Tag: pubmed

J Cardiovasc Electrophysiol. 2022 Nov 15. doi: 10.1111/jce.15747. Online ahead of print.

ABSTRACT

BACKGROUND: Implantable cardioverter defibrillators (ICD) are indicated for primary and secondary prevention of sudden cardiac arrest. Despite enhancements in design and technologies, the ICD lead is the most vulnerable component of the ICD system and failure of ICD leads remains a significant clinical problem. A novel, small diameter, lumenless, catheter delivered, defibrillator lead was developed with the aim to improve long term reliability.

METHODS AND RESULTS: The Lead Evaluation for Defibrillation and Reliability (LEADR) study is a multi-center, single-arm, Bayesian, adaptive design, pre-market interventional pivotal clinical study. Up to 60 study sites from around the world will participate in the study. Patients indicated for a de novo ICD will undergo defibrillation testing at implantation and clinical assessments at baseline, implant, pre-hospital discharge, 3 months, 6 months, and every 6 months thereafter until official study closure. Patients may be participating for a minimum of 18 months to approximately 3 years. Fracture-free survival will be evaluated using a Bayesian statistical method that incorporates both virtual patient data (combination of bench testing to failure with in-vivo use condition data) with clinical patients. The clinical subject sample size will be determined using decision rules for number of subject enrollments and follow-up time based upon the observed number of fractures at certain time points in the study. The adaptive study design will therefore result in a minimum of 500 and a maximum of 900 patients enrolled.

CONCLUSION: The LEADR Clinical Study was designed to efficiently provide evidence for short- and long-term safety and efficacy of a novel lead design using Bayesian methods including a novel virtual patient approach. This article is protected by copyright. All rights reserved.

PMID:36378803 | DOI:10.1111/jce.15747

IEEE Trans Med Imaging. 2022 Nov 15;PP. doi: 10.1109/TMI.2022.3220706. Online ahead of print.

ABSTRACT

Dataset auditing for machine learning (ML) models is a method to evaluate if a given dataset is used in training a model. In a Federated Learning setting where multiple institutions collaboratively train a model with their decentralized private datasets, dataset auditing can facilitate the enforcement of regulations, which provide rules for preserving privacy, but also allow users to revoke authorizations and remove their data from collaboratively trained models. This paper first proposes a set of requirements for a practical dataset auditing method, and then present a novel dataset auditing method called Ensembled Membership Auditing (EMA). Its key idea is to leverage previously proposed Membership Inference Attack methods and to aggregate data-wise membership scores using statistic testing to audit a dataset for a ML model. We have experimentally evaluated the proposed approach with benchmark datasets, as well as 4 X-ray datasets (CBIS-DDSM, COVIDx, Child-XRay, and CXR-NIH) and 3 dermatology datasets (DERM7pt, HAM10000, and PAD-UFES-20). Our results show that EMA meet the requirements substantially better than the previous state-of-the-art method. Our code is at: https://github.com/Hazelsuko07/EMA.

PMID:36378795 | DOI:10.1109/TMI.2022.3220706

Circ Cardiovasc Imaging. 2022 Nov;15(11):e013676. doi: 10.1161/CIRCIMAGING.121.013676. Epub 2022 Nov 15.

ABSTRACT

BACKGROUND: The FUEL trial (Fontan Udenafil Exercise Longitudinal) demonstrated statistical improvements in exercise capacity following 6 months of treatment with udenafil (87.5 mg po BID). The effect of udenafil on echocardiographic measures of single ventricle function in this cohort has not been studied.

METHODS: The 400 enrolled participants were randomized 1:1 to udenafil or placebo. Protocol echocardiograms were obtained at baseline and 26 weeks after initiation of udenafil/placebo. Linear regression compared change from baseline indices of single ventricle systolic, diastolic and global function, atrioventricular valve regurgitation, and mean Fontan fenestration gradient in the udenafil cohort versus placebo, controlling for ventricular morphology (left ventricle versus right ventricle/other) and baseline value.

RESULTS: The udenafil participants (n=191) had significantly improved between baseline and 26 weeks visits compared to placebo participants (n=195) in myocardial performance index (P=0.03, adjusted mean difference [SE] of changes between groups -0.03[0.01]), atrioventricular valve inflow peak E (P=0.009, 3.95 [1.50]), and A velocities (P=0.034, 3.46 [1.62]), and annular Doppler tissue imaging-derived peak e’ velocity (P=0.008, 0.60[0.23]). There were no significant differences in change in single ventricle size, systolic function, atrioventricular valve regurgitation severity, or mean fenestration gradient. Participants with a dominant left ventricle had significantly more favorable baseline values of indices of single ventricle size and function (lower volumes and areas, E/e’ ratio, systolic:diastolic time and atrioventricular valve regurgitation, and higher annular s’ and e’ velocity).

CONCLUSIONS: FUEL participants who received udenafil demonstrated a statistically significant improvement in some global and diastolic echo indices. Although small, the changes in diastolic function suggest improvement in pulmonary venous return and/or augmented ventricular compliance, which may help explain improved exercise performance in that cohort.

REGISTRATION: URL: https://clinicaltrials.gov; Unique Identifier: NCT02741115.

PMID:36378780 | DOI:10.1161/CIRCIMAGING.121.013676

Genet Epidemiol. 2022 Nov 15. doi: 10.1002/gepi.22507. Online ahead of print.

ABSTRACT

The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters. The posterior distributions of the parameters are not in explicit form and we use a reversible jump Markov chain Monte Carlo based technique is used to simulate the parameters from the posteriors. The end products of the proposed method are estimated cluster configuration of the proteins (variables) along with the number of clusters. The Bayesian method is flexible enough to cluster the proteins as well as estimate the number of clusters. The performance of the proposed method has been substantiated with extensive simulation studies and one protein expression data with a hereditary disposition in breast cancer where the proteins are coming from different pathways.

PMID:36378773 | DOI:10.1002/gepi.22507

Circ Cardiovasc Qual Outcomes. 2022 Nov;15(11):e009333. doi: 10.1161/CIRCOUTCOMES.122.009333. Epub 2022 Nov 15.

ABSTRACT

BACKGROUND: Cardiovascular disease is the leading cause of death in the United States; however, women and rural residents face notable health disparities compared with male and urban counterparts. Community-engaged programs hold promise to help address disparities through health behavior change and maintenance, the latter of which is critical to achieving clinical improvements and public health impact.

METHODS: A cluster-randomized controlled trial of Strong Hearts, Healthy Communities-2.0 conducted in medically underserved rural communities examined health outcomes and maintenance among women aged ≥40 years, who had a body mass index >30 or body mass index 25 to 30 and also sedentary. The multilevel intervention provided 24 weeks of twice-weekly classes with strength training, aerobic exercise, and skill-based nutrition education (individual and social levels), and civic engagement components related to healthy food and physical activity environments (community, environment, and policy levels). The primary outcome was change in weight; additional clinical and functional fitness measures were secondary outcomes. Mixed linear models were used to compare between-group changes at intervention end (24 weeks); subgroup analyses among women aged ≥60 years were also conducted. Following a 24-week no-contact period, data were collected among intervention participants only to evaluate maintenance.

RESULTS: Five communities were randomized to the intervention and 6 to the control (87 and 95 women, respectively). Significant improvements were observed for intervention versus controls in body weight (mean difference: -3.15 kg [95% CI, -4.98 to -1.32]; P=0.008) and several secondary clinical (eg, waist circumference: -3.02 cm [-5.31 to -0.73], P=0.010; systolic blood pressure: -6.64 mmHg [-12.67 to -0.62], P=0.031; percent body fat: -2.32% [-3.40 to -1.24]; P<0.001) and functional fitness outcomes; results were similar for women aged ≥60 years. The within-group analysis strongly suggests maintenance or further improvement in outcomes at 48 weeks.

CONCLUSIONS: This cardiovascular disease prevention intervention demonstrated significant, clinically meaningful improvements and maintenance among rural, at-risk older women.

REGISTRATION: URL: https://www.

CLINICALTRIALS: gov; Unique identifier: NCT03059472.

PMID:36378768 | DOI:10.1161/CIRCOUTCOMES.122.009333

Am J Community Psychol. 2022 Nov 15. doi: 10.1002/ajcp.12633. Online ahead of print.

ABSTRACT

This study fills a methodological gap in racial justice research by assessing the utility and validity of the Black Community Activism Orientation Scale (BCAOS) in a racially and ethnically diverse sample of college-going young adults (N = 624, M = 19.4 years, SD = 1.89) from 10 colleges in the United States. Confirmatory factor analysis was conducted to estimate the goodness of fit of the proposed three-factor model and assess the validity of the BCAOS. Findings from the confirmatory factor analysis provide statistical support for use of the BCAOS as a measure of racial justice activism in support of Black communities among racially and ethnically diverse college-going young adults. Findings from the study also suggest that White college students and men are less oriented toward racial justice activism than women and racially marginalized students. Convergent and discriminant validity were established through bivariate correlations of the BCAOS factors with other civic development measures. As more and more young people consider the importance of standing against racial oppression, the BCAOS has utility as an assessment instrument in future racial justice research, education, intervention, and youth programming efforts.

PMID:36378747 | DOI:10.1002/ajcp.12633

JMIR Perioper Med. 2022 Nov 15;5(1):e34600. doi: 10.2196/34600.

ABSTRACT

BACKGROUND: Patients with early breast cancer undergoing primary surgery, who have low axillary nodal burden, can safely forego axillary node clearance (ANC). However, routine use of axillary ultrasound (AUS) leads to 43% of patients in this group having ANC unnecessarily, following a positive AUS. The intersection of machine learning with medicine can provide innovative ways to understand specific risks within large patient data sets, but this has not yet been trialed in the arena of axillary node management in breast cancer.

OBJECTIVE: The objective of this study was to assess if machine learning techniques could be used to improve preoperative identification of patients with low and high axillary metastatic burden.

METHODS: A single-center retrospective analysis was performed on patients with breast cancer who had a preoperative AUS, and the specificity and sensitivity of AUS were calculated. Standard statistical methods and machine learning methods, including artificial neural network, naive Bayes, support vector machine, and random forest, were applied to the data to see if they could improve the accuracy of preoperative AUS to better discern high and low axillary burden.

RESULTS: The study included 459 patients; 142 (31%) had a positive AUS; among this group, 88 (62%) had 2 or fewer macrometastatic nodes at ANC. Logistic regression outperformed AUS (specificity 0.950 vs 0.809). Of all the methods, the artificial neural network had the highest accuracy (0.919). Interestingly, AUS had the highest sensitivity of all methods (0.777), underlining its utility in this setting.

CONCLUSIONS: We demonstrated that machine learning improves identification of the important subgroup of patients with no palpable axillary disease, positive ultrasound, and more than 2 metastatically involved nodes. A negative ultrasound in patients with no palpable lymphadenopathy is highly indicative of low axillary burden, and it is unclear whether sentinel node biopsy adds value in this situation. Further studies with larger patient numbers focusing on specific breast cancer subgroups are required to refine these techniques in this setting.

PMID:36378516 | DOI:10.2196/34600

JMIR Med Inform. 2022 Nov 15;10(11):e37976. doi: 10.2196/37976.

ABSTRACT

BACKGROUND: Since the use of electronic health records (EHRs) in an automated way, pharmacovigilance or pharmacoepidemiology studies have been used to characterize the therapy using different algorithms. Although progress has been made in this area for monotherapy, with combinations of 2 or more drugs the challenge to characterize the treatment increases significantly, and more research is needed.

OBJECTIVE: The goal of the research was to develop and describe a novel algorithm that automatically returns the most likely therapy of one drug or combinations of 2 or more drugs over time.

METHODS: We used the Information System for Research in Primary Care as our reference EHR platform for the smooth algorithm development. The algorithm was inspired by statistical methods based on moving averages and depends on a parameter Wt, a flexible window that determines the level of smoothing. The effect of Wt was evaluated in a simulation study on the same data set with different window lengths. To understand the algorithm performance in a clinical or pharmacological perspective, we conducted a validation study. We designed 4 pharmacological scenarios and asked 4 independent professionals to compare a traditional method against the smooth algorithm. Data from the simulation and validation studies were then analyzed.

RESULTS: The Wt parameter had an impact over the raw data. As we increased the window length, more patient were modified and the number of smoothed patients augmented, although we rarely observed changes of more than 5% of the total data. In the validation study, significant differences were obtained in the performance of the smooth algorithm over the traditional method. These differences were consistent across pharmacological scenarios.

CONCLUSIONS: The smooth algorithm is an automated approach that standardizes, simplifies, and improves data processing in drug exposition studies using EHRs. This algorithm can be generalized to almost any pharmacological medication and model the drug exposure to facilitate the detection of treatment switches, discontinuations, and terminations throughout the study period.

PMID:36378514 | DOI:10.2196/37976

J Gerontol A Biol Sci Med Sci. 2022 Nov 15:glac230. doi: 10.1093/gerona/glac230. Online ahead of print.

ABSTRACT

BACKGROUND: A systematic review and network meta-analysis was undertaken to examine the effectiveness of different modes of resistance exercise velocity in fast walking speed, timed-up and go, five-times sit-to-stand, 30-sec sit-to-stand and 6-min walking tests in older adults.

METHODS: CINAHL, Embase, LILACS, PubMed, Scielo, SPORTDiscus and Web of Science databases were searched up to February 2022. Eligible randomised trials examined the effects of supervised high-velocity or traditional resistance exercise in older adults (i.e., ≥ 60 years). The primary outcome for this review was physical function measured by fast walking speed, timed up and go, five times sit-to-stand, 30-sec sit-to-stand and 6-minute walking tests, while maximal muscle power and muscle strength were secondary. A random-effects network meta-analysis was undertaken to examine the effects of different resistance exercise interventions.

RESULTS: Eighty articles describing 79 trials (n= 3,575) were included. High-velocity resistance exercise was the most effective for improving fast walking speed [standardised mean difference (SMD) -0.44, 95% CI: 0.00 to 0.87], timed-up and go (SMD -0.76, 95% CI: -1.05 to -0.47) and five times sit-to-stand (SMD -0.74, 95% CI: -1.20 to -0.27), while traditional resistance exercise was the most effective for 30-sec sit-to-stand (SMD 1.01, 95% CI: 0.68 to 1.34) and 6-min walking (SMD 0.68, 95% CI: 0.34 to 1.03).

CONCLUSION: Our study provides evidence that resistance exercise velocity effects are specific in older adults as evidenced by physical function test dependence. We suggest that prescription based on the velocity of contraction should be individualised to address specific functional needs of participants.

PMID:36378500 | DOI:10.1093/gerona/glac230

Nicotine Tob Res. 2022 Nov 15:ntac258. doi: 10.1093/ntr/ntac258. Online ahead of print.

ABSTRACT

INTRODUCTION: To help offset the increased price of cigarettes and promote brand loyalty, tobacco companies distribute coupons, particularly to price-sensitive consumers. Few studies, however, have examined the longitudinal association between coupon receipt and smoking cessation.

METHODS: Using adult data from Waves 1-5 (2013-2019) of the Population Assessment of Tobacco and Health Study, we examined the longitudinal association between coupon receipt and short-term smoking cessation. Multivariable discrete time survival models were fit to an unbalanced person-period dataset for adult respondents (18+) with current established smoking status at baseline (person n=9472, risk period n=29,784). Short-term smoking cessation was measured as discontinued cigarette use (no past 30-day cigarette use at follow-up) and self-reported complete quitting. Coupon receipt was measured as a time-varying exposure, measured in the wave preceding the outcome. Tobacco dependence and time-varying cigarette use intensity were controlled as potential confounders. Effect modification by age, sex, race/ethnicity, and education was assessed by examining interaction terms.

RESULTS: We found that adults who received a coupon were 19% less likely to quit smoking compared to adults who did not receive a coupon, adjusting for covariates (adjusted hazard rate (aHR): 0.81, 95% CI: 0.74-0.89). None of the interaction terms were statistically significant, suggesting that the association between coupon receipt and short-term smoking cessation may not differ across the sociodemographic groups that we examined.

CONCLUSIONS: Taken together, our results reveal that coupon receipt reduces the likelihood of short-term smoking cessation, and that this association does not differ by age, sex, race/ethnicity, or education.

IMPLICATIONS: Tobacco companies distribute coupons for tobacco products to price-sensitive customers in the US, and these coupons can be particularly effective in partly offsetting the impact of a tax increases and promoting brand loyalty. This study provides longitudinal evidence that coupon receipt is associated with a decrease in short-term smoking cessation among US adults who smoke cigarettes after adjusting for covariates and tobacco-related confounders. The findings from this study suggest that coupons are an effective tool for tobacco companies to prevent adults who smoke from quitting, and a national ban on coupons may help to facilitate smoking cessation.

PMID:36378499 | DOI:10.1093/ntr/ntac258