The Boomerang Effect: How Nurses' Regulation of Patients' Affect Associates With Their Own Emotional Exhaustion and Affective Experiences

Recent research has shown that the intentional regulation of others’ affect has effects not only on the target (e.g., a patient) of the regulation, but also on the agent (e.g., a nurse). In particular, the use of intentional interpersonal affect regulation strategies has been found to predict employees’ emotional exhaustion (EEx). Use of affect-worsening strategies is associated with an increase in EEx, whereas the effect of using affect-improving strategies is less clear. Another relevant consequence of interpersonal affect regulation is its effect on affective experiences, which is one of the main determinants of job attitudes. This study tests the relationships between the interpersonal affect regulation strategies that nurses use to regulate their patients’ affect and the nurses’ EEx and affective experiences. A longitudinal 2-wave field study was conducted in sample of nurses. Participants completed a questionnaire on 2 different occasions, 2 months apart (Time 1 [T1], Time 2 [T2]). Of the 141 participants at T1, 103 also completed the survey at T2. Longitudinal hierarchical regression analyses showed that using affect-worsening strategies was a significant predictor of nurse’s EEx, whereas using affect-improving strategies did not significantly predict their EEx. For affective experiences, use of affect-worsening strategies was related to nurses experiencing low-activation negative affect (e.g., feeling depressed); whereas affect-improving strategies was related to them experiencing low-activation positive affect (e.g., feeling calm). Results support the view that intentional regulation of patients’ affect needs to be considered not only in relation to the patients’ perception of service quality but also from the perspective of nurses’ well-being

Response to ‘Comment on “Replicating Health Economic Models: Firm Foundations or a House of Cards?”’

This reply refers to the comment available at: https://doi.org/10.1007/s40273-017-0576-

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer

IntroductionUrinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as “black-box” has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models. The aim of this study was to employ three different ML classifiers to predict the probability of experiencing UI in men with localized prostate cancer 1-year and 2-year after treatment and compare their accuracy and explainability. MethodsWe used the ProZIB dataset from the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) which contained clinical, demographic, and PROM data of 964 patients from 65 Dutch hospitals. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms were applied to predict (in)continence after prostate cancer treatment. ResultsAll models have been externally validated according to the TRIPOD Type 3 guidelines and their performance was assessed by accuracy, sensitivity, specificity, and AUC. While all three models demonstrated similar performance, LR showed slightly better accuracy than RF and SVM in predicting the risk of UI one year after prostate cancer treatment, achieving an accuracy of 0.75, a sensitivity of 0.82, and an AUC of 0.79. All models for the 2-year outcome performed poorly in the validation set, with an accuracy of 0.6 for LR, 0.65 for RF, and 0.54 for SVM. ConclusionThe outcomes of our study demonstrate the promise of using non-black box models, such as LR, to assist clinicians in recognizing high-risk patients and making informed treatment choices. The coefficients of the LR model show the importance of each feature in predicting results, and the generated nomogram provides an accessible illustration of how each feature impacts the predicted outcome. Additionally, the model’s simplicity and interpretability make it a more appropriate option in scenarios where comprehending the model’s predictions is essential

Replicating Health Economic Models: Firm Foundations or a House of Cards?

Health economic evaluation is a framework for the comparative analysis of the incremental health gains and costs associated with competing decision alternatives. The process of developing health economic models is usually complex, financially expensive and time-consuming. For these reasons, model development is sometimes based on previous model-based analyses; this endeavour is usually referred to as model replication. Such model replication activity may involve the comprehensive reproduction of an existing model or 'borrowing' all or part of a previously developed model structure. Generally speaking, the replication of an existing model may require substantially less effort than developing a new de novo model by bypassing, or undertaking in only a perfunctory manner, certain aspects of model development such as the development of a complete conceptual model and/or comprehensive literature searching for model parameters. A further motivation for model replication may be to draw on the credibility or prestige of previous analyses that have been published and/or used to inform decision making. The acceptability and appropriateness of replicating models depends on the decision-making context: there exists a trade-off between the 'savings' afforded by model replication and the potential 'costs' associated with reduced model credibility due to the omission of certain stages of model development. This paper provides an overview of the different levels of, and motivations for, replicating health economic models, and discusses the advantages, disadvantages and caveats associated with this type of modelling activity. Irrespective of whether replicated models should be considered appropriate or not, complete replicability is generally accepted as a desirable property of health economic models, as reflected in critical appraisal checklists and good practice guidelines. To this end, the feasibility of comprehensive model replication is explored empirically across a small number of recent case studies. Recommendations are put forward for improving reporting standards to enhance comprehensive model replicability

Generative models improve radiomics performance in different tasks and different datasets:An experimental study

PURPOSE: Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic features. In this article, we investigate the possibility of using deep learning generative models to improve the performance of radiomics from low dose CTs. METHODS: We used two datasets of low dose CT scans - NSCLC Radiogenomics and LIDC-IDRI - as test datasets for two tasks - pre-treatment survival prediction and lung cancer diagnosis. We used encoder-decoder networks and conditional generative adversarial networks (CGANs) trained in a previous study as generative models to transform low dose CT images into full dose CT images. Radiomic features extracted from the original and improved CT scans were used to build two classifiers - a support vector machine (SVM) and a deep attention based multiple instance learning model - for survival prediction and lung cancer diagnosis respectively. Finally, we compared the performance of the models derived from the original and improved CT scans. RESULTS: Denoising with the encoder-decoder network and the CGAN improved the area under the curve (AUC) of survival prediction from 0.52 to 0.57 (p-value < 0.01). On the other hand, the encoder-decoder network and the CGAN improved the AUC of lung cancer diagnosis from 0.84 to 0.88 and 0.89 respectively (p-value < 0.01). Finally, there are no statistically significant improvements in AUC using encoder-decoder networks and CGAN (p-value = 0.34) when networks trained at 75 and 100 epochs. CONCLUSION: Generative models can improve the performance of low dose CT-based radiomics in different tasks. Hence, denoising using generative models seems to be a necessary pre-processing step for calculating radiomic features from low dose CTs

Artificial intelligence-based clinical decision support in modern medical physics:Selection, acceptance, commissioning, and quality assurance

Background: Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability.Aim: In this article, we provide guidance based on literature for the different aspects involved in the adoption of a CDSS with a special focus on machine and deep learning based systems: selection, acceptance testing, commissioning, implementation and quality assurance.Results: A rigorous selection process will help identify the CDSS that best fits the preferences and requirements of the local site. Acceptance testing will make sure that the selected CDSS fulfills the defined specifications and satisfies the safety requirements. The commissioning process will prepare the CDSS for safe clinical use at the local site. An effective implementation phase should result in an orderly roll out of the CDSS to the well-trained end-users whose expectations have been managed. And finally, quality assurance will make sure that the performance of the CDSS is maintained and that any issues are promptly identified and solved.Conclusion: We conclude that a systematic approach to the adoption of a CDSS will help avoid pitfalls, improve patient safety and increase the chances of success. (C) 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.</p