptomatology and increased prevalence of atrial fibrillation. Overall, the prevalence of the use of guideline suggested therapies was 96%. After a median follow-up duration of 4years (IQR 2-6years), digoxin use was associated with increased ACM (21.8% versus 12.9%, unadjusted HR=1.81; 95% CI=1.33 to 2.45; p=0.001). This association remained significant after adjusting for the propensity score, atrial fibrillation, ejection fraction, and New York HF Class (HR=1.74; 95% CI=1.20 to 2.38; p<0.0001). CONCLUSION In this analysis of well-treated HF patients, digoxin was associated with increased ACM. Further randomized controlled trials are needed to determine whether digoxin therapy should be used in well-treated HF patients. Until then, routine use of digoxin in clinical practice should be discouraged.

Cardiovascular risk factors burden in Saudi Arabia: The Africa Middle East Cardiovascular Epidemiological (ACE) study

Background Limited data exist on the epidemiology of cardiovascular risk factors in Saudi Arabia, particularly in relation to the differences between Saudi nationals and expatriates in Saudi Arabia. The aim of this analysis was to describe the current prevalence of cardiovascular risk factors among patients attending general practice clinics across Saudi Arabia. Methods In this cross-sectional epidemiological analysis of the Africa Middle East Cardiovascular Epidemiological (ACE) study, the prevalence of cardiovascular risk factors (hypertension, diabetes, dyslipidemia, obesity, smoking, abdominal obesity) was evaluated in adults attending primary care clinics in Saudi Arabia. Group comparisons were made between patients of Saudi ethnicity (SA nationals) and patients who were not of Saudi ethnicity (expatriates). Results A total of 550 participants were enrolled from different clinics across Saudi Arabia [aged (mean ± standard deviation) 43 ± 11 years; 71% male]. Nearly half of the study cohort (49.8%) had more than three cardiovascular risk factors. Dyslipidemia was the most prevalent risk factor (68.6%). The prevalence of hypertension (47.5%) and dyslipidemia (75.5%) was higher among expatriates when compared with SA nationals (31.4% vs. 55.1%, p = 0.0003 vs. p < 0.0001, respectively). Conversely, obesity (52.6% vs. 41.0%; p = 0.008) and abdominal obesity (65.5% vs. 52.2%; p = 0.0028) were higher among SA nationals vs. expatriates. Conclusion Modifiable cardiovascular risk factors are highly prevalent in SA nationals and expatriates. Programmed community-based screening is needed for all cardiovascular risk factors in Saudi Arabia. Improving primary care services to focus on risk factor control may ultimately decrease the incidence of coronary artery disease and improve overall quality of life. The ACE trial is registered under NCT01243138.

Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford exercise testing (FIT) Project

This study evaluates and compares the performance of different machine learning techniques on predicting the individuals at risk of developing hypertension, and who are likely to benefit most from interventions, using the cardiorespiratory fitness data. The dataset of this study contains information of 23,095 patients who underwent clinician- referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up. The variables of the dataset include information on vital signs, diagnosis and clinical laboratory measurements. Six machine learning techniques were investigated: LogitBoost (LB), Bayesian Network classifier (BN), Locally Weighted Naive Bayes (LWB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Tree Forest (RTF). Using different validation methods, the RTF model has shown the best performance (AUC = 0.93) and outperformed all other machine learning techniques examined in this study. The results have also shown that it is critical to carefully explore and evaluate the performance of the machine learning models using various model evaluation methods as the prediction accuracy can significantly differ.

Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project

BackgroundPrior studies have demonstrated that cardiorespiratory fitness (CRF) is a strong marker of cardiovascular health. Machine learning (ML) can enhance the prediction of outcomes through classification techniques that classify the data into predetermined categories. The aim of this study is to present an evaluation and comparison of how machine learning techniques can be applied on medical records of cardiorespiratory fitness and how the various techniques differ in terms of capabilities of predicting medical outcomes (e.g. mortality).MethodsWe use data of 34,212 patients free of known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems Between 1991 and 2009 and had a complete 10-year follow-up. Seven machine learning classification techniques were evaluated: Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Naïve Bayesian Classifier (BC), Bayesian Network (BN), K-Nearest Neighbor (KNN) and Random Forest (RF). In order to handle the imbalanced dataset used, the Synthetic Minority Over-Sampling Technique (SMOTE) is used.ResultsTwo set of experiments have been conducted with and without the SMOTE sampling technique. On average over different evaluation metrics, SVM Classifier has shown the lowest performance while other models like BN, BC and DT performed better. The RF classifier has shown the best performance (AUC = 0.97) among all models trained using the SMOTE sampling.ConclusionsThe results show that various ML techniques can significantly vary in terms of its performance for the different evaluation metrics. It is also not necessarily that the more complex the ML model, the more prediction accuracy can be achieved. The prediction performance of all models trained with SMOTE is much better than the performance of models trained without SMOTE. The study shows the potential of machine learning methods for predicting all-cause mortality using cardiorespiratory fitness data.

Low-dose attenuation correction in diagnosis of non-alcoholic fatty liver disease

BackgroundNon-enhanced computed tomography (CT) is a valuable modality in the diagnosis of non-alcoholic fatty liver disease (NAFLD). However, it is not clear if low-dose CT attenuation correction (CTAC) scans have the same accuracy to diagnose NAFLD. Our aim is to evaluate the diagnostic accuracy of low-dose CTAC in the diagnosis of NAFLD using non-enhanced CT as a gold standard.MethodsA total of 864 patients who underwent a clinically indicated hybrid nuclear imaging scanning between May 2011 and April 2014 were included in the study. Diagnosis of fatty liver was established if an absolute liver attenuation was <40 Hounsfield units and/or a liver-to-spleen ratio was <1.1. The diagnostic accuracy parameters were calculated to detect NAFLD by low-dose CTAC using unenhanced CT as a gold standard.ResultsThe prevalence of fatty liver by diagnostic CT and low-dose attenuation correction were 9.9 and 12.9% (using liver attenuation <40HU and liver-to-spleen ratio <1.1), respectively, with 32.9 and 34.9% (using absolute liver attenuation or ratio-to-spleen criteria), correspondingly. Low-dose CTAC had sensitivity (81.3%), specificity (94.0%), positive predictive value (60.2%), and negative predictive value (97.8%) using both diagnostic criteria. Using either of the diagnostic criteria resulted in sensitivity (76.8%), specificity (83.5%), PPV (66.3%), and NPV (89.5%).ConclusionLow-dose CT could be used as a tool to rule out the presence of fatty liver if neither liver attenuation of less than 40 HU nor liver-to-spleen below 1.1 is present.

Prognostic value of exercise capacity among patients with treated depression: The Henry Ford Exercise Testing (FIT) Project

Exercise capacity is associated with survival in the general population. Whether this applies to patients with treated depression is not clear.

INCREMENTAL PROGNOSTIC VALUE OF CORONARY CALCIUM SCORING IN PATIENTS UNDERGOING 82-RUBIDIUM MYOCARDIAL PERFUSION IMAGING

Introduction: Coronary artery calcium (CAC) score and hyperaemic myocardial blood flow (HMBF) have been associated with clinical outcomes. The aim of this analysis is to determine the incremental prognostic value of CAC over HMBF in the prediction of cardiac events. Methods: A total of 2,060