Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Analytical and Clinical > Week 1 - stats > Flashcards

Week 1 - stats Flashcards

1
Q

What is analytical biochemistry?

A
  • The study of biochemical components found in a cell or other biological sample
  • Uses a broad range of techniques for separation, identification, quantification and functional characterisation of biological molecules
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Chromatography

A
  • separates components of a mixture based on their chemical properties separates and identifies components of a complex mixture.

Examples–HPLC, GC, TLC

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Electrophoresis

A

separates charged molecules based on their size and charge separates and identify proteins, DNA, and RNA

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Spectroscopy

A

uses the interaction of light with matter to study the properties of biological moleculesused to study the structure and function of proteins, nucleic acids, and other biological molecules

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

WHAT IS CLINICAL BIOCHEMISTRY?

A
  • generally concerned with analysis of bodily fluids for diagnostic and therapeutic purposes
  • an applied form of biochemistry
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

General or routine chemistry

A

Commonly ordered blood chemistries (e.g., liver and kidney function tests)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Special chemistry

A

Elaborate techniques such as electrophoresis, and manual testing methods

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Clinical endocrinology

A

The study of hormones, and diagnosis of endocrine

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Toxicology

A

The study of drugs of abuse and other chemicals

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Therapeutic Drug Monitoring

A

Measurement of therapeutic medication levels

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Urinalysis

A

Chemical analysis of urine for a wide array of diseases

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

DIAGNOSIS OF A MYOCARDIAL INFARCTION

A
  • A patient is diagnosed with myocardial infarction if 2 (probable) or 3 (definite) criteria:

→ Clinical history of ischaemic type chest pain lasting for more than 20 minutes

→ Changes in serial ECG tracings

→ Rise and fall of serum cardiac biomarkers, e.g. creatine kinase-MB or troponin

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

CARDIAC MARKERS

A

measure of damage to the cardiac muscle

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Where is CREATINE KINASE (CK) found?

A

Present in muscle (skeletal & cardiac) and brain (not liver) Dimer, with two different subunits: Muscle (M), Brain (B)

May exist as CK-MM, CK-MB, CK-BB

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

WHAT IS NEAR PATIENT TESTING?

A
  • May also be called point of care testing
  • Performed near or at site of patient–result may change care pathway for patient
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Near Patient Testing - Advantages

A
  • Generally faster than traditional lab testing
  • May require smaller sample
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Near Patient Testing - Disadvantages

A

May be less accurate–clinical staff

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

APPLICATIONS OF NEAR PATIENT TESTING

A
  • Diagnosis of infectious diseases
  • Monitoring of chronic diseases
  • Detection of drug abuse
  • Guide to other problems
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

NEAR PATIENT TESTING–DEVICES

A
  • Blood glucose meters
  • Pregnancy Tests
  • Rapid Strep Tests
  • Covid Tests
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

NEAR PATIENT TESTING - URINALYSIS
Visual’ exam

A
  • urine appearance
  • clear
  • cloudiness
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

NEAR PATIENT TESTING - URINALYSIS - Colour

A
  • Watery
  • Yellow
  • Orange
  • Red/brown B/R
  • Milky white

Unusual odour

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

NEAR PATIENT TESTING–URINALYSIS - DIPSTICK

Leukocytes number

A

small number

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

NEAR PATIENT TESTING–URINALYSIS - DIPSTICK

What does nitrates/Leukocyte Esterase presence indicate?

A

sign of infection or inflammation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

NEAR PATIENT TESTING–URINALYSIS - DIPSTICK

Urobilinogen

A

breakdown product of bilirubin, not normally present

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

NEAR PATIENT TESTING–URINALYSIS - DIPSTICK

Bilirubin

A

0-85% from haemoglobin released by breakdown of senescent red blood cells; 12-20% breakdown myoglobin, bone marrow, not normally present

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

NEAR PATIENT TESTING–URINALYSIS - DIPSTICK

Is protein [albumin] normally present?

A

not normally present

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

NEAR PATIENT TESTING–URINALYSIS - DIPSTICK

Glucose

A

present in small amount

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

NEAR PATIENT TESTING–URINALYSIS - DIPSTICK

Specific Gravity

A

= weight of urine/weight of water

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

NEAR PATIENT TESTING–URINALYSIS - DIPSTICK

Ketones

A

appear in urine as a consequence of accelerated fat metabolism; when in large amount: fruity odour “pear drops”

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

LIVER FUNCTION TESTS (LFT) - Total Protein (serum)

A

→ Albumin

→ Globulins

→ A/G ratio (albumin-globulin)

→ Protein electrophoresis

→Urine protein

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

LIVER FUNCTION TESTS (LFT) - bilirubin

A

direct + indirect = total

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

LIVER FUNCTION TESTS (LFT)

A

Total protein (serum)

Bilirubin

Aspartate transaminase (AST)

Alanine transaminase (ALT)

Gamma-glutamyl transpeptidase (GGT)

Alkaline phosphatase (ALP)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

BASIC METABOLIC PANEL (UREA & ELECTROLYTES)

A
  • Four electrolytes:→ sodium (Na+)→ potassium (K+)→ chloride (Cl-)→ bicarbonate (HCO3-)
  • blood urea (blood urea nitrogen, BUN)
  • creatinine
  • glucose
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
34
Q

COMPREHENSIVE METABOLIC PANEL

General tests

A
  • Serum glucose
  • Calcium
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
35
Q

COMPREHENSIVE METABOLIC PANEL
Kidney function assessment

A
  • Blood urea nitrogen (BUN)
  • Creatinine
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
36
Q

Liver function assessment

A
  • ALPALTAST Bilirubin
37
Q

COMPREHENSIVE METABOLIC PANEL - Electrolytes

A
  • Sodium
  • Potassium
  • Chloride
  • Carbon dioxide (CO2)
38
Q

COMPREHENSIVE METABOLIC PANEL - Protein

A
  • Albumin
  • Total protein
39
Q

Inferential Statistics

A
  • relationship between variables
  • is based of a hypothesis
  • needs a formal statistical test to guide policy making, health care choices etc
  • tests depending on the nature of the data etc. Mann-whitney U-test, t-test
  • evaluation of test results in terms of statistical significance
40
Q

Steps in Performing a Statistical Test

A
  1. formulate null and alternative hypothesis
  2. evaluate the data and choose an appropriate statistical test for the data
  3. perform the statical test
  4. obtain test statistical significance of the results
  5. reject or accept the null hypothsois
41
Q

Null hypothesis

A

H0

42
Q

Alternative Hypothesis

A

H1

43
Q

Why do we aim to reject hypothesis?

A

rejecting a hypothesis is often more feasible task than proving it

44
Q

null hypothesis

A

a null hypothesis is the statement that is considered to be true unless the data prides sufficient evidence to reject it. one does not prove null hypothesis

45
Q

statistical significance

A

the observed results is not by chance

46
Q

p-value

A

probability of observing the result or more extreme result (test statistic) given the null-hypothesis is true

47
Q

note about the use of p-value

A
  • a large p-value does never prove the absence of an effect but allows us to conclude that there is no sufficient evidence of the effect to be present
  • the cut off is 0.05 is quite arbitrary
48
Q

statistical test depends on

A

→ type of data

→ research question

→ sample size

49
Q

test of correlation

A
  1. Pearson
  2. spearman
50
Q

Test of frequency

A

test whether the observed data deviates from the expected

51
Q

Chi-Squared

A
  • represents how the observed data (o) data/frequency deviates from the expressed (E)
  • x^2 is another probability distribution and ranges from 0 to infinity
52
Q

Fisher’s Exact Test

A
  • to be used when sample size is small (cell size <5)
  • p-value is calculated exactly as opposed to an approximation
53
Q

Test of frequency examples

A

Chi-Squared

Fisher’s Exact Test

54
Q

Quantitative Normally Distributed Data

A

Student T-Test

55
Q

Why is it called a student t-test?

A

the test statistic t follows student t-distribution with n-2 degrees of freedom

56
Q

Student T-Test

A

a comparison of group means between two groups (2 sample t-test)

57
Q

Student T-Test Assumptions

A

assumes the data are normally distributed

assumes equal variance (for unequal variance Welch’s t-test)

58
Q

Testing for Normality

A

visual inspection eh. using histogram

provides a single p-value

59
Q

Testing for Normality Examples

A

d’agostino and Pearson normality test

60
Q

Evaluation Testing for Normality

A

evaluates the skewness and kurtosis of the distribution to quantify how far the distribution is from normal distribution in terms of asymmetry and shape

61
Q

Examples Quantitivative Non-Normal Data

A

Mann-Whitney U-test

More than 2 groups: ANOVA - analysis of variance

62
Q

Mann-Whitney U-test

A
  • a non-parametric test for non-normal data
  • a comparison of group means between two group
  • also called wilcoxson rank-sum test
63
Q

One-Way ANOVA

A

effect of one categorical variable on the continuous outcome variable

64
Q

Two-Way ANOVA

A

effect of two categorical variable incombination, on the continuous outcome variable

65
Q

Post-Hoc Testing

A
  • find out which groups are different from one another
  • multiple comparison between group - this is controlled by these test (adjusted p-value are produced)
66
Q

Tukey’s Honestly Significant Difference (HSD)

A
  • often referred to as Tukey’s method
  • the most commonly used
  • compares all the levels against each other
  • assumes equal variance across groups. if not statisfied, see eg. Games Howell Post Hoc Test
67
Q

More than 2 groups: Kruskal-Wallis Test

A
  • a non-parametric test
  • an extension of Mann-Whitney U-test is for 2 groups
  • based on ranking the data
68
Q

Post-Hoc Testing Example

A

Tukey’s Honestly Significant Difference (HSD)

More than 2 groups: Kruskal-Wallis Test

69
Q

What is ANOVA?

A

analysis of variance

70
Q

ANOVA

A
  • a comparison between groups mean between mutilple groups
  • compares the variance between groups to variance within groups
71
Q

What does ANOVA tell you?

A

tells only whether there is a difference in the mean levels of the outcome between the groups but does not tell in which group

72
Q

What is a Statistical Model?

A

a simplified representation of reality

a model for the association structure in the data

most common ones are regression models

73
Q

What does a regression model represent?

A

a regression model represents how a dependent (outcome) variable Y depends on one or more independent variables (covariates) X

74
Q

Linear Regression (Simple and Multiple)

A
  • a generalisation of t-test, ANOVA
  • to only for differences in group means (categorical variables) but also for association between continuous variable
  • can be included just one covariate (simple linear regression) or many (multiple linear linear regression)
  • mathematical formula
75
Q

Linear Regression (Simple and Multiple) Assumptions

A
  1. linearity
  2. = normality and homoscedasticity (constanct variance) of the errors
  3. independence of the errors
76
Q

Confidence Intervals (CI)

A

it is the point estimate beta that an error (SE) associated within telling about its variability if we repeated the test multiple times (remember SEM) - 95%

77
Q

What can we use SE bar to estimate?

A

we can use the SE to estimate where the point estimate is with a certain confidence if we were to repeat the test in multiple samples from the population

78
Q

What does it mean if Cl does not include 0?

A

if 95% CI does not include 0, there is a statistically significant association ie. p<0.05

79
Q

Standard Curve in Analytical Chemistry

A
  • in analytical chemistry, standard curve is use to determine the concentration of an unknown quantity given your data
  • this is obtained via linear agression
80
Q

Logistic Regression

A
  • for categorical outcomes Y, eg. disease status yes/no
  • the predictor variable X can be categorical or continous
  • again, can have one or multiple predictors (covariates) in the model
81
Q

Logistic Regression - How are the results given?

A

Results are given as odds ratio (OR)

→ approximate how much more likely (or unlikely) it is for the outcome to be present (event to occur) among group A than among reference group B

82
Q

Logistic Regression - What does an OR of 1 suggest?

A

OR = 1 means that there is no increased odds

83
Q

Logistic Regression - if the 95% CL includes 1, what does that mean?

A

if the 95% Cl includes 1, the association is not significant

84
Q

Odds Ratio (OR) vs Risk Ratio (RR) - if in the rare event where RR is close to OR, what does that mean?

A

(a and c are close to 0)

RR is easier to interpret but OR is more widely used because R cannot be used in designed case-control studies because we don’t know the real total number of exposed individuals/events

85
Q

Testing for Causation

A
  • randomised controlled trails - the golden standard
  • direct acyclic graphs eg, path analysis
  • mendelian randomisation - uses genetic data, very popular
  • the two later involve several assumptions
86
Q

Sensitivity

A

if I have disease X, what is the likelihood I will test positive for it?

true positive / (true pos + false negative)

87
Q

Specificity

A

if I do not have disease X, what is the likelihood I will test negative for it?

True Negatives / (True Negatives + False Positives)

88
Q

Positive Predictive Value (PPV)

A

is the proportion of those with a POSITIVE blood test that have Disease X.

“If I have a positive test, what is the likelihood I have Disease X?”

PPV = True Positives / (True Positives + False Positives)

89
Q

Negative Predictive Value (NPV)

A

is the proportion of those with a NEGATIVE blood test that have Disease X

“If I have a negative test, what is the likelihood I do not have Disease X?”

PPV = True Negatives / (True Negatives + False Negatives)

Analytical and Clinical (11 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions