Abstract: 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.
DOCUMENT
Abstract The aim of this cross-sectional study was to develop a Frailty at Risk Scale (FARS) incorporating ten well-known determinants of frailty: age, sex, marital status, ethnicity, education, income, lifestyle, multimorbidity, life events, and home living environment. In addition, a second aim was to develop an online calculator that can easily support healthcare professionals in determining the risk of frailty among community-dwelling older people. The FARS was developed using data of 373 people aged ≥ 75 years. The Tilburg Frailty Indicator (TFI) was used for assessing frailty. Multivariate logistic regression analysis showed that the determinants multimorbidity, unhealthy lifestyle, and ethnicity (ethnic minority) were the most important predictors. The area under the curve (AUC) of the model was 0.811 (optimism 0.019, 95% bootstrap CI = −0.029; 0.064). The FARS is offered on a Web site, so that it can be easily used by healthcare professionals, allowing quick intervention in promoting quality of life among community-dwelling older people.
DOCUMENT
Deze voorlichtingspublicatie is bedoeld voor allen die te maken hebben of te maken krijgen met de selectie, toepassing en uitvoering van warmtebehandelingen. Daarbij moet gedacht worden aan constructeurs, lastechnici, werkvoorbereiders, enzovoorts. Deze voorlichtingspublicatie is een update van de bestaande NIL-voorlichtingspublicatie V990906 (september 1999) "Warmtebehandeling van metalen in relatie tot de lastechniek". De updating was noodzakelijk omdat er enerzijds geen document beschikbaar was met een duidelijk overzicht van de (belangrijkste) warmtebehandelingen en anderzijds omdat de ontwikkelingen in onder andere de nieuwe staalsoorten beperkingen (kunnen) stellen aan de uitvoering van warmtebehandelingen. De in deze voorlichtingspublicatie genoemde warmtebehandelingen zijn die, welke veel voorkomen in de staalverwerkende industrie en dus ook in de lastechniek. De meest belangrijke warmtebehandelingen worden in beknopte vorm behandeld. Doel van deze publicatie is voornamelijk basisinformatie te verschaffen over de warmtebehandelingen. Warmtebehandelingen hebben effecten op de metaalkundige aspecten, zoals de structuur en de daaraan gerelateerde mechanische eigenschappen. Daarom wordt in een aantal hoofdstukken aandacht besteed aan de opbouw (kristalstructuur) van de metalen, alsmede de invloed van het opwarmen naar en (snel) afkoelen vanaf een bepaalde warmtebehandelingstemperatuur. Daarnaast wordt ook beknopt ingegaan op de uitvoering van warmtebehandelingen. Het is echter geenszins de bedoeling met deze publicatie volledig te zijn.
DOCUMENT
Background/Aims: This study examines the feasibility of a preoperative exercise program to improve the physical fitness of a patient before gastrointestinal surgery. Methods: An outpatient exercise program was developed to increase preoperative aerobic capacity, peripheral muscle endurance and respiratory muscle function in patients with pancreatic, liver, intestinal, gastric or esophageal cancer. During a consult at the outpatient clinic, patients were invited to participate in the exercise program when their surgery was not scheduled within 2 weeks. Results: The 115 participants followed on average 5.7 (3.5) training sessions. Adherence to the exercise program was high: 82% of the planned training sessions were attended, and no adverse events occurred. Mixed model analyses showed a significant increase of maximal inspiratory muscle strength (84.1-104.7 cm H2O; p = 0.00) and inspiratory muscle endurance (35.0-39.5 cm H2O; p = 0.00). No significant changes were found in aerobic capacity and peripheral muscle strength. Conclusion: This exercise program in patients awaiting oncological surgery is feasible in terms of participation and adherence. Inspiratory muscle function improved significantly as a result of inspiratory muscle training. The exercise program however failed to result in improved aerobic capacity and peripheral muscle strength, probably due to the limited number of training sessions as a result of the restricted time interval between screening and surgery.
DOCUMENT
Background: Follow‑up of curatively treated primary breast cancer patients consists of surveillance and aftercare and is currently mostly the same for all patients. A more personalized approach, based on patients’ individual risk of recurrence and personal needs and preferences, may reduce patient burden and reduce (healthcare) costs. The NABOR study will examine the (cost‑)effectiveness of personalized surveillance (PSP) and personalized aftercare plans (PAP) on patient‑reported cancer worry, self‑rated and overall quality of life and (cost‑)effectiveness. Methods: A prospective multicenter multiple interrupted time series (MITs) design is being used. In this design, 10 participating hospitals will be observed for a period of eighteen months, while they ‑stepwise‑ will transit from care as usual to PSPs and PAPs. The PSP contains decisions on the surveillance trajectory based on individual risks and needs, assessed with the ‘Breast Cancer Surveillance Decision Aid’ including the INFLUENCE prediction tool. The PAP contains decisions on the aftercare trajectory based on individual needs and preferences and available care resources, which decision‑making is supported by a patient decision aid. Patients are non‑metastasized female primary breast cancer patients (N= 1040) who are curatively treated and start follow‑up care. Patient reported outcomes will be measured at five points in time during two years of follow‑up care (starting about one year after treatment and every six months thereafter). In addition, data on diagnostics and hospital visits from patients’ Electronical Health Records (EHR) will be gathered. Primary outcomes are patient‑reported cancer worry (Cancer Worry Scale) and over‑all quality of life (as assessed with EQ‑VAS score). Secondary outcomes include health care costs and resource use, health‑related quality of life (as measured with EQ5D‑5L/SF‑12/EORTC‑QLQ‑C30), risk perception, shared decision‑making, patient satisfaction, societal participation, and cost‑effectiveness. Next, the uptake and appreciation of personalized plans and patients’ experiences of their decision‑making process will be evaluated. Discussion: This study will contribute to insight in the (cost‑)effectiveness of personalized follow‑up care and contributes to development of uniform evidence‑based guidelines, stimulating sustainable implementation of personalized surveillance and aftercare plans. Trial registration: Study sponsor: ZonMw. Retrospectively registered at ClinicalTrials.gov (2023), ID: NCT05975437.
MULTIFILE
De maritieme industrie staat voor een grote duurzaamheidsopgave, waarbij oude methodes niet meer toereikend zijn. Nieuwe technieken (zoals grootschalige sensormetingen, dataverwerking, gegevensmodellering) kunnen ondersteuning bieden bij het ontwerpen van de schepen van de toekomst. Naast dit hoofdonderwerp wordt er ook aandacht geschonken aan een stabiliteitsgame, bovenwettelijke veiligheidsmaatregelen en de digital twin.
DOCUMENT
In deze lectorale rede wordt eerst geschetst hoe de maritieme industrie zich op een kruispunt van wegen bevindt. Met op de ene weg de enorme opgave van het ontwerpen, bouwen en exploiteren van een revolutionair nieuwe generatie van schone en veilige schepen. En op de andere de beschikbaarheid van steeds betere ontwerpgereedschappen, die gedreven wordt door krachtige ontwikkelingen op het gebied van (numerieke) wiskunde, IT, mathematisch modelleren, visualisatie en simulaties. Vervolgens wordt aangegeven op welke wijze het lectoraat meent hierbij van dienst te kunnen zijn, en welke concrete onderwijs- en onderzoeksinspanningen daartoe voorgenomen zijn.
DOCUMENT
Talloze studies tonen aan dat een fysiek actieve leefstijl bloeddruk, cholesterol en gewicht verlaagt, botten en spieren versterkt en het risico van hart- en vaatziekten, darmkanker en diabetes type II vermindert. Bewegen kan dus worden gezien als een medicijn wat voor iedereen toegankelijk is.
DOCUMENT
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.
DOCUMENT
