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Module 1: Assignment: Moral Compass
September 3, 2019
Choosing A Nursing Home
September 3, 2019

Discussion Question #1:

For this assignment, you are to address the question(s) that follow and submit your response as per the guidelines stated in the syllabus. Please submit your document as a “.doc” or “.docx” file using the “Assignments” tab of the web course. Do not submit your response in the “Discussions” section of the course.

Discussion Question #1:

Using epidemiological measures, prioritize what population subgroups should be targeted in a screening program for the following health conditions: oropharyngeal cancer, testicular cancer, and skin cancer (all types).
What challenges do you foresee to the effectiveness of the screening programs in the subpopulations you have identified?
What might be some of the barriers to participation in the screening programs and how might these barriers be addressed?

Discussion Question Guidelines

• Your response to the discussion question must be of sufficient length to permit the instructor to assess your understanding of the subject matter. I would suggest a discussion posting of no less than 450 words. This assignment should include cited works as indicated with a list of references at the conclusion of the document.
• Please single-space your discussion.
• Do not attach a cover sheet/title page with your posting.
• Please make sure your response relates to the relevant concepts explored in the question and that all components of the discussion question are addressed.
• Discussions posted after the due date will not be graded.
• You must submit your response to the discussion question as a word document posted in the “Assignments” section of the web course. Use only .doc or .docx files; any files that cannot be opened will be returned to the student and the delay may result in a “missed” or “late” status for that assignment.
• Please remember to put your name on all documents submitted.
• A rubric will be posted to guide your responses to the discussion questions.

Attached are the powerpoints and the article from the module in the class
10 The Nurse Practitioner • Vol. 40, No. 8 www.tnpj.com

NP Insights

By Tom Bartol, APRN

Our current health- care culture empha- sizes evidence-based treatment. Diagnostic testing should also be

evidence-based. Tests are sometimes ordered without considering the evidence behind them. Clinicians may order a diagnostic test out of fear or to offer reassurance to the patient. Ineffi cient testing can lead to increased costs as well as unneces- sary or unwanted treatment for some patients. Using evidence to guide diagnostic testing can become part of the shared decision-making process, giving the patients a perspective about what the test might mean for them. The patient and clinician can then make a choice that fi ts with the patient’s condition as well as the patient’s desired goals and values.

This process need not add immense complexity to the decision- making process. Four steps can make the process more thoughtful and effi cient. First, determine the pretest probability of the condition you are concerned about. If you have no idea what you are looking for or have no differential diagnoses, then a test is probably not the way to begin. Second, determine what you want from the test. Do you want to rule out or rule in a disease or condition? Next, understand the sensitivity and specifi city of the test you want to use. Finally, think about what you will do with the results of the test.

■ Pre-test probability Pretest probability is the likelihood that a patient has the condition you are considering prior to testing. This can be based on the prevalence of the condition in the population. For example, the prevalence of colon cancer in the average 50-year-old female patient is about 0.1% or 1 in 1,000.1 If that female had a family history of colon cancer, heavy alcohol use, little physical activity, or other factors that increase risk for colon cancer, the pretest probability would be higher. Frequent exercise or a high-fi ber diet would lower pretest risk. Pretest probability can vary based on symptoms or clinical conditions as well. Consider the case of a 59-year-old male presenting with left-sided chest pressure. The pretest probability would be lower if the pain is sharp and aggravated with deep breathing and higher if the pain is worse with exertion, accompanied by shortness of breath, nausea, and diaphoresis. A past history of coronary artery disease (CAD) or a history of hypertension and diabetes in this patient would also increase pretest probability.

Determining pretest probability can sometimes be challenging. For various types of cancer, the pretest probability or incidence can be found on the CDC website (cdc.gov). In many cases, you will not be able to fi nd an exact percent or number for the pretest probability. Simply determining if the probability is low, medium, or high can be very helpful in making testing decisions. For example, consider pretest probability

for diabetes with two different people. The fi rst is a thin, 65-year- old male with no family history of diabetes, normal BP, and lipids who would have a low pretest probability. The second is a 58-year-old obese male with hypertension, hyperlipid- emia, and two brothers with diabe- tes; this patient would have a high pretest probability. A general sense of pretest probability for many conditions can be determined through the history and physical exam of your patient.

■ Testing goals Next, consider what your goal is for the test. Do you want to rule in a diagnosis or rule out a diagnosis? For those with a high pretest probability of a condition, you will likely be ruling in a diagnosis, and for those with low pretest probability, ruling out will be the goal of the diagnostic test.

By using pretest probability and understanding what you want to do with a test, you can compare the sensitivity and specifi city of a test to help determine how each test will help you with your goal. Understand- ing sensitivity and specifi city can be challenging for some clinicians. An easy way to remember is that a highly- sensitive test that is negative rules out a condition, whereas a highly-specifi c test that is positive rules in the condition. To help remember this, think “SNOut” for sensitivity negative rules out and “SPIn” for specifi city positive rules in.

Finally, before ordering a diagnostic test, consider the implica- tions of the results, that is, what you

Thoughtful use of diagnostic testing: Making practical sense of sensitivity, specifi city, and predictive value

Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

NP Insights

www.tnpj.com The Nurse Practitioner • August 2015 11

and the patient want to do with the test results. Consider specifi c disease implications as well as age, comor- bidities, life expectancy, and the patient’s needs, goals, and values. Should the test be positive, would the patient want or tolerate treatment? For example, would an asymptomatic 80-year-old person want to undergo treatment should a colon cancer screen be positive? What would be the potential risks and benefi ts of treatment? This should be discussed before performing the test.

■ Applying the principles Screening mammography for breast cancer can help practice applying the four testing principles. The preva- lence of breast cancer in the average 50-year-old woman is 0.28%, which would be the pretest probability.2 For every 10,000 50-year-old women, 28 of them could be expected to have breast cancer. With such a low pretest probability, as is the case with screening tests, our goal would be to rule out breast cancer. To rule out, we want a highly-sensitive test (remem- ber: “SNout”). Data vary regarding the sensitivity and specifi city of screening mammography with a range of 68% to 90% sensitivity and 82% to 97% specifi city. For our purposes, we will use 80% sensitivity and 90% specifi city.3 (See Screening mammography with prevalence of 0.28%.)

Ten thousand women are screened with mammography. Knowing the prevalence is 28/10,000, and the sensitivity is 80%, then 80% of the 28 positive cases (or 22) are identifi ed with positive screening mammography. There are six women who have breast cancer but have a negative mammogram, missed by screening or false-negative results. The 90% specifi city means that 90% of those testing negative do not have the disease. Thus, 8,975 of the 9,972 women who do not have cancer have

negative mammography. There are 10% of the 9,972 women (997 of them) who have positive mammo- grams but do not have breast cancer. These are the false-positives, and because the pretest probability of breast cancer is so low, the number of false-positive mammograms is large.

■ Positive and negative predictive values Two other useful numbers, the positive predictive value (PPV) and the negative predictive value (NPV), can be determined from this informa- tion. PPV tells us the probability that someone with a positive test (based on prevalence of disease) actually has breast cancer (higher PPV the bet- ter for ruling in the disease). NPV indicates the probability that some- one with a negative test does not have breast cancer (higher the NPV, the better for ruling out the disease).

In this case, the PPV is 22/1,019, true-positives divided by the total

number of positive mammograms, or 2%. With the prevalence, sensitiv- ity, and specifi city used, a woman with a positive screening mammo- gram has only a 2% chance of actually having breast cancer. NPV is 8,975, the number of true-negative test divided by 8,981, the total negative tests or 99.9%. This means that a woman with a negative screening mammogram in this population has a 99.9% probability of not having breast cancer. A test like this with a high specifi city or a high NPV is helpful for ruling out disease.

■ The impact of prevalence Prevalence makes a big difference (see True and false-positive results with higher prevalence of 25%). Now the PPV is 2,000/2,750 (or 73%), while the NPV is 6,750/7,250 (or 93%). With increasing prevalence (or pretest probability) of the PPV, the likelihood that a positive test really indicates presence of the disease goes up while

Screening mammography with prevalence of 0.28%3

Have breast cancer

Do not have breast cancer

Totals

Positive mammogram

22 (True positive)

997 (False positive)

1,019

Negative mammogram

6 (False negative)

8,975 (True negative)

8,981

Total 28 9,972 10,000

Sensitivity: 80% Specifi city: 90%

True and false-positive results with higher prevalence of 25%

Has disease Does not have disease

Totals

Positive test 2,000 (True positive)

750 (False positive)

2,750

Negative test 500 (False negative)

6,750 (True negative)

7,250

Total 2,500 7,500 10,000

Sensitivity: 80% Specifi city: 90%

Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

12 The Nurse Practitioner • Vol. 40, No. 8 www.tnpj.com

NP Insights

As you learn more about your patient’s symptoms and

the history, the pretest probability may go up or down.

the NPV goes slightly down. Said another way, with high prevalence, we get better at ruling in a disease and worse at ruling them out. With increased prevalence, that is, increased pretest probability, there is a higher probability that a person with a positive test really has the disease or a higher PPV. Sensitivity and specifi city alone are not enough to tell us the usefulness of a test. We must know the pretest probability, that is, the

prevalence of the disease in the population we are working with. As can be seen, the higher the pretest probability for breast cancer, be it through increased risk factors or symptoms, the higher the positive pre- dictive value and fewer false-positive results of the test.

■ Simplifying the process This can all sound confusing, but think about it in more general terms—without numbers. Consider the use of exercise treadmill testing (ETT) for someone to rule in or rule out CAD. A patient complaining of chest pain that is at low risk, for example, young and having pain with deep breathing or chest move- ment but not with exertion and no associated symptoms. would be considered to have a low pretest probability for CAD. Even without knowing the sensitivity and specifi c- ity of ETT, you know that based on the low pretest probability, there is a high likelihood of a false-positive test.

A treadmill test “just to be sure” may not be so sure in a low-risk patient with a high likelihood of false- positive results, which may lead to more unnecessary testing.

On the other hand, a person who has many risk factors for or classic symptoms of myocardial ischemia, such as left-sided chest pressure with associated shortness of breath, nausea, and diaphoresis has a high pretest probability for CAD. A negative test would not be reassuring, as there is a much higher chance of a false-negative than a true-negative result. In this case, despite a negative test, treatment as if this patient has

myocardial ischemia would be more appropriate, and it might be better to move to a higher specifi city test fi rst.

The rule to remember when trying to test thoughtfully is to think about pretest probability. Pretest probability is not a static number. As you learn more about your patient’s symptoms and the history, the pretest probability may go up or down. The key is that the lower the pretest probability (or prevalence) as we saw with screen- ing mammography, the higher the likelihood of false-positive results and the lower the PPV. If there is a high pretest probability (or preva- lence), the risk is high for false- negative results.

■ Adding the patient’s perspective Knowing prevalence or baseline risk is a helpful tool for both the clinician and the patient. Even if you do know the sensitivity and specifi city, pretest probability gives you information about what the test will tell you. With this information, using shared decision-making with your patient can help decide whether or not to perform a test. The patient can use this information to make a more informed decision knowing the risk

of the condition prior to testing. Not only are you now more thoughtful in your testing, you are using the patient as your partner in the choice. This becomes an opportunity to discuss step 4 with the patient: What will you do with the results?

Thoughtful testing will make a difference for both the clinician and the patient. Remember, when choosing diagnostic testing, consider: • What condition are you looking

for? • What is the pretest probability for

that condition? • What is the sensitivity of the test? • What is the specifi city of the test? • What are the risks of the test? • What are the benefi ts or how will it

change therapy or improve health? These questions, shared with the patient, will help lead to more deliberate and effi cient testing. Thoughtful, evidence-based testing, rather than refl ex or habit testing, can make a difference in healthcare. The goal is not to limit testing or to save money but to improve care. More tests do not mean better care, but appropriate testing does. The best reassurance we can give our patients is more information about the test and what it means for them.

REFERENCES 1. Centers for Disease Control and Prevention.

Colorectal Cancer Risk by Age. http://www.cdc. gov/cancer/colorectal/statistics/age.htm.

2. National Cancer Institute. Breast cancer risk in American women. http://www.cancer.gov/types/ breast/risk-fact-sheet.

3. Kavanagh AM, Giles GG, Mitchell H, Cawson JN. The sensitivity, specifi city, and positive predictive value of screening mammography and symptomatic status. J Med Screen. 2000;7(2):105-110.

Tom Bartol is an Advanced Practice Registered Nurse at Richmond Area Health Center, HealthReach Community Health Centers, Richmond, Me.

The author has disclosed that he has no fi nancial relationships related to this article.

Questions or comments? E-mail bartolnp@gmail. com

DOI-10.1097/01.NPR.0000469262.22776.1d

Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

 

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