Objective. After laryngectomy, the breathing resistance of heat and moisture exchangers may limit exercise capacity. Breathing gas analysis during cardiopulmonary exercise testing is not possible using regular masks. This study tested the feasibility of cardiopulmonary exercise testing with a heat and moisture exchanger in situ, using an in-house designed connector. Additionally, we explored the effect of different heat and moisture exchanger resistances on exercise capacity in this group. Methods. Ten participants underwent two cardiopulmonary exercise tests using their daily life heat and moisture exchanger (0.3 hPa or 0.6 hPa) and one specifically developed for activity (0.15 hPa). Heat and moisture exchanger order was randomised and blinded.Results. All participants completed both tests. No (serious) adverse events occurred. Only four subjects reached a respiratory exchange ratio of more than 1.1 in at least one test. Maximum exercise levels using heat and moisture exchangers with different resistances did not differ. Conclusion. Cardiopulmonary exercise testing in laryngectomees with a heat and moisture exchanger is feasible; however, the protocol does not seem appropriate to reach this group's maximal exercise capacity. Lowering heat and moisture exchanger resistance does not increase exercise capacity in this sample.
ObjectiveTo evaluate the cost-effectiveness of the Cardiac Care Bridge (CCB) nurse-led transitional care program in older (≥70 years) cardiac patients compared to usual care.MethodsThe intervention group (n = 153) received the CCB program consisting of case management, disease management and home-based cardiac rehabilitation in the transition from hospital to home on top of usual care and was compared with the usual care group (n = 153). Outcomes included a composite measure of first all-cause unplanned hospital readmission or mortality, Quality Adjusted Life Years (QALYs) and societal costs within six months follow-up. Missing data were imputed using multiple imputation. Statistical uncertainty surrounding Incremental Cost-Effectiveness Ratios (ICERs) was estimated by using bootstrapped seemingly unrelated regression.ResultsNo significant between group differences in the composite outcome of readmission or mortality nor in societal costs were observed. QALYs were statistically significantly lower in the intervention group, mean difference -0.03 (95% CI: -0.07; -0.02). Cost-effectiveness acceptability curves showed that the maximum probability of the intervention being cost-effective was 0.31 at a Willingness To Pay (WTP) of €0,00 and 0.14 at a WTP of €50,000 per composite outcome prevented and 0.32 and 0.21, respectively per QALY gained.ConclusionThe CCB program was on average more expensive and less effective compared to usual care, indicating that the CCB program is dominated by usual care. Therefore, the CCB program cannot be considered cost-effective compared to usual care.
MULTIFILE
Deployment and management of environmental infrastructures, such as charging infrastructure for Electric Vehicles (EV), is a challenging task. For policy makers, it is particularly difficult to estimate the capacity of current deployed public charging infrastructure for a given EV user population. While data analysis of charging data has shown added value for monitoring EV systems, it is not valid to linearly extrapolate charging infrastructure performance when increasing population size.We developed a data-driven agent-based model that can explore future scenarios to identify non-trivial dynamics that may be caused by EV user interaction, such as competition or collaboration, and that may affect performance metrics. We validated the model by comparing EV user activity patterns in time and space.We performed stress tests on the 4 largest cities the Netherlands to explore the capacity of the existing charging network. Our results demonstrate that (i) a non-linear relation exists between system utilization and inconvenience even at the base case; (ii) from 2.5x current population, the occupancy of non-habitual charging increases at the expense of habitual users, leading to an expected decline of occupancy for habitual users; and (iii) from a ratio of 0.6 non-habitual users to habitual users competition effects intensify. For the infrastructure to which the stress test is applied, a ratio of approximately 0.6 may indicate a maximum allowed ratio that balances performance with inconvenience. For policy makers, this implies that when they see diminishing marginal performance of KPIs in their monitoring reports, they should be aware of potential exponential increase of inconvenience for EV users.
