Abstract BackgroundFrailty is a syndrome that is defined as an accumulation of deficits in physical, psychological, and social domains. On a global scale, there is an urgent need to create frailty-ready healthcare systems due to the healthcare burden that frailty confers on systems and the increased risk of falls, healthcare utilization, disability, and premature mortality. Several studies have been conducted to develop prediction models for predicting frailty. Most studies used logistic regression as a technique to develop a prediction model. One area that has experienced significant growth is the application of Bayesian techniques, partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. ObjectiveWe compared ten different Bayesian networks as proposed by ten experts in the field of frail elderly people to predict frailty with a choice from ten dichotomized determinants for frailty. MethodsWe used the opinion of ten experts who could indicate, using an empty Bayesian network graph, the important predictors for frailty and the interactions between the different predictors. The candidate predictors were age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. The ten Bayesian network models were evaluated in terms of their ability to predict frailty. For the evaluation, we used the data of 479 participants that filled in the Tilburg Frailty indicator (TFI) questionnaire for assessing frailty among community-dwelling older people. The data set contained the aforementioned variables and the outcome ”frail”. The model fit of each model was measured using the Akaike information criterion (AIC) and the predictive performance of the models was measured using the area under the curve (AUC) of the receiver operator characteristic (ROC). The AUCs of the models were validated using bootstrapping with 100 repetitions. The relative importance of the predictors in the models was calculated using the permutation feature importance algorithm (PFI). ResultsThe ten Bayesian networks of the ten experts differed considerably regarding the predictors and the connections between the predictors and the outcome. However, all ten networks had corrected AUCs 0.700. Evaluating the importance of the predictors in each model, ”diseases or chronic disorders” was the most important predictor in all models (10 times). The predictors ”lifestyle” and ”monthly income” were also often present in the models (both 6 times). One or more diseases or chronic disorders, an unhealthy lifestyle, and a monthly income below 1,800 euro increased the likelihood of frailty. ConclusionsAlthough the ten experts all made different graphs, the predictive performance was always satisfying (AUCs 0.700). While it is true that the predictor importance varied all the time, the top three of the predictor importance consisted of “diseases or chronic disorders”, “lifestyle” and “monthly income”. All in all, asking for the opinion of experts in the field of frail elderly to predict frailty with Bayesian networks may be more rewarding than a data-driven forecast with Bayesian networks because they have expert knowledge regarding interactions between the different predictors.
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Journal of Physics: Conference Series Paper • The following article is Open access Exploring the relationship between light and subjective alertness using personal lighting conditions J. van Duijnhoven1, M.P.J. Aarts1, E.R. van den Heuvel2 and H.S.M. Kort3,4 Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 2042, CISBAT 2021 Carbon-neutral cities - energy efficiency and renewables in the digital era 8-10 September 2021, EPFL Lausanne, Switzerland Citation J. van Duijnhoven et al 2021 J. Phys.: Conf. Ser. 2042 012119 Download Article PDF References Download PDF 29 Total downloads Turn on MathJax Share this article Share this content via email Share on Facebook (opens new window) Share on Twitter (opens new window) Share on Mendeley (opens new window) Hide article information Author e-mails j.v.duijnhoven1@tue.nl Author affiliations 1 Building Lighting Group, Department of the Built Environment, Eindhoven University of Technology, Eindhoven, The Netherlands 2 Stochastics, Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands 3 Research Centre Healthy and Sustainable Living, University of Applied Sciences Utrecht, Utrecht, The Netherlands 4 Building Healthy Environments for Future Users Group, Department of the Built Environment, Eindhoven University of Technology, Eindhoven, The Netherlands DOI https://doi.org/10.1088/1742-6596/2042/1/012119 Buy this article in print Journal RSS Sign up for new issue notifications Create citation alert Abstract The discovery of the ipRGCs was thought to fully explain the mechanism behind the relationship between light and effects beyond vision such as alertness. However, this relationship turned out to be more complicated. The current paper describes, by using personal lighting conditions in a field study, further exploration of the relationship between light and subjective alertness during daytime. Findings show that this relationship is highly dependent on the individual. Although nearly all dose-response curves between personal lighting conditions and subjective alertness determined in this study turned out to be not significant, the results may be of high importance in the exploration of the exact relationship.
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Background: Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown. Aim: To estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients. Methods: An individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (PHL) to describe predictive performance in terms of discrimination and calibration. Results: The population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56-0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63-0.73; PHL was 0.658). Discussion: The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored.
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PURPOSE: Premorbid conditions affect prognosis of acutely-ill aged patients. Several lines of evidence suggest geriatric syndromes need to be assessed but little is known on their relative effect on the 30-day survival after ICU admission. The primary aim of this study was to describe the prevalence of frailty, cognition decline and activity of daily life in addition to the presence of comorbidity and polypharmacy and to assess their influence on 30-day survival.METHODS: Prospective cohort study with 242 ICUs from 22 countries. Patients 80 years or above acutely admitted over a six months period to an ICU between May 2018 and May 2019 were included. In addition to common patients' characteristics and disease severity, we collected information on specific geriatric syndromes as potential predictive factors for 30-day survival, frailty (Clinical Frailty scale) with a CFS > 4 defining frail patients, cognitive impairment (informant questionnaire on cognitive decline in the elderly (IQCODE) with IQCODE ≥ 3.5 defining cognitive decline, and disability (measured the activity of daily life with the Katz index) with ADL ≤ 4 defining disability. A Principal Component Analysis to identify co-linearity between geriatric syndromes was performed and from this a multivariable model was built with all geriatric information or only one: CFS, IQCODE or ADL. Akaike's information criterion across imputations was used to evaluate the goodness of fit of our models.RESULTS: We included 3920 patients with a median age of 84 years (IQR: 81-87), 53.3% males). 80% received at least one organ support. The median ICU length of stay was 3.88 days (IQR: 1.83-8). The ICU and 30-day survival were 72.5% and 61.2% respectively. The geriatric conditions were median (IQR): CFS: 4 (3-6); IQCODE: 3.19 (3-3.69); ADL: 6 (4-6); Comorbidity and Polypharmacy score (CPS): 10 (7-14). CFS, ADL and IQCODE were closely correlated. The multivariable analysis identified predictors of 1-month mortality (HR; 95% CI): Age (per 1 year increase): 1.02 (1.-1.03, p = 0.01), ICU admission diagnosis, sequential organ failure assessment score (SOFA) (per point): 1.15 (1.14-1.17, p < 0.0001) and CFS (per point): 1.1 (1.05-1.15, p < 0.001). CFS remained an independent factor after inclusion of life-sustaining treatment limitation in the model.CONCLUSION: We confirm that frailty assessment using the CFS is able to predict short-term mortality in elderly patients admitted to ICU. Other geriatric syndromes do not add improvement to the prediction model. Since CFS is easy to measure, it should be routinely collected for all elderly ICU patients in particular in connection to advance care plans, and should be used in decision making.
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Background: Pain assessment is a necessary step in pain management in older people in palliative care. In older people, pain assessment can be challenging due to underreporting and atypical pain manifestations by other distressing symptoms. Anxiety, fatigue, loss of appetite, insomnia, dyspnoea, and bowel problems correlate with pain in palliative care patients. Insight into these symptoms as predictors may help to identify the underlying presence of pain. This study aimed to develop a prediction model for pain in independently living frail older people in palliative care.Methods: In this cross-sectional observational study, community-care nurses from multiple organizations across the Netherlands included eligible patients (life expectancy < 1 year, aged 65+, independently living and frail). The outcome pain and symptoms were assessed by means of the Utrecht Symptom Diary. Also, demographic and illness information, including relevant covariates age, sex and living situation, was collected. Multivariable logistic regression and minimum Akaike Information Criterion(AIC) were used for model development and Receiver Operating Characteristics(ROC)-analysis for model performance. Additionally, predicted probability of pain are given for groups differing in age and sex.Results: A total of 157 patients were included. The final model consisted of insomnia(Odds Ratio[OR]=2.13, 95% Confidence Interval[CI]=1.013-1.300), fatigue(OR=3.47, 95% CI=1.107-1.431), sex(female)(OR=3.83, 95% CI=2.111-9.806) and age(OR=-1.59, 95% CI=0.922-1.008) as predicting variables. There is an overall decreasing trend for age, older persons suffer less from pain and females have a higher probability of experiencing pain. Model performance was indicated as fair with a sensitivity of 0.74(95% CI=0.64-0.83) and a positive predictive value of 0.80(95% CI=0.70-0.88).Conclusion: Insomnia and fatigue are predicting symptoms for pain, especially in women and younger patients. The use of a symptom diary in primary care can support the identification of pain.
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Purpose: Until now, it is not clear whether there are differences in patient perception between multi-bedded rooms with two and four beds. The purpose of this study was to investigate the effect of the physical (i.e. room type) and psychosocial (i.e. kindness of roommates and extraversion) aspects on the patients’ experience (i.e. pleasantness of the room, anxiety, sleep quality) in multi-bedded rooms in an oncology ward. Design/methodology/approach: A group of 84 hospitalized oncology patients completed a questionnaire on the day of departure. Room types were categorized into two groups: two-person and four-person rooms. Findings: Multivariate logistic regression analyses with the minimum Akaike Information Criterion (AIC) showed no direct main effects of room type (two vs. four-person room), kindness of roommates and extraversion on pleasantness of the room, anxiety and sleep quality. However, the authors found an interaction effect between room type and extraversion on pleasantness of the room. Patients who score relatively high in extraversion rated the room as more pleasant when they stayed in a four-person rather than a two-person room. For patients relatively low in extraversion, room type was not related to pleasantness of the room. Practical implications: The findings allow hospitals to better understand individual differences in patient experiences. Hospitals should inform patients about the benefits of the different room types and potential influences of personality (extraversion) so patients are empowered and can benefit from autonomy and the most appropriate place. Originality/value: This study emphasizes the importance of including four-person rooms in an oncology ward, while new hospital facility layouts mainly include single-bed rooms.
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INTRODUCTION: Innovations in head and neck cancer (HNC) treatment are often subject to economic evaluation prior to their reimbursement and subsequent access for patients. Mapping functions facilitate economic evaluation of new treatments when the required utility data is absent, but quality of life data is available. The objective of this study is to develop a mapping function translating the EORTC QLQ-C30 to EQ-5D-derived utilities for HNC through regression modeling, and to explore the added value of disease-specific EORTC QLQ-H&N35 scales to the model.METHODS: Data was obtained on patients with primary HNC treated with curative intent derived from two hospitals. Model development was conducted in two phases: 1. Predictor selection based on theory- and data-driven methods, resulting in three sets of potential predictors from the quality of life questionnaires; 2. Selection of the best out of four methods: ordinary-least squares, mixed-effects linear, Cox and beta regression, using the first set of predictors from EORTC QLQ-C30 scales with most correspondence to EQ-5D dimensions. Using a stepwise approach, we assessed added values of predictors in the other two sets. Model fit was assessed using Akaike and Bayesian Information Criterion (AIC and BIC) and model performance was evaluated by MAE, RMSE and limits of agreement (LOA).RESULTS: The beta regression model showed best model fit, with global health status, physical-, role- and emotional functioning and pain scales as predictors. Adding HNC-specific scales did not improve the model. Model performance was reasonable; R2 = 0.39, MAE = 0.0949, RMSE = 0.1209, 95% LOA of -0.243 to 0.231 (bias -0.01), with an error correlation of 0.32. The estimated shrinkage factor was 0.90.CONCLUSIONS: Selected scales from the EORTC QLQ-C30 can be used to estimate utilities for HNC using beta regression. Including EORTC QLQ-H&N35 scales does not improve the mapping function. The mapping model may serve as a tool to enable cost-effectiveness analyses of innovative HNC treatments, for example for reimbursement issues. Further research should assess the robustness and generalizability of the function by validating the model in an external cohort of HNC patients.
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Most multi‑problem young adults (18–27 years old) have been exposed to childhood maltreatment and/or have been involved in juvenile delinquency and, therefore, could have had Child Protection Service (CPS) interference during childhood. The extent to which their childhood problems persist and evolve into young adult‑ hood may differ substantially among cases. This might indicate heterogeneous profiles of CPS risk factors. These pro‑ files may identify combinations of closely interrelated childhood problems which may warrant specific approaches for problem recognition and intervention in clinical practice. The aim of this study was to retrospectively identify distinct statistical classes based on CPS data of multi‑problem young adults in The Netherlands and to explore whether these classes were related to current psychological dysfunctioning and delinquent behaviour. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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The objective of this study was to understand community-dwelling older people’s readiness for receiving telehealth by studying their intention to use videoconferencing and capacities for using digital technology in daily life as indicators. A mixed-method triangulation design was used. First, a cross-sectional survey study was performed to investigate older people’s intention to use videoconferencing, by testing our theoretical framework with a multilevel path analysis (phase 1). Second, for deeper understanding of older people’s actual use of digital technology, qualitative observations of older people executing technological tasks (eg, on a computer, cell phone) were conducted at their homes (phase 2).
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Background: The importance of clarifying goals and providing process feedback for student learning has been widely acknowledged. From a Self-Determination Theory perspective, it is suggested that motivational and learning gains will be obtained because in well-structured learning environments, when goals and process feedback are provided, students will feel more effective (need for competence), more in charge over their own learning (need for autonomy) and experience a more positive classroom atmosphere (need for relatedness). Yet, in spite of the growing theoretical interest in goal clarification and process feedback in the context of physical education (PE), little experimental research is available about this topic. Purpose: The present study quasi-experimentally investigated whether the presence of goal clarification and process feedback positively affects students’ need satisfaction and frustration. Method: Twenty classes from five schools with 492 seventh grade PE students participated in this quasi-experimental study. Within each school, four classes were randomly assigned to one of the four experimental conditions (n = 121, n = 117, n = 126 and n = 128) in a 2 × 2 factorial design, in which goal clarification (absence vs. presence) and process feedback (absence vs. presence) were experimentally manipulated. The experimental lesson consisted of a PE lesson on handstand (a relatively new skill for seventh grade students), taught by one and the same teacher who went to the school of the students to teach the lesson. Depending on the experimental condition, the teacher either started the lesson explaining the goals, or refrained from explaining the goals. Throughout the lesson the teacher either provided process feedback, or refrained from providing process feedback. All other instructions were similar across conditions, with videos of exercises of differential levels of difficulty provided to the students. All experimental lessons were observed by a research-assistant to discern whether manipulations were provided according to a condition-specific script. One week prior to participating in the experimental lesson, data on students’ need-based experiences (i.e. quantitatively) were gathered. Directly after students’ participation in the experimental lesson, data on students’ perceptions of goal clarification and process feedback, need-based experiences (i.e. quantitatively) and experiences in general (i.e. qualitatively) were gathered. Results and discussion: The questionnaire data and observations revealed that manipulations were provided according to the lesson-scripts. Rejecting our hypothesis, quantitative analyses indicated no differences in need satisfaction across conditions, as students were equally satisfied in their need for competence, autonomy and relatedness regardless of whether the teacher provided goal clarification and process feedback, only goal clarification, only process feedback or none. Similar results were found for need frustration. Qualitative analyses indicated that, in all four conditions, aspects of the experimental lesson made students feel more effective, more in charge over their own learning and experience a more positive classroom atmosphere. Our results suggest that under certain conditions, lessons can be perceived as highly need-satisfying by students, even if the teacher does not verbally and explicitly clarify the goals and/ or provides process feedback. Perhaps, students were able to self-generate goals and feedback based on the instructional videos.
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