Search

Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 2018 donor heart allocation policy change is warranted. This study assessed seven statistical and machine learning algorithms–Lasso, Ridge, Elastic Net, Cox Gradient Boost, Extreme Gradient Boost Linear, Extreme Gradient Boost Tree, and Random Survival Forests in a post-policy cohort of 7,160 adult heart-only transplant recipients in the Scientific Registry of Transplant Recipients (SRTR) database who received their first transplant on or after October 18, 2018. A cross-validation framework was designed in mlr. Model performance was also compared in a seasonally-matched pre-policy cohort. In the post-policy cohort, Random Survival Forests and Cox Gradient Boost had the highest performances with C-indices of 0.628 and 0.627. The relative importance of some predictive variables differed between the pre-policy and post-policy cohorts, such as the absence of ECMO in the post-policy cohort. Survival machine learning models provide reasonable prediction of 1-year posttransplant mortality outcomes and continual updating of prediction models is warranted in the contemporary era. [ABSTRACT FROM AUTHOR]

Background: Alcohol intake is associated with a higher risk of estrogen receptor-positive (ER+) breast cancer (BC), presumably through its confirmed ability to increase sex hormone levels. Whether consuming alcohol within the recommended limit of one serving per day increases sex hormone levels among postmenopausal women taking aromatase inhibitors (AI) to inhibit estrogen production remains unknown. Therefore, we compared sex hormone levels following white wine to levels following white grape juice among ER + BC survivors taking AIs. Methods: In this 10-week randomized controlled two-period crossover trial conducted from September 2022 to July 2023 among 20 postmenopausal women on AIs, we examined within-person changes in sex hormone levels following 3 weeks of 5 ounces of white wine daily versus 3 weeks of 6 ounces of white grape juice daily, with each drinking period preceded by two-week washouts and drinking period sequence allocated by randomization. Results: All 20 participants completed the trial. Compared to daily grape juice, daily wine led to decreases in total estradiol (11.1%, 95%confidence interval[CI] -49.8%,57.2%), free estradiol index (0.7%, 95%CI -2%,0.7%), and free estradiol concentration (7.7%, 95%CI -48%, 63.9%) but increases in estrone (13.8%, 95%CI -9.5%,43.1%), dehydroepiandrosterone sulfate (DHEAS; 11.4%, 95%CI -3.3%,28.4%), and testosterone (12.6%, 95%CI -0.8%,27.7%) and decreased sex hormone-binding globulin (SHBG; -2.7%, 95%CI -21.9%,21.2%). Conclusions: Five ounces of white wine daily did not lead to statistically significant increases in estradiol, but it led to changes in other sex hormones suggesting higher BC risk. Whether this level of alcohol intake diminishes AI effectiveness warrants further investigation. Trials Registration: Clinicaltrials.gov NCT05423730 registered June 14, 2022. [ABSTRACT FROM AUTHOR]

Background: There is a yet unmet opportunity to utilize data on taxes and individual behaviors to yield insight for analyzing studies involving alcohol and cigarette use. Objectives: To inform the direction and strength of their mutual associations by leveraging the fact that taxation can affect individual consumption, but individual consumption cannot affect taxation. Methods: We linked state-level data on cigarette and beer taxes in 2009–2020 with individual-level data on self-reported current cigarette and alcohol use from the Behavioral Risk Factor Surveillance System, a telephone survey by the Centers for Disease Control and Prevention that is representative of the population of each state in the United States. We constructed linear and logistic models to examine associations between a $1 increase in cigarette taxes per pack and a $1 increase in beer taxes per gallon and self-reported cigarette use and alcohol consumption (assessed as any current intake, average drinks/day, heavy drinking, and binge drinking), adjusting for survey year and individual characteristics. Results: Among 2,968,839,352 respondents (49% male), a $1 increase in beer taxes was associated with.003 (95% confidence interval [CI] −.013,.008) fewer drinks/day and lower odds of any drinking (odds ratio [OR] =.81 95%CI.80,.83), heavy drinking (OR =.96 95%CI.93,.99), binge drinking (OR =.82 95%CI.80,.83), and smoking (OR =.98 95%CI.96, 1.00). In contrast, a $1 increase in cigarette taxes was associated with lower odds of smoking (OR =.94 95%CI.94,.95) but.007 (95%CI.005,.010) more drinks/day, and higher odds of any drinking (OR = 1.10 95%CI 1.10, 1.11), heavy drinking (OR = 1.02 95%CI 1.01, 1.02), and binge drinking (OR =.82 95%CI.80,.83). Conclusion: Higher beer taxes were associated with lower odds of drinking and smoking, but higher cigarette taxes were associated with lower odds of smoking and higher alcohol consumption. These results suggest that alcohol intake may be a determinant of cigarette use rather than cigarette use as a determinant of alcohol intake. [ABSTRACT FROM AUTHOR]

The authors evaluated hospitalization cost offset of hostility management group therapy for patients with coronary heart disease (CHD) from a previously published randomized controlled trial (Y. Gidron, K. Davidson, & I. Bata, 1999). Twenty-six male patients with myocardial infarction or unstable angina were randomized to either 2 months of cognitive-behavioral group therapy or an information (control) session. Therapy patients had a shorter average length of hospital stay (n = 13, M = 0.38 days, SD = 0.96) than did control patients (n = 13, M = 2.15 days, SD = 2.6), t(15.2) = -2.29, p = 0.04, over 6 months following therapy. The average hospitalization costs were significantly lower for therapy patients (M = $245, SD = $627) than for control patients (M = $1,333, SD = $1,609), t(15.6) = -2.27, p = 0.04. The cost-offset ratio is calculated by dividing the $1,088 of hospitalization savings by the $560 of therapy expense ($1.00:$1.94), indicating that for every $1.00 spent on therapy, there is an approximate savings of $2.00 in hospitalization costs in the following 6 months. These findings support the hospitalization cost offset of hostility-reduction in CHD patients.