Consensus of Chinese experts on glioma multidisciplinary team management(2nd edition)

To increase the consistency of glioma multidisciplinary team(MDT)management across different regions and hospitals at varying levels,we have updated the Expert Consensus on MDT of Glioma in China based on the currently available evidence.This version has revised and updated the process-management rules and quality-control standards for a glioma MDT,providing reference and guidance for relevant clinical disciplines and physicians.All members of the Consensus Expert Group,abstract,background,and prospects can be seen in supplementaryle,http://links.lww.com/CM9/B999.

A Protocol for Developing Chinese Clinical Practice Guidelines of Hypertension

To improve the standard screening, diagnosis, and treatment of hypertension in patients in China;realize the standardization of clinical practice of hypertension;and improve the prevention and control level of hypertension in China, it is both important and necessary to develop a clinical practice guideline for hypertension according to a recognized methodology. Jointly sponsored by the National Center for Cardiovascular Diseases, Chinese Medical Doctor Association, Hypertension Committee of the Chinese Medical Doctor Association, Chinese Society of Cardiology, and Hypertension Committee of Cross-Straits Medicine Exchange Association, the “Chinese Clinical Practice Guidelines of Hypertension” was proposed. Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences, Guideline and Standards Research Centre of Chinese Medical Association Publishing House, Lanzhou University Institute of Health Data Science, and Lanzhou University GRADE Center will provide methodological support for the guidelines.

中国科学引文数据库影像期刊发表放射影像诊断病例报告质量的评价研究

目的评价中国科学引文数据库(CSCD)影像期刊发表的放射影像诊断病例报告的报告质量。方法本研究为横断面调查,检索2021至2022年度CSCD收录的影像期刊,根据纳入和排除标准检索2020年发表的放射影像诊断病例报告,采用国际病例报告清单(CARE)评价其报告质量。结果共纳入发表在5本影像期刊上的161篇影像诊断病例报告。纳入研究的报告率为33.5%(7.5%,93.3%),所有研究均未报告患者观点和知情同意,报告率低于10%的条目包括3a(摘要-简介病例的新颖性)、3c(摘要-诊断、治疗干预和结局)、8b(影响诊断的因素)以及8d(影响预后的因素),报告率分别为2.5%(4/161)、0.6%(1/161)、0.6%(1/161)以及4.3%(7/161);报告率在10%~50%的条目包括3b(摘要-病例特征)、4(背景介绍)、5c(既往史、家族史和社会心理因素)、7(病例的时间轴)、10(来院随访的结果)以及11a(病例处理过程中的经验和局限性),报告率分别为16.8%(27/161)、30.4%(49/161)、34.2%(55/161)、24.8%(40/161)、32.9%(53/161)和31.7%(51/161)。条目1(标题)、条目2(关键词)、条目5a(患者的一般信息)、条目5b(主诉)、条目8a(诊断方法)以及条目11c(对结论的合理解释)的报告率均超过90%。此外,本研究结果显示作者数量以及学科数量与影像诊断病例报告的质量无关。结论 CSCD影像期刊发表的放射影像诊断病例报告对CARE条目的遵循程度整体较低,影像期刊编辑、影像科医师和报告规范研究人员等应重视病例报告的撰写规范,提升病例报告的报告质量。

Application of artificial intelligence in clinical diagnosis and treatment:an overview of systematic reviews

Objective This study aimed to summarize the characteristics and methodological quality of systematic reviews on the application of artificial intelligence(AI)in clinical diagnosis and treatment.Methods We systematically searched seven English-and Chinese-language literature databases to identify sys-tematic reviews on the application of AI,deep learning,or machine learning in the diagnosis and treatment of any disease published in 2020.We evaluated the methodological quality of the included systematic reviews using“A Measurement tool for the assessment of multiple systematic reviews”(AMSTAR).We also conducted meta-analyses on the diagnostic accuracy of AI on selected disease categories with a large number of included studies and low clinical heterogeneity.Results A total of 40 systematic reviews reporting 1,083 original studies were included,covering 31 diseases from 11 groups of diseases.Eleven systematic reviews were related to neoplasms and nine were systematic reviews related to diseases of the digestive system.We selected digestive system diseases for the meta-analysis.The pooled sensitivities(with 95%confidence interval(CI))of AI to assist the diagnosis of helicobacter pylori,gastrointestinal ulcers,hemorrhage,esophageal tumors,gastric tumors,and intestinal tumors(with 95%CI)were 0.91(0.83-0.95),0.99(0.76-1.00),0.95(0.83-0.99),0.90(0.85-0.93),0.90(0.82-0.95),and 0.93(0.88-0.96),respectively,and the pooled specificities were 0.82(0.77-0.87),0.97(0.86-1.00),1.00(0.99-1.00),0.80(0.71-0.87),0.93(0.87-0.97),and 0.89(0.85-0.92),respectively.The AMSTAR items“the list of included studies”(n=39,97.5%)and“the characteristics of the included studies”(n=39,97.5%)had the highest compliance among the reviews;the compliance was relatively low to the items“the consideration of publication status”(n=1,2.5%),“the consideration of scientific quality”(n=19,47.5%),“data synthesis methods”(n=18,45.0%),and“the evaluation of publication bias”(n=13,32.5%).Conclusions The main subjects of systematic reviews on AI applications in clinical diagnosis and treatment pub-lished in 2020 were diseases of the digestive system and neoplasms.The methodological quality of the systematic reviews on AI needs to be improved,paying particular attention to publication bias and the rigorous evaluation of the quality of the included studies.

Investigation and evaluation of randomized controlled trials for interventions involving artificial intelligence

Objective Complete and transparent reporting is of critical importance for randomized controlled trials(RCTs).The present study aimed to determine the reporting quality and methodological quality of RCTs for interventions involving artificial intelligence(AI)and their protocols.Methods We searched MEDLINE(via PubMed),Embase,Web of Science,CBMdisc,Wanfang Data,and CNKI from January 1,2016,to November 11,2020,to collect RCTs involving AI.We also extracted the protocol of each included RCT if it could be obtained.CONSORT-AI(Consolidated Standards of Reporting Trials-Artificial Intelligence)statement and Cochrane Collaboration’s tool for assessing risk of bias(ROB)were used to evaluate the reporting quality and methodological quality,respectively,and SPIRIT-AI(The Standard Protocol Items:Recommendations for Interventional Trials-Artificial Intelligence)statement was used to evaluate the reporting quality of the protocols.The associations of the reporting rate of CONSORT-AI with the publication year,journal’s impact factor(IF),number of authors,sample size,and first author’s country were analyzed univariately using Pearson’s chi-squared test,or Fisher’s exact test if the expected values in any of the cells were below 5.The compliance of the retrieved protocols to SPIRIT-AI was presented descriptively.Results Overall,29 RCTs and three protocols were considered eligible.The CONSORT-AI items“title and abstract”and“interpretation of results”were reported by all RCTs,with the items with the lowest reporting rates being“funding”(0),“implementation”(3.5%),and“harms”(3.5%).The risk of bias was high in 13(44.8%)RCTs and not clear in 15(51.7%)RCTs.Only one RCT(3.5%)had a low risk of bias.The compliance was not significantly different in terms of the publication year,journal’s IF,number of authors,sample size,or first author’s country.Ten of the 35 SPIRIT-AI items(funding,participant timeline,allocation concealment mechanism,implementation,data management,auditing,declaration of interests,access to data,informed consent materials and biological specimens)were not reported by any of the three protocols.Conclusions The reporting and methodological quality of RCTs involving AI need to be improved.Because of the limited availability of protocols,their quality could not be fully judged.Following the CONSORT-AI and SPIRIT-AI statements and with appropriate guidance on the risk of bias when designing and reporting AI-related RCTs can promote standardization and transparency.