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Background. In most reports on ECMO treatment, advanced age is classified as a contraindication to VA ECMO. We attempted to investigate whether advanced age would be a main risk factor deciding VA ECMO application and performing VA ECMO support. We determined whether advanced age should be regarded as an absolute or relative contraindication to VA ECMO and could affect weaning and survival rates of VA ECMO patients. Methods. VA ECMO was performed on 135 adult patients with primary cardiogenic shock between January 2010 and December 2014. Successful weaning was defined as weaning from ECMO followed by survival for more than 48 hours. Results. Among the 135 patients, 35 survived and were discharged uneventfully, and the remaining 100 did not survive. There were significant differences in survival between age groups, and older age showed a lower survival rate with statistical significance (P = .01). By multivariate logistic regression analysis, age was not significantly associated with in-hospital mortality (P = .83) and was not significantly associated with VA ECMO weaning (P = .11). Conclusions. Advanced age is an undeniable risk factor for VA ECMO; however, patients of advanced age should not be excluded from the chance of recovery after VA ECMO treatment.

Occupant behaviour plays a significant role in shaping the dynamics of energy consumption in buildings, but the complex nature of occupant behaviour has hindered a deeper understanding of its influence. A meta-analysis was conducted on 65 published studies that used data-driven quantitative assessments to assess energy-related occupant behaviour using the Knowledge Discovery and Data Mining (KDD) framework. Hierarchical clustering was utilised to categorise different modelling techniques based on the intended outcomes of the model and the types of parameters used in various models. This study will assist researchers in selecting the most appropriate parameters and methods under various data constraints and research questions. The research revealed two distinct model categories being used to study occupant behaviour-driven energy consumption, namely (i) occupancy status models and (ii) energy-related behaviour models. Multiple studies have identified limitations on data collection and privacy concerns as constraints of modelling occupant behaviour in residential buildings. The “regression model” and its variants were found to be the preferred model types for research that models “energy-related behaviour”, and “classification models” were found to be preferable for modelling “occupancy” status. There were only limited instances of data-driven studies that modelled occupant behaviour in low-income households, and there is a need to generate region-specific models to accurately model energy-related behaviour.

IntroductionThe evaluation of the Victorian Healthy Homes Program (VHHP) will generate evidence about the efficacy and cost-effectiveness of home upgrades to improve thermal comfort, reduce energy use and produce health and economic benefits to vulnerable households in Victoria, Australia.Methods and analysisThe VHHP evaluation will use a staggered, parallel group clustered randomised controlled trial to test the home energy intervention in 1000 households. All households will receive the intervention either before (intervention group) or after (control group) winter (defined as 22 June to 21 September). The trial spans three winters with differing numbers of households in each cohort. The primary outcome is the mean difference in indoor average daily temperature between intervention and control households during the winter period. Secondary outcomes include household energy consumption and residential energy efficiency, self-reported respiratory symptoms, health-related quality of life, healthcare utilisation, absences from school/work and self-reported conditions within the home. Linear and logistic regression will be used to analyse the primary and secondary outcomes, controlling for clustering of households by area and the possible confounders of year and timing of intervention, to compare the treatment and control groups over the winter period. Economic evaluation will include a cost-effectiveness and cost-benefit analysis.Ethics and disseminationEthical approval was received from Victorian Department of Human Services Human Research Ethics Committee (reference number: 04/17), University of Technology Sydney Human Research Ethics Committee (reference number: ETH18-2273) and Australian Government Department of Veterans Affairs. Study results will be disseminated in a final report and peer-reviewed journals.Trial registration numberACTRN12618000160235.

Time series data collected in clinical trials can have varying degrees of missingness, adding challenges during statistical analyses. An additional layer of complexity is introduced for missing data in randomized controlled trials (RCT), where researchers must remain blinded between intervention and control groups. Such restriction severely limits the applicability of conventional imputation methods that would utilize other participants’ data for improved performance. This paper explores and compares various methods to impute high-resolution temperature logger data in RCT settings. In addition to the conventional non-parametric approaches, we propose a spline regression (SR) approach that captures the dynamics of indoor temperature by time of day that is unique to each participant. We investigate how the inclusion of external temperature and energy use can improve the model performance. Results show that SR imputation results in 16% smaller root mean squared error (RMSE) compared to conventional imputation methods, with the gap widening to 22% when more than half of data is missing. The SR method is particularly useful in cases where missingness occurs simultaneously for multiple participants, such as concurrent battery failures. We demonstrate how proper modelling of periodic dynamics can lead to significantly improved imputation performance, even with limited data.

Although numerous complications of the Seldinger technique have been reported in the literature, only a few complications are related to guidewires. We here report a case of a patient with a guidewire lost and retained in the aorta during vertebral artery stenting. Unfortunately, the guidewire in the aorta was not detected for 5 years, and it penetrated through the aorta into the left thorax, leading to recurrent left pneumothorax. No physician identified the wandering guidewire in the left thorax, and the recurrent left pneumothorax was only managed with closed thoracostomy drainage several times. After 4 months, the patient presented to our hospital with repeated severe chest pain, and newly developed right pneumothorax was diagnosed on chest X-rays. We meticulously evaluated the radiological findings of the other hospitals to identify the cause of the recurrent pneumothorax and discovered that the lost and wandering guidewire had crossed over from the left to the right thorax through the anterior mediastinum. The guidewire was identified as the cause of the recurrent bilateral pneumothorax, and the patient was successfully treated with video-assisted thoracoscopic surgery without any events.