Volume 171 Issue 6

Editorial Breaking bad news: explaining cancer diagnosis and prognosis Doctors and nurses need training in communicating information about cancer and responding to patients' concerns MJA 1999; 171: 288-289 For related articles see Prince, Lobb et al & Naganathan et al Most patients, if they have cancer, want to be told about it, and they want to know what the likely treatments are, the side effects of treatment and their prognosis.1 A clear understanding of prognosis can be particularly important in conditions such as breast cancer, because patients need prognostic information to make informed decisions about systemic treatment. So, how can this information best be communicated? Based on a literature review and recommendations of a consensus panel of doctors (with input from patients with cancer), Girgis and Sanson-Fisher2 published some useful guidelines on conveying information to patients about serious disease or death. Their guidelines included ensuring privacy and allowing adequate time, assessing patients' understanding, giving information about diagnosis and prognosis simply and honestly, avoiding euphemisms, encouraging patients to express feelings, being empathic, giving a broad but realistic time-frame concerning prognosis, and arranging a review. The crucial question is how well these recommendations are followed in clinical practice, as discussing prognosis should be part of the process of breaking bad news. Audiotape recordings of consultations have shown that doctors break the bad news of a cancer diagnosis to patients in a predictable and routine way regardless of patients' individual information needs.3 Although the doctors gave reassurance that something could be done, few attempted to elicit patients' thoughts and feelings about the symptoms and their cause. They gave the information in a consistent order -- diagnosis, the relevant evidence, the need for further investigations, the treatments being considered and the probable outcome -- with no heed to which issues patients wished to address first. Obvious verbal and non-verbal cues of distress were not acknowledged and patients' immediate concerns were not explored. This consultation structure led patients to believe they were not entitled to talk about their feelings or their major concerns. Consequently, their preoccupation with these feelings and concerns meant that they did not assimilate the information and advice given. On being interviewed at the end of the consultation, patients reported that they were left with important, but undisclosed, concerns and also felt that the information given had been inadequate for their needs. The important article by Lobb and colleagues4 published in this issue of the Journal looks at one aspect of breaking bad news to cancer patients -- explaining prognosis to women with breast cancer. It was clear from the women's answers to a questionnaire and a clinical vignette that many had problems understanding prognostic information in the form it is usually presented. The women also varied considerably in what prognostic information they would like to receive and how they preferred it to be presented. The study's findings showed that not all women will desire or understand standard methods of giving prognostic information. It has been suggested that giving patients the opportunity to talk with nurses after consultations in which they have been told they have cancer, or have been given complex information about cancer prognosis, would result in their disclosing concerns and misunderstandings and these could then be fed back to the treating clinicians.5 However, this solution ignores important evidence that nurses are just as reluctant as doctors to acknowledge patients' distress and elicit their underlying concerns.6 Like doctors, nurses in these situations have been found to adopt behaviours designed to prevent further disclosure.7 These "blocking behaviours" include telling patients that any distress is normal, switching the subject to neutral topics, giving information and advice before patients' concerns have been identified, focusing only on physical aspects of the condition, and using leading, closed and multiple questions.7 Doctors and nurses avoid exploring patients' feelings and concerns because they fear that it will provoke too much emotion, which could be harmful to patients.8 They feel that their training has not equipped them with the necessary skills to explore these issues and respond appropriately.9,10 Feeling that they are not being supported emotionally and practically by colleagues and supervisors has also been linked to a greater use of these "blocking behaviours".6,11 The lack of adequate training of doctors and nurses in communicating information to patients with cancer can have important negative psychological consequences for the patients. The development of clinical anxiety and depression is more likely when patients have unresolved concerns and perceive that they have been given inadequate information.12,13 So, how can these communication deficiencies be remedied? The data collected by Lobb et al4 could be used as a basis for ongoing research to identify how best to structure and describe prognostic information so that concepts such as "median survival" and "relative risk" are made understandable. It would then be possible to develop training programs in communication skills that teach doctors how to elicit patients' preferences for information about prognosis. The use of intradisciplinary10 and multidisciplinary workshops14 has been advocated. While objective evidence of the value of intradisciplinary workshops is awaited, multidisciplinary workshops have proved successful in helping doctors and nurses acquire key communication skills.14 Despite workshops being effective in changing key communication behaviours, it is not certain how much of what is learnt is applied to clinical practice. Two randomised trials are being conducted in the United Kingdom in an effort to determine this. The Cancer Research Campaign (CRC) Psychosocial Oncology Group is studying whether training doctors in small groups enables them to be more effective in communicating with patients and better able to cope with breaking bad news and dealing with patient concerns. The CRC Psychological Medicine Group is assessing whether senior doctors benefit from six sessions of individual feedback on their "bad news" consultations and whether this results in better patient recall and less patient distress in future consultations (as measured by a patient interview and the Hospital Anxiety and Depression Scale), as well as a reduction in the level of burnout in the doctors themselves. Without systematic training, the breaking of bad news and discussions of cancer prognosis are likely to fall short of existing guidelines and patients' needs and expectations. Consultations of this type can be difficult and painful. Yet, for too long, we have expected doctors and nurses to undertake these difficult tasks without the necessary training and support. G Peter Maguire Consultant Psychiatrist; and Director Cancer Research Campaign Psychological Medicine Group, Manchester, UK Meredith C, Symonds P, Webster L, et al. Informational needs of cancer patients in West Scotland: cross sectional survey of patients' views. BMJ 1996; 313: 724-726. Girgis A, Sanson-Fisher RW. Breaking bad news: consensus guidelines for medical practitioners. J Clin Oncol 1995; 13: 2449-2456. Maguire P. Breaking bad news. Cambridge: Cambridge Handbook of Psychology, Health and Medicine, 1998: 273-275. Lobb EA, Butow PN, Kenny DT, Tattersall MHN. Communicating prognosis in early breast cancer: do women understand the language used? Med J Aust 1999; 171: 290-294. Watson M, Denton S, Baum M, Greer S. Counselling breast cancer patients: a specialist nurse service. Counselling Psychol Q 1988; 1(i): 23-31. Wilkinson SM. Factors which influence how nurses communicate with cancer patients. J Adv Nurs 1991; 16: 677-688. Maguire P. Barriers to psychological care of the dying. BMJ 1985; 291: 1711-1713. Maguire P, Faulkner A, Booth K, et al. Helping cancer patients disclose their concerns. Eur J Cancer 1996; 32A: 78-81. Maguire P, Faulkner A. How to improve the counselling skills of doctors and nurses in cancer care. BMJ 1988; 297: 847-849. Fallowfield L, Lipkin M, Hall A. Teaching senior oncologists communication skills: Results from phase 1 of a comprehensive longitudinal programme in the United Kingdom. J Clin Oncol 1998; 16: 1961-1968. Booth K, Maguire P, Butterworth T, Hillier VT. Perceived professional support and the use of blocking behaviours by hospice nurses. J Adv Nurs 1996; 24: 622-527. Parle M, Jones B, Maguire P. Maladaptive coping and affective disorders in cancer patients. Psychol Med 1996; 26: 735-744. Fallowfield LJ, Hall A, Maguire GP, Baum M. Psychological outcomes of different treatment policies in women with early breast cancer outside a clinical trial. BMJ 1990; 301: 575-580. Maguire P, Booth K, Elliott C, Jones B. Helping health professionals involved in cancer care acquire key skills -- the impact of workshops. Eur J Cancer 1996; 32A: 1486-1489.

Research Communicating prognosis in early breast cancer: do women understand the language used? Elizabeth A Lobb, Phyllis N Butow, Dianna T Kenny and Martin H N Tattersall MJA 1999; 171: 290-294 For related articles see Maguire, Prince & Naganathan et al Abstract - Introduction - Methods - Results - Discussion - Acknowledgements - References - Authors' details - - More articles on Oncology Abstract Objectives: To determine the degree to which women with early breast cancer understand the prognostic information communicated by clinicians after breast cancer diagnosis, and their preferences for how this information is presented. Design: Cross-sectional survey conducted within two months of breast cancer diagnosis, using a self-administered written questionnaire. Participants and setting: One hundred women attending five Sydney teaching hospitals and one country hospital, who were diagnosed with early stage breast cancer between January and December 1997. Results: The 100 respondents represented 70% of the 143 women originally approached to participate. Many respondents did not fully understand the language typically used by surgeons and cancer specialists to describe prognosis: 53% could not calculate risk reduction (with adjuvant therapy) relative to absolute risk; 73% did not understand the term "median" survival; and 33% believed a cancer specialist could predict an individual patient's outcome. Women in professional/ paraprofessional occupations understood more prognostic information than non-professional women. There was no agreement on the descriptive equivalent of a "30%" risk, nor the numerical interpretation of a "good" chance of survival. Forty-three per cent of women preferred positively framed messages (eg, "chance of cure"), and 33% negatively framed messages (eg, "chance of relapse"). The information women most wanted was that relating to probability of cure, staging of their cancer, chances of treatment being successful, and 10-year survival figures with and without adjuvant therapy. Conclusions: Our results suggest that misunderstanding is responsible for women's confusion about breast cancer prognosis. Clinicians should use a variety of techniques to communicate prognosis and risk, and need to verify that the information has been understood. Introduction To make informed decisions women with breast cancer must understand what their prognosis is without systemic treatment, and the likely advantages and disadvantages of treatment. While most Australian doctors now tell cancer patients their diagnosis,1 prognosis is less commonly discussed.2 Reticence to provide prognostic information is often based on concerns that the information will be overwhelming, not understood or will destroy hope.3,4 However, it is not clear whether the information itself, or the language used, is the critical feature. Many patients have a poor understanding of their disease and their prognosis,5,6 or have difficulty recalling the information they have been given about their disease.7,8 Similarly, women at risk of developing breast cancer commonly misreport individual and population risk.9 Denial and minimisation of risk are also common psychological reactions to cancer risk notification after screening procedures.10 If it were possible to determine whether patients cannot understand the terminology or mathematics of risk information, or, rather, prefer not to be told or do not absorb the information, clearer directions for best clinical practice in discussing prognosis could be established. Most previous studies on risk communication in cancer have not dealt specifically with issues pertinent to women with breast cancer. In two studies that did, the women surveyed were well down the treatment path and their experience of learning their prognosis was long past.5,11 To our knowledge, there are no reports of women's understanding of specific prognostic information in early breast cancer. We investigated women's understanding of prognostic information and their preferences for the way the information on the risk of their breast cancer recurring after surgery is presented to them. Methods Survey subjects Women were recruited through their treating physician. To ensure input from a range of women, five urban centres attracting referrals from populations with different socioeconomic profiles (Royal Prince Alfred, Royal North Shore, Prince of Wales, St George, and Westmead hospitals, all in Sydney, New South Wales) and one rural centre (Tamworth Hospital, Tamworth, NSW) were approached to participate in the study. Thirteen breast surgeons and 13 medical oncologists from these centres were invited to participate in the study and all agreed. One hundred and forty-three consecutive women newly diagnosed with stage I or II breast cancer at any of the six treatment centres between January and December 1997 were contacted by letter to request their participation. The women received the letter within 2-4 weeks of making their own decisions about adjuvant treatment and within 2 months of their initial diagnosis. (The timing of questionnaire administration was carefully considered to maximise the saliency of the issues while avoiding distressing women making their own treatment decisions.) The letter was followed up by a phone call from the research coordinator, who obtained verbal consent for participation and then sent out the questionnaire by mail. One centre opted to send women a letter signed by their oncologist inviting them to participate in the study. The survey sample included patients of surgeons and medical oncologists in both private and public practice. Women from a non-English-speaking background with insufficient knowledge of English to complete the questionnaire, and women presenting with a second cancer, were excluded. Questionnaire We gathered the data using a self-administered written 17-item questionnaire, designed on the basis of a review of the literature; an analysis of 20 audiotapes of initial oncology consultations with breast cancer patients (collected from two centres -- Royal Prince Alfred Hospital and Westmead Hospital -- during another study undertaken between 1995 and 199712); and expert consultation (a working party set up by the National Breast Cancer Centre). The 20 audiotapes were transcribed and the contents analysed to identify the range of ways in which prognosis was conveyed to patients (eg, absolute and relative risk, cumulative risk, numerical or non-numerical probability, and individual versus population risk). The questionnaire investigated women's understanding of and preferences for these different formats used by doctors for disclosing prognosis and risk information. In addition, a standard hypothetical scenario of adjuvant therapy in early stage breast cancer was included, and women responded to questions applying to that scenario (Box 1). Six of the questions explored women's understanding of different ways in which the risk of breast cancer recurring after surgery could be presented. Two sample questions are shown in Box 2. A "don't know" option was not offered in the items relating to "understanding" in order to force a choice and allow an analysis of common errors in interpretation. The remaining questions focused on the importance of different prognostic information to women's decision making, and on their preferences for presentation of risk - for example: percentages versus numbers (eg, "70%" v. "7 in 10"); numerical versus verbal descriptions of risk (eg, "30%" v. "small"); and positively framed versus negatively framed statements (eg, "70% chance of remaining free of cancer" v. "30% chance of the cancer coming back"). The questionnaire also elicited the women's demographic data and details of their breast cancer diagnosis and treatment (Box 3). Statistical analysis Appropriate sample sizes were calculated using the SAM sample size software package.13 Sample size calculations were based on effect sizes from related studies in patients' level of recall after a variety of interventions. In an Australian study measuring understanding of information presented in an oncology consultation, a sample size of 47 per group was sufficient to detect statistically significant differences of 7% (P < 0.005) in recall between groups. Thus, in a comparison of two patient subgroups (eg, young v. old), a total sample size of 100 would allow us to detect a similar difference in responses in the two groups. A sample size of 100 would also allow detection of a difference of 30% or more (felt to be clinically significant) between subgroups in the proportion of women preferring one presentation of risk versus another, with a power of 0.8 and a significance level of 0.05. A "total understanding" score was calculated by summing correct responses to the six items assessing "understanding". The summary score was normally distributed (K-S Lilliefors .0514). Descriptive statistics were used to identify the percentage of patients understanding and preferring different risk information. Analysis of variance (ANOVA), Student's t tests and χ2 tests of association were used to examine the relationship between demographic variables and outcomes, as appropriate; two-sided tests were used.15 Ethical approval Approval was granted for this study by the Ethics Committee of the University of Sydney, the Central Sydney Area Health Service, the Southern Sydney Area Health Service, and individual hospital ethics committees at Westmead, Royal North Shore and Tamworth hospitals. Results Of the 118 women who agreed to participate in the survey, 100 returned questionnaires (70% of the original 143 women contacted). Demographic data Demographic characteristics of the participants are presented in Box 3. Their mean age was 56 years and most were city dwellers. Just over half had completed the Higher School Certificate, university or some form of tertiary training. The percentage of women with tertiary qualifications was 42% (compared with 37% in the general Australian population16).. Nearly two-thirds worked (or had worked) in professional or paraprofessional occupations, and 22% were working in occupations related to medicine (eg, doctor, nurse, medical receptionist, technician). Questionnaire responses A summary of the women's responses to the questionnaire is given in Box 4. Discussion We have identified some of the problems women with breast cancer experience when trying to interpret prognostic information presented by their doctors. Our results support the hypothesis that it is misunderstanding, not denial, that causes confusion. A considerable number of women in our study did not clearly understand some of the language used to describe the risk of breast cancer recurrence after surgery or how additional treatment might benefit them. Moreover, the response from this group of relatively highly educated women probably represents a "best case" scenario, and, if anything, one might expect understanding to be poorer in the general population of women with breast cancer. These findings have implications for informed consent. Clinicians need to explain what type of prognostic information can be given, and enquire how much of this information women want to hear. They should check very carefully how women have interpreted the information presented to them, and must not assume that, because a woman has already consulted a number of specialists, her prognosis has been conveyed to her and clearly understood. This applies to all patients with breast cancer, but especially those who work in unskilled occupations. It might be argued that our sample was not truly representative, as (i) the women surveyed, having recently been told their diagnosis, may not have been in the best frame of mind to answer the questionnaire clearly and impartially; and (ii) we did not include a similar group of women who had never had breast cancer. Furthermore, patient responses may have been different had the questions concerned personal experience rather than a hypothetical case scenario.5,9,10,19 However, data from Degner et al20 suggest that views expressed by people diagnosed with cancer differ considerably from those of the well population, which underscores the importance of surveying those who have actually been diagnosed with cancer. In addition, we felt that a typical case vignette was the most appropriate tool to control for the influence of individual disease variables and treatment protocols; to reduce the positive bias associated with evaluating one's own treatment team; and to examine all aspects of risk communication in adjuvant therapy. Creative measures to assist women in understanding risk statistics are needed. Bunker et al have recently proposed that a life table constructed from published statistics on national morbidity and mortality may be used to display the likelihood of developing or dying of a disease at any given moment.21 A similar approach could be used to display the likelihood of disease recurrence and premature death after cancer diagnosis. We believe that these and other information aids may contribute to informed patients' involvement in treatment decisions, and a more realistic understanding of prognosis. Acknowledgements We thank Dr Afaf Girgis, Dr Lyn Mann, Ms Kate White, Ms Joan Wilson and Ms Kim Hobbs for their assistance and advice; also the 26 clinicians who participated in this project, and the women who so willingly filled out the questionnaire. The research was funded by the National Health and Medical Research Council National Breast Cancer Centre of Australia. References Charlton RC. Breaking bad news. Med J Aust 1992; 157: 615-621. Butow PN, Kazemi J, Beeney LJ, et al. When the diagnosis is cancer: patient communication experiences and preferences. Cancer 1996; 77: 2630-2637. Oken D. What to tell cancer patients: a study of medical attitudes. JAMA 1961; 175: 1120-1128. Beisecker AE, Helmig I, Graham D, et al. Attitudes of oncologists, oncology nurses and patients from a women's clinic regarding medical decision making with older and younger breast cancer patients. Gerontologist 1994; 34: 505-512. Siminoff LA, Fetting JH, Abeloff MD. Doctor-patient communication about breast cancer adjuvant therapy. J Clin Oncol 1989; 7: 1192-1200. Sheldon JM, Fetting JH, Siminoff LA. Offering the option of randomized clinical trials to cancer patients who overestimate their prognoses with standard therapies. Cancer Invest 1993, 11: 57-62. Dunn SM, Butow PN, Tattersall MHN, et al. General information tapes inhibit recall of the cancer consultation. J Clin Oncol 1993; 11: 2279-2285. Mackillop WJ, Stewart WE, Ginsburg AD, Stewart SS. Cancer patients' perceptions of their disease and its treatment. Br J Cancer 1988; 58: 355-358. Evans DR, Blair V, Greenhalgh R, et al. The impact of genetic counselling on risk perceptions in women with a family history of breast cancer. Br J Cancer 1994; 70: 934-938. Lerman C, Rimer BK, Engstrom PF. Cancer risk notification: psychosocial and ethical implications. J Clin Oncol 1991; 9: 1275-1282. Hughes KK. Decision making by patients with breast cancer: the role of information in treatment decision selection. Oncol Nurs Forum 1993; 20: 623-628. Brown R, Dunn S, Butow P. Meeting patient expectations in the cancer consultation. Ann Oncol 1997; 8: 877-882. Glasziou P. SAM 2.1: a sample size calculator [computer program]. Sydney: NHMRC Clinical Trials Centre, University of Sydney, 1992. Armitage P, Berry G. Statistical methods in medical research. Oxford: Blackwell Scientific, 1994: 397. SPSS Advanced Statistics, TM6.1. Chicago; SPSS Inc, 1994. Australian women's year book. Canberra: Australian Bureau of Statistics, 1997. Degner LF, Kristjanson LJ, Bowman D, et al. Information needs and decisional preferences in women with breast cancer. JAMA 1997; 277: 1485-1492. Bilodeau BA, Degner LF. Informational needs, sources of information, and decisional roles in women with breast cancer. Oncol Nurs Forum 1996; 23: 691-696. Marteau TM. Framing of information: its influence upon decisions of doctors and patients. Br J Soc Psychol 1989; 28: 89-94. Degner LF, Sloan JA. Decision making during serious illness: what role do patients really want to play? J Clin Epidemiol 1992; 45: 941-950. Bunker JP, Houghton J, Baum M. Putting the risk of breast cancer in perspective. BMJ 1998; 317: 1307-1309. (Received 4 Jan, accepted 11 May, 1999) Authors' details University of Sydney, Sydney, NSW. Elizabeth A Lobb, BAdEd, MAppSci, Associate Lecturer, Medical Psychology Unit. Phyllis N Butow, PhD, MPH, Executive Director, Medical Psychology Unit; and Research Co-ordinator, Department of Psychological Medicine, Royal North Shore Hospital. Dianna T Kenny, PhD, MA, Associate Professor of Psychology, Faculty of Health Sciences. Martin H N Tattersall, MD, FRACP, Professor of Cancer Medicine, Department of Medicine. Reprints: Ms E A Lobb, Associate Lecturer, Medical Psychology Unit, Department of Psychological Medicine, University of Sydney, NSW 2006. Email: lizlobbATblackburn.med.usyd.edu.au 1: Hypothetical breast cancer scenario used in the questionnaire Sheila is a 54-year-old woman with breast cancer. Sheila has gone through the menopause. Sheila chose to have her breast cancer (tumour) removed by a lumpectomy, but she also had some of the lymph glands in her armpit removed. Sheila was advised to have radiotherapy after her lumpectomy. Sheila's breast cancer was small, and the lymph nodes under her arm were not affected with cancer. Her tumour contained receptors to oestrogen, suggesting that it may be sensitive to the effects of hormones. Sheila's doctor uses this information to decide if she would benefit from additional treatment. Sheila understands that any additional treatment other than surgery is called "adjuvant" therapy. Adjuvant therapy means giving treatment now after her surgery to try to prevent the cancer returning in the future. Following her breast cancer surgery, Sheila was told of her risk of having her cancer return (her prognosis) if she has no further treatment. This risk can be expressed in different ways. Back to text 2: Examples of questions to assess women's understanding of relative risk reduction Example A: Sheila's doctor told her that 30% of women with a cancer similar to hers will have their cancer come back within 5 years. If Sheila has additional treatment, the risk of her cancer coming back will be reduced by 30%. Tick one only If Sheila has additional treatment this means the risk of her cancer coming back within 5 years is zero. If Sheila has additional treatment this means the risk of her cancer coming back within 5 years is 21%. If Sheila has additional treatment this means the risk of her cancer coming back within 5 years is 30%. Example B: If Sheila's doctor says the median time for her breast cancer to return without further treatment is about 5 years, he/she means: The average time for Sheila's cancer to return is 5 years That 50% of women with breast cancer like Sheila's will have their cancer return within 5 years That the women whose cancer will come back will have it come back within 5 years I don't understand the word "median" Back to text 3: Demographic characteristics of the respondents (n = 100) to a questionnaire about provision of information on breast cancer prognosis*CategoryNumber of participantsAge (mean, 56 years; range, 35-88 years)Postcode City82 Country18Educational level Non-tertiary58 Tertiary42Occupation Professional/paraprofessional63 Non-professional36Marital status Married58 Other41English as first language84Working in medicine-related occupation22Time since diagnosis 1-2 months71 ≥3 months26Treatment for breast cancer Lumpectomy only14 Mastectomy only17 Lumpectomy + R38 Lumpectomy +R + C15 Mastectomy + C15Family member/friend with breast cancer Yes61 No38* Not all categories sum to 100 because of missing data. R = radiotherapy; C = chemotherapy. Back to text 4: Summary of questionnaire responses Understanding of risk Risk concepts tested Absolute risk of relapse: -- 86% of respondents gave a correct response. 30% relative risk reduction, with therapy, of an absolute risk of 30% (sample question A, Box 2): -- 47% gave a correct response; -- 28% thought additional treatment would reduce the risk of relapse to zero; and -- 25% thought the risk would remain at 30%. Median 5-year survival (sample question B, Box 2): -- 27% answered correctly; -- 43% thought it meant "average" survival; -- 10% thought that half the women not having adjuvant therapy would have their breast cancer return within 5 years; and -- 20% did not understand the term "median". Interpretation of a graphical representation of risk: -- 80% of respondents answered correctly. Distinguishing individual risk from population risk: -- 66% gave correct response. -- The remaining women believed that their cancer specialist knew whether or not they would respond to treatment. Association with demographic variables Mean number of correct responses, 3.4 (95% CI, 3.08-3.6); only one woman answered all six questions correctly. Women in professional employment (mean number of correct responses, 3.6 [95% CI, 3.2-4.0]) or paraprofessional employment (mean number of correct responses, 3.4 [95% CI, 3.0-3.8]) understood more prognostic information than women in non-professional employment (mean number of correct responses, 2.5 [95% CI, 1.7-3.4] [F2,87 = 4.24, P = 0.02]). No other variables were found to be associated with understanding of risk (eg, working in a medically related field; having tertiary qualifications; having had surgery, radiotherapy and/or chemotherapy for breast cancer; or time elapsed since consultation in which prognosis was discussed). Interpretation of words versus statistics There was no consistency in respondents' interpretation of "a good chance of remaining free of cancer" in statistical terms, nor agreement on the non-numerical interpretation of "a 30% risk" (17% thought it was a very high or high risk, 34% that it was a medium risk, and 49% that it was a low risk). Preferences for language 44% of respondents preferred "a 70% chance of cure", 13% preferred "a 7 in 10 chance", and the remainder had no preference. 53% of women preferred "a 30% chance of cancer coming back", 38% preferred "a small chance of cancer coming back", and the remainder had no preference. 49% of non-tertiary-educated women versus 24% of tertiary-educated women preferred the descriptive option (a "small" chance) (χ22 = 8.17, P = 0.02). 43% of women preferred the wording "70% chance of cure", 33% preferred "30% chance of the cancer coming back", and 25% had no preference. Preferences for framing of information Reasons given for choosing positively framed prognostic information: "a more positive/optimistic statement" and "encourages determination to manage treatment positively". Reasons given for choosing negatively framed prognostic information: "it emphasises the importance of additional treatment" and "more specific/precise". Importance attributed to prognostic information (Table) Over 90% of respondents regarded information about their chances of being cured, the staging of their cancer, and the chances that the recommended treatment would work as very important to their decision making. Nearly two-thirds of women regarded the 10-year survival rate with adjuvant therapy as very important information, and 45% wanted to know this rate without adjuvant therapy. These percentages are considerably higher than documented in previous studies.17,18 Women's ratings of the importance of different types of prognostic informationPrognostic informationVery importantSomewhat importantNot importantMy chances of being cured94%2%4%What things about my cancer influence my chances of being cured (eg, size of my cancer, whether lymph nodes are involved, etc)92%6%2%The chances that the recommended treatment will work91%7%2%How many women in my situation choosing to have the recommended treatment would be alive in 10 years60%29%11%Statistics about long term outcome of breast cancer50%32%18%How many women not choosing to have the recommended treatment are alive in 10 years45%35%20%The longest anyone in my situation has lived34%19%47%The shortest anyone in my situation has lived30%14%56%The risk of my cancer shortening my life compared with other life events (eg, heart disease, old age)45%23%32%The average time people in my situation have lived44%28%28%Back to text

Elizabeth A Lobb · Phyllis N Butow · Dianna T Kenny

Editorial Diagnosing osteoporosis: the value of quantitative ultrasound Currently, screening by quantitative ultrasound does not appear to be a good deal MJA 1999; 171: 295-296 For related articles see Maguire, Lobb et al & Naganathan et al Towards the end of the 20th century, the problems of diagnosis have not really changed. These remain how to evaluate the risk of disease, and then how to explain risk reduction clearly to patients. What has changed is our ability to predict risk, and to reduce that risk by the powerful public health, pharmacological or surgical interventions now available. What has this to do with osteoporosis, a microarchitectural disorder leading to fragility fracture? Osteoporosis is a classic chronic disorder in which the actual individual risk of clinical disease (in this case future fracture) is often difficult to quantify. The situation is similar for hypertension and hypercholesterolaemia. A few statistics may aid risk evaluation. A risk of 5%-10% or greater over five years of any osteoporotic fracture (arms, legs, pelvis, spine or rib) is generally regarded as requiring intervention. Interventions can reduce fracture risk by about 15%-50%.1,2 To prevent one fracture in such a population, between 20 and 133 patients need to be treated for five years. The risk of osteoporotic fracture depends on age. In women over the age of 65 years, the five-year risk rises dramatically from 5%, and to 20% in women over the age of 90 years.3 The presence of a previous osteoporotic fracture at least doubles the risk of future fracture.4-6 What does bone densitometry have to do with risk evaluation? It is this: when bone density is measured by dual energy x-ray absorptiometry (DEXA), then, for each standard deviation that the result falls below the mean for the individual's age (the Z score), the future risk of fracture doubles. Thus, an individual with a Z score of 22 has a fourfold greater risk of fracture than the average person of the same age. In a 65-year-old, this would give an actual five-year risk of fracture of about 30%. Because the bone density of fracture populations is independent of age, the concept of a standard deviation unit with respect to a fixed, low-risk population -- healthy 20-30-year-olds -- has been introduced (the T score) (see Figure). Significant microarchitectural deterioration (osteoporosis) is defined in bone density terms as a T score of -2.5 or less. Patients with a T score in this range have at least 5.65 times the risk of fracture relative to normal young individuals. However, their absolute five-year risk of fracture is related to the actual population risk at their age; for women aged 65-70 years this is about 18%.2 Factors other than age and bone density contribute to calculation of absolute fracture risk. Particularly important is a previous history of osteoporotic fracture. Currently, bone density risk evaluation by DEXA or quantitative computed tomography is supported by rebates from the Health Insurance Commission (HIC) for patients at high risk based on clinical information. Rebates are for evaluation of individuals who have had an osteoporotic fracture; who have clinical risk factors, such as corticosteroid treatment or premature menopause; or who have had osteoporosis diagnosed on a previous bone density test. The HIC will not fund the first bone density test in unselected individuals, otherwise known as population screening. This is because it is currently considered that bone density testing and the interventions consequent on finding high-risk individuals do not fulfil Australian cost-effectiveness criteria. However, individuals who do not meet current HIC criteria for bone density testing often decide to pay for the test themselves. How should we advise the HIC or the individual patient about the most effective screening for osteoporosis? Based on the epidemiology of fracture in Australia, it could be argued that, as well as the categories of high risk patients already outlined, all women aged 65-75 years should have bone density testing. This is because the population risk of future fracture rises dramatically in this age group. If screening is performed at an earlier age, the benefits are diluted by the small number of patients with detectable osteoporosis and the lack of controlled trial evidence that treatment prevents fracture at these ages when event rates are low. Could ultrasound become a "front end" to DEXA testing? In this issue of the Journal, the article by Naganathan et al7 is a useful contribution to the debate, providing a framework for considering the value of ultrasound screening. Naganathan et al report a simple method for calculating the benefits of testing bone structure by ultrasound compared with the current "gold standard" of DEXA. They achieved this by comparing the pre- and post-test probabilities of DEXA-defined osteoporosis (T score ≤ -2.5 at spine or hip sites) after ultrasound testing. Interestingly, they showed that 37% of their selected population had a normal combined quantitative ultrasound score, and that this finding completely excluded DEXA-defined osteoporosis. However, based on these data, an ultrasound test does not seem to be a good deal either for the individual patient or for the HIC, should it fund screening. The cost of screening 100 patients with ultrasound ($40 each) plus DEXA for those with abnormal ultrasound results ($80 x 63) would be $9040; the cost of screening with DEXA alone would be only $8000. Thus, both patients and the HIC should be advised not to pay for commercial ultrasound testing as a "front end" to DEXA at present. What about the future -- could ultrasound replace DEXA as the "gold standard" for predicting fracture? The answer is yes, possibly. The evidence-based approach would demand large prospective studies showing that ultrasound is better and cheaper than DEXA in predicting fracture, and that patients treated on the basis of ultrasound testing have a reduced risk of fracture compared with those who are not treated. To date, a couple of studies have taken the first steps to show effective fracture prediction in elderly women.8,9 It has taken 20 years to validate DEXA as a clinically useful predictor of patients who should be treated to prevent fracture, so don't hold your breath over ultrasound! Richard L Prince Associate Professor, University Department of Medicine Sir Charles Gairdner Hospital, Perth, WA Reprints: Associate Professor R L Prince, University Department of Medicine, Sir Charles Gairdner Hospital, Nedlands, WA 6009. Eddy DM, Johnston CC, Cummings SR, et al. Osteoporosis: review of the evidence for prevention, diagnosis and treatment and cost-effectiveness analysis. Osteoporos Int 1998; 8 Suppl 4: S7-S80. Cummings SR. Effect of alendronate on risk of fracture in women with low bone density but without vertebral fracture: Results from the Fracture Intervention Trial. JAMA 1998; 280: 2077-2082. Sanders KM, Seeman E, Ugoni AM, et al. The age- and gender-specific rate of fractures in Australia: a population based study. Osteoporos Int 1999. In press. Ross PD, Genant HK, Davis JW, et al. Predicting vertebral fracture incidence from prevalent fractures and bone density among non-black, osteoporotic women. Osteoporos Int 1993; 3: 120-126. Wasnich RD, Davis JW, Ross PD. Spine fracture risk is predicted by non-spine fractures. Osteoporos Int 1994; 4: 1-5. Cummings SR, Nevitt MC, Browner WS, et al. Risk factors for hip fracture in white women. N Engl J Med 1995; 332: 767-773. Naganathan V, March L, Hunter D, et al. Quantitative heel ultrasound as a predictor of osteoporosis. Med J Aust 1999; 171: 297-300. Bauer DC, Gluer CC, Cauley JA, et al. Broadband ultrasound attenuation predicts fractures strongly and independently of densitometry in older women. Arch Intern Med 1997; 157: 629-634. Porter RW, Miller C, Grainger D, Palmer SB. Prediction of hip fracture in elderly women: a prospective study. BMJ 1990; 301: 638-641. Back to text

Richard L Prince

Research Quantitative heel ultrasound as a predictor for osteoporosis Vasi Naganathan, Lyn March, David Hunter, Nick A Pocock, Joanna Markovey and Philip N Sambrook MJA 1999; 171: 297-300 For related articles, see Prince, Maguire & Lobb et al Abstract - Introduction - Methods - Results - Discussion - References - Authors' details - - More articles on Rheumatology Abstract Objective: To determine the diagnostic value of quantitative ultrasound (QUS) to predict bone mineral density (BMD) categories as defined by dual-energy x-ray absorptiometry. Design: Cross-sectional survey. Setting: Rheumatology department of a tertiary care hospital (Royal North Shore Hospital, Sydney, NSW), 1997-1998. Subjects: 326 healthy women aged 45-80 years who had volunteered for a twin study. Our study included both members of non-identical twin pairs but only one randomly selected member of identical twin pairs. Main outcome measures: BMD categories as defined by dual-energy x-ray absorptiometry of lumbar spine and left hip, and QUS of calcaneus; sensitivity, specificity and likelihood ratios (LRs) of QUS parameters to diagnose osteoporosis as defined by BMD. Results: The sensitivity of QUS to diagnose BMD osteoporosis varied between 9% and 47%, depending on the QUS parameter. The specificity of QUS was high (88%-100%). If all QUS parameters were normal, osteoporosis was unlikely (LR, 0-0.2). One QUS parameter, broadband ultrasound attenuation (BUA), was highly predictive of osteoporosis by BMD when in the osteoporotic range (LR, Infinity), but had low sensitivity (9%). QUS results in the osteoporotic range for other parameters and all QUS results in the osteopenic range were less predictive (LR, 1.0-5.2) of osteoporotic BMD. Conclusion: These results suggest that, for most of those tested for osteoporosis by QUS in the community, uncertainty remains about expected BMD. Introduction Quantitative heel ultrasound has recently been introduced in Australian pharmacies as a "screening" tool for osteoporosis. The technology is relatively cheap, radiation-free and portable, but its accuracy in diagnosing osteoporosis is unclear. Bone mineral density (BMD), measured by dual energy x-ray absorptiometry (DEXA), is the best predictor of fracture risk and is currently considered the "gold standard" for diagnosing osteoporosis. Although prospective studies have shown that quantitative ultrasound (QUS) predicts future fracture risk independently of BMD,1,2 most women with abnormal results will proceed to formal BMD measurement to assess the need for therapeutic intervention; women with normal QUS results may be reassured they do not need BMD measurement. It is unclear how many women have unnecessary further investigations or are falsely reassured. When used in this way, QUS has diagnostic value only if it can accurately predict BMD categories as determined by DEXA. Our aim was to examine the role of QUS in predicting BMD diagnostic categories. We determined conventional sensitivity and specificity, as well as likelihood ratios (LRs). These have the advantage of allowing test results to be assessed for several diagnostic categories rather than only at a single cut-off between "normal" and "abnormal".3 Methods Subjects and setting The study was a cross-sectional survey of healthy women aged 45-80 years who had volunteered to take part in a twin study. They were recruited from the Australian Twin Registry and media advertising. Our ultrasound study included both members of each non-identical twin pair and one randomly selected member of each identical twin pair. The study was conducted in the Rheumatology Department of the Royal North Shore Hospital, Sydney, NSW (a tertiary care hospital), in 1997 and 1998. It was approved by the hospital's Human Research Ethics Committee. Assessment Subjects had BMD measurements of their lumbar spine (L1-L4) and left hip (neck of femur and total hip) by DEXA using a Hologic QDR450 instrument (Hologic Inc, Waltham, Mass, USA). The same machine was used on all patients. QUS of the left calcaneus was performed on the same day using a CUBA Mark II ultrasound instrument (McCue Ultrasonics, London, UK). The two most commonly used QUS parameters were measured: broadband ultrasound attenuation (BUA), which is thought to reflect bone mass and architecture, and velocity of sound (VOS), which reflects mass and elasticity of bone.4 Analyses Each BMD and QUS value was converted to a T score (number of standard deviations from the population mean for young, healthy, sex-matched adults). This population mean was estimated from measurements in 50 women aged 20-30 years who also took part in the twin study. T scores were used to categorise BMD values as normal (T > -1) or indicating osteopenia (T, -2.5 to -1) or osteoporosis (T < -2.5), as proposed by a working party of the World Health Organization.5 QUS values were classified in the same way. Although no consensus has been reached on what T-score cut-offs and diagnostic categories to use with QUS, the instrument used commonly in Australian pharmacies uses the WHO criteria and cut-off values. Subjects were classified as having osteoporosis if at least one of the three BMD measurements (lumbar spine, neck of left femur or total left hip) indicated osteoporosis, and as having osteopenia if at least one measurement indicated osteopenia but none indicated osteoporosis. BUA and VOS results were combined as a cQUS category: this was defined as normal if both results were normal, as osteopenic if either indicated osteopenia but neither indicated osteoporosis, and as osteoporotic if either indicated osteoporosis. Some QUS scanners calculate a stiffness parameter (unrelated to mechanical stiffness) from a linear combination of normalised BUA and VOS values. We calculated stiffness in an analogous manner,6 and categorised it as normal, osteopenic or osteoporotic based on T scores in the same way as other QUS values. We calculated the sensitivity and specificity of QUS parameters in predicting BMD-defined osteoporosis and osteopenia. We also calculated the likelihood ratio (LR) for each QUS result (sensitivity/1 - specificity), defined as the ratio of the probability of the particular QUS result (normal, osteopenic or osteoporotic) in women with BMD-defined osteoporosis or osteopenia to the probability of the same result in women with normal BMD.7 As there is no consensus on what QUS cut-off values should be used to diagnose osteoporosis, the statistical analyses were repeated using a range of QUS T-score cut-off values for osteoporosis between -2.5 and -1.0. Results Subjects and osteoporosis There were 326 subjects, with mean age 58.5 years; 255 (78%) were postmenopausal. Of the 326, 47 (14%) had a BMD measurement indicating osteoporosis at one or more of the three sites where BMD was measured, and a further 160 (49%) had a measurement indicating osteopenia. QUS results are compared with BMD results in Box 1. The percentage of women with values in the osteoporotic range varied between QUS parameters (1% for BUA, 17% for VOS and the combined BUA-VOS category, and 14% for stiffness). Sensitivity and specificity of QUS Sensitivity and specificity of QUS for predicting BMD diagnostic categories are shown in Box 2. Sensitivity and specificity varied between QUS parameters. A BUA result in the osteoporotic range (T < -2.5) had very low sensitivity for predicting BMD-defined osteoporosis (9%), but high specificity (100%). In contrast, VOS, cQUS and stiffness results in the osteoporotic range had sensitivities of almost 50%, and specificities that were again high. For predicting either osteoporosis or osteopenia, stiffness had the best combination of sensitivity (77%) and specificity (81%). Positive and negative predictive values are also shown in Box 2. Negative predictive values were high (87%-91%) for QUS as a predictor of BMD-defined osteoporosis versus osteopenia/normal BMD. This indicated that a woman with BMD-defined osteoporosis was unlikely to have a QUS result in the normal-osteopenic range. Likelihood ratios of QUS LRs for different QUS results to predict BMD-defined osteoporosis are summarised in Box 2 and interpreted in Box 3. Normal QUS result: A BUA, VOS, cQUS or stiffness result in the normal range had a low LR (0-0.2) (ie, a normal result significantly lowered the odds or probability of the woman's having BMD-defined osteoporosis). Osteoporotic QUS result: A BUA result in the osteoporotic range had an LR approaching infinity and so was highly predictive of BMD-defined osteoporosis. In contrast, a VOS, cQUS or stiffness result in the osteoporotic range had a much lower LR (4.0-5.2), increasing the odds of BMD-defined osteoporosis, but to a lesser extent than a BUA result in the osteoporotic range. Osteopenic QUS result: QUS results in the osteopenic range were less predictive, as LR ranged from 1.0 to 2.4. Between 37% and 50% of subjects (depending on the QUS parameter) had results in this range (Box 1). LRs for predicting low BMD (ie, osteoporosis or osteopenia; BMD T score < -1) are also shown in Box 2, and followed a similar pattern to LRs for predicting BMD-defined osteoporosis. When QUS T-score cut-off values for osteoporosis were increased from -2.5 to -1.0, sensitivity increased, but at the expense of decreasing specificity and LR (data not shown). For example, a BUA cut-off of -1.0 gave 83% sensitivity, 69% specificity and LR, 2.6. Corresponding values for a VOS cut-off of -1.0 were 96%, 41% and 1.6. Discussion We found that QUS had variable usefulness in predicting BMD categories. Specificity for predicting BMD-defined osteoporosis was high for all QUS parameters (88%-100%), but sensitivity was low and variable (9%-47%). A BUA result in the osteoporotic range was highly predictive of BMD-defined osteoporosis (LR, Infinity), but had low sensitivity (9%). Results in the osteoporotic range for other QUS parameters and in the osteopenic range for all QUS parameters were less predictive of BMD-defined osteoporosis (LRs, 1.0-5.2). In the light of our study, how can we interpret QUS results? If a BUA result is in the osteoporotic range (LR, Infinity), then BMD-defined osteoporosis is almost certain (predictive value, 100%). However, the low sensitivity of BUA (9%) means that many women with osteoporosis would be missed if BUA alone were used. If results are normal for both BUA and VOS (cQUS normal; LR, 0), we can confidently rule out BMD-defined osteoporosis. If LR is 0, then, no matter what the pre-test probability of osteoporosis, the post-test probability will be < 5% (Box 3). All results in the osteopenic range, and VOS and stiffness results in the osteoporotic range, are less predictive of BMD category. Therefore, if QUS were performed on a population similar to ours (14% prevalence of osteoporosis), then (from Box 1) 1% would have a BUA result in the osteoporotic range (likely to have osteoporosis) and 33% would have both BUA and VOS results in the normal range (osteoporosis could fairly confidently be ruled out, with a post-test probability < 5%). However, there would be a degree of uncertainty about the remaining 66%, who would then need a DEXA scan to identify those with osteoporosis. Previous studies of QUS as a predictor of BMD have generally used conventional sensitivity and specificity analyses only, not LRs, and have not used the WHO BMD definitions. For example, two community-based cross-sectional studies on 700 postmenopausal10 and 1000 perimenopausal women,11 respectively, found that there was a 40%-50% overlap in the number of women in the lowest quartile of both DEXA and QUS measurements. Two other studies found QUS parameters to have a sensitivity of 65%-70% for BMD in the lowest quartile.6,12 Only one study other than ours has evaluated QUS in terms of WHO BMD definitions. It found BUA and VOS to have higher sensitivities, of 77% and 69%, respectively, for diagnosing osteoporosis in 100 women aged 60-69 years.13These higher sensitivities may have been due to use of higher BUA and VOS cut-off values. As expected, specificities were lower than in our study. The ultrasound instrument used in this study, the McCue Cuba Mark II, is not identical to the Achilles ultrasound instrument (Lunar, Madison, Wis, USA) used in Australian pharmacies. Nevertheless, a comparison of the two machines found that BUA measurements on a Cuba Mark II instrument were highly correlated with "stiffness" measurements on a Lunar Achilles instrument (r = 0.906; 95% CI, 0.873-0.931).14 Another study compared measurements of 30 women between the Cuba Mark II used in our study and an Achilles, finding an r value of 0.8.15 Therefore, it is unlikely that the Achilles instrument would be a significantly better predictor of BMD than our Cuba Mark II. There is no consensus on what cut-off values to use with QUS to diagnose osteoporosis. We found that changing the cut-off could achieve higher sensitivity, but only by accepting higher rates of false positives (lower specificity) and less discriminating LRs. Although there is enough evidence to support the use of QUS as an independent predictor of fracture risk,1,2 our study shows that QUS should not been seen as a substitute for BMD measurement. The results of our study suggest that, when women in the community are "screened" for osteoporosis using QUS, a few women will be confidently identified with BMD-defined osteoporosis. Another small group will be able to be reassured that they are unlikely to have osteoporosis. However, for the great majority, the presence or absence of osteoporosis will remain uncertain. References Hans D, Dargent MP, Schott AM, et al. Ultrasonographic heel measurements to predict hip fracture in elderly women: the EPIDOS prospective study. Lancet 1996; 348 (9026): 511-514. Bauer DC, Gluer CC, Cauley JA, et al. Broadband ultrasound attenuation predicts fractures strongly and independently of densitometry in older women. A prospective study. Study of Osteoporotic Fractures Research Group. Arch Intern Med 1997; 157: 629-634. Sackett DL, Haynes BR, Guyatt GH, et al. Clinical epidemiology. A basic science for clinical medicine. 2nd ed. Boston: Little, Brown and Company, 1991. Gluer CC, Wu CY, Jergas M, et al. Three quantitative ultrasound parameters reflect bone structure. Calcif Tissue Int 1994; 55: 46-52. World Health Organization Study Group. Assessment of fracture risk and its application to screening for prostmenopausal osteoporosis. World Health Organ Tech Rep Ser 1994; 843: 1-129. Herd RJ, Blake GM, Miller CG, et al. The ultrasonic assessment of osteopenia as defined by dual X-ray absorptiometry. Br J Radiol 1994; 67: 631-635. Fletcher RH, Fletcher SW, Wagner EH. Clinical epidemiology: the essentials. 3rd ed. Baltimore: Williams & Wilkins, 1988: 65. Fagan TJ. Normogram for Bayes theorem [letter]. N Engl J Med 1975: 293; 257. Australian National Consensus Conference 1996. The prevention and management of osteoporosis. Consensus statement. Med J Aust 1997; 167 Suppl: S1-S15. van Daele Burger H, Algra D, et al. Age-associated changes in ultrasound measurements of the calcaneus in men and women: the Rotterdam Study. J Bone Miner Res 1994; 9: 1751-1757. Massie A, Reid DM, Porter RW. Screening for osteoporosis: comparison between dual energy X-ray absorptiometry and broadband ultrasound attenuation in 1000 perimenopausal women. Osteoporos Int 1993; 3: 107-110. Young H, Howey S, Purdie DW. Broadband ultrasound attenuation compared with dual-energy X-ray absorptiometry in screening for postmenopausal low bone density. Osteoporos Int 1993; 3: 160-164. Langton CM, Ballard PA, Bennett DK, Purdie DW. A comparison of the sensitivity and specificity of calcaneal ultrasound measurements with clinical criteria for bone densitometry (DEXA) referral. Clin Rheumatol 1997; 16: 117-118. Greenspan SL, Bouxsein ML, Melton ME, et al. Precision and discriminatory ability of calcaneal bone assessment technologies. J Bone Miner Res 1997; 12: 1303-1313. Harris ND, Griffiths MR, Nguyen TV, et al. Quantitative ultrasound of the heel: a comparison of Lunar and McCue instruments [abstract]. Proceedings of the Australian and New Zealand Bone and Mineral Society 7th Annual Scientific Meeting. 1997. 29 Sept-1 Oct; Canberra, ACT: 62. (Received 24 Dec 1998, accepted 9 Jul 1999) Authors' details Department of Rheumatology, Royal North Shore Hospital, Sydney, NSW. Vasi Naganathan, FRACP, Research Scholar; Lyn March, FRACP, FAFPHM, Staff Specialist; David Hunter, MB BS, Advanced Physician Trainee in Rheumatology; Joanna Markovey, MSc, Research Bone Densitometry Technician; Philip N Sambrook, FRACP, MD, Head of Department. Department of Nuclear Medicine, St Vincent's Hospital, Sydney, NSW. Nick A Pocock, FRACP, MD, Senior Staff Specialist. Reprints will not be available from the authors. Correspondence: Dr V Naganathan, Department of Rheumatology, Royal North Shore Hospital, St Leonards, NSW 2065. Email: vasinATmed.usyd.edu.au 1: Association between quantitative heel ultrasound (QUS) results and bone mineral density* in 326 women aged 45-80 yearsBone mineral densityQUS resultNormal (n = 119)Osteopenia (n = 160)Osteoporosis (n = 47)Total (n = 326)Broadband ultrasound (BUA) Normal103918202 (62%) Osteopenia166935120(37%) Osteoporosis0044 (1%)Velocity of sound (VOS) Normal81332116 (36%) Osteopenia369623155 (47%) Osteoporosis2312255 (17%)Combined category (cQUS) Normal79280107 (33%) Osteopenia3810125164 (50%) Osteoporosis2312255 (17%)Stiffness Normal96444144 (44%) Osteopenia209421135 (41%) Osteoporosis3222247 (14%) * Measured by dual energy x-ray absorptiometry. Combined result for broadband ultrasound and velocity of sound: normal if both normal; osteopenic if either osteopenic and neither osteoporotic; and osteoporotic if either osteoporotic. Back to text 2: Use of ultrasound parameters to predict osteoporosis or osteopenia defined by dual energy x-ray absorptiometry (DEXA) measurement of bone mineral densityPredictive valuesLikelihood ratio for ultrasound measurement (95% confidence interval)Sensitivity SpecificityPositiveNegativeNormalOsteopeniaOsteoporosisTo predict osteoporosisBroadband ultrasound (BUA)9%100%100%87%0.2 (0.11-0.38)2.4 (1.9-3.1)InfinityVelocity of sound (VOS)46%88%40%91%0.1 (0.03-0.4)1.0 (0.7-1.4)4.0 (2.6-6.2)Combined category* (cQUS)47%88%40%91%0(1.1 (0.8-1.5)4.0 (2.6-6.2)Stiffness47%91%46%91%0.2 (0.08-0.5)1.1 (0.8-1.6)5.2 (3.2-8.4)To predict osteoporosis or osteopeniaBroadband ultrasound (BUA)52%87%87%51%0.6 (0.5-0.7)3.7 (2.3-6.0)InfinityVelocity of sound (VOS)83%68%82%70%0.25 (0.18-0.35)1.9 (1.4-2.6)15.0 (3.7-60.5)Combined category* (cQUS)86%66%82%74%0.20 (0.14-0.29)1.9 (1.4-2.5)15.2 (3.8-61.3)Stiffness77%81%87%67%0.3 (0.2-0.4)3.3 (2.2-5.0)8.4 (2.7-26.5) DEXA = dual energy x-ray absorptiometry. * Combined broadband ultrasound attenuation and velocity of sound category. Back to text 3: Interpretation of likelihood ratios The likelihood ratio (LR) indicates how much a test result raises or lowers the probability of an individual's having "disease". It can be used to determine post-test probability of a disease from the estimated pre-test probability using a normogram (Figure). Thus, if an early postmenopausal woman (aged 60-64 years) had a pre-test probability of osteoporosis of 15%,9 then, from the normogram, a cQUS result in the osteoporot ic range (LR, 4.0) would increase her probability to 40%. If she had a very high pre-test probability of osteoporosis, for example 50%, because of multiple risk factors (eg, including family history of osteoporosis and recent wrist fracture after a fall), then a cQUS result in the osteoporotic range would increase her probability to 80%. A BUA result in the osteoporotic range (LR, Infinity) would make BMD-defined osteoporosis highly likely, no matter what the pre-test risk. However, a cQUS result in the normal range (LR, 0) would make BMD-defined osteoporosis unlikely. QUS results in the osteopenic range, with LRs closer to unity (LR, 1.0-2.4), would make BMD category far less certain. Normogram for applying likelihood ratios, adapted from Fagan.8 Lines show post-test probabilities when LR = 4.0 and pre-test probabilities are 15% and 50%, respectively. Back to text

Vasi Naganathan · Lyn March · David Hunter · Nick A Pocock · Joanna Markovey · Philip N Sambrook