Delirium screening in an acute care setting with a machine learning classifier based on routinely collected nursing data: A model development study

Delirium screening in acute care settings is a resource intensive process with frequent deviations from screening protocols. A predictive model relying only on daily collected nursing data for delirium screening could expand the populations covered by such screening programs. Here, we present the results of the development and validation of a series of machine-learning based delirium prediction models. For this purpose, we used data of all patients 18 years or older which were hospitalized for more than a day between January 1, 2014, and December 31, 2018, at a single tertiary teaching hospital in Zurich, Switzerland. A total of 48,840 patients met inclusion criteria. 18,873 (38.6%) were excluded due to missing data. Mean age (SD) of the included 29,967 patients was 71.1 (12.2) years and 12,231 (40.8%) were women. Delirium was assessed with the Delirium Observation Scale (DOS) with a total score of 3 or greater indicating that a patient is at risk for delirium. Additional measures included structured data collected for nursing process planning and demographic characteristics. The performance of the machine learning models was assessed using the area under the receiver operating characteristic curve (AUC). The training set consisted of 21,147 patients (mean age 71.1 (12.1) years; 8,630 (40.8%) women|) including 233,024 observations with 16,167 (6.9%) positive DOS screens. The test set comprised 8,820 patients (median age 71.1 (12.4) years; 3,601 (40.8%) women) with 91,026 observations with 5,445 (6.0%) positive DOS screens. Overall, the gradient boosting machine model performed best with an AUC of 0.933 (95% CI, 0.929 - 0.936). In conclusion, machine learning models based only on structured nursing data can reliably predict patients at risk for delirium in an acute care setting. Prediction models, using existing data collection processes, could reduce the resources required for delirium screening procedures in clinical practice

Structural neuroimaging of hippocampus and amygdala subregions in posttraumatic stress disorder: A scoping review

Numerous studies have explored the relationship between posttraumatic stress disorder (PTSD) and the hippo-campus and the amygdala because both regions are implicated in the disorder’s pathogenesis and pathophysiology. Nevertheless, those key limbic regions consist of functionally and cytoarchitecturally distinct substructures that may play different roles in the etiology of PTSD. Spurred by the availability of automatic segmentation software, structural neuroimaging studies of human hippocampal and amygdala subregions have proliferated in recent years. Here, we present a preregistered scoping review of the existing structural neuroimaging studies of the hippocampus and amygdala subregions in adults diagnosed with PTSD. A total of 3513 studies assessing subregion volumes were identified, 1689 of which were screened, and 21 studies were eligible for this review (total N = 2876 individuals). Most studies examined hippocampal subregions and reported decreased CA1, CA3, dentate gyrus, and subiculum volumes in PTSD. Fewer studies investigated amygdala subregions and reported altered lateral, basal, and central nuclei volumes in PTSD. This review further highlights the conceptual and methodological limitations of the current literature and identifies future directions to increase understanding of the distinct roles of hippocampal and amygdalar subregions in posttraumatic psychopathology

Math Anxiety Is Related to Math Difficulties and Composed of Emotion Regulation and Anxiety Predisposition: A Network Analysis Study

Current evidence suggests emotion regulation is an important factor in both math anxiety and math performance, but the interplay between these constructs is unexamined. Given the multicomponent structure of math anxiety, emotion regulation, and math performance, here, we aimed to provide a comprehensive model of the underlying nature of the links between these latent variables. Using the innovative network analysis approach, the study visualized the underlying links between directly observable and measurable variables that might be masked by traditional statistical approaches. One hundred and seventeen adults completed a battery of tests and questionnaires on math anxiety, emotion regulation, and math performance. The results revealed: (1) state math anxiety (the emotional experience in math-related situations), rather than trait math anxiety, was linked to anxiety predisposition, subjective valence of math information, and difficulties in emotion regulation; (2) the link between state math anxiety and math performance partialed out the link between trait math anxiety and performance. The study innovatively demonstrates the need to differentiate between traits and tendencies to the actual emotional experience and emotion regulation used in math anxiety. The results have important implications for the theoretical understanding of math anxiety and future discussions and work in the field