A scoping review on quality assessment tools used in systematic reviews and meta-analysis of real-world studies

Anti-TNF Alpha and Risk of Lymphoma in Rheumatoid Arthritis: A Systematic Review and Meta-Analysis

Background and Objectives: Anti-tumor necrosis factor-alpha (TNF-α) agents are effective in treating rheumatoid arthritis (RA) but may entail a risk of lymphoma due to TNF-α's role in immune surveillance. This systematic review and meta-analysis assesses the risk of lymphoma in patients with RA treated with anti-TNF agents versus patients treated with methotrexate and/or a placebo. Materials and Methods: The Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Embase, PubMed, and Google Scholar were systematically searched for relevant literature. Data were extracted and analyzed to determine risk ratios (RRs) and 95% confidence intervals (CIs), with heterogeneity assessed using I2 statistics. Methodological quality and risk of bias were assessed using the Cochrane Risk of Bias tool for randomized controlled trials (RCTs) and the Newcastle-Ottawa Scale for observational studies. Results: The search yielded 932 articles, 13 of which were retained for qualitative review and 12 for quantitative synthesis. Overall, the studies reviewed included 181,735 participants: 3772 from six RCTs and 177,963 from seven observational studies. The meta-analysis of RCTs revealed no significant difference in the risk of lymphoma between patients receiving anti-TNF-α therapy and patients on conventional treatments, with an overall RR of 1.43 (95% CI: 0.32-5.16) and I2 of 0%. Conversely, observational studies showed some variability, with an overall RR of 1.43 (95% CI: 0.59-3.47) and significant heterogeneity (I2 = 95%), whereas others indicated a potentially elevated risk of lymphoma in specific subgroups but had inconsistent results. Conclusions: The systematic and meta-analysis revealed no significant difference in the risk of lymphoma for patients with RA treated with anti-TNF-α agents versus conventional therapies. However, given the limitations of the studies included, additional research is needed to validate the results and explore potential risk factors contributing to the development of lymphoma in patients with RA.

Development of quality assessment tool for systematic reviews and meta-analyses of real-world studies: a Delphi consensus survey

The increasing adoption of real-world studies in healthcare for decision making and planning has further necessitated the need for a specific quality assessment tool for evidence synthesis. This study aimed to develop a quality assessment tool for systematic reviews (SR) and meta-analysis (MA) involving real-world studies (QATSM-RWS) using a formal consensus method. Based on scoping review, the authors identified a list of items for possible inclusion in the quality assessment tool. A Delphi survey was formulated based on the identified items. A total of 89 experts, purposively recruited, with research experience in real-world data were invited to participate in the first round of Delphi survey. The participants who responded in the first Delphi round were invited to participate (n = 15) in the phrasing of the items. Strong level of agreement was found on the proposed list of items after the first round of Delphi. A rate of agreement ≥ 0.70 was used to define which items to keep in the tool. A list of 14 items emerged as suitable for QATSM-RWS. The items were structured under five domains: introduction, methods, results, discussions, and others. All participants agreed with the proposed phrasing of the items. This is the first study that has developed a specific tool that can be used to appraise the quality of SR and MA involving real-world studies. QATSM-RWS may be used by policymakers, clinicians, and practitioners when evaluating and generating real-world evidence. This tool is now undergoing validation process.

Prevalence, perceptions, and experiences of frailty among older migrants from Low- and Middle-Income Countries to High Income Countries: A mixed method systematic review

BACKGROUND

Frailty is increasingly becoming a public health concern, especially among vulnerable populations. Older migrants from Low- and Middle-Income Countries to High Income Countries present with poorer health and are at increased risk of becoming frail. This review aims to explore the prevalence, perceptions, and experiences of frailty among older migrants from Low- and Middle-Income Countries to High Income Countries.

METHODS

This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis. Five electronic databases were comprehensively searched for relevant literature published from January 1, 2000, to April 30, 2023. Quality appraisal for the quantitative studies was done with the Joanna Brigg's critical appraisal tool for analytic cross-sectional studies, and the qualitative studies were assessed with the Critical Appraisal Skill Program tool for qualitative studies.

RESULT

Seven studies met the inclusion criteria. Frailty was assessed using modified versions of the Frailty Phenotype and Frailty Index. The prevalence of frailty using the Frailty Phenotype was 16.6 %, and 17 % to 61.9 % according to the Frailty Index. The perceptions and experiences of frailty were characterised by chronic ill-health and a review of healthy pre-migration and early migration lives.

CONCLUSION

Despite the variation in frailty assessment methods, the high prevalence of frailty among older migrants was highlighted across the included studies. The perceptions and experiences of frailty reflect a state of resignation which can complicate the state of frailty. There is the need for ongoing research among migrant groups to identify their predisposition to frailty for early intervention.

Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review

BACKGROUND

The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies.

OBJECTIVE

The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home.

METHODS

A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis.

RESULTS

Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used.

CONCLUSION

Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.