Clinical sub-phenotypes of Staphylococcus aureus bacteraemia.

BACKGROUND: Staphylococcus aureus bacteraemia (SAB) is a clinically heterogeneous disease. The ability to identify sub-groups of patients with shared traits (sub-phenotypes) is an unmet need that could allow patient stratification for clinical management and research. We aimed to test the hypothesis that clinically-relevant sub-phenotypes can be reproducibly identified amongst patients with SAB. METHODS: We studied three cohorts of hospitalised adults with monomicrobial SAB: a UK retrospective observational study (Edinburgh cohort, n=458), the UK ARREST randomised trial (n=758), and the Spanish SAFO randomised trial (n=214). Latent class analysis was used to identify sub-phenotypes using routinely-collected clinical data, without considering outcomes. Mortality and microbiologic outcomes were then compared between sub-phenotypes. RESULTS: Included patients had predominantly methicillin-susceptible SAB (1366/1430,95.5%). We identified five distinct, reproducible clinical sub-phenotypes: (A) SAB associated with older age and comorbidity, (B) nosocomial intravenous catheter-associated SAB in younger people without comorbidity, (C) community-acquired metastatic SAB, (D) SAB associated with chronic kidney disease, and (E) SAB associated with injection drug use. Survival and microbiologic outcomes differed between the sub-phenotypes. 84-day mortality was highest in sub-phenotype A, and lowest in B and E. Microbiologic outcomes were worse in sub-phenotype C. In a secondary analysis of the ARREST trial, adjunctive rifampicin was associated with increased 84-day mortality in sub-phenotype B and improved microbiologic outcomes in sub-phenotype C. CONCLUSIONS: We have identified reproducible and clinically-relevant sub-phenotypes within SAB, and provide proof-of-principle of differential treatment effects. Through clinical trial enrichment and patient stratification, these sub-phenotypes could contribute to a personalised medicine approach to SAB.

Development, internal reliability and preliminary construct validity of the Dutch Dietary Intention Evaluation Tool for In-patients (DIETI).

BACKGROUND AND AIMS: Diet is important in prevention and management of non-communicable disease and in particular, cardiovascular disease. Recently, more hospitals gear towards healthier dietary policies, however, a tool to assess the effect of these interventions in patient populations is currently lacking. The Theory of Planned Behavior (TPB) is generally used to assess health-related behavior and offers a framework for development of questionnaires. In this study, we aim to evaluate the reliability, internal consistency and preliminary construct validity of the newly developed Dietary Intention Evaluation Tool for In-hospital patients (DIETI) which is based on the TPB. METHODS AND RESULTS: An expert panel constructed the item list of the DIETI. A total of 312 patients admitted to the cardiology ward filled out the DIETI. Explanatory- and confirmatory factor analysis showed that our tool adequately discerns five TPB-consistent factors regarding a healthy diet in hospitalized patients. (N = 312, for the CFA model χ2 = 313.072 (df = 160, p < 0.001, CFI = 0.939, RMSEA = 0.058). Subsequent analysis of reliability showed satisfactory to strong internal consistency of the questionnaire as a whole and all subscales (Cronbach's alpha for the subscales ranging between 0.65 and 0.88). CONCLUSIONS: We conclude that the DIETI is an internally reliable tool to assess behavioral intentions regarding a healthy diet of in-hospital patients. Thus, this questionnaire can be used to evaluate the effect of dietary interventions aimed at hospitalized patients.

Enhancing psychological capital and personal growth initiative: working on strengths or deficiencies.

Personal growth initiative (PGI), defined as being proactive about one's personal development, is critical to graduate students' academic success. Prior research has shown that students' PGI can be enhanced through interventions that focus on stimulating developmental activities. Within this study, we aimed to investigate whether an intervention that stimulates development in the area of one's personal strengths (strengths intervention) has more beneficial effects on students' PGI than an intervention that stimulates development in the area of individual deficiencies (deficiency intervention). We conducted 2 longitudinal field experiments to investigate the effects of the 2 interventions on students' PGI (Experiment 1) and the potential mediating role of psychological capital (PsyCap) in this regard (Experiment 2). In Experiment 1, 105 (N = 105) university students participated in either a strengths intervention or a deficiency intervention. Results indicated that the strengths intervention increased the students' PGI in the short but not in the long term, whereas the deficiency intervention did not affect PGI. Ninety students (N = 90) participated in Experiment 2, in which we slightly refined both interventions by putting a stronger emphasis on the ongoing development of strengths (strengths intervention) or correction of deficiencies (deficiency intervention) by adding posttraining assignments. Results suggested that participating in both interventions led to increases in PGI over a 3-month period, but that these increases were bigger for the strengths intervention group. Furthermore, the relationship between the strengths intervention and PGI was mediated by hope as one component of PsyCap.

Unravelling the skills of data scientists: A text mining analysis of Dutch university master programs in data science and artificial intelligence.

The growing demand for data scientists in both the global and Dutch labour markets has led to an increase in data science and artificial intelligence (AI) master programs offered by universities. However, there is still a lack of clarity regarding the specific skills of data scientists. This study addresses this issue by employing Correlated Topic Modeling (CTM) to analyse the content of 41 master programs offered by 11 Dutch universities and an interuniversity combined program. We assess the differences and similarities in the core skills taught by these programs, determine the subject-specific and general nature of the skills, and provide a comparison between the different types of universities offering these programs. Our analysis reveals that data processing, statistics, research, and ethics are the core competencies in Dutch data science and AI master programs. General universities tend to focus on research skills, while technical universities lean more towards IT and electronics skills. Broad-focussed data science and AI programs generally concentrate on data processing, information technology, electronics, and research, while subject-specific programs give priority to statistics and ethics. This research enhances the understanding of the diverse skills of Dutch data science graduates, providing valuable insights for employers, academic institutions, and prospective students.

A two-step estimator for multilevel latent class analysis with covariates.

We propose a two-step estimator for multilevel latent class analysis (LCA) with covariates. The measurement model for observed items is estimated in its first step, and in the second step covariates are added in the model, keeping the measurement model parameters fixed. We discuss model identification, and derive an Expectation Maximization algorithm for efficient implementation of the estimator. By means of an extensive simulation study we show that (1) this approach performs similarly to existing stepwise estimators for multilevel LCA but with much reduced computing time, and (2) it yields approximately unbiased parameter estimates with a negligible loss of efficiency compared to the one-step estimator. The proposal is illustrated with a cross-national analysis of predictors of citizenship norms.

Identifying barriers and facilitators to adopting healthier dietary choices in clinical care: a cross-sectional observational study.

Background and aims: Adopting healthier diets can drastically improve societal health. Our environment plays a crucial role in daily dietary choices and hospitals in particular can stimulate patients to adopt healthier eating habits. Unfortunately, no robust clinically applicable cuing tools exist to help guide in-hospital dietary interventions. The purpose of this study was to identify patient-related barriers and facilitators to adopting healthier dietary choices. Methods and results: This cross-sectional observational study was conducted on the cardiology ward of a university medical center between June 2020 and January 2021. Of the 594 patients asked and the 312 completed surveys on healthy eating intentions, 285 responses were considered for analysis. Notably, the majority of respondents were male (68.8%), with an average hospital stay of 3.3 days. The results indicate that cardiac patients attribute significantly greater influence on their dietary behavior to doctors compared to other caregivers, including dieticians (X2 = 37.09, df = 9, p < 0.001). Also, younger patients (below 70 years of age) were more inclined to plan changing dietary behavior than older patients. Most mentioned facilitators for adopting a healthier diet were more information/counseling, help in preparing food, support from family and friends, and more emphasis from a doctor. Conclusion: The study highlights the importance of involving doctors in formulating dietary policies and patient-directed interventions within hospital settings. It also sheds light on the barriers and facilitators for promoting healthier dietary behaviors among patients during their hospitalization.

Relating latent class membership to external variables: An overview.

In this article we provide an overview of existing approaches for relating latent class membership to external variables of interest. We extend on the work of Nylund-Gibson et al. (Structural Equation Modeling: A Multidisciplinary Journal, 2019, 26, 967), who summarize models with distal outcomes by providing an overview of most recommended modeling options for models with covariates and larger models with multiple latent variables as well. We exemplify the modeling approaches using data from the General Social Survey for a model with a distal outcome where underlying model assumptions are violated, and a model with multiple latent variables. We discuss software availability and provide example syntax for the real data examples in Latent GOLD.

Two-Step Estimation of Models Between Latent Classes and External Variables.

We consider models which combine latent class measurement models for categorical latent variables with structural regression models for the relationships between the latent classes and observed explanatory and response variables. We propose a two-step method of estimating such models. In its first step, the measurement model is estimated alone, and in the second step the parameters of this measurement model are held fixed when the structural model is estimated. Simulation studies and applied examples suggest that the two-step method is an attractive alternative to existing one-step and three-step methods. We derive estimated standard errors for the two-step estimates of the structural model which account for the uncertainty from both steps of the estimation, and show how the method can be implemented in existing software for latent variable modelling.