General Lab 1

Multidisciplinary specialty with several interacting sub disciplines

Laboratory Medicine

requires the medical laboratory scientist to appreciate the clinical significance of test results and therefore produce accurate and consistent results

Laboratory testing

what are laboratory testing results used for

confirm a clinical suspicion, exclude a diagnosis, assist in treatment, provide a prognosis, screen for disease, establish physiological disturbance

Process of screening

to classify a persons with disease from those without disease (to form a baseline)

Diagnostic purpose

to classify patients into disease categories, based on manifestations, etiology, prognosis, and/or treatment

Sensitive (diagnostic)

positive result when have disease

Sensitive (analytical)

detects small amounts of analyte

Specific (diagnostic)

negative results when not have disease

Specific (analytical)

detects only the analyte want detected

Prognostic purpose

to predict a physiological/pathological outcome

specific (analytical)

detects only the analyte want detected

precise (analytical)

agreement between replicate values

sensitive (analytical)

Detects small amounts of analyte

Accurate (analytical)

Closeness with the true value

Profiles

tests to screen asymptomatic persons for evidence of disease for risk factors

routine

tests are performed during regular work hours on fixed schedules, 4-24 hours.

ASAP

tests are performed with the next batch of specimen, 1-3 hours.

STAT

test are needed urgently and at any hour of the day, interrupt flow of routine work, 15-60 minutes.

Critical values

results that represent life-threatening state unless some action is taken in a very short time

cumulative reports

results presented as a table on a single sheet; enables physicians to see progress of a patient at a glance; generated by computer

Checks with prior values (delta checks)

compare the new value for an analyte with the last value obtained for the same analyte

Ethical issues - confidentiality

genetic information and patient medical information

Ethical issues - allocation of resources

resources are finite, trade off between cost and quality

ethical issues - conflict of interest

concerns over interrelationship between practitioners and manufactures lead to financial reviews, laws developed to precent fraud, abuse and waste in healthcare reimbursement programs.

Ethical issues - publishing

editors are responsible for developing polices that ensure fair consistent and ethical review, publishers ensure there are no conflict of interest, authors are accountable for honest and complete reporting, reviewers are in charge of providing an impartial assessment

Population

complete set of all observations that might occur as a result of performing a particular procedure according to specified conditions, all possible values for a particular characteristic.

Characteristics in population e described by

parameters (population mean, median, population variance, population sd)

sample

subgroup of observations taken from the population used to form conclusions about population characteristics

random sample

each member of the population has an equal chance of being selected

characteristics of random sample are described by

statistics (sample mean, sample sd, cov)

Statistical analysis and interpretation

depends on the assumption of a random sample from a fixed population

Frequency distribution

graphical device for displaying a large set of data; histogram

Frequency distribution tends to form a

normal or Gaussian distribution

Gaussian distribution (normal distribution)

relative frequency distribution that forms a bell-shaped curve (stats are assumed from, tails of curve are asymptotic)

1SD of the mean

68.2

2SD of the mean

95.44

3SD of the mean

99.27

a constant relationship exists between

sd and probability of values occuring in a population or sample

variations

of a normal curve depends on how the data points are concentrated around the cent and toward the tails of the curve

four characteristics of distribution curve

measure of central tendency, variability, symmetry of the curve about the center, and kurtosis.

Measure of central tendency

how data points fit around the center (or highest point or points) of the distribution (unimodal, multimodal)

Line of central tendency

area in the distribution pattern where most of the observations seem to accumulate

Measure of variability

how widely the data points are scattered around the center of the distribution (flat = greater pattern of variation vs a sharp peak)

Symmetry of the curve about the center

whether the data points are evenly arranged around the measure of central tendency or skweed with the tail to the right (positive) or to the left (negative)

Kurtosis

how the data points are distributed along the base of the distribution diagram, with regard to the relative frequency of data appearing at the center point and each flank, with thinning of data between the center and the tails

Kurtosis increases

as tails become heavier (negative)

Kurtosis decreases

as tails become lighter (positive)

Arithmetic mean

set of data is obtained by adding all the numbers in the set and dividing the sum by the number of values in that set

median

middle value of a body data ranking variables in order of increasing magnitude; the 50th percentile. (with even amount of numbers, average of middle two)

mode

most frequently occurring variable in a mass of data; the value at the peak of the frequency distribution curve (ex: 90, 70, 22, 70)

percentile

percentage of scores in the whole distribution that fall below that score that is being compared

location of a percentile equation

Lp=(n+1)(P/100) (P= position of finding)

Lp example of reading results = 1.75 (data set is given) (42, 61, 75, 91, 101, 104)

1 tells us the % is located after the first number, (1) in the data set .75 tells how far apart, 61-43 =18, 18(.075) = 13.5 +43 = 56.5

range

spread of data around the mean

variance (s²)

reflects dispersion around the mean and is the square of the standard deviation

variance (s²) formula

s²= sum (each score - mean)² / n-1

Standard deviation

measures dispersion of the variable about the mean and is the square root of the variance

standard deviation formula

S = sq rt of sum (each score - mean)² / n-1

Coefficient of variation (CV)

the ratio of the standard deviation to the mean (also known as relative standard deviation, compares the dispersion of two similar sets of data)

Coefficient of variation formula

CV = sd/mean x 100

Null hypothesis

probability theory that states there is no difference between two sets of values which are being compared (F test)

F test

test statistic based on the comparison of the variance values from two or more series of numbers

F test formula

F = S² (new)/S² (old)

Null hypothesis is rejected when

the observed F values is greater than the critical F value

T test

test statistic based on the comparison of mean values from two or more series of number or methods

t test formula (just need to recognize not solve)

t = d/sd sq rt n

Null hypothesis is rejected in t values when

t value is greater than the critical t values

Linear regression

equation that expressed the linear relationship between two variables (describes the graphical plot of test values versus reference values)

least squares analysis

technique to determine the best fit for the line by measuring the distance from each point to the line, squaring the distance then totaling the squares (the line with the lowest total is called the least squares line and should be the best line)

Correlation Coefficient (r values)

determine if two series of numbers are related positively, negatively, or not at all.

when selecting a new method, one has to consider

medical usefulness, analytical performances, and other practical criteria

method evaluation four key points

1. application of clinical significance to the whole task

2. development of analytical goals before you begin

3. appropotae experiments to produce correct data

4. objective conclusions about the method

Analytical method characteristics

practical, reliable, analytical range, analytical sensitivity, detection limit, blank measurement, analytical specificity, interferences, recovery

trueness

the closeness of agreement of the mean with the true value

precision

is the closeness of agreement between independent results of measurements obtained under stipulated conditions, repeatability or reproducibility

repeatability

closeness of agreement between results of successive measurements carried out under the same conditions

Reproducibility

closeness of agreement between results of measurements performed under changed condition of measurements

Bias

measure of the systematic error, the difference between the average value and the true value

Imprecision (SD)

measure of the dispersion of random errors

error of measurement

comprises both random and systemic influences

calibration

function that describes the relationship between instrument signal (y) and concentration of analyte (x)

total error

measure of random and systemic errors

random error

random deviation is due to chance (1 in every 20 tests fall outside the 2SD 95%)

systematic error

measure of the agreement between the measured quantity and the true value

constant systematic error (systematic error)

error that is always in one direction and has the same magnitude regardless of concentration of the sample

proportional systematic error (type of systemic error)

has the same percentage of the concentration being measured so the absolute magnitude increases as the concentration increases

Assay comparison

the comparison of measurements from two methods using statistical procedures

Replication experiment

performed by making measurements on a series of aliquots within a specified time period, estimates random error

Recovery experiment

estimates proportional systematic error

recovery experiment procedure

baseline made with solvent and patient serum, test sample is standard and patient serum, test sample 2 is standard x 2 and patient serum

recovery experiment formulas

percent recovery = conc recovered/conc added x 100 conc added = std conc x mL of std/mL of standard+mL of serum conc recovered = measured conc (diluted test) - measured conc (baseline)

Interference experiment

used to determine the effect of other substances in the sample

Interference experiment procedure

baseline with solvent + patient sample, test sample 1 with interfering std + patient sample, test sample 2 interfering std x 2 + patient sample

interference experiment formulas

interference = measured conc(diluted test) - measured conc (baseline)

diagnostic accuracy

fraction of true classification out of all classifications

diagnostic accuracy formula

TN + TP/TN + TP + FP + FN

true positives

patients with a condition who are correctly classified by a test to have the condition

false negatives

patients with the condition who are classified by the test as not having the condition

true negatives

patients without the condition who are correctly classified by a test to not have the condition

false positives

patients without the condition who are classified by the test as having the condition