Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Abstract

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current “one size fits all” protocolised care to adaptive, model-based “one method fits all” personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

“…It is apparent that mathematical models of tumor-immune interaction with respect to HER2-targeted therapy and/or immune checkpoint blockade can be used to explore tumor dynamics in detail and to answer questions that are difficult to answer by clinical analysis [54,71,145]. Since 1954, mathematical model-based analysis has contributed heavily to various areas of cancer research such as drug scheduling, estimating drug response in terms of desired effect, testing research hypothesis, and to study interdependence and sensitivity of various parameters involved in cancer dynamics [39,40,70,[154][155][156][157]. At this point in time, it is worth noting that nuclear physics, neuroscience, epidemiology, and physical chemistry are fundamental areas of research that witnessed a big leap forward due to the integration of empirical and theoretical works.…”

“…On the other hand, the results obtained from theoretical analysis using mathematical models (e.g., tumor doubling time, optimal drug dose, predicted tumor volume, estimated time for relapse of disease) are used to optimize the clinical experiment and proposed therapeutic strategy [31,[35][36][37][38]. Specifically, mathematical models can be used to analyze drug distribution (pharmacokinetics), drug response (pharmacodynamics) to monotherapy and combination therapy, development of drug resistance, and effect of drug toxicity related to cancer treatment [39,40]. Even though substantial efforts have been dedicated to the development of mathematical modeling of various types of cancers and their treatments, these contributions have been isolated from the clinical framework of cancer care and management.…”

A combined SHM/IDA method for assessing collapse capacity and risk in subsequent ground motions

“…While realistic, it may not hold for a given structure, impacting the benefit obtained from the resulting IDA and risk assessment when degradation is included. There is thus a need for what is effectively a "virtual structure" with predictive capacity for future events, but built off identified current values, similar to biomedical engineering "virtual patients" [41][42][43][44]. Thus, a more complex smooth hysteretic model, such as a Bouc-Wen model [45,46] could be used to capture the continuous change of plasticity in the future work.…”

Environmental microbiology – the next 20 years: bioconnectivity and meta'omics 2.0

“…Significant advances will also come from the development and study of lab-based artificial/synthetic in vitro immune system models (Geering and Fussenegger, 2015) which, in turn, will inform in silico virtual patient models for research and development purposes and clinical practice (Daudy, 2015;Chase et al, 2018). With advancements in the sophistication of data collection and analysis techniques, virtual patient algorithms will be increasingly populated with individual patient immune system responses and risk profiles, leading to truly personalised and holistic medical prediction methods.…”