Evaluation of preoperative difficult airway prediction methods for adult patients without obvious airway abnormalities: a systematic review and meta-analysis.

BACKGROUND: This systematic review aims to assist clinical decision-making in selecting appropriate preoperative prediction methods for difficult tracheal intubation by identifying and synthesizing literature on these methods in adult patients undergoing all types of surgery. METHODS: A systematic review and meta-analysis were conducted following PRISMA guidelines. Comprehensive electronic searches across multiple databases were completed on March 28, 2023. Two researchers independently screened, selected studies, and extracted data. A total of 227 articles representing 526 studies were included and evaluated for bias using the QUADAS-2 tool. Meta-Disc software computed pooled sensitivity (SEN), specificity (SPC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Heterogeneity was assessed using the Spearman correlation coefficient, Cochran's-Q, and I2 index, with meta-regression exploring sources of heterogeneity. Publication bias was evaluated using Deeks' funnel plot. RESULTS: Out of 2906 articles retrieved, 227 met the inclusion criteria, encompassing a total of 686,089 patients. The review examined 11 methods for predicting difficult tracheal intubation, categorized into physical examination, multivariate scoring system, and imaging test. The modified Mallampati test (MMT) showed a SEN of 0.39 and SPC of 0.86, while the thyromental distance (TMD) had a SEN of 0.38 and SPC of 0.83. The upper lip bite test (ULBT) presented a SEN of 0.52 and SPC of 0.84. Multivariate scoring systems like LEMON and Wilson's risk score demonstrated moderate sensitivity and specificity. Imaging tests, particularly ultrasound-based methods such as the distance from the skin to the epiglottis (US-DSE), exhibited higher sensitivity (0.80) and specificity (0.77). Significant heterogeneity was identified across studies, influenced by factors such as sample size and study design. CONCLUSION: No single preoperative prediction method shows clear superiority for predicting difficult tracheal intubation. The evidence supports a combined approach using multiple methods tailored to specific patient demographics and clinical contexts. Future research should focus on integrating advanced technologies like artificial intelligence and deep learning to improve predictive models. Standardizing testing procedures and establishing clear cut-off values are essential for enhancing prediction reliability and accuracy. Implementing a multi-modal predictive approach may reduce unanticipated difficult intubations, improving patient safety and outcomes.

Establishment of a medical device adverse event management system for hospitals.

BACKGROUND: The management of medical device adverse event (MDAE) is one of the most important aspects of improving medical quality and safety management. Nonetheless, hospitals still lack standardized and unified initiatives to improve MDAE management. METHODS: This study, thus, established a MDAE monitoring system on May 1 in 2011 for suspected adverse events and designed a hospital-based dynamic warning system, aiming to standardize the process of MDAE handling and provide real-time monitoring for MDAEs in a hospital. This system was used in the First Affiliated Hospital of Zhejiang University School of Medicine. Numbers and the compound growth rate of MDAE reports from 2010 to 2020 were compared to test the effectiveness of the MDAE monitoring system. Numbers of MDAE reported to the National Adverse Event Monitoring System were also compared over 2013 to 2020, due to the loss of data before 2013 after shutdown of the old system. Efficacy and usability of the hospital-based dynamic warning system was then verified by analyzing risk and warning levels of MDAEs in 2020. Descriptive statistics was used for data analysis in this study. RESULTS: Results showed that the compound annual growth rates of MDAE reports and those submitted to the National Adverse Event Monitoring System from 2013 to 2020 were 35.0% and 31.5%, respectively. A standardized management of MDAE with full participant, timely response and effective feedback was formed in the hospital by establishment of the MDAE system. CONCLUSIONS: This system effectively improved the monitoring level of MDAEs, helping to improve early detection, early warning, and early intervention of risk of medical device. This study may provide suggestions for medical institutions to establish a MDAE monitoring system, and may promote development of medical quality and safety management for hospitals to some extent.

Media use degree and depression: A latent profile analysis from Chinese residents.

Background: Previous studies have emphasized the media as an essential channel for understanding information about depression. However, they have not divided groups according to the degree of media use to study their differences in depression. Therefore, this study aims to explore the influence of media use on depression and the influencing factors of depression in people with different media use degrees. Methods: Based on seven items related to media use, a total of 11, 031 respondents were categorized by the frequency of media use using latent profile analysis (LPA). Secondly, multiple linear regression analyzes were conducted to analyze the effects of depression in people with different degrees of media use. Finally, factors influencing depression among people with different degrees of media use were explored separately. Results: All respondents were classified into three groups: media use low-frequency (9.7%), media use general (67.1%), and media use high-frequency (23.2%). Compared with media use general group, media use low-frequency (ß = 0.019, p = 0.044) and media use high-frequency (ß = 0.238, p < 0.001) groups are significantly associated with depression. The factors influencing depression in the population differed between media use low-frequency, media use general, and media use high-frequency groups. Conclusion: The government and the appropriate departments should develop targeted strategies for improving the overall health status of people with different media use degrees.

Adenosine A2A receptor blockade improves neuroprosthetic learning by volitional control of population calcium signal in M1 cortical neurons.

Volitional control is at the core of brain-machine interfaces (BMI) adaptation and neuroprosthetic-driven learning to restore motor function for disabled patients, but neuroplasticity changes and neuromodulation underlying volitional control of neuroprosthetic learning are largely unexplored. To better study volitional control at annotated neural population, we have developed an operant neuroprosthetic task with closed-loop feedback system by volitional conditioning of population calcium signal in the M1 cortex using fiber photometry recording. Importantly, volitional conditioning of the population calcium signal in M1 neurons did not improve within-session adaptation, but specifically enhanced across-session neuroprosthetic skill learning with reduced time-to-target and the time to complete 50 successful trials. With brain-behavior causality of the neuroprosthetic paradigm, we revealed that proficiency of neuroprosthetic learning by volitional conditioning of calcium signal was associated with the stable representational (plasticity) mapping in M1 neurons with the reduced calcium peak. Furthermore, pharmacological blockade of adenosine A2A receptors facilitated volitional conditioning of neuroprosthetic learning and converted an ineffective volitional conditioning protocol to be the effective for neuroprosthetic learning. These findings may help to harness neuroplasticity for better volitional control of neuroprosthetic training and suggest a novel pharmacological strategy to improve neuroprosthetic learning in BMI adaptation by targeting striatal A2A receptors.

Invasive Brain Machine Interface System.

Because of high spatial-temporal resolution of neural signals obtained by invasive recording, the invasive brain-machine interfaces (BMI) have achieved great progress in the past two decades. With success in animal research, BMI technology is transferring to clinical trials for helping paralyzed people to restore their lost motor functions. This chapter gives a brief review of BMI development from animal experiments to human clinical studies in the following aspects: (1) BMIs based on rodent animals; (2) BMI based on non-human primates; and (3) pilot BMIs studies in clinical trials. In the end, the chapter concludes with a summary of potential opportunities and future challenges in BMI technology.

Gesture Decoding Using ECoG Signals from Human Sensorimotor Cortex: A Pilot Study.

Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system. High decoding accuracy of the three gestures was achieved in both offline analysis (85.7%, 84.5%, and 69.7%) and online tests (80% and 82%, tested on two participants only). We found that the decoding performance was maintained even with a subset of channels selected by a greedy algorithm. More importantly, these selected channels were mostly distributed along the central sulcus and clustered in the area of 3 interelectrode squares. Our findings of the reduced and clustered distribution of ECoG channels further supported the feasibility of clinically implementing the ECoG-based BMI system for the control of hand gestures.