Module 1 Introduction to Pathophysiology Flashcards
What is pathophysiology? What does it explain?
- 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
What is normality?
A state in which a set of objects or values is in the standard range. Usually reflects a state in which disease is absent.
How can normality be described?
May be described differently depending on a variety of factors including:
- Genetics
- Age
- Gender
- The physical environment
- Time variations
What are the 2 types of diagnostic tests?
qualitative or quantitative
Describe qualitative tests. Provide an example
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
Describe quantitative tests
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
What is the use of clinical tests?
To confirm the presence of a disease or further the diagnostic process.
Differentiate between sensitivity and specificity
- 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) - 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)
Differentiate between positive and negative predictive value
- 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) - Negative Predictive Value: The proportion of jobs identified as being low risk that really are low risk.
- > TN/(TN+FN)
What is the purpose of tests
to determine who has disease and who does not.
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
- High predictive value – many true positive or negative results; few false positive or negative results
- Low predictive value – many false positive or negative results; few true positive and negative results
When considering the risk associated with a certain factor, it is important to know whether one is dealing with ______ risk or ______ risk.
- Absolute
2. Relative
What is absolute risk
- 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
What is relative risk
- 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)
Example of relative risk
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.
What is the purpose of confidence intervals (CI)
used in statistics to help give a measure of confidence in the accuracy of a result
What is a level C confidence interval for a relative risk estimate
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.
What is a typical C
Usually C = 95%.
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]
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
in order for a RR estimate to be considered clinically meaningful, the corresponding CI must not cross a value of ___
1
Why must Cl not cross a value of 1
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.
What is the odds ratio
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
Example of odds ratio
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
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:
- 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
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)
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.
Note that in this example the relative risk is 5.7714 while the odds ratio is 6.9792, what does it mean
- 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.
What is absolute risk reduction
ARR = the diff between the absolute risk of one group and the absolute risk of another (control group)
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
0.05 - 0.02 = 0.03 or 3%
What is RRR
Relative risk reduction = the % reduction in risk in one group compared to the control group risk rate
What is the eqn for relative risk reduction?
RRR = (CER – EER) / CER
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.
CER is the control (placebo) group event rate (in this case 5% or 0.05)
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.
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%)
Is checking the RRR enough?
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.
What is NNT
Number Needed to Treat = the # of patients that must be treated in order to prevent one adverse outcome (e.g. death or heart attack)
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
For calculation: NNT = 1 / ARR
So in our example, NNT = 1 / 0.03 = 33
What is survival analysis
- 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
What are survival curves
curves that begin with 100% survival rate of the study population and then show survival rates for successive times for a certain time period
What does the Kaplan-Meier Analysis measure
Measures the ratio of surviving patients (those free from an untoward outcome) divided by the total # of patients at risk for the outcome
How often is the Kaplan-Meier Analysis done
This ratio is recalculated every time a patient has an outcome
For Kaplan-Meier Analysis, the time intervals used for data are dependent on when
patients have an outcome
What is A Kaplan-Meier curve
a survival curve depicting these calculated ratios vs. time
For comparing risk in two different groups (for example, male/female or diabetic/non-diabetic) we can compare _____
their Kaplan-Meier curves
Two curves that are close together or cross ____ likely to represent a statistically significant risk difference
are not
If two curves are not close together and do not cross, then we want to determine ______
if there is a statistically significant difference in their risk levels
How to determined if there is a statistically significant difference in their risk levels? Con of this approach
- 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
What is the Log-rank test? Helps determine what?
- 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
Procedure to perform the Log-Rank test?
- 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
Explain this graph
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
What is independence? Example
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.
What is dependence? Example
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).
What is a independent risk factor
- 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
Define disease
- 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.
Define pathology
- 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.
Pathogenesis
The process of development of an illness or abnormal condition. Includes the structural and biochemical alterations induced in the cells and organs.
What is a sign? Primary vs. secondary?
An objective indication of disease as perceived by an examiner. E.g., fever, rash.
Primary: intrinsically associated with disease
Secondary: Consequence of disease
What is a Symptom
A subjective indication of disease or a change in condition as perceived by a patient.
What is a Syndrome
A complex of signs and symptoms resulting from a common cause or appearing in combination with a clinical picture of disease or inherited abnormality.
Define Etiology
- The study of all factors that may be involved in the development of disease.
- Includes:
Susceptibility of patient
Causative agent
Mode of invasion
Etiologic Vs Risk Factors
- 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.
Define Epidemiology
- The study of the dynamics of disease within a population. Multiple factors are involved.
- These include:
genetics
ecology
socioeconomics
demographics
