Chemotherapy and Tyrosine Kinase Inhibitors in the last month of life in patients with metastatic lung cancer: A patient file study in the Netherlands

Objective: Chemotherapy in the last month of life for patients with metastatic lung cancer is often considered as aggressive end-of-life care. Targeted therapy with Tyrosine Kinase Inhibitors (TKIs) is a relatively new treatment of which not much is known yet about use in the last month of life. We examined what percentage of patients received chemotherapy or TKIs in the last month of life in the Netherlands. Methods: Patient files were drawn from 10 hospitals across the Netherlands. Patients had to meet the following eligibility criteria: metastatic lung cancer; died between June 1, 2013 and July 31, 2015. Results: From the included 1,322 patients, 39% received no treatment for metastatic lung cancer, 52% received chemotherapy and 9% received TKIs. A total of 232 patients (18%) received treatment in the last month of life (11% chemotherapy, 7% TKIs). From the patients who received chemotherapy, 145 (21%) received this in the last month of life and 79 (11%) started this treatment in the last month of life. TKIs were given and started more often in the last month of life: from the patients who received TKIs, 87 (72%) received this treatment in the last month of life and 15 (12%) started

Interpretable machine learning models for classifying low back pain status using functional physiological variables.

PURPOSE:To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS:Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS:Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text]  = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] =  0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION:The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material

Dutch Oncology COVID-19 consortium:Outcome of COVID-19 in patients with cancer in a nationwide cohort study

Aim of the study: Patients with cancer might have an increased risk for severe outcome of coronavirus disease 2019 (COVID-19). To identify risk factors associated with a worse outcome of COVID-19, a nationwide registry was developed for patients with cancer and COVID-19. Methods: This observational cohort study has been designed as a quality of care registry and is executed by the Dutch Oncology COVID-19 Consortium (DOCC), a nationwide collaboration of oncology physicians in the Netherlands. A questionnaire has been developed to collect pseudonymised patient data on patients' characteristics, cancer diagnosis and treatment. All patients with COVID-19 and a cancer diagnosis or treatment in the past 5 years are eligible. Results: Between March 27th and May 4th, 442 patients were registered. For this first analysis, 351 patients were included of whom 114 patients died. In multivariable analyses, age ≥65 years (p < 0.001), male gender (p = 0.035), prior or other malignancy (p = 0.045) and active diagnosis of haematological malignancy (p = 0.046) or lung cancer (p = 0.003) were independent risk factors for a fatal outcome of COVID-19. In a subgroup analysis of patients with active malignancy, the risk for a fatal outcome was mainly determined by tumour type (haematological malignancy or lung cancer) and age (≥65 years). Conclusion: The findings in this registry indicate that patients with a haematological malignancy or lung cancer have an increased risk of a worse outcome of COVID-19. During the ongoing COVID-19 pandemic, these vulnerable patients should avoid exposure to severe acute respiratory syndrome coronavirus 2, whereas treatment adjustments and prioritising vaccination, when available, should also be considered

Dynamische werkplekken: wat vinden gebruikers ervan?

Aan zittend werk kleven gezondheidsrisico’s. Dynamische werkplekken, werkplekken waaraan (computer)werkzaamheden gecombineerd worden met lichaamsbeweging, kunnen deze mogelijk verminderen. Wij evalueerden drie dynamische werkplekken: een loopband, een fietsergometer en een zittende elliptische trainer (ZET) bij negentien kantoormedewerkers met zittend werk. www.humanfactors.n

Musculoskeletal discomfort during VDU tasks; input for a smart office chair

TNO and BMA Ergonomics are developing a so-called smart office chair. This chair is supposed to provide feedback on postures and movements during seated office work. The feedback should enable the user (i.e. the worker doing VDU tasks) to perform his or her work with less discomfort and in a more productive way. One part of this project is a study about the development of musculoskeletal discomfort during VDU work, sitting in various postures. Nineteen subjects did 2 types of VDU work in a laboratory setting: call-centre and data-entry. They were instructed to work in a certain sitting posture for 30 minutes, without pauses. After that, they could relax for 30 minutes. Four sitting postures were imposed; the 5th one was each individual’s freely chosen working posture: the reference posture. Every 3 minutes, local perceived discomfort (LPD) of 5 body regions was determined, by letting subjects rate their LPD on a 10-point-scale. From that we calculated the relative LPD-dose, setting the dose of the ergonomic optimal posture to 100%. LPD-doses were not significantly different between the 2 VDU-tasks. Different sitting postures did lead to significantly different LPD-doses; the lowest in the ergonomic optimal posture and the highest one in the ‘vulture’ posture. From the individual development of LPD during 30 minutes of VDU work and a prior evaluation of the health risks of prolonged LPD, we can deduce the period of time a subject is allowed to work in a specific posture. These data can serve as input for a demonstration model of the smart office chair