NAPLEX Calculation & Biostats

1 tsp = ? mL

5

1 tbsp = ? mL

15

1 fl oz = ? mL

30

1 cup = ? oz = ? mL

8 oz, 240 mL

1 pint = ? oz = ? mL

16 oz, 480 mL

1 quart = ? pint = ? mL

2 pint, 960 mL

1 gallon = ? quart = ? mL

4 quart, 3840 mL

1 kg = ? lbs

2.2

1 oz = ? grams

28.4

1 grain = ? mg

65

1 inch = ? cm

2.54

% w/v equation

x (g)/100 mL

% v/v equation

x (mL)/100 mL

% w/w equation

x (g)/100 g

Parts per million—> percentage

move the decimal left 4 places

Osmolarity calculation (mOsmol/L)

mOsmol/L = wt of substance (g/L)/MW (g/mol)

moles calculation

mol = g/MW

Isotonicity (E)

E = (58.5 i)/(MW of drug * 1.8)

Nutrition (calories)

Enteral carbs, protein

4 kcal/g

Enteral fat

9 kcal/g

Parental dextrose

3.4 kcal/g

Parental amino acid

4 kcal/g

ILE 10%

1.1 kcal/mL

ILE 20%

2 kcal/mL

ILE 30%

3 kcal/mL

Amount of fluid needs

When wt >20 kg, 1500 mL + (20 mL)(wt in kg - 20)

• Typically 30 - 40 mL/kg/day

Grams of nitrogen intake

Nitrogen (g) = protein intake (g)/ 6.25

Corrected calcium for albumin <3.5

Ca core (mg/dL) = serum Ca + [(4 - albumin) * 0.8]

BMI

BMI = kg/m² or lbs/in² * 703

Ideal BW

Male = 50 + 2.3 * (number of inches over 5 feet)

Female = 45.5 + 2.3 * (number of inches over 5 feet)

1 feet = 12 inches

Adjusted BW

AdjBW = IBW + 0.4 * (TBW - IBW)

Medication dosage using IBW

Acyclovir, Aminophylline, Levothyroxine, Theophylline (obese)

Medication dosage using AdjBW

Aminoglycoside (obese)

Dehydration

BUN:SCr > 20:1

Cockcroft-Gault Equation

CrCl = (140 - age)/(72 * SCr) * wt (kg) (* 0.85 if female)

Arterial blood gas- acidosis vs alkalosis

Acidosis

• pH < 7.35

• respiratory pCO2 > 45

• metabolic HCO3 < 22

Alkalosis

• pH > 7.45

• respiratory pCO2 < 35

• metabolic HCO3 > 26

Sampling bias

When participants selected are not representative of the whole population

Selection bias

When there are baseline differences between the study groups, such as demographics or disease severity, that prevents the groups from being comparable and generalizable.

Selection bias can result from lack of randomization or strict study exclusion criteria

Performance bias

When the study group are not treated equally.

E.g., if the study is not blinded

Detection bias

When outcomes are measured differently between the study groups.

To avoid detection bias, the study should be blinded on both team (research vs study)

Attribution bias

When study participants with specific characteristics

E.g., less severe disease, higher dropout rate

Reporting bias

When the decision to report on a finding is influenced by the result

E.g., researchers reports positive efficacy data more than the adverse event data

Publication bias

When only research with positive results is published, and studies that had negative results or that did not have statistically significant results do not get published

Continuous data

Interval data: no meaningful zero (zero does not equal to one)

• E.g., celcius

Ratio data: has meaningful zero

• E.g., heart rate

Discrete/ categorical data

Nominal data: subjects are sorted into arbitrary categories, and the order doesn’t matter

• E.g., female= 1, male= 0 or vice versa

Ordinal data: subjects are ranked and has logical order

• E.g., pain scale

Measurement of central tendency

Mean, Median, Mode (the most frequent, preferred for nominal data)

Skewed distribution

Data graphs are not symmetrical. 68% of the values dont fall within 1SD

• When there’s more low value data & outliners are the higher value, data is skewed to the right (positive skew)

• When there’re more high value data & outliners are the low values, data is skewed to the left (negative skew)

Null hypothesis (H0)

H0= there’s no statistically significant difference between groups

Researchers need to reject H0 to show superiority

Alpha

Maximun permissible error margin

Alpha is the threshold for rejecting a H0

Typical alpha value in medical research— 5% (0.05)

P value is compared to alpha— <5% (0.05)

Confidence interval

CI also provide information on significance + precision of the result

CI= 1 - alpha

Narrow CI means high precision, vice versa

Type I errors- False Positives

E.g., conclusion is wrong, but H0 is rejected because alpha= 0.05 & P< 0.05

Type II errors- False Negatives

Type II error can be denoted as beta. Type II error increases when the sample size is too small. Power analysis should be performed to determine the sample size

E.g., H0 is accepted when it should be rejected

Power

The probability that a test will reject the null hypothesis correctly

Power= 1 - beta

Relative risk

The ratio of risk in the exposed group divided by risk in the control group

Risk= (# of pts experienced ADR) / (total # of pts)

RR= (risk of treatment group) / (risk of control group)

• RR >1, greater risk

• RR =1, no differences

• RR <1, lower risk

Relative risk reduction

RRR indicates how much the risk is reduced in the treatment group

RRR= (% risk in control - % risk in treatment) / (% risk in control) or

RRR= 1 - RR

Absolute risk reduction

ARR includes the reduction in risk and the incidence rate of the outcome

ARR= (% risk in control) - (% risk in treatment)

Number needed to treat

NNT is the number of patients who need to be treated for a