Hospital-level associations with 30-day patient mortality after cardiac surgery: a tutorial on the application and interpretation of marginal and multilevel logistic regression

Background: Marginal and multilevel logistic regression methods can estimate associations between hospital-level factors and patient-level 30-day mortality outcomes after cardiac surgery. However, it is not widely understood how the interpretation of hospital-level effects differs between these methods. Methods. The Australasian Society of Cardiac and Thoracic Surgeons (ASCTS) registry provided data on 32,354 patients undergoing cardiac surgery in 18 hospitals from 2001 to 2009. The logistic regression methods related 30-day mortality after surgery to hospital characteristics with concurrent adjustment for patient characteristics. Results: Hospital-level mortality rates varied from 1.0% to 4.1% of patients. Ordinary, marginal and multilevel regression methods differed with regard to point estimates and conclusions on statistical significance for hospital-level risk factors; ordinary logistic regression giving inappropriately narrow confidence intervals. The median odds ratio, MOR, from the multilevel model was 1.2 whereas ORs for most patient-level characteristics were of greater magnitude suggesting that unexplained between-hospital variation was not as relevant as patient-level characteristics for understanding mortality rates. For hospital-level characteristics in the multilevel model, 80% interval ORs, IOR-80%, supplemented the usual ORs from the logistic regression. The IOR-80% was (0.8 to 1.8) for academic affiliation and (0.6 to 1.3) for the median annual number of cardiac surgery procedures. The width of these intervals reflected the unexplained variation between hospitals in mortality rates; the inclusion of one in each interval suggested an inability to add meaningfully to explaining variation in mortality rates. Conclusions: Marginal and multilevel models take different approaches to account for correlation between patients within hospitals and they lead to different interpretations for hospital-level odds ratios. © 2012 Sanagou et al; licensee BioMed Central Ltd

Team climate, intention to leave and turnover among hospital employees: Prospective cohort study

<p>Abstract</p> <p>Background</p> <p>In hospitals, the costs of employee turnover are substantial and intentions to leave among staff may manifest as lowered performance. We examined whether team climate, as indicated by clear and shared goals, participation, task orientation and support for innovation, predicts intention to leave the job and actual turnover among hospital employees.</p> <p>Methods</p> <p>Prospective study with baseline and follow-up surveys (2–4 years apart). The participants were 6,441 (785 men, 5,656 women) hospital employees under the age of 55 at the time of follow-up survey. Logistic regression with generalized estimating equations was used as an analysis method to include both individual and work unit level predictors in the models.</p> <p>Results</p> <p>Among stayers with no intention to leave at baseline, lower self-reported team climate predicted higher likelihood of having intentions to leave at follow-up (odds ratio per 1 standard deviation decrease in team climate was 1.6, 95% confidence interval 1.4–1.8). Lower co-worker assessed team climate at follow-up was also association with such intentions (odds ratio 1.8, 95% confidence interval 1.4–2.4). Among all participants, the likelihood of actually quitting the job was higher for those with poor self-reported team climate at baseline. This association disappeared after adjustment for intention to leave at baseline suggesting that such intentions may explain the greater turnover rate among employees with low team climate.</p> <p>Conclusion</p> <p>Improving team climate may reduce intentions to leave and turnover among hospital employees.</p

NMR Structure of the Human Prion Protein with the Pathological Q212P Mutation Reveals Unique Structural Features

Prion diseases are fatal neurodegenerative disorders caused by an aberrant accumulation of the misfolded cellular prion protein (PrPC) conformer, denoted as infectious scrapie isoform or PrPSc. In inherited human prion diseases, mutations in the open reading frame of the PrP gene (PRNP) are hypothesized to favor spontaneous generation of PrPSc in specific brain regions leading to neuronal cell degeneration and death. Here, we describe the NMR solution structure of the truncated recombinant human PrP from residue 90 to 231 carrying the Q212P mutation, which is believed to cause Gerstmann-Sträussler-Scheinker (GSS) syndrome, a familial prion disease. The secondary structure of the Q212P mutant consists of a flexible disordered tail (residues 90–124) and a globular domain (residues 125–231). The substitution of a glutamine by a proline at the position 212 introduces novel structural differences in comparison to the known wild-type PrP structures. The most remarkable differences involve the C-terminal end of the protein and the β2–α2 loop region. This structure might provide new insights into the early events of conformational transition of PrPC into PrPSc. Indeed, the spontaneous formation of prions in familial cases might be due to the disruptions of the hydrophobic core consisting of β2–α2 loop and α3 helix

Complex systems and the technology of variability analysis

Characteristic patterns of variation over time, namely rhythms, represent a defining feature of complex systems, one that is synonymous with life. Despite the intrinsic dynamic, interdependent and nonlinear relationships of their parts, complex biological systems exhibit robust systemic stability. Applied to critical care, it is the systemic properties of the host response to a physiological insult that manifest as health or illness and determine outcome in our patients. Variability analysis provides a novel technology with which to evaluate the overall properties of a complex system. This review highlights the means by which we scientifically measure variation, including analyses of overall variation (time domain analysis, frequency distribution, spectral power), frequency contribution (spectral analysis), scale invariant (fractal) behaviour (detrended fluctuation and power law analysis) and regularity (approximate and multiscale entropy). Each technique is presented with a definition, interpretation, clinical application, advantages, limitations and summary of its calculation. The ubiquitous association between altered variability and illness is highlighted, followed by an analysis of how variability analysis may significantly improve prognostication of severity of illness and guide therapeutic intervention in critically ill patients

School Effects on the Wellbeing of Children and Adolescents

Well-being is a multidimensional construct, with psychological, physical and social components. As theoretical basis to help understand this concept and how it relates to school, we propose the Self-Determination Theory, which contends that self-determined motivation and personality integration, growth and well-being are dependent on a healthy balance of three innate psychological needs of autonomy, relatedness and competence. Thus, current indicators involve school effects on children’s well-being, in many diverse modalities which have been explored. Some are described in this chapter, mainly: the importance of peer relationships; the benefits of friendship; the effects of schools in conjunction with some forms of family influence; the school climate in terms of safety and physical ecology; the relevance of the teacher input; the school goal structure and the implementation of cooperative learning. All these parameters have an influence in promoting optimal functioning among children and increasing their well-being by meeting the above mentioned needs. The empirical support for the importance of schools indicates significant small effects, which often translate into important real-life effects as it is admitted at present. The conclusion is that schools do make a difference in children’s peer relationships and well-being

Numerical methods for the design and description of in vitro expansion processes of human mesenchymal stem cells

Human mesenchymal stem cells (hMSCs) are a valuable source of cells for clinical applications (e.g., treatment of acute myocardial infarction or inflammatory diseases), especially in the field of regenerative medicine. However, for autologous (patient-specific) and allogeneic (off-the-shelf) hMSC-based therapies, in vitro expansion is necessary prior to the clinical application in order to achieve the required cell numbers. Safe, reproducible, and economic in vitro expansion of hMSCs for autologous and allogeneic therapies can be problematic because the cell material is restricted and the cells are sensitive to environmental changes. It is beneficial to collect detailed information on the hydrodynamic conditions and cell growth behavior in a bioreactor system, in order to develop a so called “Digital Twin” of the cultivation system and expansion process. Numerical methods, such as Computational Fluid Dynamics (CFD) which has become widely used in the biotech industry for studying local characteristics within bioreactors or kinetic growth modelling, provide possible solutions for such tasks. In this review, we will present the current state-of-the-art for the in vitro expansion of hMSCs. Different numerical tools, including numerical fluid flow simulations and cell growth modelling approaches for hMSCs, will be presented. In addition, a case study demonstrating the applicability of CFD and kinetic growth modelling for the development of an microcarrier-based hMSC process will be shown

Willing to wait?: The influence of patient wait time on satisfaction with primary care

<p>Abstract</p> <p>Background</p> <p>This study examined the relationship between patient waiting time and willingness to return for care and patient satisfaction ratings with primary care physicians.</p> <p>Methods</p> <p>Cross-sectional survey data on a convenience sample of 5,030 patients who rated their physicians on a web-based survey developed to collect detailed information on patient experiences with health care. The survey included self-reported information on wait times, time spent with doctor, and patient satisfaction.</p> <p>Results</p> <p>Longer waiting times were associated with lower patient satisfaction (p < 0.05), however, time spent with the physician was the strongest predictor of patient satisfaction. The decrement in satisfaction associated with long waiting times is substantially reduced with increased time spent with the physician (5 minutes or more). Importantly, the combination of long waiting time to see the doctor and having a short doctor visit is associated with very low overall patient satisfaction.</p> <p>Conclusion</p> <p>The time spent with the physician is a stronger predictor of patient satisfaction than is the time spent in the waiting room. These results suggest that shortening patient waiting times at the expense of time spent with the patient to improve patient satisfaction scores would be counter-productive.</p

Physician Language Ability and Cultural Competence: An Exploratory Study of Communication with Spanish-speaking Patients

OBJECTIVE: We studied physician–patient dyads to determine how physician self-rated Spanish-language ability and cultural competence affect Spanish-speaking patients’ reports of interpersonal processes of care. SETTING/PARTICIPANTS: Questionnaire study of 116 Spanish-speaking patients with diabetes and 48 primary care physicians (PCPs) at a public hospital with interpreter services. MEASURES: Primary care physicians rated their Spanish ability on a 5-point scale and cultural competence by rating: 1) their understanding of the health-related cultural beliefs of their Spanish-speaking patients; and 2) their effectiveness with Latino patients, each on a 4-point scale. We assessed patients’ experiences using the interpersonal processes of care (IPC) in diverse populations instrument. Primary care physician responses were dichotomized, as were IPC scale scores (optimal vs nonoptimal). We analyzed the relationship between language and two cultural competence items and IPC, and a summary scale and IPC, using multivariate models to adjust for known confounders of communication. RESULTS: Greater language fluency was strongly associated with optimal IPC scores in the domain of elicitation of and responsiveness to patients, problems and concerns [Adjusted Odds Ratio [AOR], 5.25; 95% confidence interval [CI], 1.59 to 17.27]. Higher score on a language-culture summary scale was associated with three IPC domains – elicitation/responsiveness (AOR, 6.34; 95% CI, 2.1 to 19.3), explanation of condition (AOR, 2.7; 95% CI, 1.0 to 7.34), and patient empowerment (AOR, 3.13; 95% CI, 1.2 to 8.19)—and not associated with two more-technical communication domains. CONCLUSION: Physician self-rated language ability and cultural competence are independently associated with patients’ reports of interpersonal process of care in patient-centered domains. Our study provides empiric support for the importance of language and cultural competence in the primary care of Spanish-speaking patients