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

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.

Figures

Figure 1

Typical progression of pharmacometric model development commonly used to support pharmacotherapeutic decision support systems.

Figure 2

Schematic of three-tier system architecture of hospital pharmacotherapy decision support system comprising a back end database tier, a business logic middle tier and data presentation/user interface front-end tier.

Figure 3

Diagnostic plots from preliminary methotrexate population pharmacokinetic model. (A) Observed versus population predicted concentrations. (B) Observed versus individual predicted concentrations. Open circles represent MTX plasma concentrations from patients predicted to have normal renal function. Open triangles represent patients predicted to have reduced MTX clearance.

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.

Figure 5

Workflow of MTX dashboard operation.