Modelling physiological deterioration in post-operative patient vital-sign data

Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients’ vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a “normal” recovery was constructed using a kernel density estimate, and tested with “abnormal” data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from “normal” patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen “abnormal” data, suggesting that such techniques may be used to provide early warning of adverse physiological events

Digital health system for personalised COPD long-term management

Background Recent telehealth studies have demonstrated minor impact on patients affected by long-term conditions. The use of technology does not guarantee the compliance required for sustained collection of high-quality symptom and physiological data. Remote monitoring alone is not sufficient for successful disease management. A patient-centred design approach is needed in order to allow the personalisation of interventions and encourage the completion of daily self-management tasks. Methods A digital health system was designed to support patients suffering from chronic obstructive pulmonary disease in self-managing their condition. The system includes a mobile application running on a consumer tablet personal computer and a secure backend server accessible to the health professionals in charge of patient management. The patient daily routine included the completion of an adaptive, electronic symptom diary on the tablet, and the measurement of oxygen saturation via a wireless pulse oximeter. Results The design of the system was based on a patient-centred design approach, informed by patient workshops. One hundred and ten patients in the intervention arm of a randomised controlled trial were subsequently given the tablet computer and pulse oximeter for a 12-month period. Patients were encouraged, but not mandated, to use the digital health system daily. The average used was 6.0 times a week by all those who participated in the full trial. Three months after enrolment, patients were able to complete their symptom diary and oxygen saturation measurement in less than 1 m 40s (96% of symptom diaries). Custom algorithms, based on the self-monitoring data collected during the first 50 days of use, were developed to personalise alert thresholds. Conclusions Strategies and tools aimed at refining a digital health intervention require iterative use to enable convergence on an optimal, usable design. ‘Continuous improvement’ allowed feedback from users to have an immediate impact on the design of the system (e.g., collection of quality data), resulting in high compliance with self-monitoring over a prolonged period of time (12-month). Health professionals were prompted by prioritisation algorithms to review patient data, which led to their regular use of the remote monitoring website throughout the trial

Trial of Remote Continuous versus Intermittent NEWS monitoring after major surgery (TRaCINg): protocol for a feasibility randomised controlled trial

Background: Despite medical advances, major surgery remains high risk. Up to 44% of patients experience postoperative complications, which can have huge impacts for patients and the healthcare system. Early recognition of postoperative complications is crucial in reducing morbidity and preventing long-term disability. The current standard of care is intermittent manual vital signs monitoring, but new wearable remote monitors offer the benefits of continuous vital signs monitoring without limiting the patient’s mobility. The aim of this study is to evaluate the feasibility, acceptability and clinical impacts of continuous remote monitoring after major surgery. Methods: The study is a randomised, controlled, unblinded, parallel group, feasibility trial. Adult patients undergoing elective major surgery will be invited to participate if they have the capacity to provided informed, written consent and do not have a cardiac pacemaker or an allergy to adhesives. Participants will be randomly assigned to receive continuous remote monitoring and normal National Early Warning Score (NEWS) monitoring (intervention group) or normal NEWS monitoring alone (control group). Continuous remote monitoring will be achieved using the SensiumVitals® wireless patch which is worn on the patient’s chest and monitors heart rate, respiratory rate and temperature continuously and alerts the nurse when there is deviation from pre-set physiological norms. Participants will be followed up throughout their hospital admission and for 30 days after discharge. Feasibility will be assessed by evaluating recruitment rate, adherence to protocol and randomisation, and the amount of missing data. The acceptability of the patch to nursing staff and patients will be assessed using questionnaires and interviews. Clinical outcomes will include time to antibiotics in cases of sepsis, length of hospital stay, number of critical care admissions and rate of readmission within 30 days of discharge. Discussion: Early detection and treatment of complications minimises the need for critical care, improves patient outcomes, and produces significant cost savings for the healthcare system. Remote continuous monitoring systems have the potential to allow earlier detection of complications, but evidence from the literature is mixed. Demonstrating significant benefit over intermittent monitoring to offset the practical and economic implications of continuous monitoring requires well-controlled studies in high-risk populations to demonstrate significant differences in clinical outcomes; this feasibility trial seeks to provide evidence of how best to conduct such a confirmatory trial. Trial registration: This study is listed on the ISRCTN registry with study ID ISRCTN16601772

Maze solvers demystified and some other thoughts

There is a growing interest towards implementation of maze solving in spatially-extended physical, chemical and living systems. Several reports of prototypes attracted great publicity, e.g. maze solving with slime mould and epithelial cells, maze navigating droplets. We show that most prototypes utilise one of two phenomena: a shortest path in a maze is a path of the least resistance for fluid and current flow, and a shortest path is a path of the steepest gradient of chemoattractants. We discuss that substrates with so-called maze-solving capabilities simply trace flow currents or chemical diffusion gradients. We illustrate our thoughts with a model of flow and experiments with slime mould. The chapter ends with a discussion of experiments on maze solving with plant roots and leeches which show limitations of the chemical diffusion maze-solving approach.Comment: This is a preliminary version of the chapter to be published in Adamatzky A. (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Theopoetics: Kierkegaard and the vocation of the Christian creative artist

In this doctoral dissertation I examine the development of Kierkegaard's sense of vocation as a Christian creative artist by research into his journals and published works, as well as investigating how this was influenced by his scriptural hermeneutic. I then attempt to sketch some starting points for a theology of Christian creative artwork contextualised within modern theological aesthetics by drawing upon this examination. I argue that Kierkegaard began writing without documented reflection on his intentions and communicative methodology, but was nonetheless a religious author from the start of his career, as his text The Point of View for my Work as an Author later claimed. I trace how he began with a more "indirect" approach in his writing and gradually developed a theory of "indirect communication", though there were more "direct" elements present in his work from the beginning (the "first authorship"), yet as he continued in his authorial career he became ever more "direct" in his mode of communication (the "second authorship"), until it eventually became exclusively more "direct" religious writing (the "attack on Christendom"). I conclude that the most concise and complete formulation of Kierkegaard's mature conception of his task as a Christian artist becomes "to communicate Christianity in Christendom" in a more direct mode—to explain straightforwardly what authentic Christianity is in an age of cultural, purely nominal religion. I allow that this task is in some ways unique to his own historical situation but contend nonetheless that a consideration of it is profitable for contemporary theology because of the many different ways that he attempted to carry it out. In Kierkegaardian terms, and following on from resources in Kierkegaard and his use of scripture, I argue constructively from all of this that more "direct" communication is the more valuable form of communication to the Christian creative artist for theological reasons, but that more "indirect" communication can still be useful, in the task of communicating creatively through art

Finite-sample and asymptotic sign-based tests for parameters of non-linear quantile regression with Markov noise

One of the most noticeable features of sign-based statistical procedures is an opportunity to build an exact test for simple hypothesis testing of parameters in a regression model. In this article, we expanded a sing-based approach to the nonlinear case with dependent noise. The examined model is a multi-quantile regression, which makes it possible to test hypothesis not only of regression parameters, but of noise parameters as well

Artificial intelligence in health care: enabling informed care

We read with interest the Lancet Editorial on artificial intelligence (AI) in health care (Dec 23, 2017, p 2739).1 Deep learning as a form of AI risks being overhyped. Deep neural networks contain multiple layers of nodes connected by adjustable weights. Learning occurs by adjusting these weights until the desired input-to-output function is achieved.2 With many millions of weights, huge amounts of data are required for learning, a process facilitated by recent increases in computational power. However, the learning algorithm, known as the error back-propagation algorithm, was invented in the 1980s and has been used to train neural networks ever since. Two decades ago, our neural network system scored sleep and diagnosed sleep disorders.3 Our machine learning algorithm,4, 5 which now provides early warning of deterioration in many hospitals, was commercialised a decade ago.6 A key change occurred in the early 2000s. Since then, error back-propagation learns features directly from the input data, rather than relying on expert-selected features (eg, microaneurysms for a neural network assessing diabetic retinopathy). The first layers become implicit feature detectors. The success of deep learning has been shown mainly in problems with inputs of image (or image-like) data, as shown in medical image analysis,7, 8 speech recognition, and board game playing. Deep learning also lacks explanatory power; deep neural networks cannot explain how a diagnosis is reached and the features enabling discrimination are not easily identifiable. Clinicians should be aware of the capabilities as well as current limitations of AI. Properly integrated AI will improve patient outcomes and health-care efficiency. Augmented intelligence at the point of care is likely to precede AI without human involvement. LT and PW are supported by the Biomedical Research Centre, Oxford. Both authors have received funding from the National Institute for Health Research. The authors have developed an electronic observations application for which Drayson Health has purchased a sole licence. Drayson Health has a research agreement with the University of Oxford and has paid LT personal fees for consultancy as a member of its Strategic Advisory Board. Drayson Health might pay PW consultancy fees in the future

Semiconductor wireless technology for chronic disease management

With most chronic diseases, monitoring of one or more physiological variables (vital signs) can inform the management of the patient. In active monitoring, the patient adjusts medication dosage according to the value of the measured variable. Passive monitoring is associated with the more advanced stages of chronic diseases and requires the use of wearable sensors. 'Digital plasters' which exploit recent advances in semiconductor technology can now provide continuous monitoring and wireless transmission of patients' vital signs for several days. The ideal digital plaster, or adhesive patch, for long-term passive monitoring would incorporate both electrical and optical measurements. Other promising technologies for the future include implantable sensors and non-contact vital sign imaging

A Comparison of the Ability of the Physiologic Components of Medical Emergency Team Criteria and the U.K. National Early Warning Score to Discriminate Patients at Risk of a Range of Adverse Clinical Outcomes.

OBJECTIVE: To compare the ability of medical emergency team criteria and the National Early Warning Score to discriminate cardiac arrest, unanticipated ICU admission and death within 24 hours of a vital signs measurement, and to quantify the associated workload. DESIGN: Retrospective cohort study. SETTING: A large U.K. National Health Service District General Hospital. PATIENTS: Adults hospitalized from May 25, 2011, to December 31, 2013. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We applied the National Early Warning Score and 44 sets of medical emergency team criteria to a database of 2,245,778 vital signs sets (103,998 admissions). The National Early Warning Score's performance was assessed using the area under the receiver-operating characteristic curve and compared with sensitivity/specificity for different medical emergency team criteria. Area under the receiver-operating characteristic curve (95% CI) for the National Early Warning Score for the combined outcome (i.e., death, cardiac arrest, or unanticipated ICU admission) was 0.88 (0.88-0.88). A National Early Warning Score value of 7 had sensitivity/specificity values of 44.5% and 97.4%, respectively. For the 44 sets of medical emergency team criteria studied, sensitivity ranged from 19.6% to 71.2% and specificity from 71.5% to 98.5%. For all outcomes, the position of the National Early Warning Score receiver-operating characteristic curve was above and to the left of all medical emergency team criteria points, indicating better discrimination. Similarly, the positions of all medical emergency team criteria points were above and to the left of the National Early Warning Score efficiency curve, indicating higher workloads (trigger rates). CONCLUSIONS: When medical emergency team systems are compared to a National Early Warning Score value of greater than or equal to 7, some medical emergency team systems have a higher sensitivity than National Early Warning Score values of greater than or equal to 7. However, all of these medical emergency team systems have a lower specificity and would generate greater workloads