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Determination of Operating Characteristics and Performance Requirements for a Methotrexate Forecasting
Algorithm Used for Clinical Decision Support in Pediatric Oncology Patients
Erin Cummings, Bhuvana Jayaraman, John Mondick, and Jeffrey S. Barrett
Laboratory for Applied PK/PD, Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia; Philadelphia, PA
BACKGROUND
MTX TDM
MTX Cleared
• Urine pH must be ≥ 7
• 25 mg/ml solution in Dextrose
5% in water (D5W)
• Maximum absolute dose: 20g
• Begins 24 hours after the start of
MTX infusion
• Results plotted on protocolspecific nomogram
• Continues daily until MTX level
≤ 0.1 µM
• MTX level ≤ 0.1 µM
• Patient can be discharged
Before Administration
0 – 24 Hours
OBJECTIVES
24 Hours - Discharge
Prehydration
Continuing Hydration
LVR Administration
• 750 ml/m2 of D5 0.22% NaCl
with 40 mEq/L NaHCO3 is given
over 1 hour
• If urine pH < 7, 0.5 mEq/L
NaHCO3 is given over 30
minutes. Repeated if urine pH is
< 7 after 1 hour
• D5 0.22% NaCl with 40 mEq/L
NaHCO3 at 100 ml/m2/hr
• Urine pH is measured every 8
hours. If pH < 7, 10 ml/kg
hydration fluid is given over 30
minutes and pH measurements
are taken
• Lasts until MTX level ≤ 0.1 µM
• LVR starts 24 - 42 hours after the
start of MTX infusion as 15
mg/m2 IVSS over 15 minutes,
every 6 hours
• Dose can be modified based on
protocol-specific nomogram
because of excretion delay
• Lasts until MTX level ≤ 0.1 µM
FIGURE 1 A timeline representing the protocol for an inpatient visit at CHOP
where HDMTX is administered.
FIGURE 2 An
example of a
protocolspecific
nomogram used
by physicians
for LVR dosing
based on patient
plasma MTX
concentrations.
To perform a clinical validation of the forecasting algorithm used for the
MTX dashboard of the PKB with actual retrospective data assembled from
our Electronic Medical Records (EMR) system.
The analysis of variance performed in SAS, showed that both run number
and the number of observations had a P-value <0.05, making them
statistically significant.
Table 2 Tukey studentized range test for the difference between PRED and
observation based on run number. Only comparisons made with the first run
were shown to be statistically significant.
METHODS
Run No. Comparison
Under IRB approval from CHOP patient data for the validation was
obtained from the EMR system. Patients were selected so that there
would be variation in age, sex, weight, gender and height. 28 patients
visiting CHOP between September 29, 2004 and November 20, 2006
were used as the validation set in this retrospective study.
Table 1 Demographics of historical and validation patient populations.
No. Patients
No. Observations
Age Range (years)
Gender (%M/F)
Weight (kg)
Historical Patients
240
2176
1 - 80
48/52
6.6 - 157
Data Collection from Electronic
Medical Records
Validation Patients
28
110
0 - 21
64/36
7.6 - 119.6
Control File with Historical Priors
for First TDM Observation
Dfference Between Means
12.503
*
1–3
14.930
1–4
14.478
1–5
14.406
2–3
2.427
2–4
1.975
2–5
1.904
3–4
0.452
3–5
0.523
4-5
0.071
A
7
6
5
4
3
2
0
0
1
2
3
4
5
6
7
8
9
10
NONMEM-Ready Data Set
FIGURE 3 A screencapture of the PKB. This
view shows the projected
MTX plasma concentrations
overlaid on a nomogram.
This is used in order to
determine if LVR rescue
therapy is needed and what
the dosage should be.
LST File,
Runlog File
C
Repeated up to
Four Observations
SAS Script:
Update Parameters from Runlog in Control File
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
New Control File for Next TDM Observation
FIGURE 4 Validation Workflow
Analysis of variance was performed in SAS to determine the affects of run
number and the nested effect of number of observations on the difference
of population prediction (PRED) and observation. Statistical significance
was considered at p < 0.05. A Tukey test was utilized to compare the
means for each run.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Observed
0.8
0.9
1
(n=28)
(n=23)
(n=16)
(n=6)
(CLN – Clearance in patients with normal renal function. CLR - Clearance in patients with reduced renal function, V1 –
Volume of distribution in central compartment, V2 - Volume of distribution in peripheral compartment, Q – Intercompartmental clearance)
No. Patients
CLN
(L/hr)
CLR
(L/hr)
V1
(L)
V2
(L)
Q
(L/hr)
Population
Distribution
Theta 1
Theta 2
Theta 3
Theta 4
Theta 5
Theta 6
7.490
2.550
36.00
3.330
0.0980
0.5590
28
7.62 ± 0.256
2.56 ± .0250
35.9 ± 0.0935
3.33 ± 0.0281
0.11 ± 0.028
0.59 ± 0.025
*
Run 2
28
7.84 ± 0.569
2.56 ± 0.0273
35.8 ± 0.168
3.32 ± 0.326
0.15 ± 0.043
0.64 ± 0.036
*
Run 3
23
8.11 ± 0.844
2.57 ± 0.0309
35.7 ± 0.241
3.37 ± 0.524
0.17 ± 0.052
0.68 ± 0.055
Run 4
16
8.15 ± 1.23
2.59 ± 0.0788
35.7 ± 0.318
3.52 ± 0.752
0.18 ± 0.062
0.72 ± 0.044
Run 5
6
6.96 ± 1.56
2.71 ± 0.262
35.62 ± 0.280
4.25 ± 0.777
0.12 ± 0.037
0.73 ± 0.067
B
400
300
Based on this clinical validation, the MTX dashboard will be superior to
the current nomogram-based criterion which now guides MTX
pharmacotherapy in regards to reducing medication errors, earlier
detection of nephrotoxicity and need for rescue therapy. Some limitations
of the current analysis:
 Unreliability of the EMR system being used as “gold standard”.
 Small sampling of actual patient population receiving HDMTX.
 Patients are assumed to be admitted simultaneously (naïve analysis).
200
100
0
Subject ID
Prior to production version of the PKB:
500
0.8
(n=28)
Run 5
CONCLUSIONS
1
0.9
Run 4
Run 1
NONMEM Execution and Runlog Script
Predicted
The MTX model was developed as a part of the Pediatric Knowledgebase
(PKB), a web-based physician designed informatics systems that uses
real-time modeling in order to predict the risk of toxicity.
Observed
Run 3
TABLE 3 Stability of Priors from Repeated Runs.
Historical
500
8
Run 2
Parameter
*
10
9
Run 1
FIGURE 7 Box and Whisker Representation of the percent error of PRED
with respect to DV against run number. The 25th and 75th percentiles are shown
within the boxes. Values that fell out of the range shown for Percent Error were
included in the calculation from the plot.
Statistically Significant
1–2
1
MTX Model
275
250
225
200
175
150
125
100
75
50
25
0
Percent Error
Figure 5 Relationship between the difference of PRED and observation and the
number of observations and run number.
Percent Error (%)
MTX Administration
Percent Error (%)
MTX Patient Experience
Methotrexate disposition is described using a two-compartment model
with first-order elimination. Inter-subject variability is described with an
exponential error model and the residual error is expressed by a
proportional error model. Two clearance distributions exist in the model;
for a patient population with normal renal function and for one with
compromised renal function. Based on a patients MTX plasma
concentrations, a patients is assigned to one of the two populations.
Bayesian prediction of MTX concentrations is utilized along with the
NONMEM PRIOR subroutine, which incorporates the population priors
into the forecasting model. Parameter estimates for the historical patients
were CLN (7.49 L/h, 6.37 %CV), CLR (2.55 L/h, 75.37 %CV), V1 (36 L,
19.03 %CV), V2 (3.33 L, 52.15 %CV) and Q (.0984 L/h, 12.25 %CV).
Predicted
Methotrexate (MTX), an antifol agent used in the treatment of various
pediatric cancers, ranks second in oncology agents utilized at The
Children’s Hospital of Philadelphia (CHOP). As a chemotherapeutic
agent, high-dose MTX (HDMTX, > 1 g/m2) can be life-threatening
because it induces nephrotoxicity at high or prolonged low exposures.
Leucovorin (LVR) is used as a rescue therapy to manage this. Based on
CHOP protocols, Therapeutic Drug Monitoring (TDM) of MTX is
performed, which aids in pharmacokinetic (PK) modeling since there is
high inter- and intra- patient variability.
RESULTS
D
400
300
200
100
 Clustered validation of MTX model.
 Investigation of how often population parameters should update.
 Determination if the time LVR therapy is suggested to commence by
the algorithm corresponds to the protocol-defined time.
 The dashboard will be integrated with the CHOP EMR systems.
0
Subject ID
FIGURE 6 Diagnostic plots comparing the difference between having two or
three TDM observations available. (A) PRED and observation for the second
observation. (B) Percent Error for each patient having two observations. (C)
PRED and observation for the third observation. (D) Percent Error for each
patient having three observations.
REFERENCES
Barrett JS, Mondick JT, Narayan M, Vijayakumar K, Vijayakumar S.
Integration of Modeling and Simulation into Hospital-based Decision
Support Systems Guiding Pediatric Pharmacotherapy. BMC Medical
Informatics and Decision Making 8:6, 2008.