Partner-Involved Financial Incentives for Smoking Cessation in Dual-Smoker Couples: A Randomized Pilot Trial.

INTRODUCTION: Members of dual-smoker couples (in which both partners smoke) are unlikely to try to quit smoking and are likely to relapse if they do make an attempt. The purpose of this study was to investigate the feasibility, tolerability, and preliminary outcomes of dyadic adaptations of financial incentive treatments (FITs) to promote smoking cessation in dual-smoker couples. AIMS AND METHODS: We enrolled 95 dual-smoker couples (N = 190) in a three-arm feasibility RCT comparing two partner-involved FITs (single vs. dual incentives) against a no-treatment control condition. Participants in all conditions were offered nicotine replacement and psychoeducation. A 3-month follow-up provided information about retention, tolerability (ie, self-reported benefits and costs of the study), and preliminary efficacy (ie, program completion, quit attempts, point-prevalent abstinence, and joint quitting). RESULTS: Results suggest dyadic adaptations were feasible to implement (89% retention rate) and highly tolerable for participants (p < .001). Neither feasibility nor tolerability varied across the treatment arm. Preliminary efficacy outcomes indicated partner-involved FITs have promise for increasing smoking cessation in dual-smoker couples (OR = 2.36-13.06). CONCLUSIONS: Dyadic implementations of FITs are feasible to implement and tolerable to participants. IMPLICATIONS: The evidence that dyadic adaptations of FITs were feasible and tolerable, and the positive preliminary efficacy outcomes suggest that adequately powered RCTs formally evaluating the efficacy of dyadic adaptations of FITs for dual-smoker couples are warranted.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology.

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Machine learning based prediction of prolonged duration of mechanical ventilation incorporating medication data.

Rationale: Duration of mechanical ventilation is associated with adverse outcomes in critically ill patients and increased use of resources. The increasing complexity of medication regimens has been associated with increased mortality, length of stay, and fluid overload but has never been studied specifically in the setting of mechanical ventilation. Objective: The purpose of this analysis was to develop prediction models for mechanical ventilation duration to test the hypothesis that incorporating medication data may improve model performance. Methods: This was a retrospective cohort study of adults admitted to the ICU and undergoing mechanical ventilation for longer than 24 hours from October 2015 to October 2020. Patients were excluded if it was not their index ICU admission or if the patient was placed on comfort care in the first 24 hours of admission. Relevant patient characteristics including age, sex, body mass index, admission diagnosis, morbidities, vital signs measurements, severity of illness, medication regimen complexity as measured by the MRC-ICU, and medical treatments before intubation were collected. The primary outcome was area under the receiver operating characteristic (AUROC) of prediction models for prolonged mechanical ventilation (defined as greater than 5 days). Both logistic regression and supervised learning techniques including XGBoost, Random Forest, and Support Vector Machine were used to develop prediction models. Results: The 318 patients [age 59.9 (SD 16.9), female 39.3%, medical 28.6%] had mean 24-hour MRC-ICU score of 21.3 (10.5), mean APACHE II score of 21.0 (5.4), mean SOFA score of 9.9 (3.3), and ICU mortality rate of 22.6% (n=72). The strongest performing logistic model was the base model with MRC-ICU added, with AUROC of 0.72, positive predictive value (PPV) of 0.83, and negative prediction value (NPV) of 0.92. The strongest overall model was Random Forest with an AUROC of 0.78, a PPV of 0.53, and NPV of 0.90. Feature importance analysis using support vector machine and Random Forest revealed severity of illness scores and medication related data were the most important predictors. Conclusions: Medication regimen complexity is significantly associated with prolonged duration of mechanical ventilation in critically ill patients, and prediction models incorporating medication information showed modest improvement in this prediction.

Individualizing Dual Antiplatelet Therapy (DAPT) Duration Based on Bleeding Risk, Ischemic Risk, or Both: An Analysis From the DAPT Study.

BACKGROUND: Guidelines recommend individualization of dual antiplatelet therapy (DAPT) duration. Whether to guide decisions based on bleeding risk, ischemic risk or a combination is not known. AIMS: To compare a bleeding prediction model, an ischemic prediction model, and the DAPT score in guiding DAPT duration. METHODS: 11,648 patients in the DAPT Study were categorized into higher and lower risk using a bleeding model, an ischemic model, and the DAPT score. Effect of 30 vs. 12 months of DAPT on bleeding events, ischemic events, and the combination (net-adverse clinical events [NACE]) was assessed. RESULTS: Among patients stratified with the bleeding model, 30 vs. 12 months of DAPT resulted in similar ischemic and bleeding event rates. With the ischemic model, however, higher risk patients had a greater reduction in ischemic events with extended duration of DAPT (difference in risk differences [DRD]: -2.6%, 95% CI: -3.9 to -1.3%; p < 0.01), and a smaller increase in bleeding (DRD: -1.0%, 95% CI: -2.1-0.0%; p = 0.04). Similarly, high DAPT score patients had a greater reduction in ischemic events with extended DAPT duration (DRD: -2.4%, 95%: CI: -3.6 to -1.1%; p < 0.01) and a smaller increase in bleeding (DRD: -1.2%, 95%: CI: -2.2-0.0%; p = 0.02). Although NACE was similar for bleeding risk groups, NACE was significantly reduced with extended DAPT in the higher ischemic risk and high DAPT score groups. CONCLUSIONS: In this low-bleeding risk population, stratifying patients based on predicted ischemic risk and the DAPT score best discerned benefit versus harm of extended DAPT duration on ischemic events, bleeding events, and NACE. CONDENSED ABSTRACT: Duration of dual antiplatelet therapy (DAPT) should be guided by an individualized risk assessment. Bleeding risk tools have emerged to identify patients at high bleeding risk for whom truncated DAPT therapy may be safest. In a lower bleeding risk population, however, whether DAPT duration should be guided by bleeding risk, ischemic risk, or a combination is unknown. In this analysis, implementation of a score based on ischemic risk prediction and the DAPT score (a combination of ischemic and bleeding risk) best predicted ischemic events, bleeding events, and net-adverse clinical events (NACE).