Impact of Preoperative Prealbumin on Outcomes After Cardiac Surgery

Background: Preoperative malnutrition is increasingly prevalent in patients undergoing cardiac surgery. Although prealbumin is a widely used indicator of nutrition status, its use in the preoperative assessment of patients undergoing cardiac surgery is not well defined. The purpose of this study is to determine the impact of preoperative prealbumin levels on outcomes after cardiac surgery. Materials and Methods: Data were prospectively gathered from February 2013 to July 2013 on 69 patients undergoing cardiac surgery. Prealbumin levels were obtained within 24 hours of surgery. Patients were divided into 2 groups based on a prealbumin cutoff value of 20 mg/dL. Results: Of the 69 patients, 32 (46.4%) had a preoperative prealbumin ≤20 mg/dL. There was no correlation between prealbumin levels and body mass index (r = ?0.13, P = .28). Likewise, there was no correlation between preoperative albumin and prealbumin levels (r = 0.09, P = .44). Nine of 32 (28.1%) patients with low preoperative prealbumin levels had postoperative infections compared with 2 of 37 (5.4%) patients with high prealbumin levels (P = .010). Patients with low prealbumin levels also had increased risk of postoperative intubation for >12 hours (P = .010). Conclusions: Patients undergoing cardiac surgery with preoperative prealbumin levels of ≤20 mg/dL have an increased risk for postoperative infections and the need for longer mechanical ventilation. If feasible, nutrition optimization of such patients may be considered prior to cardiac surgery.     

Computer‐Based Malnutrition Risk Calculation May Enhance the Ability to Identify Pediatric Patients at Malnutrition‐Related Risk for Unfavorable Outcome

Background: The study aimed to test the hypothesis that computer‐based calculation of malnutrition risk may enhance the ability to identify pediatric patients at malnutrition‐related risk for an unfavorable outcome. The Pediatric Digital Scaled MAlnutrition Risk screening Tool (PeDiSMART), incorporating the World Health Organization (WHO) growth reference data and malnutrition‐related parameters, was used. Materials and Methods: This was a prospective cohort study of 500 pediatric patients aged 1 month to 17 years. Upon admission, the PeDiSMART score was calculated and anthropometry was performed. Pediatric Yorkhill Malnutrition Score (PYMS), Screening Tool Risk on Nutritional Status and Growth (STRONGkids), and Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP) malnutrition screening tools were also applied. PeDiSMART's association with the clinical outcome measures (weight loss/nutrition support and hospitalization duration) was assessed and compared with the other screening tools. Results: The PeDiSMART score was inversely correlated with anthropometry and bioelectrical impedance phase angle (BIA PhA). The score's grading scale was based on BIA Pha quartiles. Weight loss/nutrition support during hospitalization was significantly independently associated with the malnutrition risk group allocation on admission, after controlling for anthropometric parameters and age. Receiver operating characteristic curve analysis showed a sensitivity of 87% and a specificity of 75% and a significant area under the curve, which differed significantly from that of STRONGkids and STAMP. In the subgroups of patients with PeDiSMART‐based risk allocation different from that based on the other tools, PeDiSMART allocation was more closely related to outcome measures. Conclusion: PeDiSMART, applicable to the full age range of patients hospitalized in pediatric departments, graded according to BIA PhA, and embeddable in medical electronic records, enhances efficacy and reproducibility in identifying pediatric patients at malnutrition‐related risk for an unfavorable outcome. Patient allocation according to the PeDiSMART score on admission is associated with clinical outcome measures.     

Links between Adolescents' Closeness to Adoptive Parents and Attachment Style in Young Adulthood

This study examined whether adolescents' closeness to adoptive parents (APs) predicted attachment styles in close relationships outside their family during young adulthood. In a longitudinal study of domestic infant adoptions, closeness to adoptive mother and adoptive father was assessed in 156 adolescents (M = 15.7 years). Approximately 9 years later (M = 25.0 years), closeness to parents was assessed again as well as attachment style in their close relationships. Multilevel modeling was used to predict attachment style in young adulthood from the average and discrepancy of closeness to adolescents' adoptive mothers and fathers and the change over time in closeness to APs. Less avoidant attachment style was predicted by stronger closeness to both APs during adolescence. Increased closeness to APs over time was related to less anxiety in close relationships. Higher closeness over time to either AP was related to less avoidance and anxiety in close relationships.     

An Extension and Moderational Analysis of the Family Stress Model Focusing on African American Adolescents

This study focuses on 422 African American families with two caregivers as children transition into adolescence. The family stress model was tested longitudinally using structural equation modeling. Results showed that economic stress was associated with economic pressure and depressive symptoms among caregivers, which in turn was related to caregiver conflict. Higher levels of conflict had a negative influence on parenting quality at Wave 2, and this was linked to adolescent functioning. The depressive symptomology of secondary caregivers (SCs) exerted a greater indirect effect in the models than that of primary caregivers (PCs) and had a direct relationship to adolescent's positive adjustment. Moderational effects of caregiver conflict on the link between economic pressure/adolescent outcomes and economic pressure/hostile parenting were also tested. The case for the importance of including PCs and SCs of children and adolescents in future research focusing on African American families facing economic hardship is made.     

Impact of Coping Resources on the Well‐Being of Custodial Grandmothers and Grandchildren

The authors blended elements from the Stress Process Model and the Family Stress Model to investigate the direct and indirect effects of custodial grandmothers' (CGMs') coping resources (i.e., active strategies, passive strategies, and social support) on their psychological distress, their parenting practices, and their grandchild's internalizing and externalizing symptoms. Participants included African American and White CGMs (N = 733, M_age = 56) who provided full‐time care to a grandchild (M_age = 9.8). Structural equation modeling revealed that social support and active coping were related to lower CGM distress and less ineffective parenting, whereas passive coping was associated with increased distress and more ineffective parenting. Ineffective parenting had direct effects on grandchildren's outcomes, whereas CGM coping resources had direct effects on ineffective parenting and indirect effects (through ineffective parenting) on grandchildren's externalizing and internalizing difficulties. The authors conclude that CGM coping resources affect the psychological well‐being of both generations.     

Intraparental Inconsistency: The Influence of Parenting Behaviors on Aggression and Depression in Children

The authors examined several different predictive and mediation models of longitudinal parental inconsistency over a 3‐year time span. They hypothesized that parental behavior (communication/affection, kindness, and positive control) mediated the relationship between parental inconsistency (mother or father) and two emotional problems in children: (a) aggression and (b) depression. Data were obtained from a 3‐wave study (2007, 2008, and 2009) of 523 Spanish families with children ranging from 9–15 years of age at the beginning of Wave 1 (41.3% boys). Structural equation models revealed that multiple dimensions of parenting (mother or father) fully or partially mediated the relationship between longitudinal parental inconsistency and the child's adjustment. Communication/affection and kindness are the main processes through which parental inconsistency affects a child's aggression and depression. These results represent an important contribution to the improvement of parenting models of relationships between parental inconsistency and child adjustment.     

Optimal treatment allocation for placebo‐treatment comparisons in trials with discrete‐time survival endpoints

In many randomized controlled trials, treatment groups are of equal size, but this is not necessarily the best choice. This paper provides a methodology to calculate optimal treatment allocations for longitudinal trials when we wish to compare multiple treatment groups with a placebo group, and the comparisons may have unequal importance. The focus is on trials with a survival endpoint measured in discrete time. We assume the underlying survival process is Weibull and show that values for the parameters in the Weibull distribution have an impact on the optimal treatment allocation scheme in an interesting way. Additionally, we incorporate different cost considerations at the subject and measurement levels and determine the optimal number of time periods. We also show that when many events occur at the beginning of the trial, fewer time periods are more efficient. As an application, we revisit a risperidone maintenance treatment trial in schizophrenia and use our proposed methodology to redesign it and compare merits of our optimal design. Copyright © 2015 John Wiley & Sons, Ltd.     

Analysis of transtheoretical model of health behavioral changes in a nutrition intervention study—a continuous time Markov chain model with Bayesian approach

Continuous time Markov chain (CTMC) models are often used to study the progression of chronic diseases in medical research but rarely applied to studies of the process of behavioral change. In studies of interventions to modify behaviors, a widely used psychosocial model is based on the transtheoretical model that often has more than three states (representing stages of change) and conceptually permits all possible instantaneous transitions. Very little attention is given to the study of the relationships between a CTMC model and associated covariates under the framework of transtheoretical model. We developed a Bayesian approach to evaluate the covariate effects on a CTMC model through a log‐linear regression link. A simulation study of this approach showed that model parameters were accurately and precisely estimated. We analyzed an existing data set on stages of change in dietary intake from the Next Step Trial using the proposed method and the generalized multinomial logit model. We found that the generalized multinomial logit model was not suitable for these data because it ignores the unbalanced data structure and temporal correlation between successive measurements. Our analysis not only confirms that the nutrition intervention was effective but also provides information on how the intervention affected the transitions among the stages of change. We found that, compared with the control group, subjects in the intervention group, on average, spent substantively less time in the precontemplation stage and were more/less likely to move from an unhealthy/healthy state to a healthy/unhealthy state. Copyright © 2015 John Wiley & Sons, Ltd.     

Global Analysis of Methylation Profiles From High Resolution CpG Data

New high throughput technologies are now enabling simultaneous epigenetic profiling of DNA methylation at hundreds of thousands of CpGs across the genome. A problem of considerable practical interest is identification of large scale, global changes in methylation that are associated with environmental variables, clinical outcomes, or other experimental conditions. However, there has been little statistical research on methods for global methylation analysis using technologies with individual CpG resolution. To address this critical gap in the literature, we develop a new strategy for global analysis of methylation profiles using a functional regression approach wherein we approximate either the density or the cumulative distribution function (CDF) of the methylation values for each individual using B‐spline basis functions. The spline coefficients for each individual are allowed to summarize the individual's overall methylation profile. We then test for association between the overall distribution and a continuous or dichotomous outcome variable using a variance component score test that naturally accommodates the correlation between spline coefficients. Simulations indicate that our proposed approach has desirable power while protecting type I error. The method was applied to detect methylation differences, both genome wide and at LINE1 elements, between the blood samples from rheumatoid arthritis patients and healthy controls and to detect the epigenetic changes of human hepatocarcinogenesis in the context of alcohol abuse and hepatitis C virus infection. A free implementation of our methods in the R language is available in the Global Analysis of Methylation Profiles (GAMP) package at http://research.fhcrc.org/wu/en.html .