Withholding and withdrawing life-sustaining treatment : criminal responsibility for established medical practice?

The law recognises the right of a competent adult to refuse medical treatment even if this will lead to death. Guardianship and other legislation also facilitates the making of decisions to withhold or withdraw life-sustaining treatment in certain circumstances. Despite this apparent endorsement that such decisions can be lawful, doubts have been raised in Queensland about whether decisions to withhold or withdraw life-sustaining treatment would contravene the criminal law, and particularly the duty imposed by the Criminal Code (Qld) to provide the “necessaries of life”. This article considers this tension in the law and examines various arguments that might allow for such decisions to be made lawfully. It ultimately concludes, however, that criminal responsibility may still arise and so reform is needed.

Identification of alcohol involvement in injury-related hospitalisations using routine data compared to medical record review

Objective: To quantify the extent to which alcohol related injuries are adequately identified in hospitalisation data using ICD-10-AM codes indicative of alcohol involvement. Method: A random sample of 4373 injury-related hospital separations from 1 July 2002 to 30 June 2004 were obtained from a stratified random sample of 50 hospitals across 4 states in Australia. From this sample, cases were identified as involving alcohol if they contained an ICD-10-AM diagnosis or external cause code referring to alcohol, or if the text description extracted from the medical records mentioned alcohol involvement. Results: Overall, identification of alcohol involvement using ICD codes detected 38% of the alcohol-related sample, whilst almost 94% of alcohol-related cases were identified through a search of the text extracted from the medical records. The resultant estimate of alcohol involvement in injury-related hospitalisations in this sample was 10%. Emergency department records were the most likely to identify whether the injury was alcohol-related with almost three-quarters of alcohol-related cases mentioning alcohol in the text abstracted from these records. Conclusions and Implications: The current best estimates of the frequency of hospital admissions where alcohol is involved prior to the injury underestimate the burden by around 62%. This is a substantial underestimate that has major implications for public policy, and highlights the need for further work on improving the quality and completeness of routine administrative data sources for identification of alcohol-related injuries.

Analysing Seasonal Health Data

Seasonal patterns have been found in a remarkable range of health conditions, including birth defects, respiratory infections and cardiovascular disease. Accurately estimating the size and timing of seasonal peaks in disease incidence is an aid to understanding the causes and possibly to developing interventions. With global warming increasing the intensity of seasonal weather patterns around the world, a review of the methods for estimating seasonal effects on health is timely. This is the first book on statistical methods for seasonal data written for a health audience. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarising and modelling these data. It has a practical focus and uses interesting examples to motivate and illustrate the methods. The statistical procedures and example data sets are available in an R package called ‘season’. Adrian Barnett is a senior research fellow at Queensland University of Technology, Australia. Annette Dobson is a Professor of Biostatistics at The University of Queensland, Australia. Both are experienced medical statisticians with a commitment to statistical education and have previously collaborated in research in the methodological developments and applications of biostatistics, especially to time series data. Among other projects, they worked together on revising the well-known textbook "An Introduction to Generalized Linear Models," third edition, Chapman Hall/CRC, 2008. In their new book they share their knowledge of statistical methods for examining seasonal patterns in health.

Methods and preliminary results for a data linkage project to determine long-term survival after intensive care unit admission

Aims: To describe a local data linkage project to match hospital data with the Australian Institute of Health and Welfare (AIHW) National Death Index (NDI) to assess longterm outcomes of intensive care unit patients. Methods: Data were obtained from hospital intensive care and cardiac surgery databases on all patients aged 18 years and over admitted to either of two intensive care units at a tertiary-referral hospital between 1 January 1994 and 31 December 2005. Date of death was obtained from the AIHW NDI by probabilistic software matching, in addition to manual checking through hospital databases and other sources. Survival was calculated from time of ICU admission, with a censoring date of 14 February 2007. Data for patients with multiple hospital admissions requiring intensive care were analysed only from the first admission. Summary and descriptive statistics were used for preliminary data analysis. Kaplan-Meier survival analysis was used to analyse factors determining long-term survival. Results: During the study period, 21 415 unique patients had 22 552 hospital admissions that included an ICU admission; 19 058 surgical procedures were performed with a total of 20 092 ICU admissions. There were 4936 deaths. Median follow-up was 6.2 years, totalling 134 203 patient years. The casemix was predominantly cardiac surgery (80%), followed by cardiac medical (6%), and other medical (4%). The unadjusted survival at 1, 5 and 10 years was 97%, 84% and 70%, respectively. The 1-year survival ranged from 97% for cardiac surgery to 36% for cardiac arrest. An APACHE II score was available for 16 877 patients. In those discharged alive from hospital, the 1, 5 and 10-year survival varied with discharge location. Conclusions: ICU-based linkage projects are feasible to determine long-term outcomes of ICU patients

Lifting the lifegiving dollar : case samples of creative practice in health and medical research philanthropy

This study aims to stimulate thought, debate and action for change on this question of more vigorous philanthropic funding of Australian health and medical research (HMR). It sharpens the argument with some facts and ideas about HMR funding from overseas sources. It also reports informed opinions from those working, giving and innovating in this area. It pinpoints the range of attitudes to HMR giving, both positive and negative. The study includes some aspects of Government funding as part of the equation, viewing Government as major HMR givers, with particular ability to partner, leverage and create incentives. Stimulating new philanthropy takes active outreach. The opportunity to build more dialogue between the HMR industry and the wider community is timely given the ‘licence to practice’ issues and questioned trust that applies currently somewhat both to science and to the charitable sector. This interest in improving HMR philanthropy also coincides with the launch last year by the Federal Government of Nonprofit Australia Limited (NAL), a group currently assessing infrastructure improvements to the charitable sector. History suggests no one will create this change if Research Australia does not. However, interest in change exists in various quarters. For Research Australia to successfully change the culture of Australian HMR giving, the process will drive the outcomes. Obviously stakeholder buy-in and partners will be needed and the ultimate blueprint for greater philanthropic HMR funding here will not be this document. Instead it will be the one that wears the handprint and ‘mindprint’ of the many architects and implementers interested in promoting HMR philanthropy, from philanthropists to nonprofit peaks to government policy arms. As the African proverb says, ‘If you want to go fast, go alone; but if you want to go far, go with others’.

Withholding and withdrawing life-sustaining medical treatment

• At common law, a competent adult can refuse life-sustaining medical treatment, either contemporaneously or through an advance directive which will operate at a later time when the adult’s capacity is lost. • Legislation in most Australian jurisdictions also provides for a competent adult to complete an advance directive that refuses life-sustaining medical treatment. • At common law, a court exercising its parens patriae jurisdiction can consent to, or authorise, the withdrawal or withholding of life-sustaining medical treatment from an adult or child who lacks capacity if that is in the best interests of the person. A court may also declare that the withholding or withdrawal of treatment is lawful. • Guardianship legislation in most jurisdictions allows a substitute decision-maker, in an appropriate case, to refuse life-sustaining medical treatment for an adult who lacks capacity. • In terms of children, a parent may refuse life-sustaining medical treatment for his or her child if it is in the child’s best interests. • While a refusal of life-sustaining medical treatment by a competent child may be valid, this decision can be overturned by a court. • At common law and generally under guardianship statutes, demand for futile treatment need not be complied with by doctors.

Securing medical research data with a rights management system

We propose a digital rights management approach for sharing electronic health records for research purposes and argue advantages of the approach. We give an outline of our implementation, discuss challenges that we faced and future directions.

Creation of a validated 3D finite element model of an ovine tibia

Introduction Ovine models are widely used in orthopaedic research. To better understand the impact of orthopaedic procedures computer simulations are necessary. 3D finite element (FE) models of bones allow implant designs to be investigated mechanically, thereby reducing mechanical testing. Hypothesis We present the development and validation of an ovine tibia FE model for use in the analysis of tibia fracture fixation plates. Material & Methods Mechanical testing of the tibia consisted of an offset 3-pt bend test with three repetitions of loading to 350N and return to 50N. Tri-axial stacked strain gauges were applied to the anterior and posterior surfaces of the bone and two rigid bodies – consisting of eight infrared active markers, were attached to the ends of the tibia. Positional measurements were taken with a FARO arm 3D digitiser. The FE model was constructed with both geometry and material properties derived from CT images of the bone. The elasticity-density relationship used for material property determination was validated separately using mechanical testing. This model was then transformed to the same coordinate system as the in vitro mechanical test and loads applied. Results Comparison between the mechanical testing and the FE model showed good correlation in surface strains (difference: anterior 2.3%, posterior 3.2%). Discussion & Conclusion This method of model creation provides a simple method for generating subject specific FE models from CT scans. The use of the CT data set for both the geometry and the material properties ensures a more accurate representation of the specific bone. This is reflected in the similarity of the surface strain results.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Weaving yarns : the lived experience of Indigenous Australians with adult-onset disability in Brisbane

Indigenous Australians have lower levels of health than mainstream Australians and (as far as statistics are able to indicate) higher levels of disability, yet there is little information on Indigenous social and cultural constructions of disability or the Indigenous experience of disability. This research seeks to address these gaps by using an ethnographic approach, couched within a critical medical anthropology (CMA) framework and using the “three bodies” approach, to study the lived experience of urban Indigenous people with an adult-onset disability. The research approach takes account of the debate about the legitimacy of research into Indigenous Australians, Foucault‟s governmentality, and the arguments for different models of disability. The possibility of a cultural model of disability is raised. After a series of initial interviews with contacts who were primarily service providers, more detailed ethnographic research was conducted with three Indigenous women in their homes and with four groups of Indigenous women and men at an Indigenous respite centre. The research involved multiple visits over a period extending more than two years, and the establishment of relationships with all participants. An iterative inductive approach utilising constant comparison (i.e. a form of grounded theory) was adopted, enabling the generation and testing of working hypotheses. The findings point to the lack of an Indigenous construct of disability, related to the holistic construction of health among Indigenous Australians. Shame emerges as a factor which affects the way that Indigenous Australians respond to disability, and which operates in apparent contradiction to expectations of community support. Aspects of shame relate to governmentality, suggesting that self-disciplinary mechanisms have been taken up and support the more obvious exertion of government power. A key finding is the strength of Indigenous identity above and beyond other forms of identification, e.g. as a person with a disability, expressed in forms of resistance by individuals and service providers to the categories and procedures of the mainstream. The implications of a holistic construction of health are discussed in relation to the use of CMA, the interpretation of the “three bodies”, governmentality and resistance. The explanatory value of the concept of sympatricity is discussed, as is the potential value of a cultural model of disability which takes into account the cultural politics of a defiant Indigenous identity.