True Confessions of Neutral Mechanical Disciple-How I Learned to Love a Patient-Specific Target.

INTRODUCTION: Classical neutral mechanical alignment in total knee arthroplasty (TKA) has been a standard paradigm, while more recently, other alignment schemas, such as kinematic, individualized, and functional, have been explored. This study aimed to investigate the effect of three-dimensional (3D) computed tomography (CT)-based surgical robotics inputs on a classically trained surgeon's TKA component positions and alignment targets over time. MATERIALS AND METHODS: Data from 1,394 consecutive robotically-assisted TKAs by a single surgeon from 2016 to 2020 were analyzed. Metrics collected included pre-balance planned implant component positions, final planned implant component positions after soft tissue balancing, and constitutional alignment from CT scans. Joint line obliquity was plotted against the arithmetic hip-knee angle (aHKA) using coronal plane alignment of the knee (CPAK). Three categories of alignment strategy were defined: true mechanical alignment (tMA), adjusted mechanical alignment (aMA), and no mechanical alignment (noMA). RESULTS: A shift to overall varus component positioning was observed over the years. Joint line obliquity according to CPAK showed a wider spread in later years, and the distribution of tibial and femoral coronal alignment angles expanded over time. CONCLUSION: The study revealed a change in alignment targets and final positioning of components away from neutral biomechanical axes in a large volume of TKAs by a single, classically trained surgeon over five years of using a robotic arm-assisted TKA system with CT-based planning. The most dominant factor for this change was the use of 3D CT planning, allowing the surgeon to assess patient-specific anatomy and plan accordingly. Outcome data is needed to determine if this change in behavior and surgical technique was beneficial. In summary, using a CT scan-based robotically assisted technique led to a gradual and complete shift from tMA to predominantly a non-mechanically aligned philosophy in TKA.

Leveraging large, real-world data through machine-learning to increase efficiency in robotic-assisted total knee arthroplasty.

PURPOSE: Increased operative time can be due to patient, surgeon and surgical factors, and may be predicted by machine learning (ML) modeling to potentially improve staff utilization and operating room efficiency. The purposes of our study were to: (1) determine how demographic, surgeon, and surgical factors affected operative times, and (2) train a ML model to estimate operative time for robotic-assisted primary total knee arthroplasty (TKA). METHODS: A retrospective study from 2007 to 2020 was conducted including 300,000 unilateral primary TKA cases. Demographic and surgical variables were evaluated using Wilcoxon/Kruskal-Wallis tests to determine significant factors of operative time as predictors in the ML models. For the ML analysis of robotic-assisted TKAs (> 18,000), two algorithms were used to learn the relationship between selected predictors and operative time. Predictive model performance was subsequently assessed on a test data set comparing predicted and actual operative time. Root mean square error (RMSE), R2 and percentage of predictions with an error < 5/10/15 min were computed. RESULTS: Males, BMI > 40 kg/m2 and cemented implants were associated with increased operative time, while age > 65yo, cementless, and high surgeon case volume had reduced operative time. Robotic-assisted TKA increased operative time for low-volume surgeons and decreased operative time for high-volume surgeons. Both ML models provided more accurate operative time predictions than standard time estimates based on surgeon historical averages. CONCLUSIONS: This study demonstrated that greater surgeon case volume, cementless fixation, manual TKA, female, older and non-obese patients reduced operative time. ML prediction of operative time can be more accurate than historical averages, which may lead to optimized operating room utilization. LEVEL OF EVIDENCE: III.

Predicting robotic-assisted total knee arthroplasty operating time : benefits of machine-learning and 3D patient-specific data.

AIMS: No predictive model has been published to forecast operating time for total knee arthroplasty (TKA). The aims of this study were to design and validate a predictive model to estimate operating time for robotic-assisted TKA based on demographic data, and evaluate the added predictive power of CT scan-based predictors and their impact on the accuracy of the predictive model. METHODS: A retrospective study was conducted on 1,061 TKAs performed from January 2016 to December 2019 with an image-based robotic-assisted system. Demographic data included age, sex, height, and weight. The femoral and tibial mechanical axis and the osteophyte volume were calculated from CT scans. These inputs were used to develop a predictive model aimed to predict operating time based on demographic data only, and demographic and 3D patient anatomy data. RESULTS: The key factors for predicting operating time were the surgeon and patient weight, followed by 12 anatomical parameters derived from CT scans. The predictive model based only on demographic data showed that 90% of predictions were within 15 minutes of actual operating time, with 73% within ten minutes. The predictive model including demographic data and CT scans showed that 94% of predictions were within 15 minutes of actual operating time and 88% within ten minutes. CONCLUSION: The primary factors for predicting robotic-assisted TKA operating time were surgeon, patient weight, and osteophyte volume. This study demonstrates that incorporating 3D patient-specific data can improve operating time predictions models, which may lead to improved operating room planning and efficiency. Cite this article: Bone Jt Open 2022;3(5):383-389.

N-Doped Carbon Dot Hydrogels from Brewing Waste for Photocatalytic Wastewater Treatment.

The brewery industry annually produces huge amounts of byproducts that represent an underutilized, yet valuable, source of biobased compounds. In this contribution, the two major beer wastes, that is, spent grains and spent yeasts, have been transformed into carbon dots (CDs) by a simple, scalable, and ecofriendly hydrothermal approach. The prepared CDs have been characterized from the chemical, morphological, and optical points of view, highlighting a high level of N-doping, because of the chemical composition of the starting material rich in proteins, photoluminescence emission centered at 420 nm, and lifetime in the range of 5.5-7.5 ns. With the aim of producing a reusable catalytic system for wastewater treatment, CDs have been entrapped into a polyvinyl alcohol matrix and tested for their dye removal ability. The results demonstrate that methylene blue can be efficiently adsorbed from water solutions into the composite hydrogel and subsequently fully degraded by UV irradiation.

Predictive Models for Clinical Outcomes in Total Knee Arthroplasty: A Systematic Analysis.

BACKGROUND: Predictive modeling promises to improve our understanding of what variables influence patient satisfaction after total knee arthroplasty (TKA). The purpose of this article was to systematically review the relevant literature using predictive models of clinical outcomes after TKA. The aim was to identify the predictor strategies used for systematic data collection with the highest likelihood of success in predicting clinical outcomes. METHODS: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol systematic review was conducted using 3 databases (MEDLINE, EMBASE, and PubMed) to identify all clinical studies that had used predictive models or that assessed predictive features for outcomes after TKA between 1996 and 2020. The ROBINS-I tool was used to evaluate the quality of the studies and the risk of bias. RESULTS: A total of 75 studies were identified of which 48 met our inclusion criteria. Preoperative predictive factors strongly associated with postoperative clinical outcomes were knee pain, knee-specific Patient-Reported Outcome Measure (PROM) scores, and mental health scores. Demographic characteristics, pre-existing comorbidities, and knee alignment had an inconsistent association with outcomes. The outcome measures that correlated best with the predictive models were improvement of PROM scores, pain scores, and patient satisfaction. CONCLUSIONS: Several algorithms, based on PROM improvement, patient satisfaction, or pain after TKA, have been developed to improve decision-making regarding both indications for surgery and surgical strategy. Functional features such as preoperative pain and PROM scores were highly predictive for clinical outcomes after TKA. Some variables such as demographics data or knee alignment were less strongly correlated with TKA outcomes. LEVEL OF EVIDENCE: Systematic review - Level III.

Glucose Control, Disease Burden, and Educational Gaps in People With Type 1 Diabetes: Exploratory Study of an Integrated Mobile Diabetes App.

BACKGROUND: Self-monitoring and self-management, crucial for optimal glucose control in type 1 diabetes, requires many disease-related decisions per day and imposes a substantial disease burden on people with diabetes. Innovative technologies that integrate relevant measurements may offer solutions that support self-management, decrease disease burden, and benefit diabetes control. OBJECTIVE: The objective of our study was to evaluate a prototype integrated mobile phone diabetes app in people with type 1 diabetes. METHODS: In this exploratory study, we developed an app that contained cloud-stored log functions for glucose, carbohydrates (including a library), insulin, planned exercise, and mood, combined with a bolus calculator and communication functions. Adults with diabetes tested the app for 6 weeks. We assessed the feasibility of app use, user experiences, perceived disease burden (through questionnaires), insulin dose and basal to bolus ratio, mean glucose level, hemoglobin A1c, and number of hypoglycemic events. RESULTS: A total of 19 participants completed the study, resulting in 5782 data entries. The most frequently used feature was logging blood glucose, insulin, and carbohydrates. Mean diabetes-related emotional problems (measured with the Problem Areas in Diabetes scale) scores decreased from 14.4 (SD 10.0) to 12.2 (SD 10.3; P=.04), and glucose control improved, with hemoglobin A1c decreasing from 7.9% (mean 62.3, SD 8 mmol/mol) to 7.6% (mean 59.8, SD 7 mmol/mol; P=.047). The incidence of hypoglycemic events did not change. Participants were generally positive about the app, rating it as "refreshing," and as providing structure by reinforcing insulin-dosing principles. The app revealed substantial knowledge gaps. Logged data enabled additional detailed analyses. CONCLUSIONS: An integrated mobile diabetes app has the potential to improve diabetes self-management and provide tailored educational support, which may decrease disease burden and benefit diabetes control.

Cost-Effectiveness Analysis in Telehealth: A Comparison between Home Telemonitoring, Nurse Telephone Support, and Usual Care in Chronic Heart Failure Management.

OBJECTIVES: To assess the cost effectiveness of home telemonitoring (HTM) and nurse telephone support (NTS) compared with usual care (UC) in the management of patients with chronic heart failure, from a third-party payer's perspective. METHODS: We developed a Markov model with a 20-year time horizon to analyze the cost effectiveness using the original study (Trans-European Network-Home-Care Management System) and various data sources. A probabilistic sensitivity analysis was performed to assess the decision uncertainty in our model. RESULTS: In the original scenario (which concerned the cost inputs at the time of the original study), HTM and NTS interventions yielded a difference in quality-adjusted life-years (QALYs) gained compared with UC: 2.93 and 3.07, respectively, versus 1.91. An incremental net monetary benefit analysis showed €7,697 and €13,589 in HTM and NTS versus UC at a willingness-to-pay (WTP) threshold of €20,000, and €69,100 and €83,100 at a WTP threshold of €80,000, respectively. The incremental cost-effectiveness ratios were €12,479 for HTM versus UC and €8,270 for NTS versus UC. The current scenario (including telenurse cost inputs in NTS) yielded results that were slightly different from those for the original scenario, when comparing all New York Heart Association (NYHA) classes of severity. NTS dominated HTM, compared with UC, in all NYHA classes except NYHA IV. CONCLUSIONS: This modeling study demonstrated that HTM and NTS are viable solutions to support patients with chronic heart failure. NTS is cost-effective in comparison with UC at a WTP of €9000/QALY or higher. Like NTS, HTM improves the survival of patients in all NYHA classes and is cost-effective in comparison with UC at a WTP of €14,000/QALY or higher.

Key aspects related to implementation of risk stratification in health care systems-the ASSEHS study.

BACKGROUND: The lack of proven efficacy of new healthcare interventions represents a problem for health systems globally. It is partly related to suboptimal implementation processes, leading to poor adoption of new interventions. Activation of Stratification Strategies and Results of the interventions on frail patients of Healthcare Services (ASSEHS) EU project (N° 2013 12 04) aims to study current existing health Risk Stratification (RS) strategies and tools on frail elderly patients. This paper aims at identifying variables that make the implementation of population RS tools feasible in different healthcare services. METHODS: Two different methods have been used to identify the key elements in stratification implementation; i) a Scoping Review, in order to search and gather scientific evidence and ii) Semi-structured interviews with six key experts that had been actively involved in the design and/or implementation of RS strategies. It aims to focus the implementation construct on real-life contextual understandings, multi-level perspectives, and cultural influences. RESULTS: A Feasibility Framework has been drawn. Two dimensions impact the feasibility of RS: (i) Planning, deployment and change management and (ii) Care intervention. The former comprises communication, training and mutual learning, multidisciplinarity of the team, clinicians' engagement, operational plan and ICT display and functionalities. The latter includes case finding and selection of the target population, pathway definition and quality improvement process. CONCLUSIONS: The Feasibility Framework provides a list of key elements that should be considered for an effective implementation of population risk stratification interventions. It helps to identify, plan and consider relevant elements to ensure a proper RS implementation.

Fast mental states decoding in mixed reality.

The combination of Brain-Computer Interface (BCI) technology, allowing online monitoring and decoding of brain activity, with virtual and mixed reality (MR) systems may help to shape and guide implicit and explicit learning using ecological scenarios. Real-time information of ongoing brain states acquired through BCI might be exploited for controlling data presentation in virtual environments. Brain states discrimination during mixed reality experience is thus critical for adapting specific data features to contingent brain activity. In this study we recorded electroencephalographic (EEG) data while participants experienced MR scenarios implemented through the eXperience Induction Machine (XIM). The XIM is a novel framework modeling the integration of a sensing system that evaluates and measures physiological and psychological states with a number of actuators and effectors that coherently reacts to the user's actions. We then assessed continuous EEG-based discrimination of spatial navigation, reading and calculation performed in MR, using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Dynamic single trial classification showed high accuracy of LDA and SVM classifiers in detecting multiple brain states as well as in differentiating between high and low mental workload, using a 5 s time-window shifting every 200 ms. Our results indicate overall better performance of LDA with respect to SVM and suggest applicability of our approach in a BCI-controlled MR scenario. Ultimately, successful prediction of brain states might be used to drive adaptation of data representation in order to boost information processing in MR.

Prediction of P300 BCI aptitude in severe motor impairment.

Brain-computer interfaces (BCIs) provide a non-muscular communication channel for persons with severe motor impairments. Previous studies have shown that the aptitude with which a BCI can be controlled varies from person to person. A reliable predictor of performance could facilitate selection of a suitable BCI paradigm. Eleven severely motor impaired participants performed three sessions of a P300 BCI web browsing task. Before each session auditory oddball data were collected to predict the BCI aptitude of the participants exhibited in the current session. We found a strong relationship of early positive and negative potentials around 200 ms (elicited with the auditory oddball task) with performance. The amplitude of the P2 (r  =  -0.77) and of the N2 (r  =  -0.86) had the strongest correlations. Aptitude prediction using an auditory oddball was successful. The finding that the N2 amplitude is a stronger predictor of performance than P3 amplitude was reproduced after initially showing this effect with a healthy sample of BCI users. This will reduce strain on the end-users by minimizing the time needed to find suitable paradigms and inspire new approaches to improve performance.

Semantic conditioning of salivary pH for communication.

OBJECTIVES: Semantic conditioning of salivary pH was investigated as a new paradigm for binary communication. METHODS AND MATERIALS: In a sample of eleven healthy participants, affirmation or negation of presented statements were paired with milk and lemon to condition changes in salivary pH level. RESULTS: Significant differences between the conditioned reactions were found at the group level. However, the analysis of pH changes on single-subject level revealed significant differences between affirmative and negative responses to the presented statements only for isolated samples in few participants. When classifying a change in pH value of more than .01 as correct response to a statement, only responses to affirmative statements reached mean accuracies of more than 60%. CONCLUSION: Improvements in the paradigm are necessary before testing it with the critical target population of patients to prove its profit for basic yes/no communication in case no other reliable means of communication could be preserved.

Brain communication in the locked-in state.

Patients in the completely locked-in state have no means of communication and they represent the target population for brain-computer interface research in the last 15 years. Although different paradigms have been tested and different physiological signals used, to date no sufficiently documented completely locked-in state patient was able to control a brain-computer interface over an extended time period. We introduce Pavlovian semantic conditioning to enable basic communication in completely locked-in state. This novel paradigm is based on semantic conditioning for online classification of neuroelectric or any other physiological signals to discriminate between covert (cognitive) 'yes' and 'no' responses. The paradigm comprised the presentation of affirmative and negative statements used as conditioned stimuli, while the unconditioned stimulus consisted of electrical stimulation of the skin paired with affirmative statements. Three patients with advanced amyotrophic lateral sclerosis participated over an extended time period, one of which was in a completely locked-in state, the other two in the locked-in state. The patients' level of vigilance was assessed through auditory oddball procedures to study the correlation between vigilance level and the classifier's performance. The average online classification accuracies of slow cortical components of electroencephalographic signals were around chance level for all the patients. The use of a non-linear classifier in the offline classification procedure resulted in a substantial improvement of the accuracy in one locked-in state patient achieving 70% correct classification. A reliable level of performance in the completely locked-in state patient was not achieved uniformly throughout the 37 sessions despite intact cognitive processing capacity, but in some sessions communication accuracies up to 70% were achieved. Paradigm modifications are proposed. Rapid drop of vigilance was detected suggesting attentional variations or variations of circadian period as important factors in brain-computer interface communication with locked-in state and completely locked-in state.

Semantic classical conditioning and brain-computer interface control: encoding of affirmative and negative thinking.

The aim of the study was to investigate conditioned electroencephalography (EEG) responses to factually correct and incorrect statements in order to enable binary communication by means of a brain-computer interface (BCI). In two experiments with healthy participants true and false statements (serving as conditioned stimuli, CSs) were paired with two different tones which served as unconditioned stimuli (USs). The features of the USs were varied and tested for their effectiveness to elicit differentiable conditioned reactions (CRs). After acquisition of the CRs, these CRs to true and false statements were classified offline using a radial basis function kernel support vector machine. A mean single-trial classification accuracy of 50.5% was achieved for differentiating conditioned "yes" versus "no" thinking and mean accuracies of 65.4% for classification of "yes" and 68.8% for "no" thinking (both relative to baseline) were found using the best US. Analysis of the area under the curve of the conditioned EEG responses revealed significant differences between conditioned "yes" and "no" answers. Even though improvements are necessary, these first results indicate that the semantic conditioning paradigm could be a useful basis for further research regarding BCI communication in patients in the complete locked-in state.

Brain-computer interface and semantic classical conditioning of communication in paralysis.

We propose a classical semantic conditioning procedure to allow basic yes-no communication in the completely locked-in state as an alternative to instrumental-operant learning of brain responses, which is the common approach in brain-computer interface research. More precisely, it was intended to establish cortical responses to the trueness of a statement irrespective of the particular constituent words and letters or sounds of the words. As unconditioned stimulus short aversive stimuli consisting of 1-ms electrical pulses were used. True and false statements were presented acoustically and only the true statements were immediately followed by electrical stimuli. 15 healthy participants and one locked-in ALS patient underwent the experiment. Three different classifiers were employed in order to differentiate between the two cortical responses by means of electroencephalographic recordings. The offline analysis revealed that semantic classical conditioning can be applied successfully to enable basic communication using a non-muscular channel.

Time-frequency analysis of short-lasting modulation of EEG induced by intracortical and transcallosal paired TMS over motor areas.

Dynamic changes in spontaneous electroencephalogram (EEG) rhythms can be seen to occur with a high rate of variability. An innovative method to study brain function is by triggering oscillatory brain activity with transcranial magnetic stimulation (TMS). EEG-TMS coregistration was performed on five healthy subjects during a 1-day experimental session that involved four steps: baseline acquisition, unconditioned single-pulse TMS, intracortical inhibition (ICI, 3 ms) paired-pulse TMS, and transcallosal stimulation over left and right primary motor cortex (M1). A time-frequency analysis based on the wavelet method was used to characterize rapid modifications of oscillatory EEG rhythms induced by TMS. Single, paired, and transcallosal TMS applied on the sensorimotor areas induced rapid desynchronization over the frontal and central-parietal electrodes mainly in the alpha and beta bands, followed by a rebound of synchronization, and rapid synchronization of delta and theta activity. Wavelet analysis after a perturbation approach is a novel way to investigate modulation of oscillatory brain activity. The main findings are consistent with the concept that the human motor system may be based on networklike oscillatory cortical activity and might be modulated by single, paired, and transcallosal magnetic pulses applied to M1, suggesting a phenomenon of fast brain activity resetting and triggering of slow activity.