Predictive Power of the "Trigger Tool" for the detection of adverse events in general surgery: a multicenter observational validation study

Background In spite of the global implementation of standardized surgical safety checklists and evidence-based practices, general surgery remains associated with a high residual risk of preventable perioperative complications and adverse events. This study was designed to validate the hypothesis that a new “Trigger Tool” represents a sensitive predictor of adverse events in general surgery. Methods An observational multicenter validation study was performed among 31 hospitals in Spain. The previously described “Trigger Tool” based on 40 specific triggers was applied to validate the predictive power of predicting adverse events in the perioperative care of surgical patients. A prediction model was used by means of a binary logistic regression analysis. Results The prevalence of adverse events among a total of 1,132 surgical cases included in this study was 31.53%. The “Trigger Tool” had a sensitivity and specificity of 86.27% and 79.55% respectively for predicting these adverse events. A total of 12 selected triggers of overall 40 triggers were identified for optimizing the predictive power of the “Trigger Tool”. Conclusions The “Trigger Tool” has a high predictive capacity for predicting adverse events in surgical procedures. We recommend a revision of the original 40 triggers to 12 selected triggers to optimize the predictive power of this tool, which will have to be validated in future studies

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Social robots in therapy and care

Purpose of Review This work presents a comprehensive overview of social robots in therapy and the healthcare of children, adults, and elderly populations. According to recent evidence in this field, the primary outcomes and limitations are highlighted. This review points out the implications and requirements for the proper deployment of social robots in therapy and healthcare scenarios. Recent Findings Social robots are a current trend that is being studied in different healthcare services. Evidence highlights the potential and favorable results due to the support and assistance provided by social robots. However, some side effects and limitations are still under research. Summary Social robots can play various roles in the area of health and well-being. However, further studies regarding the acceptability and perception are still required. There are challenges to be addressed, such as improvements in the functionality and robustness of these robotic systems

Sensing methodologies for gait parameters estimation and control

One of the essential aspects of assistive and rehabilitation robots is related to gait evaluation and analysis. In general, multiple sensing technologies are available to acquire gait information. Among these are sensing devices involving inertial sensors, ultrasonic sensors, laser rangefinders, and plantar pressure sensors. From the information provided by these devices, it is possible to calculate spatiotemporal gait parameters such as cadence, speed, step length, among others. It is also possible to detect and characterize the phases of the gait cycle. Accordingly, this chapter presents a description of the most relevant gait indicators and some wearable sensors that allow their acquisition. Finally, there are described two usage scenarios for a lower-limb exoskeleton and a robotic walker. These scenarios describe two methodologies to extract gait parameters from an inertial sensor and a laser rangefinder

Physical human-robot interaction influence in ASD therapy through an affordable soft social robot

In the latest years, there has been a rise in social robotics' interest as a support tool in various scopes. For instance, social robots have been used in Autism treatments, improving social skills, social interaction, and children's daily activities performance. Although several studies elucidate the benefits of social robots in the ASD community, few focus on evaluating and promoting physical interaction. Thus, this study presents the development and assessment of a social robotic platform based on soft actuation to promote physical interaction. A total of 35 children diagnosed with autism were involved in this study. The primary outcomes show that physical interaction does not significantly influence the patient's performance in the activity. However, the clinicians remark that encouragement and motivation increase when the children were allowed to interact with the robot physically. Also, 52.9% of the control group children elucidate the intention of physically interact with the robot, suggesting this behavior is an essential way of communication

A data-driven approach to physical fatigue management using wearable sensors to classify four diagnostic fatigue states

Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%

Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals

Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0 . 75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation

Limitations of audiovisual speech on robots for second language pronunciation learning

The perception of audiovisual speech plays an important role in infants' first language acquisition and continues to be important for language understanding beyond infancy. Beyond that, the perception of speech and congruent lip motion supports language understanding for adults, and it has been suggested that second language learning benefits from audiovisual speech, as it helps learners distinguish speech sounds in the target language. In this paper, we study whether congruent audiovisual speech on a robot facilitates the learning of Japanese pronunciation. 27 native-Dutch speaking participants were trained in Japanese pronunciation by a social robot. The robot demonstrated 30 Japanese words of varying complexity using either congruent audiovisual speech, incongruent visual speech, or computer-generated audiovisual speech. Participants were asked to imitate the robot's pronunciation, recordings of which were rated by native Japanese speakers. Against expectation, the results showed that congruent audiovisual speech resulted in lower pronunciation performance than low-fidelity or incongruent speech. We show that our learners, being native Dutch speakers, are only very weakly sensitive to audiovisual Japanese speech which possibly explains why learning performance does not seem to benefit from audiovisual speech

Therapy with T-FLEX ankle-exoskeleton for motor recovery : a case study with a stroke survivor

Stroke is the main neurological condition causing disability worldwide. Physical therapy and robotic devices have been used in rehabilitation to recover lost locomotor functions. Despite the advantages of using robots in rehabilitation scenarios, some joints remain with alterations after therapy processes (e.g., the ankle joint). This paper presents a single case study of a patient with chronic stroke who participated in 18 sessions to assess the effects of T-FLEX in lower limb kinematics, spatiotemporal parameters, and muscular activity. To this end, each session consisted of two modalities: (1) 90-degree knee flexion, and (2) complete knee extension. The results showed improvement in the participant's spatiotemporal and kinematic parameters, as well as in the foot clearance during the swing phase. Regarding the muscular activity, the first sessions showed considerable increases related to the patient's inactivity. However, as the experiment proceeded, this value decreased as a consequence of the adaptation to the device. Regarding the electrical activity measured during each session, both muscles (i.e., gastrocnemius and tibialis anterior) tended to increase at the end-stage