Recently, real world studies (RWS) have received increasing attentions. Such studies typically involve patient information, and their results may have potentially significant impact on patient well-being and safety. When reviewing the protocol of real world studies, ethical issues should be carefully considered and assessed. This paper discussed three issues, including the overview of bioethics and its application to classic clinical trials, key features of RWS, and medical ethical considerations on RWS.
The aim of this study is to explore the effects of continuous theta-burst transcranial magnetic stimulation (cTBS) on functional brain network in emotion processing. Before and after the intervention of cTBS over left dorsolateral prefrontal cortex (DLPFC) of ten participants who were asked to perform the emotion gender recognition task, we recorded their scalp electroencephalograms (EEG). Then we used the phase synchronization of EEG to measure the connectivity between two nodes. We then calculated the network efficiency to describe the efficiency of information transmission in brain regions. Our research showed that after the intervention of cTBS and the stimulation of the emotion face picture, there was an obvious enhancement in the event-related spectral perturbation after stimuli onset in beta band in 100–300 ms. Under the stimulation of different emotion picture, the values of global phase synchronization for negative and neutral stimuli were enhanced compared to positive ones. And the increased small-worldness was found in emotional processing. In summary, based on the effect of activity change in the left DLPFC on emotion processing brain network, the emotional processing mechanism of brain networks were preliminary explored and it provided the reference for the research of emotion processing brain network in the future.
Assessment of Real World Observational Studies (ArRoWS) is a tool developed by the Leicester Real World Evidence (LRWE) Unit of the Diabetes Research Centre of the University of Leicester in the United Kingdom to assess the quality of real world evidence research, and has been reported to have good practicability. ArRoWS can be used to quickly and specifically assess the quality of real world evidence research that uses electronic health record information. The tool contains 16 items, nine of which are common items, and seven of which are related to specific research designs. The current study introduces the development background, development process, assessment items, assessment criteria, and application methods of ArRoWS and other related aspects, to provide references for real world researchers in China.
Structured template and reporting tool for real world evidence (STaRT-RWE) was developed by a team led by professor Shirley V Wang of Brigham and Women's Hospital, Harvard Medical School, which is to plan and report on the implementation of real world evidence (RWE) studies on the safety and efficacy of treatments. The template, published in the journal BMJ in January 2021, has been endorsed by the International Society of PharmacoEpidemiology and the Transparency Initiative promoted by the International Society of Pharmacoeconomics and Outcome Research. This article interprets its entries to promote the understanding and application of STaRT-RWE by domestic scholars engaged in real world study, and help to improve the transparency, repeatability, and accuracy of RWE research.
Real-world evidence represents critical evidence to support post-marketing drug monitoring, assessment and policy decisions, and has received extensive attentions. However, an explicit over-arching design and conceptual framework for this specific area is lacking. Divergent opinions on the production of real world evidence are often present among researchers; and understanding about their implications also differ among policy makers and evidence users. In this article, we have proposed, from the regulatory and clinical perspectives, a conceptual framework on the use of real world data for post-marketing drug studies, assessment and policy decisions.
In recent years, real-world evidence data (RWD) and real-world evidence (RWE) have gained substantial attentions from healthcare practitioners and health authorities worldwide. In particular, the needs from regulatory bodies have promoted the production and use of real-world evidence. In the context of drug and device evaluation and regulation decisions, the pattern for using real world evidence may differ. This article aimed to discuss the potential uses of RWE for pre-approval clinical evaluation, post-approval monitoring and evaluation, and associated regulatory decisions, which may ultimately improve the production and use of RWE for regulatory decisions.
ObjectiveTo analyze the neoadjuvant therapy of colorectal cancer in this center in the background of real world data by studying Database from Colorectal Cancer (DACCA) in West China Hospital of Sichuan University.MethodsData was selected from DACCA who was updated on August 15, 2019. After deleting duplicate value, patients whose tumor location and tumor pathologic characteristic showed colon or rectum, as well as adenocarcinoma, mucinous adenocarcinoma, and signet ring cell carcinoma were enrolled.ResultsThere were 2 783, 2 789, 2 790, 2 811, 4 148,3 824, 4 191, 3 676, 4 090, and 499 valid data of T, N, and M stages, clinical stages, tumor site, distance from tumor to anal dentate line, tumor pathologic characteristics, degree of tumor differentiation, neoadjuvant therapy, and compliance, respectively. There were 1 839 lines that " nature of the tumor pathology” was not empty and neoadjuvant scheme for the pure chemotherapy, radiotherapy alone or radiation, and chemotherapy, including 50 lines of signet ring cell carcinoma (2.7%), 299 lines of mucous adenocarcinoma (16.3%), 1 490 lines of adenocarcinoma (81.0%), various kinds of pathology in selection of neoadjuvant therapy difference was statistically significant (χ2=9.138, P=0.041). Except for the data lines with null value in the column of " operation date”, there were 2 234 (82.1%) and 486 (17.9%) effective data lines of " recommended” and " not recommended” for the use of neoadjuvant therapy, respectively. In the years with a large amount of data, among the patients who completed neoadjuvant therapy, the proportion of patients meeting the recommended indications was 27.4%–67.6%, with an average of 47.4%. Patients who did not meet the recommended indications but were recommended (off-label use) accounted for 7.3%–70.0%, with an average of 39.8%. According to regression analysis, the proportion in line with the recommendation (\begin{document}$\hat y $\end{document}=–0.032 5x+66.003 2, P=0.020) varies with the year, and the overall trend shows a gradual decline. The proportion of the use of super indications (\begin{document}$\hat y $\end{document}=–0.054 5x+110.174 6, P=0.002) changed with the year, and the overall trend showed a decline. A total of 1 161 valid data with non-null values of " eoadjuvant therapy regimen” and " recommended or not recommended” showed statistically significant difference in the use rate of neoadjuvant therapy among patients with different recommendation groups (χ2=9.244, P=0.002). " Patient compliance” was shown as " active cooperation” and " passive acceptance”, and " neoadjuvant therapy” was shown as " radiotherapy alone”" chemotherapy alone”, and " chemoradiotherapy” were 470 lines. There was no statistically significant difference in neoadjuvant therapy between patients receiving active and passive treatment (χ2=0.537, P=0.841). The effective data of clinical remission degree meeting the research conditions were 388 lines, including 121 lines of complete response (31.2%), 180 lines of partial response (46.4%), 79 lines of stable disease (20.4%), and 8 lines of progressive disease (2.1%). There was no statistically significant difference in clinical response degree among patients with different neoadjuvant therapy (H=0.435, P=0.783). There were 346 lines with effective data of pathologic tumor regression grade (TRG) meeting the study conditions, including 47 lines with TRG0 (13.6%), 39 lines with TRG1 (11.3%), 180 lines with TRG2 (52.0%), and 80 lines with TRG3 (23.1%). There was no statistical difference in the degree of TRG among patients with different neoadjuvant therapy (H=1.816, P=0.518).ConclusionsThe real world study reflects that in the western regional medical center, the demand for neoadjuvant therapy among the patients with colorectal cancer covered is huge. Although the implementation of neoadjuvant therapy is greatly influenced by the doctor’s recommendation behavior, the selection and recommendation of neoadjuvant therapy according to some specific clinical application guidelines are not fully met. The impact of more behavioral factors requires further in-depth analysis and research.
Objective To investigate the differences in the topology of functional brain networks between populations with good spatial navigation ability and those with poor spatial navigation ability. Methods From September 2020 to September 2021, 100 college students from PLA Army Border and Coastal Defense Academy were selected to test the spatial navigation ability. The 25 students with the highest spatial navigation ability were selected as the GN group, and the 25 with the lowest spatial navigation ability were selected as the PN group, and their resting-state functional MRI and 3D T1-weighted structural image data of the brain were collected. Graph theory analysis was applied to study the topology of the brain network, including global and local topological properties. Results The variations in the clustering coefficient, characteristic path length, and local efficiency between the GN and PN groups were not statistically significant within the threshold range (P>0.05). The brain functional connectivity networks of the GN and PN groups met the standardized clustering coefficient (γ)>1, the standardized characteristic path length (λ)≈1, and the small-world property (σ)>1, being consistent with small-world network property. The areas under curve (AUCs) for global efficiency (0.22±0.01 vs. 0.21±0.01), γ value (0.97±0.18 vs. 0.81±0.18) and σ value (0.75±0.13 vs. 0.64±0.13) of the GN group were higher than those of the PN group, and the differences were statistically significant (P<0.05); the between-group difference in AUC for λ value was not statistically significant (P>0.05). The results of the nodal level analysis showed that the AUCs for nodal clustering coefficients in the left superior frontal gyrus of orbital region (0.29±0.05 vs. 0.23±0.07), the right rectus gyrus (0.29±0.05 vs. 0.23±0.09), the middle left cingulate gyrus and its lateral surround (0.22±0.02 vs. 0.25±0.02), the left inferior occipital gyrus (0.32±0.05 vs. 0.35±0.05), the right cerebellar area 3 (0.24±0.04 vs. 0.26±0.03), and the right cerebellar area 9 (0.22±0.09 vs. 0.13±0.13) were statistically different between the two groups (P<0.05). The differences in AUCs for degree centrality and nodal efficiency between the two groups were not statistically significant (P>0.05). Conclusions Compared with people with good spatial navigation ability, the topological properties of the brains of the ones with poor spatial navigation ability still conformed to the small-world network properties, but the connectivity between brain regions reduces compared with the good spatial navigation ability group, with a tendency to convert to random networks and a reduced or increased nodal clustering coefficient in some brain regions. Differences in functional brain network connectivity exist among people with different spatial navigation abilities.
Real-world data studies have experienced rapid development in recent years, however, misunderstandings persist. This paper aims to improve practice and promote standardization by updating the categorization of real-world data, proposing two conceptual frameworks for conducting real-world data studies, developing the concepts of research data infrastructure and clarifying the misconceptions on registry database, and discussing future development.
ObjectiveTo compare the 5-year survival rates between two different follow-up patterns of postoperative stage Ⅰ-ⅢA non-small cell lung cancer (NSCLC) patients.MethodsPathological stage Ⅰ-ⅢA NSCLC 11 958 patients who underwent surgical resection and received follow-up within 6 months after initial diagnosis through telephone follow-up system were included in nine hospitals from July 2014 to July 2020. The patients were divided into two groups including a proactive follow-up group (n=3 825) and a passive follow-up group (n=8133) according to the way of following-up. There were 6 939 males and 5 019 females aged 59.8±9.5 years. The Kaplan-Meier and Cox proportional hazards regression model were used.ResultsThe median follow-up frequency was 8.0 times in the proactive follow-up group and 7.0 times in the passive follow-up group. The median call duration was 3.77 minutes in the proactive follow-up group and 3.58 minutes in the passive follow-up group. The 5-year survival rate was 81.8% and 74.2% (HR=0.60, 95CI 0.53-0.67, P<0.001) in the proactive follow-up group and the passive follow-up group, respectively. Multivariate analysis showed that follow-up pattern, age, gender and operation mode were independent prognostic factors, and the results were consistent in all subgroups stratified by clinical stages.ConclusionThe proactive follow-up leads to better overall survival for resected stage Ⅰ-ⅢA NSCLC patients, especially in the stage ⅢA.