Increased sedation requirements in patients receiving extracorporeal membrane oxygenation for respiratory and cardiorespiratory failure

Critically ill patients receiving extracorporeal membrane oxygenation (ECMO) are often noted to have increased sedation requirements. However, data related to sedation in this complex group of patients is limited. The aim of our study was to characterise the sedation requirements in adult patients receiving ECMO for cardiorespiratory failure. A retrospective chart review was performed to collect sedation data for 30 consecutive patients who received venovenous or venoarterial ECMO between April 2009 and March 2011. To test for a difference in doses over time we used a regression model. The dose of midazolam received on ECMO support increased by an average of 18 mg per day (95% confidence interval 8, 29 mg, P=0.001), while the dose of morphine increased by 29 mg per day (95% confidence interval 4, 53 mg, P=0.021) The venovenous group received a daily midazolam dose that was 157 mg higher than the venoarterial group (95% confidence interval 53, 261 mg, P=0.005). We did not observe any significant increase in fentanyl doses over time (95% confidence interval 1269, 4337 µg, P=0.94). There is a significant increase in dose requirement for morphine and midazolam during ECMO. Patients on venovenous ECMO received higher sedative doses as compared to patients on venoarterial ECMO. Future research should focus on mechanisms behind these changes and also identify drugs that are most suitable for sedation during ECMO.

Dying in hospital: medical failure or natural outcomes?

The purpose of this study was to describe patterns of medical and nursing practice in the care of patients dying of oncological and hematological malignancies in the acute care setting in Australia. A tool validated in a similar American study was used to study the medical records of 100 consecutive patients who died of oncological or hematological malignancies before August 1999 at The Canberra Hospital in the Australian Capital Territory. The three major indicators of patterns of end-of-life care were documentation of Do Not Resuscitate (DNR) orders, evidence that the patient was considered dying, and the presence of a palliative care intention. Findings were that 88 patients were documented DNR, 63 patients' records suggested that the patient was dying, and 74 patients had evidence of a palliative care plan. Forty-six patients were documented DNR 2 days or less prior to death and, of these, 12 were documented the day of death. Similar patterns emerged for days between considered dying and death, and between palliative care goals and death. Sixty patients had active treatment in progress at the time of death. The late implementation of end-of-life management plans and the lack of consistency within these plans suggested that patients were subjected to medical interventions and investigations up to the time of death. Implications for palliative care teams include the need to educate health care staff and to plan and implement policy regarding the management of dying patients in the acute care setting. Although the health care system in Australia has cultural differences when compared to the American context, this research suggests that the treatment imperative to prolong life is similar to that found in American-based studies.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

Machine prognostics based on health state estimation using SVM

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation, and can also improve productivity and enhance system safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and an assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of machines based on health state probability estimation and involving historical knowledge embedded in the closed loop diagnostics and prognostics systems. The technique uses a Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation, which can affect the accuracy of prediction. To validate the feasibility of the proposed model, real life historical data from bearings of High Pressure Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life. The results obtained were very encouraging and showed that the proposed prognostic system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

The effect of exercise training on sleep quality in heart failure

Background: Heart failure is a serious condition estimated to affect 1.5-2.0% of the Australian population with a point prevalence of approximately 1% in people aged 50-59 years, 10% in people aged 65 years or more and over 50% in people aged 85 years or over (National Heart Foundation of Australian and the Cardiac Society of Australia and New Zealand, 2006). Sleep disturbances are a common complaint of persons with heart failure. Disturbances of sleep can worsen heart failure symptoms, impair independence, reduce quality of life and lead to increased health care utilisation in patients with heart failure. Previous studies have identified exercise as a possible treatment for poor sleep in patients without cardiac disease however there is limited evidence of the effect of this form of treatment in heart failure. Aim: The primary objective of this study was to examine the effect of a supervised, hospital-based exercise training programme on subjective sleep quality in heart failure patients. Secondary objectives were to examine the association between changes in sleep quality and changes in depression, exercise performance and body mass index. Methods: The sample for the study was recruited from metropolitan and regional heart failure services across Brisbane, Queensland. Patients with a recent heart failure related hospital admission who met study inclusion criteria were recruited. Participants were screened by specialist heart failure exercise staff at each site to ensure exercise safety prior to study enrolment. Demographic data, medical history, medications, Pittsburgh Sleep Quality Index score, Geriatric Depression Score, exercise performance (six minute walk test), weight and height were collected at Baseline. Pittsburgh Sleep Quality Index score, Geriatric Depression Score, exercise performance and weight were repeated at 3 months. One hundred and six patients admitted to hospital with heart failure were randomly allocated to a 3-month disease-based management programme of education and self-management support including standard exercise advice (Control) or to the same disease management programme as the Control group with the addition of a tailored physical activity program (Intervention). The intervention consisted of 1 hour of aerobic and resistance exercise twice a week. Programs were designed and supervised by an exercise specialist. The main outcome measure was achievement of a clinically significant change (.3 points) in global Pittsburgh Sleep Quality score. Results: Intervention group participants reported significantly greater clinical improvement in global sleep quality than Control (p=0.016). These patients also exhibited significant improvements in component sleep disturbance (p=0.004), component sleep quality (p=0.015) and global sleep quality (p=0.032) after 3 months of supervised exercise intervention. Improvements in sleep quality correlated with improvements in depression (p<0.001) and six minute walk distance (p=0.04). When study results were examined categorically, with subjects classified as either "poor" or "good" sleepers, subjects in the Control group were significantly more likely to report "poor" sleep at 3 months (p=0.039) while Intervention participants were likely to report "good" sleep at this time (p=0.08). Conclusion: Three months of supervised, hospital based, aerobic and resistance exercise training improved subjective sleep quality in patients with heart failure. This is the first randomised controlled trial to examine the role of aerobic and resistance exercise training in the improvement of sleep quality for patients with this disease. While this study establishes exercise as a therapy for poor sleep quality, further research is needed to investigate the effect of exercise training on objective parameters of sleep in this population.

Not all systematic reviews are systematic : a meta-review of the quality of systematic reviews for non-invasive remote monitoring in heart failure

Remote monitoring for heart failure has been evaluated in numerous systematic reviews. The aim of this meta-review was to appraise their quality and synthesise results. We electronically searched online databases, performed a forward citation search and hand-searched bibliographies. Systematic reviews of remote monitoring interventions that were used for surveillance of heart failure patients were included. Seven (41%) systematic reviews pooled results for meta-analysis. Eight (47%) considered all non-invasive remote monitoring strategies. Five (29%) focused on telemonitoring. Four (24%) included both non-invasive and invasive technologies. According to AMSTAR criteria, ten (58%) systematic reviews were of poor methodological quality. In high quality reviews, the relative risk of mortality in patients who received remote monitoring ranged from 0.53 (95% CI=0.29-0.96) to 0.88 (95% CI=0.76-1.01). High quality reviews also reported that remote monitoring reduced the relative risk of all-cause (0.52; 95% CI=0.28-0.96 to 0.96; 95% CI=0.90–1.03) and heart failure-related hospitalizations (0.72; 95% CI=0.64–0.81 to RR 0.79; 95% CI=0.67-0.94) and, as a consequence, healthcare costs. As the high quality reviews reported that remote monitoring reduced hospitalizations, mortality and healthcare costs, research efforts should now be directed towards optimising these interventions in preparation for more widespread implementation.

Not all systematic reviews are systematic : a meta-review of the quality of current systematic reviews and meta-analyses for remote monitoring in heart failure

Background/aims: Remote monitoring for heart failure has not only been evaluated in a large number of randomised controlled trials, but also in many systematic reviews and meta-analyses. The aim of this meta-review was to identify, appraise and synthesise existing systematic reviews that have evaluated the effects of remote monitoring in heart failure. Methods: Using a Cochrane methodology, we electronically searched all relevant online databases and search engines, performed a forward citation search as well as hand-searched bibliographies. Only fully published systematic reviews of invasive and/or non-invasive remote monitoring interventions were included. Two reviewers independently extracted data. Results: Sixty-five publications from 3333 citations were identified. Seventeen fulfilled the inclusion and exclusion criteria. Quality varied with A Measurement Tool to Assess Systematic Reviews (AMSTAR scores) ranging from 2 to 11 (mean 5.88). Seven reviews (41%) pooled results from individual studies for meta-analysis. Eight (47%) considered all non-invasive remote monitoring strategies. Four (24%) focused specifically on telemonitoring. Four (24%) included studies investigating both non-invasive and invasive technologies. Population characteristics of the included studies were not reported consistently. Mortality and hospitalisations were the most frequently reported outcomes 12 (70%). Only five reviews (29%) reported healthcare costs and compliance. A high degree of heterogeneity was reported in many of the meta-analyses. Conclusions: These results should be considered in context of two negative RCTs of remote monitoring for heart failure that have been published since the meta-analyses (TIM-HF and Tele-HF). However, high quality reviews demonstrated improved mortality, quality of life, reduction in hospitalisations and healthcare costs.

The benefits of thermal clothing during winter in patients with heart failure : a pilot randomised controlled trial

Background Many Australian cities experience large winter increases in deaths and hospitalisations. Flu outbreaks are only part of the problem and inadequate protection from cold weather is a key independent risk factor. Better home insulation has been shown to improve health during winter, but no study has examined whether better personal insulation improves health. Data and Methods We ran a randomised controlled trial of thermal clothing versus usual care. Subjects with heart failure (a group vulnerable to cold) were recruited from a public hospital in Brisbane in winter and followed-up at the end of winter. Those randomised to the intervention received two thermal hats and tops and a digital thermometer. The primary outcome was the number of days in hospital, with secondary outcomes of General Practitioner (GP) visits and self-rated health. Results The mean number of days in hospital per 100 winter days was 2.5 in the intervention group and 1.8 in the usual care group, with a mean difference of 0.7 (95% CI: –1.5, 5.4). The intervention group had 0.2 fewer GP visits on average (95% CI: –0.8, 0.3), and a higher self-rated health, mean improvement –0.3 (95% CI: –0.9, 0.3). The thermal tops were generally well used, but even in cold temperatures the hats were only worn by 30% of subjects. Conclusions Thermal clothes are a cheap and simple intervention, but further work needs to be done on increasing compliance and confirming the health and economic benefits of providing thermals to at-risk groups.

The effectiveness of health education using the teach-back method on adherence and self-management in chronic disease: A systematic review protocol

Review question/objective What is the effect of using the teach-back method for health education to improve adherence to treatment regimen and self-management in chronic disease? Inclusion criteria Types of participants This review will consider all studies that include adult patients (aged 18 years and over) in any healthcare setting, either as inpatients (eg acute care, medical and surgical wards) or those who attend primary health care, family medical practice, general medical practice, clinics, outpatient departments, rehabilitation or community settings. Participants need to have been diagnosed as having one or more chronic diseases including heart failure, diabetes, cardiovascular disease, cancer, respiratory disease, asthma, chronic obstructive pulmonary disease, chronic kidney disease, arthritis, epilepsy or a mental health condition. Studies that include seriously ill patients, and/or those who have impairments in verbal communication and cognitive function will be excluded. Types of intervention This review will consider studies that investigate the use of the teach-back method alone or in combination with other supporting education, either in routine or research intervention education programs; regardless of how long the programs were and whether or not a follow-up was conducted. The intervention could be delivered by any healthcare professional. The comparator will be any health education for chronic disease that does not include the teach-back method. Types of outcomes Primary outcomes of interest are disease-specific knowledge, adherence, and self-management knowledge, behavior and skills measured using patient report, nursing observation or validated measurement scales. Secondary outcomes include knowledge retention, self-efficacy, hospital readmission, hospitalization, and quality of life, also measured using patient report, nursing observation, hospital records or validated measurement scales.

Privacy oriented access control for electronic health records

Information privacy is a critical success/failure factor in information technology supported healthcare (eHealth). eHealth systems utilise electronic health records (EHR) as the main source of information, thus, implementing appropriate privacy preserving methods for EHRs is vital for the proliferation of eHealth. Whilst information privacy may be a fundamental requirement for eHealth consumers, healthcare professionals demand non-restricted access to patient information for improved healthcare delivery, thus, creating an environment where stakeholder requirements are contradictory. Therefore, there is a need to achieve an appropriate balance of requirements in order to build successful eHealth systems. Towards achieving this balance, a new genre of eHealth systems called Accountable-eHealth (AeH) systems has been proposed. In this paper, an access control model for EHRs is presented that can be utilised by AeH systems to create information usage policies that fulfil both stakeholders’ requirements. These policies are used to accomplish the aforementioned balance of requirements creating a satisfactory eHealth environment for all stakeholders. The access control model is validated using a Web based prototype as a proof of concept.

A cohort study of Chlamydia trachomatis treatment failure in women : a study protocol

Background Chlamydia trachomatis is the most commonly diagnosed bacterial sexually transmitted infection in the developed world and diagnosis rates have increased dramatically over the last decade. Repeat infections of chlamydia are very common and may represent re-infection from an untreated partner or treatment failure. The aim of this cohort study is to estimate the proportion of women infected with chlamydia who experience treatment failure after treatment with 1 gram azithromycin. Methods/design This cohort study will follow women diagnosed with chlamydia for up to 56 days post treatment. Women will provide weekly genital specimens for further assay. The primary outcome is the proportion of women who are classified as having treatment failure 28, 42 or 56 days after recruitment. Comprehensive sexual behavior data collection and the detection of Y chromosome DNA and high discriminatory chlamydial genotyping will be used to differentiate between chlamydia re-infection and treatment failure. Azithromycin levels in high-vaginal specimens will be measured using a validated liquid chromatography – tandem mass spectrometry method to assess whether poor azithromycin absorption could be a cause of treatment failure. Chlamydia culture and minimal inhibitory concentrations will be performed to further characterize the chlamydia infections. Discussion Distinguishing between treatment failure and re-infection is important in order to refine treatment recommendations and focus infection control mechanisms. If a large proportion of repeat chlamydia infections are due to antibiotic treatment failure, then international recommendations on chlamydia treatment may need to be re-evaluated. If most are re-infections, then strategies to expedite partner treatment are necessary.