Limits...
Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy.

Barrett JS, Mondick JT, Narayan M, Vijayakumar K, Vijayakumar S - BMC Med Inform Decis Mak (2008)

Bottom Line: We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available.The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pediatrics, Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, USA. barrettj@email.chop.edu

ABSTRACT

Background: Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.

Methods: Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available. These models are re-executed with individual patient data allowing for patient-specific guidance via a Bayesian forecasting approach. The models are called and executed in an interactive manner through our web-based dashboard environment which interfaces to the hospital's electronic medical records system.

Results: The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events. Projected plasma concentrations are viewable against protocol-specific nomograms to provide dosing guidance for potential rescue therapy with leucovorin. These data are also viewable against common biomarkers used to assess patient safety (e.g., vital signs and plasma creatinine levels). As additional data become available via therapeutic drug monitoring, the model is re-executed and projections are revised.

Conclusion: The management of pediatric pharmacotherapy can be greatly enhanced via the immediate feedback provided by decision analytics which incorporate the current, best-available knowledge pertaining to dose-exposure and exposure-response relationships, especially for narrow therapeutic agents that are difficult to manage.

Show MeSH

Related in: MedlinePlus

Screen captures from the current MTX dashboard design showing (A) the most recent MTX dose event with the complementary monitored MTX plasma concentrations and safety markers, (B) the MTX exposure projected after the dosing guidance menu button is selected, (C) the view from Figure 4B overlaid against a nomogram used to assess the potential for MTX toxicity with consideration for drug rescue with leucovorin and (D) the update of the model fit when the additional blood collection time points were added to the patient data set.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2254609&req=5

Figure 4: Screen captures from the current MTX dashboard design showing (A) the most recent MTX dose event with the complementary monitored MTX plasma concentrations and safety markers, (B) the MTX exposure projected after the dosing guidance menu button is selected, (C) the view from Figure 4B overlaid against a nomogram used to assess the potential for MTX toxicity with consideration for drug rescue with leucovorin and (D) the update of the model fit when the additional blood collection time points were added to the patient data set.

Mentions: The prototype methotrexate dashboard module is focused on visualizing the current and recent dosing events relative to markers which indicate the potential for drug-induced toxicity. It also forecasts methotrexate exposures within the current dosing regimen to project the potential for toxicity and provide dosing guidance. The user will access the MTX dashboard from our existing medical records system, Sunrise Clinical Manager (SCM). Eventually, the dashboard will be migrated to the EPIC system which is being integrated with the hospital network in phases. From either system, there will be multiple points of entry with the most likely access coming from the patient medical record number. Figure 4 shows screen captures of the current MTX dashboard design. The dashboard contains three tab views (Drug Formulary, Patient, and Patient History). The details of the entire MTX dashboard will be defined in a separate publication. Briefly, the Drug Formulary tab presents formulary data supplied by Lexicomp restructured in a more web-friendly and content rich layout. The Patient History tab provides historical views of previous experience with an agent in the same patient from previous hospital visits. The first panel (Figure 4A) shows the initial entry screen with the patient tab shown. All modeling and simulation is executed via the patient tab. The initial screen (Figure 4A) shows the most recent MTX dose event with the complementary monitored MTX plasma concentrations and safety markers. Based on input from our pediatric oncology community, the dashboard contains a subset of all data captured within SCM with selections from drop down windows possible. Serum creatinine and total bilirubin concentrations are shown plotted with MTX concentrations from the last dose event. Double clicking on any of the three plots will yield an expanded single plot with axes, scale and units shown.


Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy.

Barrett JS, Mondick JT, Narayan M, Vijayakumar K, Vijayakumar S - BMC Med Inform Decis Mak (2008)

Screen captures from the current MTX dashboard design showing (A) the most recent MTX dose event with the complementary monitored MTX plasma concentrations and safety markers, (B) the MTX exposure projected after the dosing guidance menu button is selected, (C) the view from Figure 4B overlaid against a nomogram used to assess the potential for MTX toxicity with consideration for drug rescue with leucovorin and (D) the update of the model fit when the additional blood collection time points were added to the patient data set.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2254609&req=5

Figure 4: Screen captures from the current MTX dashboard design showing (A) the most recent MTX dose event with the complementary monitored MTX plasma concentrations and safety markers, (B) the MTX exposure projected after the dosing guidance menu button is selected, (C) the view from Figure 4B overlaid against a nomogram used to assess the potential for MTX toxicity with consideration for drug rescue with leucovorin and (D) the update of the model fit when the additional blood collection time points were added to the patient data set.
Mentions: The prototype methotrexate dashboard module is focused on visualizing the current and recent dosing events relative to markers which indicate the potential for drug-induced toxicity. It also forecasts methotrexate exposures within the current dosing regimen to project the potential for toxicity and provide dosing guidance. The user will access the MTX dashboard from our existing medical records system, Sunrise Clinical Manager (SCM). Eventually, the dashboard will be migrated to the EPIC system which is being integrated with the hospital network in phases. From either system, there will be multiple points of entry with the most likely access coming from the patient medical record number. Figure 4 shows screen captures of the current MTX dashboard design. The dashboard contains three tab views (Drug Formulary, Patient, and Patient History). The details of the entire MTX dashboard will be defined in a separate publication. Briefly, the Drug Formulary tab presents formulary data supplied by Lexicomp restructured in a more web-friendly and content rich layout. The Patient History tab provides historical views of previous experience with an agent in the same patient from previous hospital visits. The first panel (Figure 4A) shows the initial entry screen with the patient tab shown. All modeling and simulation is executed via the patient tab. The initial screen (Figure 4A) shows the most recent MTX dose event with the complementary monitored MTX plasma concentrations and safety markers. Based on input from our pediatric oncology community, the dashboard contains a subset of all data captured within SCM with selections from drop down windows possible. Serum creatinine and total bilirubin concentrations are shown plotted with MTX concentrations from the last dose event. Double clicking on any of the three plots will yield an expanded single plot with axes, scale and units shown.

Bottom Line: We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available.The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pediatrics, Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, USA. barrettj@email.chop.edu

ABSTRACT

Background: Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.

Methods: Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available. These models are re-executed with individual patient data allowing for patient-specific guidance via a Bayesian forecasting approach. The models are called and executed in an interactive manner through our web-based dashboard environment which interfaces to the hospital's electronic medical records system.

Results: The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events. Projected plasma concentrations are viewable against protocol-specific nomograms to provide dosing guidance for potential rescue therapy with leucovorin. These data are also viewable against common biomarkers used to assess patient safety (e.g., vital signs and plasma creatinine levels). As additional data become available via therapeutic drug monitoring, the model is re-executed and projections are revised.

Conclusion: The management of pediatric pharmacotherapy can be greatly enhanced via the immediate feedback provided by decision analytics which incorporate the current, best-available knowledge pertaining to dose-exposure and exposure-response relationships, especially for narrow therapeutic agents that are difficult to manage.

Show MeSH
Related in: MedlinePlus