Volume 207 Issue 4

CTC is a safe and accurate cancer detection technique widely used overseas but underused here

Abdominal Radiology Group of Australia and New Zealand

A truly comprehensive approach to tobacco control should include interventions targeting high risk groups

Billie Bonevski · Ron Borland · Christine L Paul · Robyn L Richmond · Michael Farrell · Amanda Baker · Coral E Gartner · Sharon Lawn · David P Thomas · Natalie Walker

A commitment to understanding and improving medical expertise is fundamental to training, practice and evaluation of doctors’ performance

Jeffrey CL Looi · Choong-Siew Yong

An atypical infective colitis masquerading as autoinflammatory colitis

Ben Gerhardy · Robyn A Nagel · Ross Sellars · Aliki Andreou

A 77-year-old woman presented with longstanding left leg weakness and falls

Kay Hau Choy · Sumaya Alukaidey

DRE can be embarrassing for the patient and, at times, for the doctor

Christopher S Pokorny

Medical researchers often attempt to understand whether a risk factor is involved in the aetiology of disease or whether an intervention reduces disease; this has been the subject of another article in this series.1 We typically do this by proposing scientific hypotheses from which testable statistical hypotheses can be developed. For example, our scientific hypothesis could be that people with irritable bowel syndrome (IBS) have higher risk of depression compared with those who do not have IBS. A small study might show that one in ten in the control group have depression, compared with two in ten in the IBS group. This might reflect a real difference in the rate of depression between IBS and non-IBS populations, or the difference of one person between groups might simply be the play of chance. An empirical approach to answering this question might be to repeat the study multiple times, with larger numbers, and to look at the consistency of the results. Unfortunately, this is an expensive and time-consuming solution. In the early 20th century, statisticians proposed different solutions. Fisher (1890–1962) suggested that researchers should start with a statistical hypothesis, commonly in the null form (no difference in incidence of depression in patients with IBS compared with those without depression); the compatibility between the observed data and this null hypothesis could be measured using a P value.2,3 If this was high, one would tend to accept the explanation that the data were consistent with the hypothesis. If it was low, one would tend to reject the hypothesis; no a priori threshold was specified for the P value and it was seen as part of a continuum, with the reader deciding where to set the threshold and how to incorporate other information.3 Neyman and Pearson sought to remove this subjectivity and focus on decision making, suggesting that one should also state an alternate hypothesis — for example, that there is an increased risk of depression in patients with IBS compared with those without depression. The discrepancy between the observed data and what would be expected under the null hypothesis would be calculated (the test statistic) and compared with an a priori threshold. If the test statistic fell below this threshold, the data were judged to be compatible with the null hypothesis. If the test statistic was above the threshold, the null hypothesis would be rejected. These opposing statisticians became embroiled in a long running and bitter feud,4 and we have been left with a legacy that reflects a combination of the two approaches — we frame a null and alternate hypothesis (Neyman and Pearson) but calculate a P value (Fisher). Even recently, the American Statistical Association felt the need to release a statement on the purpose and uses of P values.5 Many clinicians find the null hypothesis counterintuitive, and it is worth exploring why it was adopted. First, it is the simplest hypothesis to test; for example, that any difference between IBS and non-IBS populations in stress observed in our sample is due to chance. Second, it is more efficient to disprove a hypothesis than to prove it; as the philosopher of science Karl Popper argued, falsifiability is a more reliable criterion of truth than verifiability.3,4 It is important to note that rejecting the null hypothesis is not actually specific evidence for the alternate hypothesis, although it is customary to take it as such. Understanding P values Consider a clinician who observes that patients with IBS have higher stress scores on the Depression Anxiety Stress Scale6 than patients without IBS. The physician observes that individuals with IBS have a higher mean (x̄) score (mean, 15; SD, 10; n = 20) than non-IBS individuals (mean, 9; SD, 8; n = 42). The scientific hypothesis is that patients with IBS have higher stress scores than those without IBS. The corresponding statistical (null) hypothesis is that there is no difference in stress scores on average between those with and without IBS and the difference observed is simply due to chance. As noted above, the empirical approach would be to repeat this experiment hundreds of times and look for consistency. The beauty of statistics is that it saves us the time and cost of doing this. The P value estimates how likely we are to see this difference, or whether there was no real difference in mean stress scores in the population, or one more extreme, if it was simply the play of chance, without us having to actually do the repeated experiments. One way of viewing a hypothesis test is as an evaluation of the signal-to-noise ratio. In our example, the signal is the difference between IBS and non-IBS means (15 − 9 = 6 points) and the noise is the uncertainty around that difference between means (reflected through the standard error). In this instance, the appropriate test statistic is an unpaired t test, which is appropriate for the comparison of means of two independent samples of individuals (although there are many different tests appropriate to different scenarios). The t test is calculated as: As the numerator (difference between means) increases relative to the denominator (standard error of the difference), we could say that the signal-to-noise ratio increases, increasingly arguing against the null hypothesis and therefore supporting the alternate hypothesis. However, at what point does the ratio become large enough for the clinician to conclude that stress is a feature of IBS? The t value here (2.42) corresponds to a P value of 0.02. The interpretation of this P value is that if we were to repeat the experiment 100 times, and there were truly no difference in the population means, we would expect that two times out of 100 we would see a difference this large or larger. Fisher would leave it to us to decide whether this was rare enough for us to accept our hypothesis of an association, while Neyman and Pearson would say that anything less than 0.05 (five times out of 100), for example, is rare enough to reject the null hypothesis. Flaws in the argument Understanding the P value this way makes it clear that we are dealing with probabilities not certainties, so errors are possible. One can falsely reject the null hypothesis, concluding that IBS is associated with higher stress levels when it is not. The chance of this happening is another way to understand the P value and is also known as a type I error. The converse is the probability that there is no real difference between the IBS and non-IBS populations. The opposite error is also possible: accepting the null hypothesis of no association when there actually is an association — this is known as a type II error. The converse is the likelihood that there is a difference between IBS and non-IBS groups, which we rightly conclude by rejecting the null hypothesis — this is also called the power of the study. The likelihood of making these type I and II errors is partly influenced by our sample size, which is why epidemiologists and statisticians give such importance to calculating a sample size before a study begins. Let us return to our example of the possible association between IBS and stress. After the data have been collected, the hypothesis testing process involves calculating a test statistic. In this case, the unpaired t test is calculated as: Any quantity in the equation that makes t larger will lead to a smaller P value and increase the likelihood of rejecting the null hypothesis. This means that either the numerator becomes larger or the denominator becomes smaller. The denominator becomes smaller if either the standard deviation (s) is small or the sample size (n) is large. We do not have direct control over standard deviation but we can directly control sample size. Therefore, we might falsely accept the null hypothesis (type II error) either because the standard deviation is large (eg, there is a high degree of measurement error) or because the sample size is small. A red flag for a possible type II error is a clinically meaningful effect size (numerator) but a P value just above our predefined level of statistical significance. Good practice in designing medical studies is to calculate what sample size is required to achieve adequate statistical power (typically ≥ 0.8) at a given level of statistical significance (typically < 0.05), but only if the effect size is large enough to be clinically meaningful. Similarly, it is good practice to report either the a priori sample size calculation or the statistical power available for the sample size achieved. Conclusion Statistical hypothesis tests require clinical and biological science in their construction and clinical interpretation in applying their results (Box). While they can be viewed as a “black box”, with little or no understanding of their statistical basis, understanding their construction and origins yields important insights into the design of medical studies, including why a prior sample size calculation is important. Box – The role of statistical hypothesis tests in empirical studies

Michael P Jones · Alissa Beath · Christopher Oldmeadow · John R Attia

Safety of biologics therapy

Nicholas Farinola

Beyond its role in therapy, FMT is providing insights into the importance of intestinal microbiota in health and disease

Lawrence J Brandt

Overseas experience offers insights into providing sustainable health care for remote communities

Roger P Strasser

Rural clinical schools are critical for increasing the proportion of local graduates in the rural workforce

Denese Playford · Hanh Ngo · Surabhi Gupta · Ian B Puddey

Guidelines for improving education, developing criteria for assessing non-response, and guiding informed consent are needed

Roshan Razik* · Majdi Osman* · Alexandra Lieberman · Jessica R Allegretti · Zain Kassam

New guidelines classify PEI as definite, possible or unlikely, and provide a diagnostic algorithm for early diagnosis and management

Australasian Pancreatic Club Pancreatic Enzyme Replacement Therapy Guidelines Working Group

Moderate quality evidence supports its value, but the best route of administration and preparation remain to be established

Paul Moayyedi · Yuhong Yuan · Harith Baharith · Alexander C Ford

Coeliac disease is an immune-mediated systemic disease triggered by exposure to gluten

Marjorie M Walker · Jonas F Ludvigsson · David S Sanders

To the Editor:I thank Kritharides and colleagues1 for their review Cardiovascular disease in patients with schizophrenia. I agree with them and support their work, which seeks to improve the physical health of patients living in the community with a chronic mental illness such as schizophrenia, through an innovative, coordinated and multidisciplinary model of care. Clozapine side effects, including risks of myocarditis and cardiomyopathy, hypercholesterolaemia and weight gain, reduce years of life and require medical attention. But the management is not always straightforward. One challenge is patient compliance with often demanding allied health therapies. How do we keep our patients motivated to continue with prescribed regular exercise most days of the week? How do we encourage compliance with a weight-reducing, low salt, low glycaemic index diet? Multidisciplinary primary care and specialist teams may consider a rehabilitation approach to complement the model of care. Two essential elements are goal setting and measurement of function.2 Some patients will be motivated by their personal goals (eg, getting back to weighing 80 kg or playing a game of table tennis) and other patients will appreciate their gain in terms of function (eg, walking up the stairs without a rest or shopping for groceries independently) more so than in terms of presented data (eg, cholesterol levels or absolute cardiovascular risk reduction). For motivating patients with schizophrenia and significant cardiovascular risk, a rehabilitation approach may be worth a try.

David Skalicky

In reply

Leonard Kritharides · Vincent Chow · Tim JR Lambert

To the Editor: We read with interest the recent study by Freed and Allen on the cost to patients of consulting a private specialist physician.1 Although not the main focus of the study, we were intrigued by the disproportionately low bulk-billing rates by physicians in Western Australia. This was highlighted in the local media,2 with an implication that WA physicians are out of step with interstate colleagues on billing practices. We acknowledge there was no such assertion in the article by Freed and Allen.1 The study findings are based on Medicare data supplied by the Commonwealth Department of Human Services.1 However, the data do not appear to differentiate between Medicare billing in private physicians’ rooms (which is the intended target of the study) or elsewhere. Hence, the bulk-billing findings may be confounded by occasions of Medicare billing occurring in outpatient clinics run by public hospitals. Public hospital services are usually funded by state governments. Nevertheless, Commonwealth (ie, Medicare) funded clinics are permitted under an interpretation of the Health Insurance Act 1973 that allows private services to be rendered by specialists within a public hospital.3 Anyone with a Medicare card is eligible to be considered a “private patient”. If bulk-billed, the patient will not suffer any financial disadvantage — or notice any difference — compared with attending an ordinary state government funded clinic. In most cases, revenue from Medicare is not retained by the specialist, but donated to the hospital to defray clinic costs.4 This model permits the creation of new fee-free hospital outpatient services that would otherwise be unsustainable within the existing state funding. In view of the large number of outpatient visits to public hospitals, there could be an inflation of statewide physician bulk-billing rates where Medicare funded hospital clinics are widespread. In our experience, such clinics are rare or non-existent in WA. It would be interesting to reappraise bulk-billing rates if billing episodes occurring at public hospitals could be excluded. We suspect that bulk-billing rates occurring entirely within private specialist rooms are not significantly different between jurisdictions.

Gregory SY Ong · Senq J Lee · Dejan Radeski

To the Editor: The demand for human leukocyte antigen (HLA) typing in the diagnostic work-up of coeliac disease (CD) in Australia has driven a 14-fold rise in testing since 2003 (Medicare Benefits Schedule data, item 71151). Although HLA typing offers limited specificity for CD, its clinical utility results from its exceptional negative predictive value (> 99%) when the specific HLA susceptibility genotypes are not detected.1 Unlike traditional tests for CD, HLA typing results are informative even when the patient is following a gluten free diet. While the accuracy of HLA typing in CD has not been reported, HLA test results are assumed by clinicians to be definitive. Our findings challenge this view. Discrepancies between several patients’ clinical diagnosis of CD and their negative HLA-DQ2 and -DQ8 typing results in AJD’s practice led to repeat HLA testing with another laboratory. The subsequent reporting of a genotype consistent with CD prompted a clinical audit (2013–2016). Of 211 patients with HLA typing results, nine had been coperformed by two separate laboratories (laboratories 1 and 2), either deliberately or inadvertently. Of these nine patients, six returned conflicting results. An additional DNA sample from all six patients was sent for HLA genotyping by a reference laboratory, where genetic susceptibility for CD was confirmed in five patients (Box). Laboratories 1 and 2 differed in the detection of risk alleles and in the interpretation of or reporting of the results in all six cases. Laboratory 2 identified an at-risk allele in only two of the six patients, and of the four patients with a reported negative genotype, two were subsequently confirmed to have definite CD. These preliminary findings raise serious concerns about CD HLA testing errors that adversely affect patient care. Although identified in Queensland, these laboratories routinely outsource their HLA typing to laboratories in New South Wales and Victoria, indicating that several Australian states are involved. We are particularly concerned about laboratories new to HLA testing or laboratories that are not participating in stringent quality assessment programs as the sourced reference laboratory does. Therefore, we suggest that an assessment of the performance and quality control measures of all laboratories offering HLA typing is urgently needed. Consistent adoption of evidence-based guidelines that describe optimal HLA testing and reporting1 should form part of the solution. Box – Human leukocyte antigen (HLA) typing results from three laboratories† Patient Laboratory 1 Laboratory 2 Reference laboratory Confirmed CD “Consistent with DQ2 phenotype; susceptible for CD” Genotype not supplied; “Not susceptible for CD” HLA-DQ2.2/2.5; susceptible to CD Incorrect Confirmed CD “Consistent with DQ2 phenotype; susceptible for CD” Genotype not supplied; “Not susceptible for CD” HLA-DQ2.2; susceptible to CD Incorrect CD excluded “Consistent with DQ2 phenotype; susceptible for CD” Genotype not supplied; “Not susceptible for CD” HLA-DQ2.2; susceptible to CD Incorrect CD not excluded; on GFD “Consistent with DQ8 phenotype; susceptible for CD” Genotype not supplied; “Not susceptible for CD” No susceptibility to CD detected Incorrect Normal CD serology “DQ2 and DQ8 not identified; no genotype susceptibility for CD” “DQA1*0505 has been detected; small percentage susceptible to CD” DQA1*05 (HLA-DQ7); low risk susceptibility to CD Incorrect CD excluded “DQ2 and DQ8 not identified; no genotype susceptibility for CD” “DQA1*0505 has been detected; small percentage susceptible to CD” DQA1*05 (HLA-DQ7); low risk susceptibility to CD Incorrect CD = coeliac disease. GFD = gluten free diet. ND = not detected. † The reference laboratory was in the Victorian Transplantation and Immunogenetics Service in Melbourne. In addition to the incorrect typing results, laboratory 2 failed to report the specific alleles detected and laboratory 1 failed to distinguish between HLA-DQ2.5 and DQ2.2.

A James M Daveson · Michael Varney · Kate E Jackson · Jason A Tye-Din

In the article “Risk-adjusted hospital mortality rates for stroke: evidence from the Australian Stroke Clinical registry (AuSCR)”, published in the 1 May 2017 issue of the Journal (Med J Aust 2017; 206: 345-350), there was an error in Box 2 on page 348. Further, the sentence at the head of the second column beneath this figure should read: “The change in rank position according to different RAMRs was clearest for hospital 20, which was ranked number 12 in the full Registry model, but number 25 in the hospital admissions model.” The corrected article is available at https://www.mja.com.au/doi/10.5694/mja16.00525.

Professor Narci Teoh has come a long way since watching her father work as the fi rst obstetrician in their part of Malaysia

Cate Swannell

Professor Anton Peleg, a group leader from the Monash Biomedicine Discovery Institute and also the Director of the Department of Infectious Diseases at the Alfred Hospital and Monash University, has received a Practitioner Fellowship from the National Health and Medical Research Council (NHMRC). Professor Peleg’s NHMRC Practitioner Fellowship – the only one received in Victoria this year – will focus on Novel solutions to antimicrobial resistant pathogens. Professor Peleg’s research will focus on four key themes. These include: basic mechanistic studies of antimicrobial resistance to identify new drug targets; the development of a new therapeutic paradigm for highly resistant pathogens - anti-virulence strategies; translational studies on eliminating biofilm infections; and clinical studies to optimise patient outcomes from antibiotic resistant infections. In collaboration with researchers and clinicians across Monash-affiliated hospitals, the university and research institutes, Professor Peleg will co-lead, with Professor Dena Lyras, another group leader at the Monash BDI, the Research Centre for Hospital Infections (RCHI). The RCHI is a Monash-wide initiative to foster innovative research and develop novel solutions to the problem of antimicrobial resistance and hospital infections. https://www.monash.edu/medicine/news/latest/articles/nhmrc-fellowship-to-help-professor-tackle-antimicrobial-resistant-infections Monash University scientist, Professor Jamie Rossjohn, has been elected to the Fellowship of the Academy of Medical Sciences in the UK. The Academy of Medical Sciences Fellows are considered the UK’s leading medical scientists, elected for their contribution to biomedical and health research, the generation of new knowledge in medical sciences and its translation into benefits to society. Professor Rossjohn, Head of the Infection and Immunity Program at the Monash Biomedicine Discovery Institute (BDI) and also Professor of Structural Immunology at Cardiff University, Wales, was one of 46 researchers to receive this accolade. Professor Rossjohn, ARC Laureate Fellow, is recognised internationally for his contributions to the field of immunology. He has provided the basis of key immune recognition events by T cells. He has shown how T cells recognise polymorphic Human Leukocyte Antigen (HLA) molecules and unearthed mechanisms of HLA polymorphism impacting on drug and food hypersensitivities. Moreover, he has pioneered our understanding of lipid- and metabolite-based immunity. https://www.monash.edu/medicine/news/latest/articles/monash-scientist-honoured-by-prestigious-uk-medical-fellowship Monash Health Translation Precinct (MHTP) biostatistician Dr StellaMay Gwini has received the Professor Damien Jolley Award for excellence in statistics within a doctoral thesis. Dr Gwini’s award was for her PhD research that examined the health of Australian veterans of the 1990–1991 Gulf War. Dr Gwini said that on return from the 1990–1991 Gulf War, veterans complained of many unexplainable symptoms. “My thesis examined how symptom reporting had changed over time among Gulf War veterans. We further investigated the impact high symptomatology had on health service use,” Dr Gwini said. “The research indicated that symptom reporting increased over time and high symptom reporting was associated with increased chronic disease incidence in the longer-term. Health service use by veterans reporting many symptoms (but without chronic diseases) was similar to that of veterans with some chronic disease diagnosis indicating that the high unexplained symptom reporting exerted a sizeable health burden on veterans and the health system.” As a research fellow in the Department of Epidemiology and Preventive Medicine, Dr Gwini’s role at MHTP and the School of Clinical Sciences at Monash Health is to assist researchers and students in research design and statistical aspects of their projects. https://www.monash.edu/medicine/news/latest/articles/mhtp-biostatistician-acknowledged-for-outstanding-statistical-modelling The Kirby Institute’s Professor Anthony Kelleher has started in the role of Acting Dean of the Faculty of Medicine at the University of New South Wales. Professor Kelleher will be filling the leadership role until the current dean Professor Rodney Phillips returns from extended absence at the end of 2017, UNSW President and Vice-Chancellor Professor Ian Jacobs said. “Professor Kelleher’s stellar research career, his leadership in the strategic review of the Infection, Immunity and Inflammation theme, his important contribution to the Sydney Partnership for Health, Education, Research and Enterprise (SPHERE), and his extensive clinical experience makes him well placed to lead the Faculty during this critical time,” said Professor Jacobs. Professor Kelleher said he hoped to work with everyone to maintain the momentum initiated by Professor Phillips and senior members of the Faculty. “I am excited and honoured but somewhat daunted by the challenges of this position especially with the substantial recalibrations and realignments required to effectively pursue the 2025 Strategy,” he said. “I am particularly interested in maintaining the high levels of satisfaction among our students and initiating the development of a strategy to most effectively navigate the recent and continuing changes in the research funding landscape.” https://med.unsw.edu.au/news/anthony-kelleher-work-acting-dean-medicine University of NSW PhD student Dr Adeniyi Borire has been recognised for a manuscript on the effects of haemodialysis on intraneural blood flow in end-stage kidney disease. Dr Borire has won the American Association of Neuromuscular and Electrodiagnostic Medicine (AANEM) 2017 Golseth Young Investigator Award. His research with PhD supervisors Professors Arun Krishnan and Matthew Kiernan and Dr Neil Simon and other co-authors was judged on scientific merit, methodology, manuscript form and Dr Borire’s contributions to the project. In the research, neuromuscular ultrasound was used to quantify intraneural blood flow (detectable blood flow within nerves) in 18 patients with end-stage kidney disease. Current ultrasound technology cannot detect blood flow under normal physiological conditions, however blood flow becomes detectable when nerves are diseased, because there is usually an increase in blood flow when tissue injury occurs. The research found even a single session of haemodialysis made significant improvements to blood flow, highlighting the therapeutic effect of dialysis on nerve structure and function. Dr Borire’s PhD is focused on the development of biomarkers for the early detection of axonal neuropathies, using neuromuscular ultrasound. He will formally receive his prize in the US later this year, and his abstract will be published in the journal Muscle and Nerve. The award was established in 1998 to honour Dr James Golseth, a founding member of the AANEM, and is presented annually for original research on neuromuscular and electrodiagnostic medicine. https://med.unsw.edu.au/news/unsw-young-investigator-awarded-kidney-disease-research