BMJ Evidence-Based Medicine最新文献

Objectives: To investigate the relationship between alcohol consumption and dementia.

Design: Prospective cohort and case-control analyses combined with linear and non-linear Mendelian randomisation.

Setting: Two large-scale population-based cohorts: the US Million Veteran Programme and the UK Biobank. Genetic analyses used summary statistics from genome-wide association studies (GWAS).

Participants: 559 559 adults aged 56-72 years at baseline were included in observational analyses (mean follow-up: 4 years in the US cohort; 12 years in the UK cohort). Genetic analyses used summary data from multiple large GWAS consortia (2.4 million participants).

Main outcome measures: Incident all-cause dementia, determined through health record linkage, and genetic proxies.

Results: During follow-up, 14 540 participants developed dementia and 48 034 died. Observational phenotype-only analyses revealed U-shaped associations between alcohol and dementia risk: higher risk was observed among non-drinkers, heavy drinkers (>40 drinks per week; HR 1.41, 95% CI 1.15 to 1.74), and those with alcohol use disorder (AUD) (HR 1.51, 95% CI 1.42 to 1.60) compared with light drinkers. In contrast, Mendelian randomisation genetic analysis identified a monotonic increase in dementia risk with greater alcohol consumption. A 1 SD increase in log-transformed drinks per week was associated with a 15% dementia increase (inverse-variance weighted (IVW) OR 1.15, 95% CI 1.03 to 1.27). A twofold increase in AUD prevalence was associated with a 16% increase in dementia risk (IVW OR 1.16, 95% CI 1.03 to 1.30). Alcohol intake increased dementia, but individuals who developed dementia also experienced a decline in alcohol intake over time, suggesting reverse causation-where early cognitive decline leads to reduced alcohol consumption-underlies the supposed protective alcohol effects in observational studies.

Conclusions: These findings provide evidence for a relationship between all types of alcohol use and increased dementia risk. While correlational observational data suggested a protective effect of light drinking, this could be in part attributable to reduced drinking seen in early dementia; genetic analyses did not support any protective effect, suggesting that any level of alcohol consumption may contribute to dementia risk. Public health strategies that reduce the prevalence of alcohol use disorder could potentially lower the incidence of dementia by up to 16%.

Objective: This study aims to comprehensively review the literature on the use of speech biomarkers in disease diagnosis and monitoring, focusing on recording protocols, speech tasks, speech features and processing algorithms.

Study design: Systematic review and meta-analysis.

Data sources: We conducted a search of six databases: PubMed, Embase, Scopus, Web of Science, PsycINFO and IEEE Xplore, covering studies published from database inception to May 2024.

Main outcome measures: The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2) and the Quality Assessment of Prognostic Accuracy Studies (QUAPAS). Pooled sensitivity and specificity were calculated using a random-effects model. Subgroup analyses examined potential sources of heterogeneity, such as disease type, language, speech tasks, features and algorithms.

Results: A total of 96 studies were included, with 83 adopting a cross-sectional design and 50 having sample sizes of fewer than 100 participants. Assessment with QUADAS-2 and QUAPAS revealed that most included studies exhibited a high risk of bias in patient selection and index test domains, while concerns regarding applicability were generally low across studies. These studies covered 20 different diseases, with cognitive disorders, depression and Parkinson's disease being the most frequently studied. The pooled sensitivity and specificity for diagnostic models were 0.80 (95% CI 0.74 to 0.86) and 0.77 (95% CI 0.69 to 0.84) for psychiatric disorders (11 studies, n=2577); 0.85 (95% CI 0.83 to 0.88) and 0.83 (95% CI 0.79 to 0.86) for cognitive disorders (27 studies, n=2068); and 0.81 (95% CI 0.76 to 0.85) and 0.83 (95% CI 0.78 to 0.88) for movement disorders (20 studies, n=852). Further subgroup analyses identified recording device, language, speech task, speech features and algorithm selection as significant contributors to heterogeneity.

Conclusions: This review and meta-analysis of 96 studies highlights the influence of devices, environments, languages, tasks, features and algorithms on speech model performance across diseases. While speech biomarkers show promise for screening and monitoring-particularly via smartphones-the high risk of bias in many studies, especially in patient selection and index test interpretation, limits the strength of current evidence. Future large-scale, prospective studies are needed to validate generalisability and support clinical implementation.

Prospero registration number: CRD42024551962.

Background: Evaluation of the quality of evidence in systematic reviews (SRs) is essential for assertive decision-making. Although Grading of Recommendations Assessment, Development and Evaluation (GRADE) affords a consolidated approach for rating the level of evidence, its application is complex and time-consuming. Artificial intelligence (AI) can be used to overcome these barriers.

Design: Analytical experimental study.

Objective: The objective is to develop and appraise a proof-of-concept AI-powered tool for the semiautomation of an adaptation of the GRADE classification system to determine levels of evidence in SRs with meta-analyses compiled from randomised clinical trials.

Methods: The URSE-automated system was based on an algorithm created to enhance the objectivity of the GRADE classification. It was developed using the Python language and the React library to create user-friendly interfaces. Evaluation of the URSE-automated system was performed by analysing 115 SRs from the Cochrane Library and comparing the predicted levels of evidence with those generated by human evaluators.

Results: The open-source URSE code is available on GitHub (http://www.github.com/alisson-mfc/urse). The agreement between the URSE-automated GRADE system and human evaluators regarding the quality of evidence was 63.2% with a Cohen's kappa coefficient of 0.44. The metrics of the GRADE domains evaluated included accuracy and F1-scores, which were 0.97 and 0.94 for imprecision (number of participants), 0.73 and 0.7 for risk of bias, 0.9 and 0.9 for I² values (heterogeneity) and 0.98 and 0.99 for quality of methodology (A Measurement Tool to Assess Systematic Reviews), respectively.

Conclusion: The results demonstrate the potential use of AI in assessing the quality of evidence. However, in consideration of the emphasis of the GRADE approach on subjectivity and understanding the context of evidence production, full automation of the classification process is not opportune. Nevertheless, the combination of the URSE-automated system with human evaluation or the integration of this tool into other platforms represents interesting directions for the future.

Objectives: This study aims to address the status of children's and caregivers' participation in the development of paediatric core outcome sets (COS).

Methods: We included all paediatric COS from a previous systematic review and searched the Core Outcome Measures in Effectiveness Trials database to 26 February 2024 for recent paediatric COS. We used descriptive and thematic analysis methods to present the characteristics of the included COS and to describe children's and caregivers' participation in the development, including any facilitators and barriers. We assessed the degree of participation of children and caregivers in two steps: by rating whether their views were considered in forming the outcome list (yes/no) and then whether their views were integrated in determining the most important outcomes (fully integrated/partially integrated/not integrated).

Results: A total of 114 paediatric COS were included. 60 (53%) COS involved children and caregivers in the development process. 29 (48%) of the 60 COS considered children's and caregivers' views in forming the initial outcome list, which was most often conducted by interview (n=12 of 29, 41%). Regarding determining the most important outcomes, 35 (58%) of the 60 COS fully integrated children's and caregivers' views, and the most common method was the Delphi survey with consensus meeting (n=29 of 35, 83%); the youngest child participants were aged 7 years. The most frequently mentioned facilitator of children's and caregivers' participation was the engagement of patient groups or organisations.

Conclusion and relevance: We evaluated the degree of children's and caregivers' participation in the development of COS and found that strategies to promote children's and caregivers' participation should be constructed.