Hybrid modelling for stroke care: Review and suggestions of new approaches for risk assessment and simulation of scenarios

A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects

BACKGROUND

The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans.

METHODS

The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox.

RESULTS

The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables.

CONCLUSIONS

The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.

Clinical decision support for chemotherapy-induced neutropenia using a hybrid pharmacodynamic/machine learning model

Consensus guidelines recommend use of granulocyte colony stimulating factor in patients deemed at risk of chemotherapy-induced neutropenia, however, these risk models are limited in the factors they consider and miss some cases of neutropenia. Clinical decision making could be supported using models that better tailor their predictions to the individual patient using the wealth of data available in electronic health records (EHRs). Here, we present a hybrid pharmacokinetic/pharmacodynamic (PKPD)/machine learning (ML) approach that uses predictions and individual Bayesian parameter estimates from a PKPD model to enrich an ML model built on her data. We demonstrate this approach using models developed on a large real-world data set of 9121 patients treated for lymphoma, breast, or thoracic cancer. We also investigate the benefits of augmenting the training data using synthetic data simulated with the PKPD model. We find that PKPD-enrichment of ML models improves prediction of grade 3-4 neutropenia, as measured by higher precision (61%) and recall (39%) compared to PKPD model predictions (47%, 33%) or base ML model predictions (51%, 31%). PKPD augmentation of ML models showed minor improvements in recall (44%) but not precision (56%), and data augmentation required careful tuning to control overfitting its predictions to the PKPD model. PKPD enrichment of ML shows promise for leveraging both the physiology-informed predictions of PKPD and the ability of ML to learn predictor-outcome relationships from large data sets to predict patient response to drugs in a clinical precision dosing context.

Perspectives of Evidence-Based Therapy Management

BACKGROUND

Therapeutics that specifically address biological processes often require a much finer selection of patients and subclassification of diseases. Thus, diagnostic procedures must describe the diseases in sufficient detail to allow selection of appropriate therapy and to sensitively track therapy response. Anatomical features are often not sufficient for this purpose and there is a need to image molecular and pathophysiological processes.

METHOD

Two imaging strategies can be pursued: molecular imaging attempts to image a few biomarkers that play key roles in pathological processes. Alternatively, patterns describing a biological process can be identified from the synopsis of multiple (non-specific) imaging markers, possibly in combination with omics and other clinical findings. Here, AI-based methods are increasingly being used.

RESULTS

Both strategies of evidence-based therapy management are explained in this review article and examples and clinical successes are presented. In this context, reviews of clinically approved molecular diagnostics and decision support systems are listed. Furthermore, since reliable, representative, and sufficiently large datasets are further important prerequisites for AI-assisted multiparametric analyses, concepts are presented to make data available in a structured way, e. g., using Generative Adversarial Networks to complement databases with virtual cases and to build completely anonymous reference databases.

CONCLUSION

Molecular imaging and computer-assisted cluster analysis of diagnostic data are complementary methods to describe pathophysiological processes. Both methods have the potential to improve (evidence-based) the future management of therapies, partly on their own but also in combined approaches.

KEY POINTS

· Molecular imaging and radiomics provide valuable complementary disease biomarkers.. · Data-driven, model-based, and hybrid model-based integrated diagnostics advance precision medicine.. · Synthetic data generation may become essential in the development process of future AI methods..

Perspectives of Evidence-Based Therapy Management

BACKGROUND

 Therapeutics that specifically address biological processes often require a much finer selection of patients and subclassification of diseases. Thus, diagnostic procedures must describe the diseases in sufficient detail to allow selection of appropriate therapy and to sensitively track therapy response. Anatomical features are often not sufficient for this purpose and there is a need to image molecular and pathophysiological processes.

METHOD

 Two imaging strategies can be pursued: molecular imaging attempts to image a few biomarkers that play key roles in pathological processes. Alternatively, patterns describing a biological process can be identified from the synopsis of multiple (non-specific) imaging markers, possibly in combination with omics and other clinical findings. Here, AI-based methods are increasingly being used.

RESULTS

 Both strategies of evidence-based therapy management are explained in this review article and examples and clinical successes are presented. In this context, reviews of clinically approved molecular diagnostics and decision support systems are listed. Furthermore, since reliable, representative, and sufficiently large datasets are further important prerequisites for AI-assisted multiparametric analyses, concepts are presented to make data available in a structured way, e. g., using Generative Adversarial Networks to complement databases with virtual cases and to build completely anonymous reference databases.

CONCLUSION

 Molecular imaging and computer-assisted cluster analysis of diagnostic data are complementary methods to describe pathophysiological processes. Both methods have the potential to improve (evidence-based) the future management of therapies, partly on their own but also in combined approaches.

KEY POINTS

· Molecular imaging and radiomics provide valuable complementary disease biomarkers.. · Data-driven, model-based, and hybrid model-based integrated diagnostics advance precision medicine.. · Synthetic data generation may become essential in the development process of future AI methods..

CITATION FORMAT

· Kiessling F, Schulz V, . Perspectives of Evidence-Based Therapy Management. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1752-0839.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer's disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

Age Specific Models to Capture the Change in Risk Factor Contribution by Age to Short Term Primary Ischemic Stroke Risk

Age is one of the most important risk factors when it comes to stroke risk prediction. However, including age as a risk factor in a stroke prediction model can give rise to a number of difficulties. Age often dominates the risk score, and also not all risk factors contribute proportionally to stroke risk by age. In this study we investigate a number of common stroke risk factors, using Framingham heart study data from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center to determine if they appear to contribute proportionally by age to a stroke risk score. As we find evidence that there is some non-proportionality by age, we then create a set of logistic regression risk models that each predict the 5 year stroke risk for a different age group. The age group models are shown to be better calibrated when compared to a model for all ages that includes age as a risk factor. This suggests that to get better predictions for stroke risk it may be necessary to consider alternative methods for including age in stroke risk prediction models that account for the non-proportionality of the other risk factors as age changes.

Assessing Patient Engagement in Health Care: Proposal for a Modeling and Simulation Framework for Behavioral Analysis

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/30092.