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Background Recent world population predictions show that the world population aged >=65 years will increase from 10% in 2022 to 16% in 2050. Population aging is accompanied by an increase in people with disability. It is important to pay special attention to people with disability, as these people are at high risk of adverse outcomes. Our study aimed to investigate the transitions of disability among Dutch community-dwelling older people aged 75 years or older, using a follow-up of nine years. We used socio-demographic factors gender, age, marital status, education, and income, but also lifestyle, diseases, and life events to predict the disability transitions over time. Methods We used a sample of 484 people that was randomly drawn from the municipality of Roosendaal (the Netherlands), a municipality with 78,000 inhabitants. A subset of people who completed part A of the Tilburg Frailty Indicator (TFI) at baseline and the Groningen Activity Restriction Scale (GARS) questionnaires was used with a nine-year follow-up. Paired Wilcoxon tests were used to compare the consecutive measurements. Socio-demographic factors gender, age, marital status, education, and income, but also lifestyle, diseases, and life events were included to predict the disability transitions over time. For the univariable and multivariable analysis of the measurements over time with the predictor variables, we used generalized estimation equations (GEE). A p-value <0.05 was considered significant. R version 3.4.4 was used for all analyses. Results Of the participants, 65% were younger than 80 years, 50% were married or cohabiting, 87% reported a healthy lifestyle, and 63% had no diseases or chronic disorders. Each year, more participants changed from status not disabled to disabled than vice versa. The GEE analyses showed that lifestyle (‘not healthy’) and diseases or chronic disorders (‘two or more’) were significant in the multivariable analysis for the disab

Background: Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands. Methods: We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error. Results: The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets. Conclusion: The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.

Disability is associated with lower quality of life and premature death in older people. Therefore, prevention and intervention targeting older people living with a disability is important. Frailty can be considered a major predictor of disability. In this study, we aimed to develop nomograms with items of the Tilburg Frailty Indicator (TFI) as predictors by using cross-sectional and longitudinal data (follow-up of five and nine years), focusing on the prediction of total disability, disability in activities of daily living (ADL), and disability in instrumental activities of daily living (IADL). At baseline, 479 Dutch community-dwelling people aged ≥75 years participated. They completed a questionnaire that included the TFI and the Groningen Activity Restriction Scale to assess the three disability variables. We showed that the TFI items scored different points, especially over time. Therefore, not every item was equally important in predicting disability. ‘Difficulty in walking’ and ‘unexplained weight loss’ appeared to be important predictors of disability. Healthcare professionals need to focus on these two items to prevent disability. We also conclude that the points given to frailty items differed between total, ADL, and IADL disability and also differed regarding years of follow-up. Creating one monogram that does justice to this seems impossible.

Introduction External validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting. Methods We compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1) the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2) the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury. Results The permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples) and heterogeneous in scenario 2 (in 17%-39% of simulated samples). Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2. Conclusion The recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect r

Background: The use of alternative modeling techniques for predicting patient survival is complicated by the fact that some alternative techniques cannot readily deal with censoring, which is essential for analyzing survival data. In the current study, we aimed to demonstrate that pseudo values enable statistically appropriate analyses of survival outcomes when used in seven alternative modeling techniques. Methods: In this case study, we analyzed survival of 1282 Dutch patients with newly diagnosed Head and Neck Squamous Cell Carcinoma (HNSCC) with conventional Kaplan-Meier and Cox regression analysis. We subsequently calculated pseudo values to reflect the individual survival patterns. We used these pseudo values to compare recursive partitioning (RPART), neural nets (NNET), logistic regression (LR) general linear models (GLM) and three variants of support vector machines (SVM) with respect to dichotomous 60-month survival, and continuous pseudo values at 60 months or estimated survival time. We used the area under the ROC curve (AUC) and the root of the mean squared error (RMSE) to compare the performance of these models using bootstrap validation. Results: Of a total of 1282 patients, 986 patients died during a median follow-up of 66 months (60-month survival: 52% [ 95% CI: 50%-255%]). The LR model had the highest optimism corrected AUC (0.791) to predict 60-month survival, followed by the SVM model with a linear kernel (AUC 0.787). The GLM model had the smallest optimism corrected RMSE when continuous pseudo values were considered for 60-month survival or the estimated survival time followed by SVM models with a linear kernel. The estimated importance of predictors varied substantially by the specific aspect of survival studied and modeling technique used. Conclusions: The use of pseudo values makes it readily possible to apply alternative modeling techniques to survival problems, to compare their performance and to search further for promising alternative mode

Kennis en expertise spelen een grote rol in watergovernance. Hoewel veel discussies alleen over feitelijkheden lijken te gaan, spelen verschillen in waarden een belangrijke rol. Ook in kennis en expertise spelen waarden mee. Waarden beïnvloeden onvermijdelijk waar experts op letten en hoe zij interpreteren wat zij zien. Dit leidt in de praktijk van watergovernance vaak tot verschillende, tegenstrijdige kennisclaims. In dit artikel worden twee voorbeelden gepresenteerd van hoe met dergelijke conflicten omgegaan kan worden: de ontwikkeling van het waterfront Almere en de dijkversterking Commandeurspolder. Eén mogelijkheid is om de kennis en expertise en daarmee de waarden van de verschillende partijen aan elkaar te koppelen, bijvoorbeeld via een open-planproces zoals in Almere. Of koppelen of juist ontkoppelen een goede optie is, hangt onder andere af van de juridische verantwoordelijkheden en de taakopvatting van de betrokken partijen. Verder moeten de mogelijke baten van koppelen, zoals minder maatschappelijke weerstand en minder afbreukrisico, afgewogen worden tegen de kosten van open-planprocessen, die vaak langdurig zijn, een erg politiek karakter kunnen krijgen, en een onzekere uitkomst hebben.

Background: Prediction rules for intracranial traumatic findings in patients with minor head injury are designed to reduce the use of computed tomography (CT) without missing patients at risk for complications. This study investigates whether alternative modelling techniques might improve the applicability and simplicity of such prediction rules. Methods: We included 3181 patients with minor head injury who had received CT scans between February 2002 and August 2004. Of these patients 243 (7.6%) had intracranial traumatic findings and 17 (0.5%) underwent neurosurgical intervention. We analyzed sensitivity, specificity and area under the ROC curve (AUC-value) to compare the performance of various modelling techniques by 10 x 10 cross-validation. The techniques included logistic regression, Bayes network, Chi-squared Automatic Interaction Detection (CHAID), neural net, support vector machines, Classification And Regression Trees (CART) and "decision list" models. Results: The cross-validated performance was best for the logistic regression model (AUC 0.78), followed by the Bayes network model and the neural net model (both AUC 0.74). The other models performed poorly (AUC < 0.70). The advantage of the Bayes network model was that it provided a graphical representation of the relationships between the predictors and the outcome. Conclusions: No alternative modelling technique outperformed the logistic regression model. However, the Bayes network model had a presentation format which provided more detailed insights into the structure of the prediction problem. The search for methods with good predictive performance and an attractive presentation format should continue.

Deltas in Depth Theme 7: Decision support instruments for climate adaptation policy, Session DD 7.5: Novel approaches. Exploratory modeling in support of robust policies for flood risk management, Dr. Tineke Ruijgh-van der Ploeg, Delft University of Technology, the Netherlands (2010). Presented at the international conference Deltas in Times of Climate Change, 29 September - 1 October, Rotterdam, the Netherlands.

OBJECTIVES: To confirm the feasibility of nurse practitioner interventionin non-high-risk patients with recent myocardial infarction (MI). DESIGN: Observational study. SETTING: Acute coronary care unit in a teaching hospital. METHODS: We performed an open-label feasibility study to identify non-high-risk MI patients and evaluate the outcome of a new nurse practitioner intervention programme. The initial pilot phase served to identify the non-high-risk population. In the subsequent confirmation phase, 500 consecutive non-high-risk post-MI patients with preserved LV function without heart failure were included to receive nurse practitioner management. The nurse practitioner intervention started on transfer from the coronary care unit to the cardiology ward and continued thereafter for up to 30 days. MAIN OUTCOME MEASURES: Time to first event analysis of death from all causes or repeat myocardial infarction. RESULTS: 500 Patients without signs of heart failure or depressed LV function were identified as nonhigh- risk and eligible for inclusion in the nurse practitioner intervention programme. In the implementation phase, none of the patients died and 0.9% developed a repeat myocardial infarction after 30 days of follow-up. Compared with the pilot phase, patients in the implementation phase spent fewer days in hospital (mean 11.1 versus 6.2 days; p<0.001). CONCLUSION: It is feasible to identify non-high-risk post-MI patients, who can be managed adequately by a nurse practitioner. Embedding experienced nurse practitioners within critical care pathways may result in significant decreases in length of hospital stay. (Neth Heart J 2009;17:61-7.Neth Heart J 2009;17:61-7.).