S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test.

BACKGROUND: Computer-aided detection of cognitive impairment garnered increasing attention, offering older adults in the community access to more objective, ecologically valid, and convenient cognitive assessments using multimodal sensing technology on digital devices. METHODOLOGY: In this study, we aimed to develop an automated method for screening cognitive impairment, building on paper- and electronic TMTs. We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE). Within S2VQ-VAE, we incorporated intra- and inter-class correlation losses to disentangle class-related factors. These factors were then combined with various real-time obtainable features (including demographic, time-related, pressure-related, and jerk-related features) to create a robust feature engineering block. Finally, we identified the light gradient boosting machine as the optimal classifier. The experiments were conducted on a dataset collected from older adults in the community. RESULTS: The experimental results showed that the proposed multi-type feature fusion method outperformed the conventional method used in paper-based TMTs and the existing VAE-based feature extraction in terms of screening performance. CONCLUSIONS: In conclusion, the proposed deep representation learning method significantly enhances the cognitive diagnosis capabilities of behavior-based TMTs and streamlines large-scale community-based cognitive impairment screening while reducing the workload of professional healthcare staff.

Use of Patient-Reported Outcome Measures in Lower Extremity Research.

Background: A large number of patient reported outcome measures (PROMs) have been developed for specific lower extremity orthopaedic pathologies. However, a consensus as to which PROMs are recommended for use in evaluating treatment outcomes for patients with hip, knee, ankle and/or foot pathology based on the strength of their psychometric properties is lacking. Objective: To identify PROMs that are recommended in systematic reviews (SRs) for those with orthopaedic hip, knee, foot, and ankle pathologies or surgeries and identify if these PROMs are used in the literature. Study design: Umbrella Review. Methods: PubMed, Embase, Medline, Cochrane, CINAHL, SPORTDisucs and Scopus were searched for SRs through May 2022. A second search was done to count the use of PROMs in seven representative journals from January 2011 through May 2022.SRs that recommended the use of PROMs based on their psychometric properties were included in the first search. SRs or PROMs not available in the English were excluded. The second search included clinical research articles that utilized a PROM. Case reports, reviews, and basic science articles were excluded. Results: Nineteen SRs recommended 20 PROMs for 15 lower extremity orthopaedic pathologies or surgeries. These results identified consistency between recommended PROMs and utilization in clinical research for only two of the 15 lower extremity pathologies or surgeries. This included the use of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Copenhagen Hip and Groin Outcome Score to assess outcomes (HAGOS) for those with knee osteoarthritis and groin pain, respectively. Conclusion: A discrepancy was found between the PROMs that were recommended by SRs and those used to assess clinical outcomes in published research. The results of this study will help to produce more uniformity with the use of PROMs that have the most appropriate psychometric properties when the reporting treatment outcomes for those with extremity pathologies. Level of evidence: 3a.

Cross-Culturally Adapted Versions of Patient Reported Outcome Measures for the Lower Extremity.

Background: A large number of patient reported outcome measures (PROMs) have been developed in the English language for various lower extremity orthopaedic pathologies. Twenty different PROMs were recommended for 15 specific musculoskeletal lower extremity pathologies or surgeries. However, the availability of cross-culturally adapted versions of these recommended PROMs is unknown. Purpose: The purpose of this study was to identify the cross-culturally adapted versions of recommended PROMs for individuals experiencing orthopedic lower extremity pathologies or undergoing surgeries, and to identify the psychometric evidence that supports their utilization. Study design: Literature Review. Methods: PubMed, Embase, Medline, Cochrane, CINAHL, SPORTDisucs and Scopus were searched for cross-culturally adapted translated studies through May 2022. The search strategy included the names of the 20 recommended PROMs from previous umbrella review along with the following terms: reliability, validity, responsiveness, psychometric properties and cross-cultural adaptation. Studies that presented a non-English language version of the PROM with evidence in at least one psychometric property to support its use were included. Two authors independently evaluated the studies for inclusion and independently extracted data. Results: Nineteen PROMS had cross-culturally adapted and translated language versions. The KOOS, WOMAC, ACL-RSL, FAAM, ATRS, HOOS, OHS, MOXFQ and OKS were available in over 10 different language versions. Turkish, Dutch, German, Chinese and French were the most common languages, with each language having more than 10 PROMs with psychometric properties supporting their use. The WOMAC and KOOS were both available in 10 languages and had all three psychometric properties of reliability, validity, and responsiveness supporting their use. Conclusion: Nineteen of the 20 recommended instruments were available in multiple languages. The PROM most frequently cross-culturally adapted and translated were the KOOS and WOMAC. PROMs were most frequently cross-culturally adapted and translated into Turkish. International researchers and clinicians may use this information to more consistently implement PROMs with the most appropriate psychometric evidence available to support their use. Level of evidence: 3a.

The Role of Gut Microbiota in Various Neurological and Psychiatric Disorders-An Evidence Mapping Based on Quantified Evidence.

Methods: We searched PubMed, Cochrane Library, and Epistemonikos to identify systematic reviews and meta-analysis (SRs). We searched for neurological diseases and psychiatric disorders, including Alzheimer's disease (AD), attention deficit hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), autism spectrum disorder (ASD), anorexia nervosa (AN), bipolar disorder (BD), eating disorder (ED), generalized anxiety disorder (GAD), major depressive disorder (MDD), multiple sclerosis (MS), obsessive compulsive disorder (OCD), Parkinson's disease (PD), posttraumatic stress disorder (PTSD), spinal cord injury (SCI), schizophrenia, and stroke. We used A Measurement Tool to Assess Systematic Reviews (AMSTAR-2) to evaluate the quality of included SRs. We also created an evidence map showing the role of gut microbiota in neurological diseases and the certainty of the evidence. Results: In total, 42 studies were included in this evidence mapping. Most findings were obtained from observational studies. According to the AMSTAR-2 assessment, 21 SRs scored "critically low" in terms of methodological quality, 16 SR scored "low," and 5 SR scored "moderate." A total of 15 diseases have been investigated for the potential association between gut microbiome alpha diversity and disease, with the Shannon index and Simpson index being the most widely studied. A total of 12 diseases were investigated for potential link between beta diversity and disease. At the phylum level, Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, and Verrucomicrobia were more researched. At the genus level, Prevotella, Coprococcus, Parabacteroides, Phascolarctobacterium, Escherichia Shigella, Alistipes, Sutteralla, Veillonella, Odoribacter, Faecalibacterium, Bacteroides, Bifidobacterium, Dialister, and Blautia were more researched. Some diseases have been found to have specific flora changes, and some diseases have been found to have common intestinal microbiological changes. Conclusion: We found varied levels of evidence for the associations between gut microbiota and neurological diseases; some gut microbiota increased the risk of neurological diseases, whereas others showed evidence of benefit that gut microbiota might be promising therapeutic targets for such diseases.

Predicting Adverse Events During Six-Minute Walk Test Using Continuous Physiological Signals.

Background and Objective: The 6-min walk test (6MWT) is a common functional assessment test, but adverse events during the test can be potentially dangerous and can lead to serious consequences and low quality of life. This study aimed to predict the occurrence of adverse events during 6MWT, using continuous physiological parameters combined with demographic variables. Methods: 578 patients with respiratory disease who had performed standardized 6MWT with wearable devices from three hospitals were included in this study. Adverse events occurred in 73 patients (12.6%). ECG, respiratory signal, tri-axial acceleration signals, oxygen saturation, demographic variables and scales assessment were obtained. Feature extraction and selection of physiological signals were performed during 2-min resting and 1-min movement phases. 5-fold cross-validation was used to assess the machine learning models. The predictive ability of different models and scales was compared. Results: Of the 16 features selected by the recursive feature elimination method, those related to blood oxygen were the most important and those related to heart rate were the most numerous. Light Gradient Boosting Machine (LightGBM) had the highest AUC of 0.874 ± 0.063 and the AUC of Logistic Regression was AUC of 0.869 ± 0.067. The mMRC (Modified Medical Research Council) scale and Borg scale had the lowest performance, with an AUC of 0.733 and 0.656 respectively. Conclusion: It is feasible to predict the occurrence of adverse event during 6MWT using continuous physiological parameters combined with demographic variables. Wearable sensors/systems can be used for continuous physiological monitoring and provide additional tools for patient safety during 6MWT.

Correlation Between Post-operative Sense of Coherence and Family Function in Patients With Type A Aortic Dissection.

Objective: To analyze the relationship between post-operative sense of coherence and family function in patients with type A aortic dissection (AD). Methods: Ninety patients with AD treated from January 2019 to December 2020 were selected as the research subjects. All patients received surgical treatments. Two weeks after the operation, the Sense of Coherence Scale (SOC-13) and Family APGAR index scale (APGAR) were used to evaluate the patients' sense of coherence and family function. Baseline data of all patients were collected, the SOC-13 scores of patients with type A AD with different demographic characteristics were compared, and the relationship between family function and patients' sense of coherence was analyzed. Results: The ninety patients with type A AD had a low level of psychological consistency, and the average SOC-13 score was 49.84 ± 3.89 points. The SOC-13 score of patients with type A AD with family monthly incomes <5,000 yuan and moderate and severe family dysfunction was lower than that of patients with family monthly incomes ≥5,000 yuan and good family function. The difference was statistically significant (P < 0.05). There was no statistically significant difference in the SOC-13 scores of patients with type A AD with different demographic characteristics (P > 0.05). The results of multiple linear regression analysis showed that family monthly income <5,000 yuan and moderate and severe family dysfunction might be general influencing factors of sense of coherence among patients with type A AD (P < 0.05). Y = 43.333 + 6.667X1 + 16.730X2 was obtained. Conclusion: The post-operative sense of coherence of patients with type A AD may be affected by family function.

Qualitative study of willingness and demand for participation in decision-making regarding anticoagulation therapy in patient undergoing heart valve replacement.

BACKGROUND: Promoting patient participation in decision-making aims to maintain the partnership between doctors and patients, reflect the patients' goals, values, and preferences, and achieve patient-centered care. Realizing patient-centered care, shared collaboration between doctors and patients, and the decision-making process that considers the patients' priorities and goals are the keys to high-quality health care. Therefore, it is indispensable to analyze the patients' willingness to participate in the decision-making process and related participation needs regarding anticoagulation treatment for patients undergoing valve replacement. PURPOSE: To analyze the patients' willingness to participate in the decision-making process and the participation needs of patients undergoing mechanical cardiac valve replacement in the process of anticoagulation therapy to provide a basis for promoting patients' participation in decision-making. METHODS: Using phenomenological research methods, data were collected through semistructured interviews. Patients were interviewed after mechanical valve replacement from June to August 2021 in a Grade A hospital in Nanjing. Data were analyzed according to the Colaizzi phenomenology method. RESULTS: Three major themes were identified from the data: strong willingness to participate but low actual participation, supportive needs, and family members' participation. CONCLUSIONS: This study guided interventions to encourage patients who underwent heart valve replacement to participate in the decision-making process. From the patient's perspective, obtaining support in the decision-making process and caregiver enthusiasm is important. This study prompted thoughts about the use of auxiliary tools and provided a reliable basis for constructing decision-making auxiliary programs to guide clinical practice.

Electroacupuncture modulates the intestinal microecology to improve intestinal motility in spinal cord injury rats.

Spinal cord injury (SCI) is a disease involving gastrointestinal disorders. The underlying mechanisms of the potential protective effects of electroacupuncture (EA) and 5-hydroxytryptamine (5-HT) system on SCI remain unknown. We investigated whether EA improves gut microbial dysbiosis in SCI and regulates the 5-HT system. 16S rDNA gene sequencing was applied to investigate alterations in the gut microbiome of the rats. Faecal metabolites and the expression of the 5-HT system were detected. EA and faecal microbiota transplantation (FMT) treatment facilitated intestinal transmission functional recovery and restored the colon morphology of SCI rats. The composition of the intestinal microbiota, including numbers of phylum Proteobacteria, class Clostridia, order Bacteroidales, and genus Dorea, were amplified in SCI rats, and EA and FMT significantly reshaped the intestinal microbiota. SCI resulted in disturbed metabolic conditions in rats, and the EA and FMT group showed increased amounts of catechin compared with SCI rats. SCI inhibited 5-HT system expression in the colon, which was significantly reversed by EA and FMT treatment. Therefore, EA may ameliorate SCI by modulating microbiota and metabolites and regulate the 5-HT system. Our study provides new insights into the pathogenesis and therapy of SCI from the perspective of microbiota and 5-HT regulation.

Repetitive Transcranial Magnetic Stimulation for Neuropathic Pain on the Non-Motor Cortex: An Evidence Mapping of Systematic Reviews.

OBJECTIVE: This study was aimed to summarize and analyze the quality of the available evidence in systematic reviews (SRs) of repetitive transcranial magnetic stimulation (rTMS) on the non-motor cortex (non-M1) for neuropathic pain (NP) through an evidence mapping approach. METHODS: We follow the Global Evidence Mapping (GEM) methodology. Searches were conducted in PubMed, EMBASE, Epistemonikos, and the Cochrane Library. The study type was restricted to SRs with or without meta-analysis. All literature published before January 23, 2021, were included. The methodological quality of the included SRs was assessed using A Measurement Tool to Assess Systematic Reviews (AMSTAR-2). Data were extracted according to a defined population-intervention-comparison-outcome (PICO) framework from primary studies that included SRs. The same PICO was categorized into PICOs according to interventions (stimulation target, frequency, number of sessions (short: 1-5 sessions, medium: 5-10 sessions, and long: >10 sessions)) and comparison (sham rTMS or other targets). The evidence mapping was presented in tables and a bubble plot. RESULTS: A total of 23 SRs were included. According to the AMSTAR-2, 20 SRs scored "very low" in terms of methodological quality, 2 SRs scored "low," and 1 SR scored "high." A total of 17 PICOs were extracted. The dorsolateral prefrontal cortex (DLPFC) is the most studied of the non-motor cortex targets. PICOs of DLPFC, premotor cortex (PMC), frontal cortex, and secondary somatosensory cortex (S2) were mainly categorized with a "potentially better" conclusion. High-frequency (5-20 Hz) rTMS of non-M1 usually lead to "potentially better" conclusions. CONCLUSIONS: DLPFC, PMC, frontal cortex, and S2 seem to be promising new targets for rTMS treatment of certain NP. Evidence mapping is a useful and reliable methodology to identify and present the existing evidence gap that more research efforts are necessary in order to highlight the optimal stimulation protocols for non-M1 targets and standardize parameters to fill the evidence gaps of rTMS. Further investigation is advised to improve the methodological quality and the reporting process of SRs.

Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study.

BACKGROUND: With the development and promotion of wearable devices and their mobile health (mHealth) apps, physiological signals have become a research hotspot. However, noise is complex in signals obtained from daily lives, making it difficult to analyze the signals automatically and resulting in a high false alarm rate. At present, screening out the high-quality segments of the signals from huge-volume data with few labels remains a problem. Signal quality assessment (SQA) is essential and is able to advance the valuable information mining of signals. OBJECTIVE: The aims of this study were to design an SQA algorithm based on the unsupervised isolation forest model to classify the signal quality into 3 grades: good, acceptable, and unacceptable; validate the algorithm on labeled data sets; and apply the algorithm on real-world data to evaluate its efficacy. METHODS: Data used in this study were collected by a wearable device (SensEcho) from healthy individuals and patients. The observation windows for electrocardiogram (ECG) and respiratory signals were 10 and 30 seconds, respectively. In the experimental procedure, the unlabeled training set was used to train the models. The validation and test sets were labeled according to preset criteria and used to evaluate the classification performance quantitatively. The validation set consisted of 3460 and 2086 windows of ECG and respiratory signals, respectively, whereas the test set was made up of 4686 and 3341 windows of signals, respectively. The algorithm was also compared with self-organizing maps (SOMs) and 4 classic supervised models (logistic regression, random forest, support vector machine, and extreme gradient boosting). One case validation was illustrated to show the application effect. The algorithm was then applied to 1144 cases of ECG signals collected from patients and the detected arrhythmia false alarms were calculated. RESULTS: The quantitative results showed that the ECG SQA model achieved 94.97% and 95.58% accuracy on the validation and test sets, respectively, whereas the respiratory SQA model achieved 81.06% and 86.20% accuracy on the validation and test sets, respectively. The algorithm was superior to SOM and achieved moderate performance when compared with the supervised models. The example case showed that the algorithm was able to correctly classify the signal quality even when there were complex pathological changes in the signals. The algorithm application results indicated that some specific types of arrhythmia false alarms such as tachycardia, atrial premature beat, and ventricular premature beat could be significantly reduced with the help of the algorithm. CONCLUSIONS: This study verified the feasibility of applying the anomaly detection unsupervised model to SQA. The application scenarios include reducing the false alarm rate of the device and selecting signal segments that can be used for further research.

[Investigation on new paradigm of clinical physiological monitoring by using wearable devices].

As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable continuous physiological monitoring until January 2020, with data from a total of 1 198 people (632 cases) screened for signals through signal quality algorithms and manual interpretation were available for analysis, accounting for 94.48 % (96.19%) of the total. Through continuous physiological data analysis and manual correction, sleep apnea event, nocturnal hypoxemia, tachycardia, and ventricular premature beats were detected in 232 (36.65%), 58 (9.16%), 30 (4.74%), and 42 (6.64%) of the total patients, while the number of these abnormal events recorded in the archives was 4 (0.63%), 0 (0.00%), 24 (3.80%), and 15 (2.37%) cases. The statistical analysis of sleep apnea event outcomes revealed that patients with chronic diseases were more likely to have sleep apnea events than healthy individuals, and the incidence was higher in men (62.93%) than in women (37.07%). The results indicate that wearable physiological monitoring technology can provide a new monitoring mode for inpatients, capturing more abnormal events and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.

Evidence Mapping Based on Systematic Reviews of Repetitive Transcranial Magnetic Stimulation on the Motor Cortex for Neuropathic Pain.

BACKGROUND AND OBJECTIVE: There is vast published literature proposing repetitive transcranial magnetic stimulation (rTMS) technology on the motor cortex (M1) for the treatment of neuropathic pain (NP). Systematic reviews (SRs) focus on a specific problem and do not provide a comprehensive overview of a research area. This study aimed to summarize and analyze the evidence of rTMS on the M1 for NP treatment through a new synthesis method called evidence mapping. METHODS: Searches were conducted in PubMed, EMBASE, Epistemonikos, and The Cochrane Library to identify the studies that summarized the effectiveness of rTMS for NP. The study type was restricted to SRs with or without meta-analysis. All literature published before January 23, 2021, was included. Two reviewers independently screened the literature, assessed the methodological quality, and extracted the data. The methodological quality of the included SRs was assessed by using the A Measurement Tool to Assess Systematic Reviews (AMSTAR-2). Data were extracted following a defined population, intervention, comparison, and outcome (PICO) framework from primary studies that included SRs. The same PICO was categorized into PICOs according to interventions [frequency, number of sessions (short: 1-5 sessions, medium: 5-10 sessions, and long: >10 sessions)] and compared. The evidence map was presented in tables and a bubble plot. RESULTS: A total of 38 SRs met the eligibility criteria. After duplicate primary studies were removed, these reviews included 70 primary studies that met the scope of evidence mapping. According to the AMSTAR-2 assessment, the quality of the included SRs was critically low. Of these studies, 34 SRs scored "critically low" in terms of methodological quality, 2 SR scored "low," 1 SR scored "moderate," and 1 SR scored "high." CONCLUSION: Evidence mapping is a useful methodology to provide a comprehensive and reliable overview of studies on rTMS for NP. Evidence mapping also shows that further investigations are necessary to highlight the optimal stimulation protocols and standardize all parameters to fill the evidence gaps of rTMS. Given that the methodological quality of most included SRs was "critically low," further investigations are advised to improve the methodological quality and the reporting process of SRs.