Measurement of breath acetone in patients referred for an oral glucose tolerance test.

Breath acetone concentrations were measured in 141 subjects (aged 19-91 years, mean = 59.11 years, standard deviation = 12.99 years), male and female, undergoing an oral glucose tolerance test (OGTT), having been referred to clinic on suspicion of type 2 diabetes. Breath samples were measured using an ion-molecule-reaction mass spectrometer, at the commencement of the OGTT, and after 1 and 2 h. Subjects were asked to observe the normal routine before and during the OGTT, which includes an overnight fast and ingestion of 75 g glucose at the beginning of the routine. Several groups of diagnosis were identified: type 2 diabetes mellitus positive (T2DM), n = 22; impaired glucose intolerance (IGT), n = 33; impaired fasting glucose, n = 14; and reactive hypoglycaemia, n = 5. The subjects with no diagnosis (i.e. normoglycaemia) were used as a control group, n = 67. Distributions of breath acetone are presented for the different groups. There was no evidence of a direct relationship between blood glucose (BG) and acetone measurements at any time during the study (0 h: p = 0.4482; 1 h: p = 0.6854; and 2 h: p = 0.1858). Nor were there significant differences between the measurements of breath acetone for the control group and the T2DM group (0 h: p = 0.1759; 1 h: p = 0.4521; and 2 h: p = 0.7343). However, the ratio of breath acetone at 1 h to the initial breath acetone was found to be significantly different for the T2DM group compared to both the control and IGT groups (p = 0.0189 and 0.011, respectively). The T2DM group was also found to be different in terms of ratio of breath acetone after 1 h to that at 2 h during the OGTT. And was distinctive in that it showed a significant dependence upon the level of BG at 2 h (p = 0.0146). We conclude that single measurements of the concentrations of breath acetone cannot be used as a potential screening diagnostic for T2DM diabetes in this cohort, but monitoring the evolution of breath acetone could open a non-invasive window to aid in the diagnosis of metabolic conditions.

Cognitive decline in the elderly after surgery and anaesthesia: results from the Oxford Project to Investigate Memory and Ageing (OPTIMA) cohort.

Concerns have been raised about the effects on cognition of anaesthesia for surgery, especially in elderly people. We recorded cognitive decline in a cohort of 394 people (198 women) with median (IQR) age at recruitment of 72.6 (66.6-77.8) years, of whom 109 had moderate or major surgery during a median (IQR) follow-up of 4.1 (2.0-7.6) years. Cognitive decline was more rapid in people who on recruitment were: older, p = 0.0003; male, p = 0.027; had worse cognition, p < 0.0001; or carried the ε4 allele of apoliprotein E (APOEε4), p = 0.008; and after an operation if cognitive impairment was already diagnosed, p = 0.0001. Cognitive decline appears to accelerate after surgery in elderly patients diagnosed with cognitive impairment, but not other elderly patients.

A chemometric study on human breath mass spectra for biomarker identification in cystic fibrosis.

Alveolar breath samples from a small case-control study population have been collected and measured via ion-molecule reaction mass spectrometry, and a constructive statistical approach to the identification of volatile biomarkers has been formulated by applying multivariate statistical methods on the mass spectra. The nature of the data is such that the number of variables largely exceeds the observations, representing a typical experimental scenario when breath analysis is conducted using mass spectrometry. Principal components analysis has been performed on the high dimensional dataset of molecular abundances, providing evidence of case separation and reducing the number of functional discriminators by almost 90%. Afterwards, a deductive approach based on a binary regression was conducted on the reduced dataset, providing an entirely reliable case discrimination model exclusively depending on the concentrations in the breath mixture of 3 out of a total of 97 metabolites.

Refinement of the basic treatment equivalent model to reflect radiotherapy treatment throughput using Australasian data.

A model of radiotherapy linear accelerator throughput has been developed and shown to be a more sensitive measure of throughput than current measures of throughput. The present study aims to develop a more sensitive basic treatment equivalent (BTE) model that still measures linear accelerator throughput and considers some of the shortcomings of the previous model. All radiation oncology departments in Australia and New Zealand were invited to participate. Departments were asked to time with a stopwatch all episodes of radiotherapy treatment over a 4-week period. Data collected for each treatment fraction included treatment intent, tumour site, patient age, Eastern Cooperative Oncology Group (ECOG) performance status, number of fields used, number of wedges used, number of junctions, number of shielding blocks used, whether the treatment was the first fraction, the use of general anaesthesia and whether port films or electronic portal imaging was used. Twenty-six departments of radiation oncology (70%) participated in this trial. A total of 7929 fractions of treatment, administered to 2424 patients, were timed. The factors found to most significantly impact on treatment duration on multivariate analysis were the type of fraction (first fraction was longer than subsequent fractions), type of beam (electrons were quicker than photons, which were quicker than mixed), number of fields, number of shields, number of junctions, number of port films and performance status (ECOG < 2 vs > 2). The age of the patient, number of compensators and the sex of the patient were not significant. The relationships between factors were assessed, and models of measuring linear accelerator throughput which consider complexity corrections were derived. It is possible to show that linear accelerator throughput is poorly measured by just considering numbers of patients or fields treated per unit time; and that other factors that impact on treatment duration must be considered. A more sensitive model of patient throughput is suggested; but even when a large number of factors are considered, some insensitivity still remains in the model.

An Australasian assessment of the basic treatment equivalent model derived from New South Wales data.

The current method of assessment of radiation oncology linear accelerator throughput is either by patients per unit time or fields per unit time. This, however, does not take into consideration the complexity of different treatment techniques or of casemix. A model has been developed in an earlier study, called 'basic treatment equivalent' (BTE), to measure patient throughput of a linear accelerator, which includes consideration of the complexity of treatment techniques. The present study compared the BTE model with the current best measure of patient throughput of fields per hour. All 37 departments in Australia and New Zealand were invited to participate in testing the model, and 36 agreed to participate. The study period for each department was a consecutive 4 weeks between August and December, 1996. The prospective data collected were the total BTE units treated per linear accelerator per day, the total number of patients and fields treated per linear accelerator per day, and the total linear accelerator hours of operation per day excluding calibration time and significant breaks of linear accelerator time such as planned meal breaks. The treatment breaks between consecutive treatment fractions were not excluded from the linear accelerator treatment time. The throughput data for 36 departments (92 linear accelerators) were collected over the 4-week study period. The average throughput for the departments was 10.8 fields per hour and 4.2 patients per hour. The average BTE per department was 5.7 BTE per hour. The average BTE per episode per department was 1.38. The BTE model was found to be a more sensitive measure of productivity compared with fields per hour (P < 0.001). Some treatment techniques were thought to be not well represented by the BTE formula, particularly those techniques where junctions were present. The BTE model is a more sensitive measure than fields per hour and better reflects the variations in complexity in techniques. Despite this result there is further refinement to be performed to make the model even more sensitive.

Basic Treatment Equivalent (BTE): a new measure of linear accelerator workload.

The measurement of linear accelerator workload in radiation oncology departments is usually based on the number of fields treated per unit time. However, this approach ignores variations in treatment complexity. This prospective study, was designed to measure treatment workload directly, taking into account the variations in complexity of different treatment techniques. From this, a model was to be developed, which would be simple to apply and reproducible, both within and between radiation oncology departments in Australasia. It would provide a realistic basis for assessing treatment costs and enable the comparison of patient throughput between departments. This paper describes the derivation of the model. Over a 4-week period in the Radiation Oncology Department of Westmead Hospital, all fractions of radiotherapy were timed. The data collected included: tumour site; treatment intent; number of fields; number of wedges, compensators and shielding blocks; fraction number; patient age; performance status; and need for general anaesthesia. Multivariate modelling was performed to identify factors that significantly affected fraction duration, so that these could be used to develop a model of resource utilization. The durations of 2371 fractions were measured in 219 patients. Seventy-five per cent of fractions were given with radical intent. The factors found to influence fraction duration on multivariate modelling were: number of fields; number of shielding blocks; first treatment fraction; need for anaesthesia; and performance status. The number of wedges and compensators were also found to be significant but were not included in the model in order to maintain simplicity. This was felt to be necessary if the model is to be applied to the widest possible variety of machines. A model of resources utilization called 'Basic Treatment Equivalent' (BTE) was derived, which incorporated these factors. When tested at Westmead Hospital, this model accurately reflected the predicted BTE value over a further 1-week study period. This model of linear accelerator use, which incorporates complexity has been derived and evaluated in one radiation oncology department. This requires further prospective testing before its widespread use. The model appears to reflect linear accelerator workload better than previous measures. An Australasian study to validate the model further will be undertaken. If adopted, this model has implications for comparative workload reports, diagnostic-related groups, waiting list calculations, and patient scheduling.

An assessment of the Basic Treatment Equivalent (BTE) model as measure of radiotherapy workload.

Current methods of linear accelerator workload analysis in radiation oncology use patients per hour or fields per hour as the basic unit of measurement but fail to take account of the variations in complexity of different treatment techniques. The Basic Treatment Equivalent (BTE) model of productivity assessment has been derived as a potentially better measure of workload because it includes a complexity factor. This model has now been tested prospectively in ten radiation oncology departments in New South Wales and compared with the numbers of fields and patients per hour. Over a 4-week period there were 50,115 fields administrated in 18,466 fractions in 441 hours of machine time in ten radiation oncology departments. The average productivity results for all departments were 4.18 patients, 11.25 fields and 5.66 BTE per hour. When compared with patients per hour and fields per hour, there was less variability of BTE per patient per hour in all departments, suggesting that most departments deliver radiation therapy in a consistent way, which is not appropriately reflected in the numbers of fields or patients per hour. Departments that were able to treat a high number of patients or fields per hour were able to do so because they used less complicated techniques or had a less complicated casemix of patients. The BTE model allows for variations in the complexity of treatment techniques, is simple to apply, and is reproducible under different conditions in different departments. Following revision of the model, an Australasian study is now proposed. The confirmation of our findings will have significant implications for resource utilization comparisons, patient time allocations, waiting list estimates and cost-benefit analysis.