Abstract
BackgroundIndividualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities.ResultsIn the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level.ConclusionsBy integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.
Highlights
Individualization and patient-specific optimization of treatment is a major goal of modern health care
Data management and exploration In order to support clinical decision-making for patientspecific therapy planning, our prototype unifies data management, data description in the form of visualizations, and patient-specific predictions based on mathematical disease models
Example applications / use cases To demonstrate the functionality of our framework as a model-based clinical decision support system, we present two prototypic applications
Summary
Individualization and patient-specific optimization of treatment is a major goal of modern health care. The rising number of different treatment modalities induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. The identification of the best patient-specific treatment options remains an open question. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, provide individualized predictions for the effect of different treatment modalities. The availability of highly effective cytotoxic agents, tumour-specific drugs, and other targeted therapy options are the mainstay of treatment for many cancer types. Risk group stratification can be useful, the identification of the best patient-specific treatment options, Hoffmann et al BMC Medical Informatics and Decision Making (2020) 20:28 such as type and dosage of drugs, remains an open question
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