ObjectiveTo evaluate the safety, efficacy, and cost-effectiveness of different uses of oxidized regenerated cellulose (ORC) in video-assisted thoracoscopic surgery (VATS) for lung cancer resection to provide a reference for the selection, clinical use, and rational utilization of absorbable hemostatic materials. MethodsA retrospective analysis of relevant data from inpatients who underwent VATS for lung cancer resection at a tertiary hospital from July 2019 to January 2020 and from July 2020 to December 2020 was conducted. Patients were divided into two groups based on the use of ORC: 1) combined use group (ORC and collagen sponge) and 2) sole-use group (ORC). Safety, efficacy, and economic outcome indicators were compared between the two groups. ResultsThe main analysis included a total of 904 patients, with 466 in the combined use group and 438 in the sole-use group. Compared to the combined use group, the sole-use group had a significantly longer hospital stay, used fewer hemostatic drugs, had a lower average cost of hemostatic materials, and a lower median total hospitalization cost (P<0.05). No statistically significant difference was found between the two groups in terms of intraoperative blood loss volume, massive blood loss rate, perioperative transfusion rate, reoperation rate, postoperative 48-hour drainage volume, bloody drainage fluid rate, or postoperative laboratory test indicators. ConclusionThere was no significant difference in the safety or efficacy of VATS for lung cancer resection between the sole use of ORC and the combined use of ORC, but the sole use of ORC was associated with a lower cost of hemostatic materials and a lower total hospitalization cost. The sole use of hemostatic gauze in VATS for lung cancer resection may be a more cost-effective choice.
With the increasing improvement of real-world evidence as a research system and guideline specification for pre-market registration and post-market regulatory decision support of clinically urgent drug and mechanical products, identifying an approach to ensure the high quality and standards of real-world data and establishing a basis for the generation of real-world evidence is receiving increasing attention and concern from regulatory authorities. Based on the experience of Boao hope city real-world data research pattern and ophthalmic data platform construction, this paper discussed the "source data-database-evidence chain" generation process, data management, and data governance in real-world study from the special features and necessity of multiple sources and heterogeneity of data, multiple research designs, and standardized regulatory requirements, and provided references for further construction of comprehensive research data platforms in the future.
Diabetic retinopathy (DR), which is a common complication of diabetic and the main cause of blindness, brings not only a heavy economic burden to society, but also seriously threatens to the patients’ quality of life. Clinical researches on the therapies of DR are active at present, but how to perform a good clinical research with scientific design should be considered with high priority. The randomized controlled trial (RCT) is considered to be the gold standard for evidence-based medicine, but RCT is not always perfect. Limitations still exist in certain circumstance and the conclusions from RCTs also need to be interpreted by an objective point of view before clinical practice. Real world study (RWS) bridges the gap between RCT and clinical practice, in which the data can be easily collected without much cost, and results might be obtained within a short period. However, RWS is also faced with the challenge of not having standardized data and being susceptible to confounding bias. The standardized single disease database for DR and propensity score matching method can provide a wide range of data sources and avoid of bias for RWS in DR.
We applied resting-state functional magnetic resonance imaging (rfMRI) combined with graph theory to analyze 90 regions of the infantile small world neural network of the whole brain. We tried to get the following two points clear:① whether the parameters of the node property of the infantile small world neural network are correlated with the level of infantile intelligence development; ② whether the parameters of the infantile small world neural network are correlated with the children's baseline parameters, i.e., the demographic parameters such as gender, age, parents' education level, etc. Twelve cases of healthy infants were included in the investigation (9 males and 3 females with the average age of 33.42±8.42 months.) We then evaluated the level of infantile intelligence of all the cases and graded by Gesell Development Scale Test. We used a Siemens 3.0T Trio imaging system to perform resting-state (rs) EPI scans, and collected the BOLD functional Magnetic Resonance Imaging (fMRI) data. We performed the data processing with Statistical Parametric Mapping 5(SPM5) based on Matlab environment. Furthermore, we got the attributes of the whole brain small world and node attributes of 90 encephalic regions of templates of Anatomatic Automatic Labeling (ALL). At last, we carried out correlation study between the above-mentioned attitudes, intelligence scale parameters and demographic data. The results showed that many node attributes of small world neural network were closely correlated with intelligence scale parameters. Betweeness was mainly centered in thalamus, superior frontal gyrus, and occipital lobe (negative correlation). The r value of superior occipital gyrus associated with the individual and social intelligent scale was -0.729 (P=0.007); degree was mainly centered in amygdaloid nucleus, superior frontal gyrus, and inferior parietal gyrus (positive correlation). The r value of inferior parietal gyrus associated with the gross motor intelligent scale was 0.725 (P=0.008); efficiency was mainly centered in inferior frontal gyrus, inferior parietal gyrus, and insular lobe (positive correlation). The r value of inferior parietal gyrus associated with the language intelligent scale was 0.738 (P=0.006); Anoda cluster coefficient (anodalCp) was centered in frontal lobe, inferior parietal gyrus, and paracentral lobule (positive correlation); Node shortest path length (nlp) was centered in frontal lobe, inferior parietal gyrus, and insular lobe. The distribution of the encephalic regions in the left and right brain was different. However, no statistical significance was found between the correlation of monolithic attributes of small world and intelligence scale. The encephalic regions, in which node attributes of small world were related to other demographic indices, were mainly centered in temporal lobe, cuneus, cingulated gyrus, angular gyrus, and paracentral lobule areas. Most of them belong to the default mode network (DMN). The node attributes of small world neural network are widely related to infantile intelligence level, moreover the distribution is characteristic in different encephalic regions. The distribution of dominant encephalic is in accordance the related functions. The existing correlations reflect the ever changing small world nervous network during infantile development.
The research on brain functional mechanism and cognitive status based on brain network has the vital significance. According to a time–frequency method, partial directed coherence (PDC), for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram (EEG) signals, this paper proposed dynamic PDC (dPDC) method to model the brain network for motor imagery. The parameters attributes (out-degree, in-degree, clustering coefficient and eccentricity) of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008, and then the interaction between different locations for the network character and significance of motor imagery was analyzed. The clustering coefficients for both groups were higher than those of the random network and the path length was close to that of random network. These experimental results show that the effective network has a small world property. The analysis of the network parameter attributes for the left and right hands verified that there was a significant difference on ROI2 (P = 0.007) and ROI3 (P = 0.002) regions for out-degree. The information flows of effective network based dPDC algorithm among different brain regions illustrated the active regions for motor imagery mainly located in fronto-central regions (ROI2 and ROI3) and parieto-occipital regions (ROI5 and ROI6). Therefore, the effective network based dPDC algorithm can be effective to reflect the change of imagery motor, and can be used as a practical index to research neural mechanisms.
Mild cognitive impairment (MCI) is a clinical transition state between age-related cognitive decline and dementia. Researchers can use neuroimaging and neurophysiological techniques to obtain structural and functional information about the human brain. Using this information researchers can construct the brain network based on complex network theory. The literature on graph theory shows that the large-scale brain network of MCI patient exhibits small-world property, which ranges intermediately between Alzheimer's disease and that in the normal control group. But brain connectivity of MCI patients presents topologically structural disorder. The disorder is significantly correlated to the cognitive functions. This article reviews the recent findings on brain connectivity of MCI patients from the perspective of multimodal data. Specifically, the article focuses on the graph theory evidences of the whole brain structural and functional and the joint covariance network disorders. At last, the article shows the limitations and future research directions in this field.
The active comparator, new user (ACNU) design is an important design developed under the concept of the target simulation experimental framework. It aims to reduce indication confounding, immortal time bias, prevalence-incidence bias, and other unmeasured confounders by simulating head-to-head randomized controlled trials. It is widely applied in scenarios such as comparing the efficacy of newly marketed drugs with existing standard treatments, evaluating drug safety and adherence, exploring drug repurposing, and optimizing algorithms for processing medical big data. This article introduces the application and practice of the ACNU design in real-world data research from aspects such as concept, development, advantages and disadvantages, and implementation points, and also presents an outlook on its application in the field of traditional Chinese medicine. It is believed that with the progress in understanding the design of observational studies of real-world data, the ACNU design is expected to be more widely applied and provide new ideas for researchers' scientific research designs.
High-quality randomized controlled trials (RCTs) are regarded as the gold standard for assessing the efficiency and safety of drugs. However, conducting RCTs is expensive and time consumed, and providing timely evidence by RCTs for regulatory agencies and medical decision-makers can be challenging, particularly for new or emerging serious diseases. Additionally, the strict design of RCTs often results in a weakly external validity, making it difficult to provide the evidence of the clinical efficacy and safety of drugs in a broader population. In contrast, large simple clinical trials (LSTs) can expedite the research process and provide better extrapolation and reliable evidence at a lower cost. This article presents the development, features, and distinctions between LSTs and RCTs, as well as special considerations when conducting LSTs, in accordance with literature and guidance principles from regulatory agencies both from China and other countries. Furthermore, this paper assesses the potential of real-world data to bolster the development of LSTs, offering relevant researchers’ insight and guidance on how to conduct LSTs.
As an important source for real-world data, existing health and medical data have gained wide attentions recently. As the first part of the serial technical guidance for real-world data and studies, this report introduced the concepts, features and potential applications of existing medical and health data, proposed recommendations for planning and developing a research database using existing health and medical data, and developed essential indicators for assessing the quality of such research databases. The technical guidance may standardize and improve the development of research database using existing health and medical data in China.
As an important policy tool, real-world evidence is the basis for health insurance catalogue adjustment, and relevant policies and regulations have been issued in foreign countries to guide the use of real-world research for health insurance access, but the field of traditional Chinese medicine in China in particular is still in the exploratory stage. Since TCM protocols are widely used in clinical practice and have significant clinical value, this paper takes TCM protocols as an example and systematically constructs a technical pathway based on real-world research to support health insurance access, including clinical needs assessment, basic requirements of protocols, key points for conducting real-world research and evaluating real-world evidence, the process of access, the strategy of access, and the dynamic monitoring of access, with the aim of providing guidance for the application of real-world research in China's health insurance catalogue adjustment. Access to real-world research to provide reference for the application of real-world research in China's health insurance.