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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.

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Related in: MedlinePlus

Workflow of MTX dashboard operation.
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Figure 5: Workflow of MTX dashboard operation.

Mentions: Figure 4C shows projected view from Figure 4B overlaid against a nomogram used to assess the potential for MTX toxicity with consideration for drug rescue with leucovorin. Several similar nomograms exist for managing MTX drug therapy. Nomograms are aligned to the clinical protocol that the patient is being treated from (protocol is shown in the dashboard in the upper right corner of each screen next to patient demographics. The dashboard contains each of the nomograms used at our institution and so correctly matches the drug exposure views to the nomogram by index to study protocol. Figure 4D illustrates the update of the model fit when the additional blood collection time points were added to the patient data set. The various stages of the MTX dashboard interface including data refresh, model update and output generation are described via the workflow diagram shown in Figure 5. While real-time access is desirable, a scheduled data refresh is more practical. This also removes the burden of data check and model update from the user operation with only calls to produce simulation plots generated by the actual user interface screen.


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)

Workflow of MTX dashboard operation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Workflow of MTX dashboard operation.
Mentions: Figure 4C shows projected view from Figure 4B overlaid against a nomogram used to assess the potential for MTX toxicity with consideration for drug rescue with leucovorin. Several similar nomograms exist for managing MTX drug therapy. Nomograms are aligned to the clinical protocol that the patient is being treated from (protocol is shown in the dashboard in the upper right corner of each screen next to patient demographics. The dashboard contains each of the nomograms used at our institution and so correctly matches the drug exposure views to the nomogram by index to study protocol. Figure 4D illustrates the update of the model fit when the additional blood collection time points were added to the patient data set. The various stages of the MTX dashboard interface including data refresh, model update and output generation are described via the workflow diagram shown in Figure 5. While real-time access is desirable, a scheduled data refresh is more practical. This also removes the burden of data check and model update from the user operation with only calls to produce simulation plots generated by the actual user interface screen.

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