The Perioperative Human Digital Twin

Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins

Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.

Depth of Anesthesia and Nociception Monitoring: Current State and Vision For 2050

Anesthesia objectives have evolved into combining hypnosis, amnesia, analgesia, paralysis, and suppression of the sympathetic autonomic nervous system. Technological improvements have led to new monitoring strategies, aimed at translating a qualitative physiological state into quantitative metrics, but the optimal strategies for depth of anesthesia (DoA) and analgesia monitoring continue to stimulate debate. Historically, DoA monitoring used patient's movement as a surrogate of awareness. Pharmacokinetic models and metrics, including minimum alveolar concentration for inhaled anesthetics and target-controlled infusion models for intravenous anesthesia, provided further insights to clinicians, but electroencephalography and its derivatives (processed EEG; pEEG) offer the potential for personalization of anesthesia care. Current studies appear to affirm that pEEG monitoring decreases the quantity of anesthetics administered, diminishes postanesthesia care unit duration, and may reduce the occurrence of postoperative delirium (notwithstanding the difficulties of defining this condition). Major trials are underway to further elucidate the impact on postoperative cognitive dysfunction. In this manuscript, we discuss the Bispectral (BIS) index, Narcotrend monitor, Patient State Index, entropy-based monitoring, and Neurosense monitor, as well as middle latency evoked auditory potential, before exploring how these technologies could evolve in the upcoming years. In contrast to developments in pEEG monitors, nociception monitors remain by comparison underdeveloped and underutilized. Just as with anesthetic agents, excessive analgesia can lead to harmful side effects, whereas inadequate analgesia is associated with increased stress response, poorer hemodynamic conditions and coagulation, metabolic, and immune system dysregulation. Broadly, 3 distinct monitoring strategies have emerged: motor reflex, central nervous system, and autonomic nervous system monitoring. Generally, nociceptive monitors outperform basic clinical vital sign monitoring in reducing perioperative opioid use. This manuscript describes pupillometry, surgical pleth index, analgesia nociception index, and nociception level index, and suggest how future developments could impact their use. The final section of this review explores the profound implications of future monitoring technologies on anesthesiology practice and envisages 3 transformative scenarios: helping in creation of an optimal analgesic drug, the advent of bidirectional neuron-microelectronic interfaces, and the synergistic combination of hypnosis and virtual reality.

Precision Anesthesia in 2050

Over the last few decades, the field of anesthesia has advanced far beyond its humble beginnings. Today's anesthetics are better and safer than ever, thanks to innovations in drugs, monitors, equipment, and patient safety.1-4 At the same time, we remain limited by our herd approach to medicine. Each of our patients is unique, but health care today is based on a one-size-fits-all approach, while our patients grow older and more medically complex every year. By 2050, we believe that precision medicine will play a central role across all medical specialties, including anesthesia. In addition, we expect that health care and consumer technology will continually evolve to improve and simplify the interactions between patients, providers, and the health care system. As demonstrated by 2 hypothetical patient experiences, these advancements will enable more efficient and safe care, earlier and more accurate diagnoses, and truly personalized treatment plans.

Development and usability testing of a patient digital twin for critical care education: a mixed methods study

Background

Digital twins are computerized patient replicas that allow clinical interventions testing in silico to minimize preventable patient harm. Our group has developed a novel application software utilizing a digital twin patient model based on electronic health record (EHR) variables to simulate clinical trajectories during the initial 6 h of critical illness. This study aimed to assess the usability, workload, and acceptance of the digital twin application as an educational tool in critical care.

Methods

A mixed methods study was conducted during seven user testing sessions of the digital twin application with thirty-five first-year internal medicine residents. Qualitative data were collected using a think-aloud and semi-structured interview format, while quantitative measurements included the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and a short survey.

Results

Median SUS scores and NASA-TLX were 70 (IQR 62.5-82.5) and 29.2 (IQR 22.5-34.2), consistent with good software usability and low to moderate workload, respectively. Residents expressed interest in using the digital twin application for ICU rotations and identified five themes for software improvement: clinical fidelity, interface organization, learning experience, serious gaming, and implementation strategies.

Conclusion

A digital twin application based on EHR clinical variables showed good usability and high acceptance for critical care education.

Systems Anesthesiology: Systems of Care Delivery and Optimization in the Operating Room

Anesthesiology presents a challenge to a traditional simplifying approach given the ever-increasing amount of medical data and a more demanding environment. Systems anesthesiology is a modern approach to perioperative care, integrating the complexity of multifactorial knowledge and data to achieve a more adequate representation of reality, while including both patient-related medical aspects as well as economic and organizational challenges. We discuss the value of some innovative technologies such as the emergence of anesthesia information systems, the use of tele-medicine, predictive monitoring, or closed-loop systems as it pertains to the changes in the current standards of care in anesthesiology. Furthermore, we highlight the importance of systems anesthesiology in operating room planning, anesthesia research, and education.

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision-based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some applications currently equal or exceed the performance of specialty physicians in areas of image interpretation. While machine vision in anesthesia has many potential applications, its development remains in its infancy in our specialty. Early research for machine vision in anesthesia has focused on automated recognition of anatomical structures during ultrasound-guided regional anesthesia or line insertion; recognition of the glottic opening and vocal cords during video laryngoscopy; prediction of the difficult airway using facial images; and clinical alerts for endobronchial intubation detected on chest radiograph. Current machine vision applications measuring the distance between endotracheal tube tip and carina have demonstrated noninferior performance compared to board-certified physicians. The performance and potential uses of machine vision for anesthesia will only grow with the advancement of underlying machine vision algorithm technical performance developed outside of medicine, such as convolutional neural networks and transfer learning. This article summarizes recently published works of interest, provides a brief overview of techniques used to create machine vision applications, explains frequently used terms, and discusses challenges the specialty will encounter as we embrace the advantages that this technology may bring to future clinical practice and patient care. As machine vision emerges onto the clinical stage, it is critically important that anesthesiologists are prepared to confidently assess which of these devices are safe, appropriate, and bring added value to patient care.

A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset

BACKGROUND

Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at-risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA-PS) score, despite reported inconsistencies with this method.

AIMS

The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day.

METHODS

Our dataset was derived from APRICOT, a prospective observational cohort study conducted by 261 European institutions in 2014 and 2015. We included only the first procedure, ASA-PS classification I to III, and perioperative adverse events not classified as drug errors, reducing the total number of records to 30 325 with an adverse event rate of 4.43%. From this dataset, a stratified train:test split of 70:30 was used to develop predictive machine learning algorithms that could identify children in ASA-PS class I to III at low risk for severe perioperative critical events that included respiratory, cardiac, allergic, and neurological complications.

RESULTS

Our selected models achieved accuracies of >0.9, areas under the receiver operating curve of 0.6-0.7, and negative predictive values >95%. Gradient boosting models were the best performing for both the booking phase and the day-of-surgery phase.

CONCLUSIONS

This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population-based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers.

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options.

Model for Predicting Complications of Hemodialysis Patients Using Data From the Internet of Medical Things and Electronic Medical Records

Intelligent models for predicting hemodialysis-related complications, i.e., hypotension and the deterioration of the quality or obstruction of the AV fistula, based on machine learning (ML) methods were established to offer early warnings to medical staff and give them enough time to provide pre-emptive treatment. A novel integration platform collected data from the Internet of Medical Things (IoMT) at a dialysis center and inspection results from electronic medical records (EMR) to train ML algorithms and build models. The selection of the feature parameters was implemented using Pearson's correlation method. Then, the eXtreme Gradient Boost (XGBoost) algorithm was chosen to create the predictive models and optimize the feature choice. 75% of collected data are used as a training dataset and the other 25% are used as a testing dataset. We adopted the prediction precision and recall rate of hypotension and AV fistula obstruction to measure the effectiveness of the predictive models. These rates were sufficiently high at approximately 71%-90%. In the context of hemodialysis, hypotension and the deterioration of the quality or obstruction of the arteriovenous (AV) fistula affect treatment quality and patient safety and may lead to a poor prognosis. Our prediction models with high accuracies can provide excellent references and signals for clinical healthcare service providers. Clinical and Translational Impact Statement-With the integrated dataset collected from IoMT and EMR, the superior predictive results of our models for complications of hemodialysis patients are demonstrated. We believe, after enough clinical tests are implemented as planned, these models can assist the healthcare team in making appropriate preparations in advance or adjusting the medical procedures to avoid these adverseevents.