Registered Interventional Clinical Trials for Old Populations With Infectious Diseases on ClinicalTrials.gov: A Cross-Sectional Study

Chen, Lingmin; Wang, Menghua; Yang, Yi; Shen, Jing; Zhang, Yonggang

doi:10.3389/fphar.2020.00942

Cited by 9 publications

(7 citation statements)

References 43 publications

(32 reference statements)

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“…Randomized controlled, masked, and appropriate patient population trials are critical components of high-quality clinical trials ( Zwierzyna et al, 2018 ). In our study, the percentage of randomized trials (70.8%) was lower than in previous studies 84.1% ( Chen et al, 2020 ); 90.7% ( Chen et al, 2019 ), which may be due to the robust effect of clinical trials that entailed cancer treatment or prevention/genetic diseases. After removing such trials, 51 trials (91.1%, 51/56) were ultimately randomized.…”

Section: Discussioncontrasting

confidence: 89%

Registered Clinical Trials Comprising Pregnant Women in China: A Cross-Sectional Study

Zhao

Luan

et al. 2022

Front. Pharmacol.

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Background: In this study, an investigation was conducted on clinical drug trials comprising pregnant women in China that provided data on the quantity, properties, source of funding, and geographical distribution regarding registration and post-marketing studies.Methods: We conducted a cross-sectional descriptive study of clinical trials of pregnant women in China on 30 December 2021, and it was registered on the official Drug Clinical Trial Information Management Platform (ChiCTR) (http://www.chinadrugtrials.org.cn) established by the State Food and Drug Administration of China (Chinese FDA).Results: This study encompassed 72 registered trials (0.46%, 72/15,539) for data analysis. Of these trials, 43.1% of trials were started between 2013 and 2016, and nearly half of the trials (48.6%) were completed. Industries were listed as the primary sponsor for 95.8% trials. Economically developed eastern China and northern China, accounting for 69.5% of the 72 registered trials, were the most frequently identified study locations. Regarding study designs of these trials, more than half of the trials (70.8%) were randomized, 61.1% were a parallel assignment, 33.3% were phase 3, and half of the trials (54.2%) were open label. In total, 23 trials met the requirements after excluding trials of cancer and/or of postmenopausal women, accounting for 0.15% of the 15,539 registered trials in the ChiCTR websites. Of the 72 clinical trials, 54 drugs for 18 indications were included. Of these indications, the highest proportion of the trials is osteoporosis (27.8%), followed by cancer (22.2%), assisted reproduction (13.9%), and other indications (13.9%).Conclusion: This survey revealed a significant shortage of the development, evaluation, and safety trials of pregnancy-related drugs in China. Modifying or adding legislation and providing financial incentives may therefore encourage pharmaceutical companies to conduct additional clinical trials on pregnant women.

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Section: Discussioncontrasting

confidence: 89%

Registered Clinical Trials Comprising Pregnant Women in China: A Cross-Sectional Study

Zhao

Luan

et al. 2022

Front. Pharmacol.

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“…According to our previous studies (14,15), we search the ClinicalTrials.gov on February 20, 2020, for trials on AI for cancer diagnosis. In case of missing any trials, we used the following words: artificial intelligence, deep learning, machine learning, etc.…”

Section: Data Searchmentioning

confidence: 99%

Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov

et al. 2020

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Objective: Clinical trials are the most effective way to judge the merits of diagnosis and treatment strategies. The in-depth mining of clinical trial data enables us to grasp the application trend of artificial intelligence (AI) for cancer diagnosis. The aim of this study was to analyze the characteristics of registered trials on AI for cancer diagnosis. Methods: Clinical trials on AI for cancer diagnosis registered on the ClinicalTrials.gov database were searched and downloaded. Statistical analysis was performed by using SPSS 20.0 software. Results: A total of 97 registered trials were included. Of them, only 27 (27.8%) were interventional trials and 70 (72.1%) were observational trials. Fifteen (15.4%) trials had been completed. Fifty trials were in recruitment, and another 18 remained unrecruited. The number of cases included in the clinical trials tended to be large, 31 (32.0%) trials including samples ranging from 100 to 499 cases and 17 (17.5%) trials including samples ranging from 500 to 999 cases. Of the 27 interventional trials, only two trials reported trials' phase. Most (85.2%) interventional trials were for diagnosis, and a few (3.7%) were for the purpose of both the diagnosis and therapy of cancers. For the observational clinical trials, 46 (65.7%) were cohort studies, and 11 (15.7%) were case-only studies. Among the observational trials, 46 (65.7%) were prospective studies and 13 (18.6%) were retrospective studies. Among 97 trials, 37 (38.1%) involved colorectal cancer, 11 (11.3%) involved breast cancer, 43 (44.3%) were for imaging diagnosis, 33 (34.0%) were for endoscopic diagnosis, and 11 (11.3%) were for pathological diagnosis. For the interventional trials, 11 trials were parallel assignment (40.7%), and 14 were single group assignment (51.9%). Among the 27 interventional trials, 18 (66.7%) trials were performed without masking, 6 (22.2%) trials were performed with single masking, only 1 (3.7%) was performed with double masking, and 2 (7.4%) was performed with triple masking. Conclusion: It appears that most registered trials on AI for cancer diagnosis are observational design, and more trials are needed in this field.

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“…ClinicalTrials.gov is a database of private and public funded clinical research conducted worldwide [18]; the researches on this website are transparent and traceable, analyzing registered trials on that will provide the progress of one field. A variety of articles have been published on ClinicalTrials.gov to analyze registered trials [19][20][21][22][23]. AI has been widely applied in the diagnosis and treatment of CLD, and the current research is addressing a range of issues and will lead to greater achievement, but few trials registered with ClinicalTrials.gov are currently known to the academic community.…”

Section: Introductionmentioning

confidence: 99%

Registered Trials of Artificial Intelligence Conducted on Chronic Liver Disease: A Cross-Sectional Study on ClinicalTrials.gov

et al. 2022

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Background. Artificial intelligence (AI) has been widely applied in the diagnosis and therapy of chronic liver disease (CLD), but there is currently little insight into the trials registered on ClinicalTrials.gov. Thus, this cross-sectional study was focused on analyzing the progress in the use of AI in CLD. Methods. Registered trials of AI applied in CLD on ClinicalTrials.gov were searched firstly. All available information was downloaded to Excel (Microsoft Excel, Rong, Rong, China), and duplicates were removed. We extracted the data of the included trials, then analyzed the characteristics of them finally. Results. Up to the 27th of May 2021, 6835 trials were identified following an initial search, and 20 registered trials were included after screening for inclusion and exclusion criteria. Among those trials, hepatocellular carcinoma (HCC, 40.0%) and nonalcoholic fatty liver disease (NAFLD, 20.0%) were the most widely applied CLDs for AI. Trials started in 2013 until 2021, with 17 trials (85%) registered after 2016. There was a large trend in trial enrolment, with 40% of them including samples more than 500. Five trials (25%) have been completed, but only one of these had available results. The most frequent sponsors and collaborators were both hospitals at 55%, followed by universities at 35% and institutes at 11%, respectively. Of the 20 trials included, 35% (7 trials) were interventional trials and 65% (13 trials) were observational trials. Among 7 interventional trials, most trials were for diagnosis purpose (42.86%, 3 trials); 4 trials (57.14%) were randomized; 3 trials (42.86%) applied behavioral intervention, 1 trial (14.29%) was in device intervention, 2 trials (28.57%) were in diagnostic test, and 1 trial intervention was unknown. Among 13 observational trials, 8 (61.54%) were cohort studies; 6 (46.15%) were prospective studies, 4 (30.77%) were retrospective studies, 2 (15.38%) were cross-sectional studies, and 1 (7.69%) did not involve a temporal perspective. Conclusion. The study is the first to focus on AI registration trials in CLD, which will aid relevant scholars in understanding the current state of the subject. This study demonstrates that additional research on AI used in the diagnosis and treatment of CLD is required, and timely publication of accessible results from registered trials is essential.

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Registered Interventional Clinical Trials for Old Populations With Infectious Diseases on ClinicalTrials.gov: A Cross-Sectional Study

Cited by 9 publications

References 43 publications

Registered Clinical Trials Comprising Pregnant Women in China: A Cross-Sectional Study

Registered Clinical Trials Comprising Pregnant Women in China: A Cross-Sectional Study

Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov

Registered Trials of Artificial Intelligence Conducted on Chronic Liver Disease: A Cross-Sectional Study on ClinicalTrials.gov

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