Objective To evaluate the reporting quality and influencing factors of patient-reported outcome (PRO) data in lung cancer randomized controlled trials (RCTs) from 2010 to 2024. Methods RCTs of lung cancer with PRO as either primary or secondary endpoints were searched from PubMed, EMbase, Medline, CNKI (China National Knowledge Infrastructure), Wanfang Data Knowledge Service Platform, and VIP Chinese Journal Service Platform between January 1, 2010 and April 20, 2024. Reporting quality of included RCT were assessed based on the CONSORT-PRO extension. Descriptive statistics and bivariate regression analysis were used to describe the reporting quality and analyze the factors influencing the reporting quality. Results A total of 740 articles were retrieved. After screening, 53 eligible lung cancer RCTs with 22 780 patients were included. The patients mainly were non-small cell lung cancer (84.91%), with the median sample size was 364 (160.50, 599.50) patients. The primary PRO tool used was the EORTC QLQ-C30 (60.38%). There were 52 studies (98.11%) whose PRO measured the domain of "symptom management of cough, dyspnea, fatigue, pain, etc.", and 45 studies (84.91%) measured "health-related quality of life." Multicenter studies accounted for 84.91%, and randomized non-blind trials accounted for 62.26%. PRO was used as the primary endpoint in 33.96% of the studies and as secondary endpoints in 66.04%. The reliability and validity of the PRO tools were explicitly mentioned in 11.32% and 7.55% of the studies, respectively. The average completeness of reporting according to the CONSORT-PRO guidelines was 60.00%, ranging from 25% to 93%. The main factors affecting the completeness of CONSORT-PRO reporting included sample size and publication year. For each additional sample size, the completeness of reporting increased by 27.5% (SE=0.000, t=2.04, P=0.046). Additionally, studies published after 2019 had a 67.2% higher completeness of reporting compared to those published in or before 2019 (SE=0.178, t=–3.273, P=0.006). Conclusion The study reveals that the overall reporting quality of PRO in lung cancer RCTs is poor. Particularly, the reporting of patient reported outcome measures reliability and validity, PRO assumptions, applicability, and handling of missing data needs further improvement. Future research should emphasize comprehensive adherence to the CONSORT-PRO guidelines.
The umbrella trial has received increasing attention in the design of clinical trials for oncology drugs in recent years. This trial design categorizes a single disease into multiple sub-types based on predictive biomarkers or other predictive factors, and simultaneously evaluates the efficacy of multiple targeted therapies. When compared with the traditional drug development model of phase Ⅰ, phaseⅡ, and phase Ⅲ randomized controlled trials, umbrella trials are a more scientifically rigorous trial design that can speed up drug evaluation to address the conflict between numerous untested drugs and diseases with a lack of effective treatment options. This article will focus on the concept, main characteristics, eligibility criteria, design and statistical considerations, ethical considerations, and future directions of umbrella trials, with the aim of providing methodological guidance for the design of clinical trials for oncology drugs.
This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.
Objective To systematically review the efficacy of long-acting antibacterial material in the prevention of secondary urinary infection. Methods PubMed, The Cochrane Library, CNKI, CBM, WanFang Data and VIP databases were electronically searched to collect randomized controlled trials (RCTs) on the efficacy of long-acting antibacterial material in the prevention of secondary urinary infection from inception to November, 2016. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies, then, meta-analysis was performed by using RevMan 5.3 software. Results A total of 16 RCTs were included. The results of meta-analysis showed that: the long-acting antibacterial material group was superior to the general intervention group in morbidity of secondary urinary infection (Peto OR=0.17, 95%CI 0.13 to 0.23, P<0.000 01), and bacterial positive rate of secondary urinary infection (Peto OR=0.15, 95%CI 0.08 to 0.27,P<0.000 01). Conclusion Current evidence shows that long-acting antibacterial material can effectively reduce the infection rates of secondary urinary infection. Due to limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
ObjectiveZero-events studies frequently occur in systematic reviews of adverse events, which consist of an important source of evidence. We aimed to examine how evidence of zero-events studies was utilized in the meta-analyses of systematic reviews of adverse events.MethodsWe conducted a survey of systematic reviews published in two periods: January 1, 2015 to January 1, 2020 and January 1, 2008, to April 25, 2011. Databases were searched for systematic reviews that conducted at least one meta-analysis of any healthcare intervention and used adverse events as the exclusive outcome. An adverse event was defined as any untoward medical occurrence in a patient or subject in healthcare practice. We summarized the frequency of occurrence of zero-events studies in eligible systematic reviews and how these studies were dealt with in the meta-analyses of these systematic reviews.ResultsWe included 640 eligible systematic reviews. There were 406 (63.45%) systematic reviews involving zero-events studies in their meta-analyses, among which 389 (95.11%) involved single-arm-zero-events studies and 223 (54.93%) involved double-arm-zero-events studies. The majority (98.71%) of these systematic reviews incorporated single-arm-zero-events studies into the meta-analyses. On the other hand, the majority (76.23%) of them excluded double-arm-zero-events studies from the meta-analyses, of which the majority (87.06%) did not discuss the potential impact of excluding such studies. Systematic reviews published at present (2015-2020) tended to incorporate zero-events studies in meta-analyses than those published in the past (2008-2011), but the difference was not significant [proportion difference=–0.09, 95%CI (–0.21, 0.03), P=0.12].ConclusionSystematic review authors routinely treated studies with zero-events in both arms as "non-informative" carriers and excluded them from their reviews. Whether studies with no events are "informative" or not, largely depends on the methods and assumptions applied, thus sensitivity analyses using different methods should be considered in future meta-analyses.