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BPK 458 > Module 1 Introduction to Pathophysiology > Flashcards

Module 1 Introduction to Pathophysiology Flashcards

1
Q

What is pathophysiology? What does it explain?

A
  1. Involves the study of the BIOLOGIC and PHYSICAL processes in ALTERED (abnormal) health states
    - > Explains structural and fxnal changes w/in body that result in signs + symptoms of disease
    - > also deals w/the effects that these changes have on overall body fxn
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2
Q

What is normality?

A

A state in which a set of objects or values is in the standard range. Usually reflects a state in which disease is absent.

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3
Q

How can normality be described?

A

May be described differently depending on a variety of factors including:

  1. Genetics
  2. Age
  3. Gender
  4. The physical environment
  5. Time variations
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4
Q

What are the 2 types of diagnostic tests?

A

qualitative or quantitative

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5
Q

Describe qualitative tests. Provide an example

A

For qualitative observations, a test is either POSITIVE (abnormal) or NEGATIVE (normal).
-> E.g. If a patient is suspected of intestinal bleeding, stool samples can be tested for the presence (positive) or absence (negative) of blood

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6
Q

Describe quantitative tests

A

Quantitative tests measure QUANTITIES (amounts or numbers).

  • > Measurements can be:
    1. normal in the absence of disease – TRUE NEG
    2. abnormal in the presence of disease – TRUE POS
    3. abnormal in the absence of disease – FALSE POS
    4. normal in the presence of disease – FALSE NEG
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7
Q

What is the use of clinical tests?

A

To confirm the presence of a disease or further the diagnostic process.

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8
Q

Differentiate between sensitivity and specificity

A
  1. Sensitivity: the probability of correctly identifying a case of high risk jobs.
    - > It is the proportion of truly risky jobs in the screened jobs
    - > TP/ (TP+FN)
  2. Specificity: the probability of correctly identifying low risk jobs.
    It is the proportion of truly low risk jobs in the screened jobs
    TN/ (TN+FP)
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9
Q

Differentiate between positive and negative predictive value

A
  1. Positive Predictive Value: the proportion of those jobs identified as being high risk by the screening tool that really are high risk.
    - > TP/ (TP+FP)
  2. Negative Predictive Value: The proportion of jobs identified as being low risk that really are low risk.
    - > TN/(TN+FN)
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10
Q

What is the purpose of tests

A

to determine who has disease and who does not.

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11
Q

Tests vary in their ability to discriminate between presence and absence of disease. What does it mean when a test has high predictive value vs. low predictive value

A
  1. High predictive value – many true positive or negative results; few false positive or negative results
  2. Low predictive value – many false positive or negative results; few true positive and negative results
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12
Q

When considering the risk associated with a certain factor, it is important to know whether one is dealing with ______ risk or ______ risk.

A
  1. Absolute

2. Relative

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13
Q

What is absolute risk

A
  • Given without any context
  • Not compared with any other risk
  • Example: A non-smoker has a 1 in 100 chances of getting lung cancer
  • > Also called a 1% risk or a 0.01 risk
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14
Q

What is relative risk

A
  • A comparison of 2 risk levels (of 2 different populations, usually one is a control group)
  • Ratio of the 2 associated absolute risks
  • RR of 1 means two absolute risks are equal.
  • Note: relative risk tells NOTHING about the actual risk (absolute risk)
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15
Q

Example of relative risk

A

If non-diabetics have a 3.5% chance (0.035 risk) and diabetics have a 20.2% chance (0.202 risk) of myocardial infarction (MI), then the relative risk is:

RR = 0.202 / 0.035

= 5.7714

So, diabetics are approximately 6 times as likely to experience MI than non-diabetics. This example emphasizes the importance of distinguishing between AR and RR. A diabetic that hears that he is six times more likely to have a heart attack will probably be more concerned than a diabetic that hears he has a 20% chance of getting the disease.

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16
Q

What is the purpose of confidence intervals (CI)

A

used in statistics to help give a measure of confidence in the accuracy of a result

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17
Q

What is a level C confidence interval for a relative risk estimate

A

an interval computed from sample data by a method that has probability C of producing an interval that contains the true value of the relative risk.

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18
Q

What is a typical C

A

Usually C = 95%.

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19
Q

Let the RR be the ratio of risk in population 1 (r1) to the risk in population 2 (r2)
We then estimate from a sample of size n that RR = r1 / r2 = 1.34
Suppose that the 95% confidence interval is calculated to be [1.26, 1.42]

A

This means that of all the confidence intervals of width 0.16 calculated from all possible samples of size n, 95% of them will contain the true RR

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20
Q

in order for a RR estimate to be considered clinically meaningful, the corresponding CI must not cross a value of ___

A

1

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21
Q

Why must Cl not cross a value of 1

A

Let RR = r1 / r2:
If RR = 1, then the risk in each population is the same
If RR < 1, the risk is greater in population 2
If RR > 1, the risk is greater in population 1
So, if the confidence interval crosses 1 (for example, say [0.94, 1.22]), we are considering both RR values that are less than 1 and greater than 1

This means that there is > 1/20 chance that the difference in the 2 curves is due to chance alone. This is too high of a chance to be satisfactory.

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22
Q

What is the odds ratio

A

the ratio of the odds of an event (i.e. a disease) occurring in one group to the odds of the same event occurring in another group (usually a control group)

Recall that “odds” is defined as the ratio of the probability of an event occurring to the probability of it not occurring

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23
Q

Example of odds ratio

A

If, in some population, 20 women in 100 smoke, then the probability of a woman being a smoker is 0.20 and the probability of a woman not being a smoker is 0.8.

Therefore, the odds that a woman is a smoker is: 0.2 / 0.8 = 0.25

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24
Q

If non-diabetics have a 3.5% chance (0.035 risk):
The odds that a non-diabetic will have a heart attack is: _____

Diabetics have a 20.2% chance (0.202 risk):
The odds that a diabetic will have a heart attack are: _____

Therefore, the odds ratio is:

A
  • Non-diabetic odds:
    0. 035 / (1-0.035)
  • diabetics odds:
    0. 202 / (1-0.202)

OR = (0.202 / 0.798) / (0.035 / 0.965) = 6.9792

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25
Q

Odds of heart disease in those w/history of smoking:
(0.10/1-0.10)

Odds of heart disease in those w/out history of smoking:
(0.05/1-0.05)

OR: (0.10/1-0.10)/(0.05/1-0.05)

A

Odds Ratio is: 400x 10/ 90x 20= 2.2

The odds of being a smoker are 2 times higher for a patient with heart disease compared to a patient with no heart disease.

26
Q

Note that in this example the relative risk is 5.7714 while the odds ratio is 6.9792, what does it mean

A
  • The relative risk is interpreted to mean that a diabetic is 5.7714 times more likely to have a heart attack than a non-diabetic
  • The odds ratio is interpreted to mean that diabetic patients have 6.9792 times the odds of having a heart attack.
27
Q

What is absolute risk reduction

A

ARR = the diff between the absolute risk of one group and the absolute risk of another (control group)

28
Q

Example of ARR: In a placebo-controlled trial of a new drug “Lowalipids”, the risk of CVD event over 10 years of follow up was 2% in the treatment group and 5% in the placebo group. WHat is the ARR by the use of Lowalipids

A

0.05 - 0.02 = 0.03 or 3%

29
Q

What is RRR

A

Relative risk reduction = the % reduction in risk in one group compared to the control group risk rate

30
Q

What is the eqn for relative risk reduction?

A

RRR = (CER – EER) / CER

31
Q

What is CER? Using example In a placebo-controlled trial of a new drug “Lowalipids”, the risk of CVD event over 10 years of follow up was 2% in the treatment group and 5% in the placebo group.

A

CER is the control (placebo) group event rate (in this case 5% or 0.05)

32
Q

What is RRR for the example

Example: In a placebo-controlled trial of a new drug “Lowalipids”, the risk of CVD event over 10 years of follow up was 2% in the treatment group and 5% in the placebo group.

A

In our example, the control group is the placebo group (0.05) and the group event rate is 0.02

RRR = (CER – EER) / CER

So, RRR = (0.05 - 0.02) / 0.05 = 0.6 (60%)

33
Q

Is checking the RRR enough?

A

This example clearly shows that there are problems with relative risk reduction, as RRR is 60% while ARR is only 2%
RRR can be misleading if it is used to make weak findings appear larger than they actually are
Don’t be misled! Always check the ARR also.

34
Q

What is NNT

A

Number Needed to Treat = the # of patients that must be treated in order to prevent one adverse outcome (e.g. death or heart attack)

35
Q

How to calculate NNT? In our example, this is interpreted as the number of patients who must choose not to smoke in order to prevent 1 patient from having a bad outcome

A

For calculation: NNT = 1 / ARR

So in our example, NNT = 1 / 0.03 = 33

36
Q

What is survival analysis

A
  • a class of statistical procedures for estimating the survival function and for making inferences about the effects of treatments, prognostic factors, exposures, and other covariates
  • used to analyze medical studies
37
Q

What are survival curves

A

curves that begin with 100% survival rate of the study population and then show survival rates for successive times for a certain time period

38
Q

What does the Kaplan-Meier Analysis measure

A

Measures the ratio of surviving patients (those free from an untoward outcome) divided by the total # of patients at risk for the outcome

39
Q

How often is the Kaplan-Meier Analysis done

A

This ratio is recalculated every time a patient has an outcome

40
Q

For Kaplan-Meier Analysis, the time intervals used for data are dependent on when

A

patients have an outcome

41
Q

What is A Kaplan-Meier curve

A

a survival curve depicting these calculated ratios vs. time

42
Q

For comparing risk in two different groups (for example, male/female or diabetic/non-diabetic) we can compare _____

A

their Kaplan-Meier curves

43
Q

Two curves that are close together or cross ____ likely to represent a statistically significant risk difference

A

are not

44
Q

If two curves are not close together and do not cross, then we want to determine ______

A

if there is a statistically significant difference in their risk levels

45
Q

How to determined if there is a statistically significant difference in their risk levels? Con of this approach

A
  • we could compare survival rates of the two curves at specific times
  • This is considered a weak approach by some because it doesn’t give a comparison of the whole survival experience of the two groups, just at single, arbitrary time points
46
Q

What is the Log-rank test? Helps determine what?

A
  • a statistical test that can be applied to the Kaplan-Meier curves to test the difference between the whole survival experience of each group
  • test helps determine that the difference in the two Kaplan-Meier curves was not due to bias from random sampling
47
Q

Procedure to perform the Log-Rank test?

A
  • Statistical software can perform this test
  • At the end of the test we are given a p-value
  • If the p-value is < or = to 0.05, we determine that the difference is statistically significant, and thus that one group is at greater risk than the other
48
Q

Explain this graph

A

The preceding graph depicts survival rates for 2 different groups of patients with Heterozygous Familial Hypercholesterolemia, classified by level of Lp(a) while on no lipid lowering medications.
It can be seen that those with Lp(a)≥800 IU/L have shorter event free survival based on the fact that their Kaplan-Meier curve is below those with Lp(a)<800 IU/L.
Since p-value is < 0.05, it is determined that the difference in survival rates (and hence in risk for CV events) is statistically significant

49
Q

What is independence? Example

A

Two factors are completely independent if a change in one factor has no effect on the other
Example:
If roll a die twice, the outcome of the first roll has no influence over the second roll.

50
Q

What is dependence? Example

A

Two factors are dependent if a change in one factor predictably affects the other
Example:

If draw names from hat without replacement, the name you draw is directly affected by the previous draw (since you can no longer draw that name as it was removed).

51
Q

What is a independent risk factor

A
  • a risk factor that retains its statistical association with the outcome when other established risk factors for the outcome are included in a statistical model
  • if a risk factor retains a statistically significant association with the outcome (i.e. CVD) even after the effects of all established risk factors are accounted for, then it is an independent risk factor
52
Q

Define disease

A
  • A disruption of homeostasis or dynamic steady state.
  • Is the sum of deviations from normal.
  • The greater the degree of deviation from normal, the more likely that disease is present.
  • Baseline evaluation determines the magnitude of deviation from normal.
53
Q

Define pathology

A
  • Study of characteristics, causes and effects of disease as observed in the structure and function of the body.
    -> Anatomic pathology
    Study of structural changes caused by disease
    ->-> Involves assessment of tissues and organs by the unaided eye, microscopy, or other imaging techniques.
  • > Clinical pathology
  • > -> Study of functional aspects of disease
  • > -> Involves laboratory study of tissue, blood, urine, or other body fluids.
54
Q

Pathogenesis

A

The process of development of an illness or abnormal condition. Includes the structural and biochemical alterations induced in the cells and organs.

55
Q

What is a sign? Primary vs. secondary?

A

An objective indication of disease as perceived by an examiner. E.g., fever, rash.

Primary: intrinsically associated with disease
Secondary: Consequence of disease

56
Q

What is a Symptom

A

A subjective indication of disease or a change in condition as perceived by a patient.

57
Q

What is a Syndrome

A

A complex of signs and symptoms resulting from a common cause or appearing in combination with a clinical picture of disease or inherited abnormality.

58
Q

Define Etiology

A
  • The study of all factors that may be involved in the development of disease.
  • Includes:
    Susceptibility of patient
    Causative agent
    Mode of invasion
59
Q

Etiologic Vs Risk Factors

A
  • Risk factors are correlational and not necessarily causal, because correlation does not prove causation. For example, being young cannot be said to cause measles, but young people have a higher rate of measles because they are less likely to have developed immunity during a previous epidemic.
  • Some people use the term risk factor to refer to any condition that increases rates of disease, and for unproven links to, or associations with disease, etc.
60
Q

Define Epidemiology

A
  • The study of the dynamics of disease within a population. Multiple factors are involved.
  • These include:
    genetics
    ecology
    socioeconomics
    demographics

BPK 458 (29 decks)

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