Technology and Health Care最新文献

Background: In 2019, approximately 330 million individuals in China were affected by cardiovascular diseases, with 11.4 million cases specifically attributed to coronary artery disease (CAD). A national public health report indicated that the mortality rate for CAD ranged from 121.59 to 130.14 per 100,000 individuals in 2019. The treatments for CAD include lifestyle changes, medications, percutaneous coronary intervention (PCI) and coronary artery bypass grafting.

Objective: To investigate the management effect of a digital health program in patients with coronary artery disease (CAD) after percutaneous coronary intervention (PCI).

Methods: This retrospective study compares blood pressure, blood glucose, low-density lipoprotein cholesterol (LDL-C), medication adherence, lifestyle modification, and readmission rate between digital health users and traditional follow-up in post-PCI CAD patients.

Results: In this study of 698 CAD patients, the 6-month readmission rate of all patients was 27.4%, with digital health users showing lower rates than those in traditional follow-up (22.6% vs. 32.1%, p= 0.005). Digital health users had significantly higher target achievements rates in blood pressure (79.7% vs. 54.7%, p< 0.001), blood glucose (98.9% vs. 82.5%, p< 0.001) and LDL-C level (71.3% vs. 52.7%, p< 0.001) at 6-month post-PCI. The digital health group had more patients adopting lifestyle changes, including quitting smoking, maintaining a healthy diet, and exercising regularly. In risk factor analysis, digital health utilization (OR = 0.60, 95%CI: 0.40-0.90, p= 0.014) and multivessel disease (double: OR = 1.72, 95%CI: 1.09-2.72, p= 0.02; triple: OR = 2.59, 95%CI: 1.61-4.17, p< 0.001) were independent predictors of CAD-related cardiovascular readmissions.

Conclusions: Post-PCI patients using digital health platforms exhibited improved blood pressure, glucose, and LDL-C control, greater treatment adherence, enhanced lifestyle changes, and reduced six-month readmission rates versus those with traditional follow-up.

Background: Knee osteoarthritis is a common degenerative joint disease where a single treatment method often fails to fully alleviate symptoms. Hence, finding effective non-invasive combined treatment approaches is particularly crucial.

Objective: The efficacy of treating knee osteoarthritis with hip abductors exercise training combined with repetitive transcranial magnetic stimulation was assessed through functional scales and objective evaluation methods.

Methods: In this four-week randomized clinical trial, 160 patients meeting inclusion criteria were randomly assigned 1:1 to group A to receive oral celecoxib and group B to receive a combination of hip abductors exercise training and repeated transcranial magnetic stimulation. The primary outcome was the western Ontario and McMaster universities osteoarthritis index. The secondary outcomes include Visual Analogue Scale, knee outcome survey activities of daily living scale, Active Range of Motion, and the Quadriceps Angle, the tibiofemoral angle, peak adductor moment, the integrated electromyography and root mean square of the surface electromyography of the lower extremity muscles. Paired sample t test was used for Within-Group comparison of outcome indicators, and independent sample t test was used for Between-Group comparison.

Results: Of the 160 randomly assigned patients, 150 completed the study. After 4 weeks, the WOMAC index decreased from 61 ± 10.83 to 40.55 ± 7.58 in the combined treatment group and from 60.97 ± 10.18 to 47.7 ± 10.13 in the celecoxib group. The effect of the combined treatment group was significantly higher than that in the celecoxib group (P< 0.001). In the combined treatment group, the score of knee joint daily living scale increased (P< 0.001), the active range of motion increased (P< 0.001), the quadriceps angle decreased (P< 0.001), the tibiofemoral angle increased (P< 0.001), and the peak adduction moment decreased (P< 0.001), integrated electromyography and root mean square increased (P< 0.001), and the effect was better than that of celecoxib group (P< 0.001). The visual analog scale score in celecoxib group was lower (P< 0.001) and knee outcome survey activities of daily living scale was higher (P< 0.001). The incidence of treatment-related adverse events was 10% in the celecoxib group and 2.5% in the combined treatment group, all of which were mild.

Conclusions: Hip abductors exercise training combined with repetitive transcranial magnetic stimulation can enhance abduction muscle strength, improve mobility, reduce joint pain, and enhance quality of life. This combined approach shows superior clinical effectiveness compared to oral celecoxib.

Background: Exploring the independent effect of mechanical discharge in response to weight loss (WL) seems necessary but remains highly challenging from a methodological point. Anti-gravity treadmills could be relevant to simulate a mechanical WL by body weight support (BWS), but their reliability remains unclear to properly explore exercise energy metabolism, especially at low degrees of alleviations.

Objective: The study aimed to evaluate the accuracy and reproducibility of an anti-gravity treadmill to generate BWS, and the reproducibility of cardiometabolic responses to an exercise performed at low degrees of BWS on this device.

Methods: Observed BWS of 18 normal-weight males was obtained twice at seven degrees of target BWS (i.e., 0, 3, 6, 9, 12, 15, 18%) using a digital scale inside the anti-gravity treadmill, and was compared to the expected BWS. Then, 15 of them performed 5-min bout of low-intensity walking exercise at these degrees of BWS in a randomized order, separated by 4-min rest. The exercise was identically repeated on three occasions separated by a minimum of 3 days. Energy metabolism and heart rate (HR) were measured throughout the exercise by indirect calorimetry and a HR monitor, respectively.

Results: The observed BWS were significantly different from the expected BWS (p< 0.001), and there was a high inter- and intra-individual variability in BWS generated by the anti-gravity treadmill. Results showed an overall good reliability of VO2 (intraclass correlation coefficients (ICC) values ranging from 0.67 to 0.85) and HR (ICC > 0.8) in response to exercise. An effect of the degree of BWS was observed for VO2 (p< 0.001), illustrating reduced values at 15% and 18% of BWS compared to 0, 3, and 6%.

Conclusions: Such device might not be adapted to simulate low degrees of WL in normal-weight males, particularly when it comes to the exploration of energy metabolism.

Background: The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important attention due to its ability to measure vital parameters such as myocardial mass, end-diastolic volume, and ejection fraction. Medical professionals realize that manually segmenting data to evaluate these processes takes a lot of time, effort when diagnosing heart diseases. Yet, manually segmenting these images is labour-intensive and may reduce diagnostic accuracy.

Objective/methods: This paper, propose a combination of different deep neural networks for semantic segmentation of the left ventricle based on Tri-Convolutional Networks (Tri-ConvNets) to obtain highly accurate segmentation. CMRI images are initially pre-processed to remove noise artefacts and enhance image quality, then ROI-based extraction is done in three stages to accurately identify the LV. The extracted features are given as input to three different deep learning structures for segmenting the LV in an efficient way. The contour edges are processed in the standard ConvNet, the contour points are processed using Fully ConvNet and finally the noise free images are converted into patches to perform pixel-wise operations in ConvNets.

Results/conclusions: The proposed Tri-ConvNets model achieves the Jaccard indices of 0.9491 ± 0.0188 for the sunny brook dataset and 0.9497 ± 0.0237 for the York dataset, and the dice index of 0.9419 ± 0.0178 for the ACDC dataset and 0.9414 ± 0.0247 for LVSC dataset respectively. The experimental results also reveal that the proposed Tri-ConvNets model is faster and requires minimal resources compared to state-of-the-art models.

Background: Nursing management considers nursing quality management at its core. Evaluation indexes of nursing quality not only control nursing quality, measure work enthusiasm, and judge the difference in nursing levels but also help nurses accurately understand the satisfaction degree of patients.

Objective: To analyze pediatric tumor nursing quality evaluation indexes and establish a scientific and rigorous index system of children's nursing quality.

Methods:   Based on the "structure-process-result" in this study, the Delphi method was used to carry out questionnaire investigations and opinion collection for domestic experts in pediatrics, pediatric oncology internal medicine, and pediatric oncology surgical care and clinical aspects two times. In addition, the Kendall coordination coefficient and analytic hierarchy process were used to determine the content of indicators at all levels, set the weight, and finally establish the quality index system of pediatric tumor nursing.

Results: The response rate of 27 experts in the first round of the questionnaire was 100%, Ca0.77, Cs,0.73, Cr0.75, and the W value of the expert coordination coefficient was 0.315. In the second round of 27 expert consultations, the reply rate was 96%, Ca0.81, Cs0.80, Cr0.805, and the specialist coordination coefficient W value was 0.369. After two rounds of consultation, the importance evaluation scores of each indicator ranged from 3.31 to 4.88 and 3.29 to 5.00, respectively, and more than 94.7% of the indicators' importance evaluation scores were higher than 3.50. Finally, 3 Level-1 indicators, 12 Level-2 indicators, and 54 Level-3 indicators are established.

Conclusion: This study has established a simple, comprehensive, and scientific quality index system for pediatric tumor nursing, highlighted the characteristics of pediatric tumor nursing, and provided an evaluation system for specialized nursing of pediatric tumors to more standardized pediatric tumor nursing and contribute to the sustainable and scientific development of pediatric tumor nursing.

Background: Chronic obstructive pulmonary disease (COPD) is a common respiratory disease globally, characterized by obstructive ventilatory disorder under pulmonary function tests. Recent years have witnessed a yearly increase in the prevalence of COPD.

Objective: To investigate the impact of respiratory virus infections on patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), and to perform sequencing typing and mutation analysis of viruses with high detection rate.

Methods: A total of 1523 inpatients with AECOPD admitted to our hospital from April 1,2020 to March 30,2022 were collected and divided into two groups: the infected group (n= 532) and the non-infected group (n= 991). The related indexes between the two groups were collected and compared (including clinical characteristics and laboratory tests that blood cell count, PCT, CRP, adenovirus, respiratory syncytial virus, rhinovirus, influenza A virus, influenza B virus, etc.).

Results: In the infected group, the proportion of patients with palpitations (49.44% VS 8.07%, P< 0.001), lipid metabolism abnormalities (18.42% VS 39.96%, P< 0.001), heart failure (39.85% VS 29.87%, P< 0.001), disease duration (17.48 ± 7.47 VS 12.45 ± 11.43 d, P< 0.001), and poor prognosis (69.55% VS 17.15%, P< 0.001) were higher than those in the non-infected group; Adenovirus (ADV) accounted for 75.94% (404/532) of all infected viruses. 31 virus strains could be categorized into 16 ADV-C1, one ADV-C5, two ADV-B3, three ADV-B7, two ADV-D17, two ADV-D19, and five ADV-D27, which were similar to the serotypes reported in severe pneumonia. Furthermore, three strains of C1 adenovirus were found to be highly homologous to the original strain AF534906 by sequencing, and the phylogenetic trees of the three main structural genes were all on the same branch as the original strain. Base mutations and amino acid variants were found in each structural gene segment. In clinical data, it's found that patients with mutations are worse than those without mutations.

Conclusion: Respiratory viruses are common in patients with poor prognosis of AECOPD, especially adenovirus, respiratory syncytial virus. Respiratory virus infections will lead to the deterioration of patients with AECOPD, accompanied by longer treatment cycles and poor prognosis.

Background: Traditional canes have no special functions besides weight support, and their long-term use may cause secondary damage to the musculoskeletal system and gait deviation.

Objective: This study aimed to investigate the usability of the cane-combined weight support feedback device (CWSFD) to identify areas requiring improvement and the current level of satisfaction.

Methods: As a collet-type device, the CWSFD has a structure allowing attachment and detachment at the lower end of a traditional mono cane. To assess the level of satisfaction and areas of improvement regarding the novel CWSFD, a usability test was conducted on 10 physical therapists with ⩾ 20 years of clinical experience. The usability test comprised a satisfaction survey questionnaire to determine the level of satisfaction, and an in-depth interview to identify the points of improvement.

Results: The usability test demonstrated a moderate or higher level of satisfaction in all tested items, and the in-depth interview revealed that the device should have a lower weight with a greater diversity of feedback.

Conclusions: We anticipate that by implementing the respective improvements, a novel device will be developed in the future; for its commercialization, studies to verify the clinical effectiveness are warranted.

Background: Schwann cell sheaths are the source of benign, slowly expanding tumours known as acoustic neuromas (AN). The diagnostic and treatment approaches for AN must be patient-centered, taking into account unique factors and preferences.

Objective: The purpose of this study is to investigate how machine learning and artificial intelligence (AI) can revolutionise AN management and diagnostic procedures.

Methods: A thorough systematic review that included peer-reviewed material from public databases was carried out. Publications on AN, AI, and deep learning up until December 2023 were included in the review's purview.

Results: Based on our analysis, AI models for volume estimation, segmentation, tumour type differentiation, and separation from healthy tissues have been developed successfully. Developments in computational biology imply that AI can be used effectively in a variety of fields, including quality of life evaluations, monitoring, robotic-assisted surgery, feature extraction, radiomics, image analysis, clinical decision support systems, and treatment planning.

Conclusion: For better AN diagnosis and treatment, a variety of imaging modalities require the development of strong, flexible AI models that can handle heterogeneous imaging data. Subsequent investigations ought to concentrate on reproducing findings in order to standardise AI approaches, which could transform their use in medical environments.