An individualized digital twin of a patient for transdermal fentanyl therapy for chronic pain management

Evolution of digital twins in precision health applications: a scoping review study

An increasing amount of research is incorporating the concept of Digital twin (DT) in biomedical and health care applications. This scoping review aims to summarize existing research and identify gaps in the development and use of DTs in the health care domain. The focus of this study lies on summarizing: the different types of DTs, the techniques employed in DT development, the DT applications in health care, and the data resources used for creating DTs. We identified fifty studies, which mainly focused on creating organ- (n=15) and patient-specific twins (n=30). The research predominantly centers on cardiology, endocrinology, orthopedics, and infectious diseases. Only a few studies used real-world datasets for developing their DTs. However, there remain unresolved questions and promising directions that require further exploration. This review provides valuable reference material and insights for researchers on DTs in health care and highlights gaps and unmet needs in this field.

The effectiveness of digital twins in promoting precision health across the entire population: a systematic review

Digital twins represent a promising technology within the domain of precision healthcare, offering significant prospects for individualized medical interventions. Existing systematic reviews, however, mainly focus on the technological dimensions of digital twins, with a limited exploration of their impact on health-related outcomes. Therefore, this systematic review aims to explore the efficacy of digital twins in improving precision healthcare at the population level. The literature search for this study encompassed PubMed, Embase, Web of Science, Cochrane Library, CINAHL, SinoMed, CNKI, and Wanfang Database to retrieve potentially relevant records. Patient health-related outcomes were synthesized employing quantitative content analysis, whereas the Joanna Briggs Institute (JBI) scales were used to evaluate the quality and potential bias inherent in each selected study. Following established inclusion and exclusion criteria, 12 studies were screened from an initial 1321 records for further analysis. These studies included patients with various conditions, including cancers, type 2 diabetes, multiple sclerosis, heart failure, qi deficiency, post-hepatectomy liver failure, and dental issues. The review coded three types of interventions: personalized health management, precision individual therapy effects, and predicting individual risk, leading to a total of 45 outcomes being measured. The collective effectiveness of these outcomes at the population level was calculated at 80% (36 out of 45). No studies exhibited unacceptable differences in quality. Overall, employing digital twins in precision health demonstrates practical advantages, warranting its expanded use to facilitate the transition from the development phase to broad application.PROSPERO registry: CRD42024507256.

Digital twins and artificial intelligence in metabolic disease research

Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.

Implementing physics-based digital patient twins to tailor the switch of oral morphine to transdermal fentanyl patches based on patient physiology

Fentanyl transdermal patches are widely implemented for cancer-induced pain treatment due to the high potency of fentanyl and gradual drug release. However, transdermal fentanyl up-titration for opioid-naïve patients is difficult, which is why opioid treatment is often started with oral/iv morphine. Based on the daily dose of morphine, the initial dose of the fentanyl patch is decided upon. After reaching a stable level of pain, the switch is made from oral/iv morphine to transdermal fentanyl. There are standard calculation tools for transferring from oral/iv morphine to transdermal fentanyl, which is the same for all patients. By considering the variations in the physiology of the patients, a unique switching strategy cannot meet the needs of different patients. This study explores the outcome in terms of pain relief and minute ventilation during opioid therapy. For this, we used physics-based simulations on a virtually-generated population of patients, and we applied the same therapy to all patients. We could show that patients' physiology, such as gender, age, and weight, greatly impact the outcome of the therapy; as such, the correlation coefficient between pain intensity and age is 0.89, and the correlation coefficient between patient's weight and maximum plasma concentration of morphine and fentanyl is -0.98 and -0.97. Additionally, a different combination of the duration of overlap between morphine and fentanyl therapy with different doses of fentanyl was considered for the virtual patients to find the best opioid-switching strategy for each patient. We explored the impact of combining physiological features to determine the best-suited strategy for virtual patients. Our findings suggest that tailoring morphine and fentanyl therapy only based on a limited number of features is insufficient, and increasing the number of impactful physiological features positively influences the outcome of the therapy.