Delirium after cardiac surgery: A critical review

Numerous articles have been published investigating the incidence of and risk factors for delirium after cardiac surgery. Smith and Dimsdale reviewed the literature on postcardiotomy delirium in 1987 using a meta-analysis of 44 research studies. However, doubts about their methods and results caused the authors to re-examine the literature using these 44 references as well as computerized literature searches to gather research and review papers from medical journals. Delirium after cardiac surgery appeared to be ill-defined in most of these studies. The methods and instruments used to assess delirium proved to be very different, and the patient samples were rather heterogeneous. Therefore, in most cases, the results are not comparable. Only a small number of the studies that were examined fit the criteria for statistical meta-analysis. On the basis of our analysis, a tentative conclusion may be drawn that the incidence of postcardiotomy delirium has declined slightly and that no strong risk factors have yet been identified

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Delirium after cardiac surgery : a prospective study

Delirium was one of the first mental disorders described by the ancient medical writers some 2500 years ago [1]. In his extensive and excellent monograph "Delirium: Acute Confusional States", Lipowski describes the historical development of the concept of this disorder in detail, from the time of Hippocrates till the twentieth century [IJ. His most important and remarkable finding in tracing the history of delirium is the accuracy and consistency of the clinical description, despite the confusing variety of terms applied over the centuries to the same set of symptoms. Hereafter, the most important facts from his historical outline on delirium will be summarized [1]. At first, in antiquity, what nowadays is called delirium was referred to as 'phrenitis'. It was regarded as an acute mental disorder usually associated with fever and characterized by cognitive and behavioural disturbances as well as disruption of sleep. Phrenitis was described as marked by restless and excited behaviour, while 'lethargus', considered as the opposite of phrenitis, featured listlessness, sleepiness, inertia, memory loss and dulling of the senses. Lethargus could change into phrenitis and vice versa. Only in the late eighteenth century the word delirium gradually came to replace both of the earlier terms. Celsus was the first medical 'Writer known to use the term 'delirium'. He, as the other ancient medical 'Writers, recognized delirium or phrenitis as one of the most important mental disorders at that time

Excess mortality in general hospital patients with delirium: A 5-year follow-up of 519 patients seen in psychiatric consultation

Mortality was determined in 519 patients with delirium who were seen in psychiatric consultation in two general hospitals. Among 419 patients with simple delirium (DSM-III: 293.00) in-hospital mortality was 26%. As compared to average hospital patients the age adjusted in-hospital excess mortality ratio varied from 6.2 for patients with malignancies to 2.1 for patients with motor system disease. After hospital discharge the 5-yr cumulative mortality was 51%. As compared to the general population excess mortality was noted in most, but not in all diagnostic subgroups. The age and sex adjusted excess mortality ratio varied from 14.1 for malignancies to 1.3 for motor system disease. The figures underline a general notion that delirium may be an indicator of disorders of grave prognosis, but mortality appears to depend more on the medical condition than on the presence of delirium

Process improvement in healthcare: Overall resource efficiency

This paper aims to develop a unifying and quantitative conceptual framework for healthcare processes from the viewpoint of process improvement. The work adapts standard models from operation management to the specifics of healthcare processes. We propose concepts for organizational modeling of healthcare processes, breaking down work into micro processes, tasks, and resources. In addition, we propose an axiological model which breaks down general performance goals into process metrics. The connexion between both types of models is made explicit as a system of metrics for process flow and resource efficiency. The conceptual models offer exemplars for practical support in process improvement efforts, suggesting to project leaders how to make a diagrammatic representation of a process, which data to gather, and how to analyze and diagnose a process's flow and resource utilization. The proposed methodology links on to process improvement methodologies such as business process reengineering, six sigma, lean thinking, theory of constraints, and total quality management. In these approaches, opportunities for process improvement are identified from a diagnosis of the process under study. By providing conceptual models and practical templates for process diagnosis, the framework relates many disconnected strands of research and application in process improvement in healthcare to the unifying pursuit of process improvement

Tailoring a cognitive behavioural model for unexplained physical symptoms to patient's perspective: a bottem-up approach.

The prevalence of unexplained physical symptoms (UPS) in primary care is at least 33%. Cognitive behavioural therapy has shown to be effective. Within cognitive behavioural therapy, three models can be distinguished: reattribution model, coping model and consequences model. The consequences model, labelling psychosocial stress in terms of consequences rather than as causes of UPS, has high acceptance among patients and is effective in academic medical care. This acceptance is lost when applied in primary care. To increase acceptance of the consequences model among patients in primary care, we tailor this model to patient's perspective by approaching the model from bottom-up instead of top-down. Subsequently, we use this tailored model in an easily accessible group training. We illu

Clinical predictors of seizure threshold in electroconvulsive therapy: a prospective study

At the start and during the course of electroconvulsive therapy (ECT), estimation of the seizure threshold (ST) is useful in weighing the expected effectiveness against the risks of side effects. Therefore, this study explores clinical factors predicting initial ST (IST) and levels of ST during the ECT course. This prospective observational study included patients aged ≥18 years receiving ECT without contraindications for dose titration. At the first and every sixth consecutive ECT session, ST level was measured. Using multivariate linear regression and multilevel models, predictors for IST and change in ST levels were examined. A total of 91 patients (mean age, 59.1 ± 15.0 years; 37 % male; 97 % diagnosis of depression) were included. In multivariable analysis, higher age (β = 0.24; P = 0.03) and bifrontotemporal (BL) electrode placement (β = 0.42; P < 0.001) were independent predictors for higher IST, explaining 49 % of its variation. Also, these two variables independently predicted higher ST levels at different time points during the course. Using multilevel models, absence of a previous ECT course(s) predicted a steeper rise in ST during the course (P = 0.03 for the interaction term time*previous ECT). The age-adjusted dose-titration method is somewhat crude, resulting in some measurement error. Concomitant medication use could have influenced ST levels. Increasing age and BL electrode placement predicted higher (I)ST, which should be taken into account when selecting ECT dosage. Previous ECT course(s) may avoid an increase in ST during the course of ECT

SKDCGN: Source-free Knowledge Distillation of Counterfactual Generative Networks using cGANs

With the usage of appropriate inductive biases, Counterfactual Generative Networks (CGNs) can generate novel images from random combinations of shape, texture, and background manifolds. These images can be utilized to train an invariant classifier, avoiding the wide spread problem of deep architectures learning spurious correlations rather than meaningful ones. As a consequence, out-of-domain robustness is improved. However, the CGN architecture comprises multiple over parameterized networks, namely BigGAN and U2-Net. Training these networks requires appropriate background knowledge and extensive computation. Since one does not always have access to the precise training details, nor do they always possess the necessary knowledge of counterfactuals, our work addresses the following question: Can we use the knowledge embedded in pre-trained CGNs to train a lower-capacity model, assuming black-box access (i.e., only access to the pretrained CGN model) to the components of the architecture? In this direction, we propose a novel work named SKDCGN that attempts knowledge transfer using Knowledge Distillation (KD). In our proposed architecture, each independent mechanism (shape, texture, background) is represented by a student 'TinyGAN' that learns from the pretrained teacher 'BigGAN'. We demonstrate the efficacy of the proposed method using state-of-the-art datasets such as ImageNet, and MNIST by using KD and appropriate loss functions. Moreover, as an additional contribution, our paper conducts a thorough study on the composition mechanism of the CGNs, to gain a better understanding of how each mechanism influences the classification accuracy of an invariant classifier. Code available at: https://github.com/ambekarsameer96/SKDCGNComment: Accepted at ECCV 2022 Workshop VIPrior