39 results
Novel mitochondrial mechanisms of cognitive regulation in subjects with cognitive impairments
- B. Bigio, R. Lima-Filho, O. Barnhill, F. Sudo, C. Drummond, N. Assunção, B. Vanderborght, F. Tovar-Moll, P. Mattos, S. Ferreira, F. De Felice, M. Lourenco, C. Nasca
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- Journal:
- European Psychiatry / Volume 66 / Issue S1 / March 2023
- Published online by Cambridge University Press:
- 19 July 2023, p. S611
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Introduction
Prior mechanistic studies in rodents showed decreased levels of the pivotal mitochondrial metabolite acetyl-L-carnitine (LAC) in relation to cognitive deficits and depressive-like behavior (Neuron 2017, 10.1016/j.neuron.2017.09.020, PNAS 2013, 10.1073/pnas.1216100110), providing the basis for the current translational study.
ObjectivesThe main objective of this work was to ascertain the role of this specific mitochondrial signaling pathway in subjects with cognitive impairments (CI), and potential sex differences in these mechanisms.
MethodsWe used computational approaches, ultraperformance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) and available plasma samples from a well-characterized cohort of 71 subjects, including subjects with CI and age- and sex-matched cognitively healthy controls (HC).
ResultsOur newest findings showed decreased levels of LAC in subjects with CI as compared to age- and sex-matched HC. We also found important sex differences in carnitine levels in relation to cognitive function as assessed by using the Mini Mental Status Exam (MMSE). Specifically, the degree of carnitine deficiency reflected the severity of cognitive dysfunction in a sex-specific manner. Using computational approaches, we found that the integration of these mitochondrial measures with canonical biomarkers improves diagnostic accuracy.
ConclusionsThe current findings of sex differences in carnitine deficiency in subjects with CI suggest a possible sex-specific mitochondrial phenotype of vulnerability to cognitive dysfunction, and point to LAC-related mitochondrial metabolism as a new signaling pathway of cognitive regulation.
Disclosure of InterestNone Declared
Obsessive-compulsive disorder in the elderly: A report from the International College of Obsessive-Compulsive Spectrum Disorders (ICOCS)
- B. Dell’Osso, B. Benatti, C.I. Rodriguez, C. Arici, C. Palazzo, A.C. Altamura, E. Hollander, N. Fineberg, D.J. Stein, H. Nicolini, N. Lanzagorta, D. Marazziti, S. Pallanti, M. Van Ameringen, C. Lochner, O. Karamustafalioglu, L. Hranov, M. Figee, L. Drummond, J. Grant, D. Denys, D. Cath, J.M. Menchon, J. Zohar
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- European Psychiatry / Volume 45 / September 2017
- Published online by Cambridge University Press:
- 23 March 2020, pp. 36-40
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Introduction:
Obsessive-compulsive disorder (OCD) is a highly disabling condition, with frequent early onset. Adult/adolescent OCD has been extensively investigated, but little is known about prevalence and clinical characterization of geriatric patients with OCD (G-OCD = 65 years). The present study aimed to assess prevalence of G-OCD and associated socio-demographic and clinical correlates in a large international sample.
Methods:Data from 416 outpatients, participating in the ICOCS network, were assessed and categorized into 2 groups, age < vs = 65 years, and then divided on the basis of the median age of the sample (age < vs = 42 years). Socio-demographic and clinical variables were compared between groups (Pearson Chi-squared and t tests).
Results:G-OCD compared with younger patients represented a significant minority of the sample (6% vs 94%, P < .001), showing a significantly later age at onset (29.4 ± 15.1 vs 18.7 ± 9.2 years, P < .001), a more frequent adult onset (75% vs 41.1%, P < .001) and a less frequent use of cognitive-behavioural therapy (CBT) (20.8% vs 41.8%, P < .05). Female gender was more represented in G-OCD patients, though not at a statistically significant level (75% vs 56.4%, P = .07). When the whole sample was divided on the basis of the median age, previous results were confirmed for older patients, including a significantly higher presence of women (52.1% vs 63.1%, P < .05).
Conclusions:G-OCD compared with younger patients represented a small minority of the sample and showed later age at onset, more frequent adult onset and lower CBT use. Age at onset may influence course and overall management of OCD, with additional investigation needed.
28 A systematic review in quality of life of patients with meningiomas: Effort towards developing a disease-specific questionnaire
- A. Mansouri, V. Lam Shin Cheung, B. Karmur, J. Lam Shin Cheung, L. Hachem, S. Taslimi, F. Nassiri, S. Suppiah, K. Drummond, T. Walbert, R. Goldbrunner, Y. Santarius, M. D. Jenkinson, J. Snyder, I. Lee, K. Devine, C. Schichor, K. D. Aldape, G. Zadeh
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- Journal:
- Canadian Journal of Neurological Sciences / Volume 45 / Issue S3 / June 2018
- Published online by Cambridge University Press:
- 27 July 2018, pp. S5-S6
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BACKGROUND: Meningiomas are the most common primary benign brain tumors in adults. Given the extended life expectancy of most meningiomas, consideration of quality of life (QOL) is important when selecting the optimal management strategy. There is currently a dearth of meningioma-specific QOL tools in the literature. OBJECTIVE: In this systematic review, we analyze the prevailing themes and propose toward building a meningioma-specific QOL assessment tool. METHODS: A systematic search was conducted, and only original studies based on adult patients were considered. QOL tools used in the various studies were analyzed for identification of prevailing themes in the qualitative analysis. The quality of the studies was also assessed. RESULTS: Sixteen articles met all inclusion criteria. Fifteen different QOL assessment tools assessed social and physical functioning, psychological, and emotional well-being. Patient perceptions and support networks had a major impact on QOL scores. Surgery negatively affected social functioning in younger patients, while radiation therapy had a variable impact. Any intervention appeared to have a greater negative impact on physical functioning compared to observation. CONCLUSION: Younger patients with meningiomas appear to be more vulnerable within social and physical functioning domains. All of these findings must be interpreted with great caution due to great clinical heterogeneity, limited generalizability, and risk of bias. For meningioma patients, the ideal QOL questionnaire would present outcomes that can be easily measured, presented, and compared across studies. Existing scales can be the foundation upon which a comprehensive, standard, and simple meningioma-specific survey can be prospectively developed and validated.
Nitrate supplementation improves physical performance specifically in non-athletes during prolonged open-ended tests: a systematic review and meta-analysis
- Helton O. Campos, Lucas R. Drummond, Quezia T. Rodrigues, Frederico S. M. Machado, Washington Pires, Samuel P. Wanner, Cândido C. Coimbra
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- British Journal of Nutrition / Volume 119 / Issue 6 / 28 March 2018
- Published online by Cambridge University Press:
- 19 March 2018, pp. 636-657
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- 28 March 2018
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Nitrate (NO3−) is an ergogenic nutritional supplement that is widely used to improve physical performance. However, the effectiveness of NO3− supplementation has not been systematically investigated in individuals with different physical fitness levels. The present study analysed whether different fitness levels (non-athletes v. athletes or classification of performance levels), duration of the test used to measure performance (short v. long duration) and the test protocol (time trials v. open-ended tests v. graded-exercise tests) influence the effects of NO3− supplementation on performance. This systematic review and meta-analysis was conducted and reported according to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A systematic search of electronic databases, including PubMed, Web of Science, SPORTDiscus and ProQuest, was performed in August 2017. On the basis of the search and inclusion criteria, fifty-four and fifty-three placebo-controlled studies evaluating the effects of NO3− supplementation on performance in humans were included in the systematic review and meta-analysis, respectively. NO3− supplementation was ergogenic in non-athletes (mean effect size (ES) 0·25; 95 % CI 0·11, 0·38), particularly in evaluations of performance using long-duration open-ended tests (ES 0·47; 95 % CI 0·23, 0·71). In contrast, NO3− supplementation did not enhance the performance of athletes (ES 0·04; 95 % CI −0·05, 0·15). After objectively classifying the participants into different performance levels, the frequency of trials showing ergogenic effects in individuals classified at lower levels was higher than that in individuals classified at higher levels. Thus, the present study indicates that dietary NO3− supplementation improves physical performance in non-athletes, particularly during long-duration open-ended tests.
About the authors
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp xviii-xxii
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7 - Multiple test results
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 16 October 2014, pp 165-208
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Summary
Even though the diagnostic radiologist examines black-and-white images, the information that is derived from the images is hardly ever black-and-white.
M.G. Myriam HuninkIntroduction
In the previous chapters we focused on dichotomous test results, e.g., fecal occult blood is either present or absent. Test results can conveniently be dichotomized, and thinking in terms of dichotomous test results is generally helpful. Distinguishing patients with and without the target disease is useful for the purpose of subsequent decision making because most medical actions are dichotomous. In reality, however, most test results have more than two possible outcomes. Test results can be categorical, ordinal, or continuous. For example, categories of a diagnostic imaging test may be defined by key findings on the images. These categories may be ordered (intuitively) according to the observer’s confidence in the diagnosis, based on the findings. As an example, abnormalities seen on mammography are commonly reported as definitely malignant, probably malignant, possibly malignant, probably benign, or definitely benign. As we shall see later in this chapter, it makes sense to order the categories (explicitly) according to increasing likelihood ratio (LR). Some test results are inherently ordinal, e.g., the five categories of a Papanicolaou smear (test for cervical cancer) are ordinal. Results of biochemical tests are usually given on a continuous scale, which may be reduced to an ordinal scale by grouping the test results. Thus, a test result on a continuous scale can be considered a result on an ordinal scale with an infinite number of very narrow categories. Scores from prediction models are on an ordinal scale if there are a finite number of possible scores, and on a continuous scale if there are an infinite number of scores. When test results are categorical, ordinal, or continuous, we have to consider many test results Ri, where i can be any value from two (the case we have considered in Chapter 5 and Chapter 6, T+ and T−) up to any number of categories. Interpretation of a test result on an ordinal scale can be considered a generalization of the situation of dichotomous test results.
2 - Managing uncertainty
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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Summary
Much of medical training consists of learning to cope with pervasive uncertainty and with the limits of medical knowledge. Making serious clinical decisions on the basis of conflicting, incomplete, and untimely data is routine.
J.D. McCueIntroduction
Much of clinical medicine and health care involves uncertainties: some reducible, but some irreducible despite our best efforts and tests. Better decisions will be made if we are open and honest about these uncertainties, and develop skills in estimating, communicating, and working with such uncertainties. What types of uncertainty exist? Consider the following example.
Needlestick injury:
It has been a hard week. It is time to go home when you are called to yet another heroin overdose: a young woman has been found unconscious outside your clinic. After giving intravenous (IV) naloxone (which reverses the effects of heroin), you are accidentally jabbed by the needle. After her recovery, despite your reassurances, the young woman flees for fear of the police. As the mêlée settles, the dread of human immunodeficiency virus (HIV) infection begins to develop. You talk to the senior doctor about what you should do. She is very sympathetic, and begins to tell you about the risks and management. The good news is that, even if the patient was HIV-positive, a needlestick injury rarely leads to HIV infection (about 3 per 1000). And if she was HIV-positive then a basic two-drug regime of antivirals such as zidovudine (AZT) plus lamivudine are likely to be able to prevent most infections (perhaps 80%).
Unfortunately, the HIV status of the young woman who had overdosed is unknown. Since she was not a patient of your clinic, you are uncertain about whether she is infected, but think that it is possible since she is an IV drug user. The Centers for Disease Control and Prevention (CDC) guidelines (1) suggest: ‘If the exposure source is unknown, use of post-exposure prophylaxis should be decided on a case-by-case basis. Consider the severity of exposure and the epidemiologic likelihood of HIV.’ What do you do?
Dedication
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 16 October 2014, pp v-vi
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11 - Estimation, calibration, and validation
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 334-355
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Summary
Essentially, all models are wrong, but some are useful.
George E. P. BoxIntroduction
As discussed in Chapter 8, ‘good decision analyses depend on both the veracity of the decision model and the validity of the individual data elements.’ The validity of each individual data element relies on the comprehensiveness of the literature search for the best and most appropriate study or studies, criteria for selecting the source studies, the design of the study or studies, and methods for synthesizing the data from multiple sources. Nonetheless, Sir Michael David Rawlins avers that ‘Decision makers have to incorporate judgements, as part of their appraisal of the evidence, in reaching their conclusions. Such judgements relate to the extent to which each of the components of the evidence base is “fit for purpose.” Is it reliable?’(1) Because the integration of a multitude of these ‘best available’ data elements forms the basis for model results, some individuals refer to decision analyses as black boxes, so this last question applies particularly to the overall model predictions. Consequently, assessing model validity becomes paramount. However, prior to assessing model validity, model construction requires attention to parameter estimation and model calibration. This chapter focuses on parameter estimation, calibration, and validation in the context of Markov and, more generally, state-transition models (Chapter 10) in which recurrent events may occur over an extended period of time. The process of parameter estimation, calibration, and validation is iterative: it involves both adjustment of the data to fit the model and adjustment of the model to fit the data.
Parameter estimation
Survival analysis involves determining the probability that an event such as death or disease progression will occur over time. The events modeled in survival analysis are called ‘failure’ events, because once they occur, they cannot occur again. ‘Survival’ is the absence of the failure event. The failure event may be death, or it may be death combined with a non-fatal outcome such as developing cancer or having a heart attack, in which case the absence of the event is referred to as event-free survival. Commonly used methods for survival analysis include life-table analysis, Kaplan–Meier product limit estimates, and Cox proportional hazards models. A survival curve plots the probability of being alive over time (Figure 11.1).
3 - Choosing the best treatment
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 53-77
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Summary
Firstly, do no (net) harm.
(adapted from) HippocratesIntroduction
Some treatment decisions are straightforward. For example, what should be done for an elderly patient with a fractured hip? Inserting a metal pin has dramatically altered the management: instead of lying in bed for weeks or months waiting for the fracture to heal while blood clots and pneumonia threatened, the patient is now ambulatory within days. The risks of morbidity and mortality are both greatly reduced. However, many treatment decisions are complex. They involve uncertainties and trade-offs that need to be carefully weighed before choosing. Tragic outcomes may occur no matter which choice is made, and the best that can be done is to minimize the overall risks. Such decisions can be difficult and uncomfortable to make. For example, consider the following historical dilemma.
Benjamin Franklin and smallpox
Benjamin Franklin argued implicitly in favor of the application to individual patients of probabilities based on previous experience with similar groups of patients. Before Edward Jenner’s discovery in 1796 of cowpox vaccination for smallpox, it was known that immunity from smallpox could be achieved by a live smallpox inoculation, but the procedure entailed a risk of death. When a smallpox epidemic broke out in Boston in 1721, the physician Zabdiel Boylston consented, at the urging of the clergyman Cotton Mather, to inoculate several hundred citizens. Mather and Boylston reported their results (1):
Out of about ten thousand Bostonians, five thousand seven hundred fifty-nine took smallpox the natural way. Of these, eight hundred eighty-five died, or one in seven. Two hundred eighty-six took smallpox by inoculation. Of these, six died, or one in forty-seven.
5 - Interpreting diagnostic information
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 16 October 2014, pp 118-144
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Summary
The interpretation of new information depends on what was already known about the patient.
Harold SoxDiagnostic information and probability revision
Physicians have at their disposal an enormous variety of diagnostic information to guide them in decision making. Diagnostic information comes from talking to the patient (symptoms, such as pain, nausea, and breathlessness), examining the patient (signs, such as abdominal tenderness, fever, and blood pressure), and from diagnostic tests (such as blood tests, X-rays, and electrocardiograms (ECGs)) and screening tests (such as Papanicolaou smears for cervical cancer or cholesterol measurements).
Physicians are not the only ones that have to interpret diagnostic information. Public policy makers in health care are equally concerned with understanding the performance of diagnostic tests. If, for example, a policy maker is considering a screening program for lung cancer, he/she will need to understand the performance of the diagnostic tests that can detect lung cancer in an early phase of the disease. In public policy making, other types of ‘diagnostic tests’ may also be relevant. For example, a survey with a questionnaire in a population sample can be considered analogous to a diagnostic test. And performing a trial to determine the efficacy of a treatment is in fact a ‘test’ with the goal of getting more information about that treatment.
list of Abbreviations
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp xvi-xvii
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6 - Deciding when to test
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 145-164
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Summary
Before ordering a test ask: What will you do if the test is positive? What will you do if the test is negative? If the answers are the same, then don’t do the test.
Poster in an Emergency DepartmentIntroduction
In the previous chapter we looked at how to interpret diagnostic information such as symptoms, signs, and diagnostic tests. Now we need to consider when such information is helpful in decision making. Even if they reduce uncertainty, tests are not always helpful. If used inappropriately to guide a decision, a test may mislead more than it leads. In general, performing a test to gain additional information is worthwhile only if two conditions hold: (1) at least one decision would change given some test result, and (2) the risk to the patient associated with the test is less than the expected benefit that would be gained from the subsequent change in decision. These conditions are most likely to be fulfilled when we are confronted with intermediate probabilities of the target disease, that is, when we are in a diagnostic ‘gray zone.’ Tests are least likely to be helpful either when we are so certain a patient has the target disease that the negative result of an imperfect test would not dissuade us from treating, or, conversely, when we are so certain that the patient does not have the target disease that a positive result of an imperfect test would not persuade us to treat. These concepts are illustrated in Figure 6.1, which divides the probability of a disease into three ranges:
do not treat (for the target disease) and do not test, because even a positive test would not persuade us to treat;
test, because the test will help with treatment decisions or with follow-up; and
treat and do not test, because even a negative test would not dissuade us from treating.
Treat implies patient management as if disease is present and may imply initiating medical therapy, performing a therapeutic procedure, advising a lifestyle or other adjuvant intervention, or a combination of these. Do not treat implies patient management as if disease is absent and usually means risk factor management, lifestyle advice, self-care and/or watchful waiting.
1 - Elements of decision making in health care
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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Summary
And take the case of a man who is ill. I call two physicians: they differ in opinion. I am not to lie down and die between them: I must do something.
Samuel JohnsonIntroduction
How are decisions made in practice, and can we improve the process? Decisions in health care can be particularly awkward, involving a complex web of diagnostic and therapeutic uncertainties, patient preferences and values, and costs. It is not surprising that there is often considerable disagreement about the best course of action. One of the authors of this book tells the following story (1):
Being a cardiovascular radiologist, I regularly attend the vascular rounds at the University Hospital. It’s an interesting conference: the Professor of Vascular Surgery really loves academic discussions and each case gets a lot of attention. The conference goes on for hours. The clinical fellows complain, of course, and it sure keeps me from my regular work. But it’s one of the few conferences that I attend where there is a real discussion of the risks, benefits, and costs of the management options. Even patient preferences are sometimes (albeit rarely) considered.
And yet, I find there is something disturbing about the conference. The discussions always seem to go along the same lines. Doctor R. advocates treatment X because he recently read a paper that reported wonderful results; Doctor S. counters that treatment X has a substantial risk associated with it, as was shown in another paper published last year in the world’s highest-ranking journal in the field; and Doctor T. says that given the current limited health-care budget maybe we should consider a less expensive alternative or no treatment at all. They talk around in circles for ten to 15 minutes, each doctor reiterating his or her opinion. The professor, realizing that his fellows are getting irritated, finally stops the discussion. Practical chores are waiting; there are patients to be cared for. And so the professor concludes: ‘All right. We will offer the patient treatment X.’ About 30% of those involved in the decision-making process nod their heads in agreement; another 30% start bringing up objections which get stifled quickly by the fellows who really do not want an encore, and the remaining 40% are either too tired or too flabbergasted to respond, or are more concerned about another objective, namely their job security.
Contents
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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8 - Finding and summarizing the evidence
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 209-236
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Summary
It is surely a great criticism of our profession that we have not organized a critical summary, by specialty or subspecialty, adapted periodically, of all relevant randomized controlled trials.
Archie CochraneIntroduction
Good decision analyses depend on both the veracity of the decision model and on the validity of the individual data elements. These elements may include probabilities (such as the pre-test probabilities, the sensitivity and specificity of diagnostic tests, the probability of an adverse event, and so on), estimates of effectiveness of interventions (such as the relative risk reduction), and the valuation of outcomes (such as quality of life, utilities, and costs). Often we lack the information needed for a confident assessment of these elements. Decision analysis, by structuring a decision problem, makes these gaps in knowledge apparent. Sensitivity analysis on these ‘soft’ numbers will also give us insight into which of these knowledge gaps is most likely to affect our decisions. These same gaps exist in less systematic decision making as well, but there is no convenient way to determine how our decisions should be affected. In this chapter we shall cover the basic methods for finding the best estimate for each of the different elements that may be included in a formal decision analysis or in less systematic decision making.
Sometimes, but not as often as one would like, the estimates one is looking for can be inferred from a published study or from a series of cases that someone has reported in the literature or recorded in a data bank. This is generally considered the most satisfactory way of assessing a probability, because it involves the use of quantitative evidence. Often we will have a choice of data sources, so it is useful to have some ‘rules’ to guide the choice of possible estimates. One helpful concept is the ‘hierarchy of evidence’ (see www.cebm.net) which explicitly ranks the available evidence; ‘perfect’ data will rarely be available, but we need to know how to choose the best from the available imperfect data. This choice will also need to be tempered by the practicalities and purpose of each decision analysis: what is feasible will differ with a range from the urgent individual patient decision to a national policy decision to fund an expensive new procedure.
4 - Valuing outcomes
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 78-117
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Summary
Values are what we care about. As such, values should be the driving force for our decision making. They should be the basis for the time and effort we spend thinking about decisions. But this is not the way it is. It is not even close to the way it is.
Ralph KeeneyIntroduction
Value judgments underlie virtually all clinical decisions. Sometimes the decision rests on a comparison of probability alone, such as the probability of surviving an acute episode of illness. In such cases, there is a single outcome measure – the probability of immediate survival – that can be averaged out to arrive at an optimal decision. In most cases, however, decisions between alternative strategies require not only estimates of the probabilities of the associated outcomes, but also value judgments about how to weigh the benefits versus the harms, and how to incorporate other factors like individual preferences for convenience, timing, who makes decisions, who else is affected by the decision, and the like. Consider the following examples.
Frontmatter
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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Index
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Decision Making in Health and Medicine
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- 05 October 2014
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- 16 October 2014, pp 414-424
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12 - Heterogeneity and uncertainty
- M. G. Myriam Hunink, Erasmus Universiteit Rotterdam, Milton C. Weinstein, Harvard University, Massachusetts, Eve Wittenberg, Michael F. Drummond, University of York, Joseph S. Pliskin, Ben-Gurion University of the Negev, Israel, John B. Wong, Tufts University, Massachusetts, Paul P. Glasziou, Bond University, Queensland
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- Book:
- Decision Making in Health and Medicine
- Published online:
- 05 October 2014
- Print publication:
- 16 October 2014, pp 356-391
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Summary
Medicine is a science of uncertainty and an art of probability.
Sir William OslerIntroduction
Decision trees and Markov cohort models, as described and illustrated in the previous chapters, are essentially macrosimulation models. Such models simulate cohorts or groups of subjects. A number of limitations exist to the use of these models. Markov cohort models, for example, have ‘no memory’, implying that subjects in a particular state are a homogeneous group. Techniques to overcome these limitations, such as expanding the number of states, using tunnel states, or using alternative modeling techniques, were discussed in Chapter 10. These techniques can get very complex when dealing with extensive heterogeneity within a population. Microsimulation using Monte Carlo analysis provides another powerful technique to account for heterogeneity across subjects. Microsimulation with Monte Carlo analysis was introduced in Chapter 10 as an alternative method for evaluating a Markov model. In this chapter it will be discussed at greater length in the context of simulating heterogeneity.
In the previous chapters we represented uncertainty with probabilities. Implicitly the assumption was that, even though we were unsure of whether an event would take place, we could nevertheless predict or estimate the probability (or relative frequency) that it would occur. In essence we were using deterministic models. In reality, however, we are also uncertain of the degree of uncertainty. In other words, rather than dealing with a fixed probability we are actually dealing with a distribution of possible values of probabilities. Not only are we uncertain about the probabilities we use in our models, but we are also uncertain about the effectiveness outcomes and cost estimates included in the analysis. Thus, every parameter value we enter into our models is better represented as a probabilistic variable rather than a deterministic variable. If there is a single uncertain parameter, e.g., the relative risk reduction of an intervention, then the 95% confidence interval (CI) of this parameter is commonly used to indicate the uncertainty of the effect. Uncertainty in two or more components requires more complex methods, such as Monte Carlo probabilistic sensitivity analysis, which we will also discuss in this chapter.