46 research outputs found
Special Considerations in the Care of Women With Advanced Heart Failure
Advanced heart failure (AHF) is associated with increased morbidity and mortality, and greater healthcare utilization. Recognition requires a thorough clinical assessment and appropriate risk stratification. There are persisting inequities in the allocation of AHF therapies. Women are less likely to be referred for evaluation of candidacy for heart transplantation or left ventricular assist device despite facing a higher risk of AHF-related mortality. Sex-specific risk factors influence progression to advanced disease and should be considered when evaluating women for advanced therapies. The purpose of this review is to discuss the role of sex hormones on the pathophysiology of AHF, describe the clinical presentation, diagnostic evaluation and definitive therapies of AHF in women with special attention to pregnancy, lactation, contraception and menopause. Future studies are needed to address areas of equipoise in the care of women with AHF
Identifying Important Risk Factors for Survival in Patient With Systolic Heart Failure Using Random Survival Forests
BACKGROUND: Heart failure survival models are typically constructed using Cox-proportional hazards regression. Regression modeling suffers from a number of limitations, including bias introduced by commonly used variable selection methods. We illustrate the value of an intuitive, robust approach to variable selection, random survival forests (RSF), in a large clinical cohort. RSF is a potentially powerful extension of Classification and Regression Trees (CART), with lower variance and bias. METHODS AND RESULTS: We studied 2231 adult systolic heart failure patients who underwent cardiopulmonary stress testing. During a mean follow-up of 5 years, 742 patients died. Thirty-nine demographic, cardiac and noncardiac co-morbidity, and stress testing variables were analyzed as potential predictors of all-cause mortality. A RSF of 2000 trees was constructed, with each tree constructed on a bootstrap sample from the original cohort. The most predictive variables were defined as those near the tree trunks (averaged over the forest). The RSF identified peak VO(2), serum BUN, and treadmill exercise time as the three most important predictors of survival. The RSF predicted survival similarly to a conventional Cox-proportional hazards model (out-of-bag C-index of 0.705 for RSF vs 0.698 for Cox-proportional hazards model). CONCLUSIONS: A random survival forests model in a cohort of heart failure patients performed as well as a traditional Cox-proportional hazard model, and may serve as a more intuitive approach for clinicians to identify important risk factors for all-cause mortality
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Advances in Cardiovascular Health in Women over the Past Decade: Guideline Recommendations for Practice
Cardiovascular disease (CVD) remains the number one cause of death in women. It is estimated that 44 million women in the United States are either living with or at risk for heart disease. This article highlights the recent significant progress made in improving care, clinical decision-making, and policy implications for women with CVD. We provide our perspective supported by evidence-based advances in cardiovascular research and clinical care guidelines in seven areas: (1) primary CVD prevention and community heart care, (2) secondary prevention of CVD, (3) stroke, (4) heart failure and cardiomyopathies, (5) ischemia with nonobstructive coronary artery disease, (6) spontaneous coronary artery dissection, and (7) arrhythmias and device therapies. Advances in these fields have improved the lives of women living with and at risk for heart disease. With increase awareness, partnership with national organizations, sex-specific research, and changes in policy, the morbidity and mortality of CVD in women can be further reduced
Importance of Treadmill Exercise Time as an Initial Prognostic Screening Tool in Patients With Systolic Left Ventricular Dysfunction
BACKGROUND: We sought to determine if treadmill exercise time may be of value as an initial prognostic screening tool in ambulatory patients with impaired systolic function referred for cardiopulmonary exercise testing. METHODS AND RESULTS: We studied 2,231 adult systolic heart failure patients (27% women) who underwent cardiopulmonary stress testing using a modified Naughton protocol. We assessed the value of treadmill exercise time for prediction of all-cause death and a composite of death or UNOS status 1 heart transplantation. During a mean follow up of 5 years, 742 (33%) patients died. There were 249 (11%) UNOS status 1 heart transplants. Treadmill exercise time was predictive of death and the composite outcome in both women and men, even after accounting for peak oxygen consumption and other clinical covariates (adjusted hazard ratio of lowest versus high sex-specific quartile for prediction of death 1.70, 95% CI 1.05–2.75, P=0.03, and for prediction of the composite outcome 1.75, 95% CI 1.15–2.66, P=0.009). For a one minute change in exercise time there was a 7% increased hazard of death (e.g. comparing 480 to 540 seconds HR 1.07, 95% CI 1.02–1.12, P=0.004). CONCLUSIONS: Since cardiopulmonary stress testing is not available in every hospital, treadmill exercise time using a modified Naughton protocol may be of value as an initial prognostic screening tool
Identifying Important Risk Factors for Survival in Patient With Systolic Heart Failure Using Random Survival Forests
BACKGROUND: Heart failure survival models are typically constructed using Cox-proportional hazards regression. Regression modeling suffers from a number of limitations, including bias introduced by commonly used variable selection methods. We illustrate the value of an intuitive, robust approach to variable selection, random survival forests (RSF), in a large clinical cohort. RSF is a potentially powerful extension of Classification and Regression Trees (CART), with lower variance and bias. METHODS AND RESULTS: We studied 2231 adult systolic heart failure patients who underwent cardiopulmonary stress testing. During a mean follow-up of 5 years, 742 patients died. Thirty-nine demographic, cardiac and noncardiac co-morbidity, and stress testing variables were analyzed as potential predictors of all-cause mortality. A RSF of 2000 trees was constructed, with each tree constructed on a bootstrap sample from the original cohort. The most predictive variables were defined as those near the tree trunks (averaged over the forest). The RSF identified peak VO(2), serum BUN, and treadmill exercise time as the three most important predictors of survival. The RSF predicted survival similarly to a conventional Cox-proportional hazards model (out-of-bag C-index of 0.705 for RSF vs 0.698 for Cox-proportional hazards model). CONCLUSIONS: A random survival forests model in a cohort of heart failure patients performed as well as a traditional Cox-proportional hazard model, and may serve as a more intuitive approach for clinicians to identify important risk factors for all-cause mortality