Description

A first-order process will increase or decrease exponentially based on the initial value and the time since starting. The time it takes to reach a given value can be predicted if certain data is available.


 

Requirements:

(1) first order rise or decline in a value

(2) constant k not known for an individual patient

 

For a value that rises as a first order process:

 

value now =

= (initial value at time 0) * EXP(k * (time interval in days))

 

For a value that decreases as a first order process:

 

value now =

= (initial value at time 0) * EXP((-1) * k * (time interval in days))

 

Doubline Time

 

If the value doubles then

 

(value now) / (initial value at time 0) = 2 =

= EXP(k * (doubling time in days))

 

and

 

LN(2) = k * (doubling time in days)

 

and

 

doubling time = LN(2) / k = 0.6931 / k

 

k =

= (LN(final value) - LN(initial value)) / (time interval in months between the 2 values)

 

k =

= LN(2) / (doubling time in months)

 

If these 2 equations are combined:

 

doubling time in months =

= LN(2) * (time interval months) / (LN(final value) - LN(initial value))

 

Generalized Version

 

Parameters:

(1) initial value

(2) value after time t

(3) target value

 

time to reach target value =

= LN((target value) / (initial value)) * (time t) / (LN(value at time t) - LN(initial value)) =

= (LN(target value) - LN(initial value)) * (time t) / (LN(value at time t) - LN(initial value))

 

Limitations:

• This assumes that the release characteristics do not change.

 


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