Health Informatics Assignment Week 3
- Calculate the following probabilities for a patient about to undergo CABG surgery (see Example 2):
- The only possible, mutually exclusive outcomes of surgery are death, relief ofsymptoms (angina and dyspnea), and continuation of symptoms. The probability of death is 0.02, and the probability of relief of symptoms is 0.80. What is the probability that the patient will continue to have symptoms?
- Two known complications of heart surgery are stroke and heart attack, with prob-abilities of 0.02 and 0.05, respectively. The patient asks what chance he or she has of having both Assume that the complications are conditionally independent, and calculate your answer.
- The patient wants to know the probability that he or she will have a stroke giventhat he or she has a heart attack as a complication of the surgery. Assume that 1 in 500 patients has both complications, that the probability of heart attack is 0.05, and that the events are independent. Calculate your answer.
- The results of a hypothetical study to measure test performance of the PCR test forHIV (see Example 1) are shown in the 2 × 2 table in Table 3.9.
- Calculate the sensitivity, specificity, disease prevalence, PV+, and PV–.
- Use the TPR and TNR calculated in part (a) to fill in the 2 × 2 table in Table 3.10. Calculate the disease prevalence, PV+, and PV–.
- You are asked to interpret a PCR HIV test in an asymptomatic man whose test waspositive when he volunteered to donate blood. After taking his history, you learn that he is an intravenous-drug user. You know that the overall prevalence of HIV infection in your community is 1 in 500 and that the prevalence in intravenous-drug users is 20 times as high as in the community at large.
- Estimate the pretest probability that this man is infected with HIV.
- The man tells you that two people with whom he shared needles subsequently diedof AIDS. Which heuristic will be useful in making a subjective adjustment to the pretest probability in part (a)?
Table 3.9. A 2 × 2 contingency table for the hypothetical study in problem 2.
PCR test result
Gold standard test positive
Gold standard test negative
PCR = polymerase chain
Table 3.10. A 2 × 2 co
ntingency table to complete for p
PCR test result
Gold standard test positive
Gold standard test negative
PCR = polymerase chain reaction.
- Use the sensitivity and specificity that you worked out in 2(a) to calculate the post-test probability of the patient having HIV after a positive and negative test. Assume that the pretest probability is 0.10.
- If you wanted to increase the post-test probability of disease given a positive testresult, would you change the TPR or TNR of the test?
- You have a patient with cancer who has a choice between surgery or chemotherapy.If the patient chooses surgery, he or she has a 2 percent chance of dying from the operation (life expectancy = 0), a 50 percent chance of being cured (life expectancy = 15 years), and a 48 percent chance of not being cured (life expectancy = 1 year). If the patient chooses chemotherapy, he or she has a 5 percent chance of death (life expectancy = 0), a 65 percent chance of cure (life expectancy = 15 years), and a 30 percent chance that the cancer will be slowed but not cured (life expectancy = 2 years). Create a decision tree. Calculate the expected value of each option in terms of life expectancy.
- You are concerned that a patient with a sore throat has a bacterial infection thatwould require antibiotic therapy (as opposed to a viral infection, for which no treatment is available). Your treatment threshold is 0.4, and based on the examination you estimate the probability of bacterial infection as 0.8. A test is available (TPR = 0.75, TNR = 0.85) that indicates the presence or absence of bacterial infection. Should you perform the test? Explain your reasoning. How would your analysis change if the test were extremely costly or involved a significant risk to the patient?
- What are the three kinds of bias that can influence measurement of test performance?Explain what each one is, and state how you would adjust the post-test probability to compensate for each.
- How could a computer system ease the task of performing a complex decision analysis? Look at the titles of Chapters 9 through 18 of this text. What role could each kind of system play in the medical-decision process?
- When you search the medical literature to find probabilities for patients similar to oneyou are treating, what is the most important question to consider? How should you adjust probabilities in light of the answer to this question?
- Why do you think physicians sometimes order tests even if the results will not affecttheir management of the patient? Do you think the reasons that you identify are valid? Are they valid in only certain situations? Explain your answers. See the January 1998 issue of Medical Decision Making for articles that discuss this question.
Clear identification of anomalous test findings can be challenging in general practice. Creating unnecessary, for example, can occasionally contribute to an irrational explosion of more examinations. Several irregular laboratory outcomes represent only subtle anomalies, and doctors may question their practical significance. For general, it is the blend of minor abnormalities as well as a low likelihood of pretest that can be difficult to assess, since it is often easier to determine on the grounds of even more distinctly irregular tests. Yes, the reasons identified are absolutely valid. Each situation comes up with the use of different method to solve it and handle it. The small number of irregular testing results if intervention limitations are implemented could be due to the fact that the greater probability of detecting an irregularity is very restricted in such situations. Doctors continue to order additional studies there are likely to be many more clinicians with irregular outcomes, demonstrating the need for fair test scheduling and a greater understanding of the effect of findings on further diagnosis. Some evidence suggests that the repeated occurrence of slightly irregular results can trigger diagnostic problems. Moreover, at the very same time, given the low likelihood of pretesting, intermittent but substantial laboratory anomalies can be observed. One way to solve the issue of inaccurate testing results could be by the use of judgment limits rather than predicted value.
- Explain the differences in three approaches to assessing patients’ preferences forhealth states: the standard gamble, the time trade-off, and the visual analog scale.
To explain any use of the standard gamble, assume they are trying to determine the usefulness of a individual to the health status of asymptomatic HIV infection. Using the traditional risk, they ask the subject to equate asymptomatic HIV infection's desirability with those of two other therapy services their utility they recognize but can delegate. In the SG system, the theoretical option is given to a participant between both the sure result, A (having lived his current life span in the "alive, synthetic voice" state), and the gamble, B. The gamble has a probability p of the best outcome imaginable (optimal health defined as 1) and a likelihood (1 – p) of the worst possible scenario (usually instant death defined as 0). The quality during which the participant is oblivious to the option between the certain result and the risk is achieved by adjusting p. At the stage of ignorance, the usefulness for the sure result, the condition of "alive, synthetic voice," is equivalent to the price of p. Therefore, if the object is oblivious to the option regarding his "alive, artificial voice" remaining life, and a chance risk. A second popular method for assessing usefulness is the timing-off methodology. In order to determine the efficacy of asymptomatic HIV infection by using duration-trade method, we ask people to calculate the amount of time in an improved level of mind (normally ideal health or best achievable health) that he or she is in will consider a longer period of time comparable to asymptomatic HIV infection. Essentially, this is the "thermometer" approach. In VAS, the participant was asked to score the condition by putting a label on a horizontally or vertically line of 100 mm which is dominated by healthy living and mortality. The measure of the number of millimeters, separated by 100, from the "death" attachment point to the threshold. The VAS doesn't really reinforce any trade-off that even a topic might intend to make in order to achieve better wellbeing, nor would it recognize in terms of risk or years of life. Other methods to measuring health outcomes have included the Well-being Scale of Efficiency, the Health Services Index and EuroQoL. Every one of those tools analyzes whether people view patient outcomes and so might be suitable to be used in analysis of decisions or cost-effectiveness.
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