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Anesthesia and Analgesia Feb 2023Several frailty screening tools have been shown to predict mortality and complications after surgery. However, these tools were developed for in-person evaluation and... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Several frailty screening tools have been shown to predict mortality and complications after surgery. However, these tools were developed for in-person evaluation and cannot be used during virtual assessments before surgery. The FRAIL (fatigue, resistance, ambulation, illness, and loss of weight) scale is a brief assessment that can potentially be conducted virtually or self-administered, but its association with postoperative outcomes in older surgical patients is unknown. The objective of this systematic review and meta-analysis (SRMA) was to determine whether the FRAIL scale is associated with mortality and postoperative outcomes in older surgical patients.
METHODS
Systematic searches were conducted of multiple literature databases from January 1, 2008, to December 17, 2022, to identify English language studies using the FRAIL scale in surgical patients and reporting mortality and postoperative outcomes, including postoperative complications, postoperative delirium, length of stay, and functional recovery. These databases included Medline, Medline ePubs/In-process citations, Embase, APA (American Psychological Association) PsycInfo, Ovid Emcare Nursing, (all via the Ovid platform), Cumulative Index to Nursing and Allied Health Literature (CINAHL) EbscoHost, the Web of Science (Clarivate Analytics), and Scopus (Elsevier). The risk of bias was assessed using the quality in prognosis studies tool.
RESULTS
A total of 18 studies with 4479 patients were included. Eleven studies reported mortality at varying time points. Eight studies were included in the meta-analysis of mortality. The pooled odds ratio (OR) of 30-day, 6-month, and 1-year mortality for frail patients was 6.62 (95% confidence interval [CI], 2.80-15.61; P < .01), 2.97 (95% CI, 1.54-5.72; P < .01), and 1.54 (95% CI, 0.91-2.58; P = .11), respectively. Frailty was associated with postoperative complications and postoperative delirium, with an OR of 3.11 (95% CI, 2.06-4.68; P < .01) and 2.65 (95% CI, 1.85-3.80; P < .01), respectively. The risk of bias was low in 16 of 18 studies.
CONCLUSIONS
As measured by the FRAIL scale, frailty was associated with 30-day mortality, 6-month mortality, postoperative complications, and postoperative delirium.
Topics: Humans; Aged; Frailty; Frail Elderly; Emergence Delirium; Geriatric Assessment; Postoperative Complications
PubMed: 36638509
DOI: 10.1213/ANE.0000000000006272 -
BMC Public Health Nov 2022Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is... (Review)
Review
BACKGROUND
Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is little investment in digital health systems and analytics; this has created a data chasm and relatively silent burden of disease. The nascent but rapidly emerging area of precision public health offers exciting new opportunities to transform our approach to NCD prevention. Precision public health uses routinely collected real-world data on determinants of health (social, environmental, behavioural, biomedical and commercial) to inform precision decision-making, interventions and policy based on social position, equity and disease risk, and continuously monitors outcomes - the right intervention for the right population at the right time. This scoping review aims to identify global exemplars of precision public health and the data sources and methods of their aggregation/application to NCD prevention.
METHODS
The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Six databases were systematically searched for articles published until February 2021. Articles were included if they described digital aggregation of real-world data and 'traditional' data for applied community, population or public health management of NCDs. Real-world data was defined as routinely collected (1) Clinical, Medication and Family History (2) Claims/Billing (3) Mobile Health (4) Environmental (5) Social media (6) Molecular profiling (7) Patient-centred (e.g., personal health record). Results were analysed descriptively and mapped according to the three horizons framework for digital health transformation.
RESULTS
Six studies were included. Studies developed population health surveillance methods and tools using diverse real-world data (e.g., electronic health records and health insurance providers) and traditional data (e.g., Census and administrative databases) for precision surveillance of 28 NCDs. Population health analytics were applied consistently with descriptive, geospatial and temporal functions. Evidence of using surveillance tools to create precision public health models of care or improve policy and practice decisions was unclear.
CONCLUSIONS
Applications of real-world data and designed data to address NCDs are emerging with greater precision. Digital transformation of the public health sector must be accelerated to create an efficient and sustainable predict-prevent healthcare system.
Topics: Humans; Noncommunicable Diseases; Public Health; Delivery of Health Care; Telemedicine; Social Media
PubMed: 36434553
DOI: 10.1186/s12889-022-14452-7 -
Body Image Jun 2023The present study's aim was to summarize existing quantitative evidence linking social physique anxiety (SPA) and eating disorders (ED). Eligible studies were searched... (Meta-Analysis)
Meta-Analysis Review
The present study's aim was to summarize existing quantitative evidence linking social physique anxiety (SPA) and eating disorders (ED). Eligible studies were searched for up to June 2, 2022 in six databases: MEDLINE, Current Contents Connect, PsycINFO, Web of Science, SciELO, and Dissertations & Theses Global. Studies were considered eligible if they included information derived from self-report instruments that allowed for computing the relationship between SPA and ED. Pooled effect sizes (r) were computed using three-level meta-analytic models. Potential sources of heterogeneity were examined using univariable and multivariable meta-regressions. Influence analyses and a three-parameter selection model (3PSM) were used for the purpose of examining the robustness of the results and publication bias, respectively. Results summarizing 170 effect sizes from 69 studies (N = 41,257) showed two main groups of findings. Firstly, that SPA and ED were very largely related (i.e., r = .51). Secondly, that this relationship was stronger (i) among individuals from Western countries, and (ii) when ED scores concerned the diagnostic feature of bulimia/anorexia nervosa involving body image disturbances. The present study adds to the current understanding of ED by suggesting that SPA is a maladaptive emotion with a potential role in the onset and maintenance of these group of pathologies.
Topics: Humans; Body Image; Feeding and Eating Disorders; Anorexia Nervosa; Bulimia Nervosa; Anxiety
PubMed: 36871312
DOI: 10.1016/j.bodyim.2023.02.008 -
JMIR Medical Education Jun 2020The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and... (Review)
Review
BACKGROUND
The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice.
OBJECTIVE
The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience.
METHODS
A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments.
RESULTS
This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula.
CONCLUSIONS
To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.
PubMed: 32602844
DOI: 10.2196/19285 -
Molecules (Basel, Switzerland) Aug 2021The biosynthesis of silver nanoparticles and the antibacterial activities has provided enormous data on populations, geographical areas, and experiments with bio silver...
The biosynthesis of silver nanoparticles and the antibacterial activities has provided enormous data on populations, geographical areas, and experiments with bio silver nanoparticles' antibacterial operation. Several peer-reviewed publications have discussed various aspects of this subject field over the last generation. However, there is an absence of a detailed and structured framework that can represent the research domain on this topic. This paper attempts to evaluate current articles mainly on the biosynthesis of nanoparticles or antibacterial activities utilizing the scientific methodology of big data analytics. A comprehensive study was done using multiple databases-Medline, Scopus, and Web of Sciences through PRISMA (i.e., Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The keywords used included 'biosynthesis silver nano particles' OR 'silver nanoparticles' OR 'biosynthesis' AND 'antibacterial behavior' OR 'anti-microbial opposition' AND 'systematic analysis,' by using MeSH (Medical Subject Headings) terms, Boolean operator's parenthesis, or truncations as required. Since their effectiveness is dependent on particle size or initial concentration, it necessitates more research. Understanding the field of silver nanoparticle biosynthesis and antibacterial activity in Gulf areas and most Asian countries also necessitates its use of human-generated data. Furthermore, the need for this work has been highlighted by the lack of predictive modeling in this field and a need to combine specific domain expertise. Studies eligible for such a review were determined by certain inclusion and exclusion criteria. This study contributes to the existence of theoretical and analytical studies in this domain. After testing as per inclusion criteria, seven in vitro studies were selected out of 28 studies. Findings reveal that silver nanoparticles have different degrees of antimicrobial activity based on numerous factors. Limitations of the study include studies with low to moderate risks of bias and antimicrobial effects of silver nanoparticles. The study also reveals the possible use of silver nanoparticles as antibacterial irrigants using various methods, including a qualitative evaluation of knowledge and a comprehensive collection and interpretation of scientific studies.
Topics: Animals; Anti-Bacterial Agents; Humans; Metal Nanoparticles; Particle Size; Silver
PubMed: 34443644
DOI: 10.3390/molecules26165057 -
Frontiers in Genetics 2023In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and... (Review)
Review
In the last years, liquid biopsy gained increasing clinical relevance for detecting and monitoring several cancer types, being minimally invasive, highly informative and replicable over time. This revolutionary approach can be complementary and may, in the future, replace tissue biopsy, which is still considered the gold standard for cancer diagnosis. "Classical" tissue biopsy is invasive, often cannot provide sufficient bioptic material for advanced screening, and can provide isolated information about disease evolution and heterogeneity. Recent literature highlighted how liquid biopsy is informative of proteomic, genomic, epigenetic, and metabolic alterations. These biomarkers can be detected and investigated using single-omic and, recently, in combination through multi-omic approaches. This review will provide an overview of the most suitable techniques to thoroughly characterize tumor biomarkers and their potential clinical applications, highlighting the importance of an integrated multi-omic, multi-analyte approach. Personalized medical investigations will soon allow patients to receive predictable prognostic evaluations, early disease diagnosis, and subsequent treatments.
PubMed: 37077538
DOI: 10.3389/fgene.2023.1152470 -
Sleep Medicine Reviews Feb 2023Cognitive models of insomnia highlight internal and external cognitive-biases for sleep-related "threat" in maintaining the disorder. This systematic review of the... (Meta-Analysis)
Meta-Analysis Review
Cognitive models of insomnia highlight internal and external cognitive-biases for sleep-related "threat" in maintaining the disorder. This systematic review of the sleep-related attentional and interpretive-bias literature includes meta-analytic calculations of each construct. Searches identified N = 21 attentional-bias and N = 8 interpretive-bias studies meeting the inclusion/exclusion criteria. Seventeen attentional-bias studies compared normal-sleepers and poor-sleepers/insomnia patients. Using a random effects model, meta-analytic data based on standardized mean differences of attentional-bias studies determined the weighted pooled effect size to be moderate at 0.60 (95%CI:0.26-0.93). Likewise, seven of eight interpretive-bias studies involved group comparisons. Meta-analytic data determined the weighted pooled effect size as moderate at .44 (95%CI:0.19-0.69). Considering these outcomes, disorder congruent cognitive-biases appear to be a key feature of insomnia. Despite statistical support, absence of longitudinal data limits causal inference concerning the relative role cognitive-biases in the development and maintenance of insomnia. Methodological factors pertaining to task design, sample and stimuli are discussed in relation to outcome variation. Finally, we discuss the next steps in advancing the understanding of sleep-related biases in insomnia.
Topics: Humans; Sleep Initiation and Maintenance Disorders; Sleep; Attention; Attentional Bias; Bias
PubMed: 36459947
DOI: 10.1016/j.smrv.2022.101713 -
PloS One 2022The consumption of raw milk from dairy cows has caused multiple food-borne outbreaks of campylobacteriosis in the European Union (EU) since 2011. Cross-contamination of... (Meta-Analysis)
Meta-Analysis
The consumption of raw milk from dairy cows has caused multiple food-borne outbreaks of campylobacteriosis in the European Union (EU) since 2011. Cross-contamination of raw milk through faeces is an important vehicle for transmission of Campylobacter to consumers. This systematic review and meta-analysis, aimed to summarize data on the prevalence and concentration of Campylobacter in faeces of dairy cows. Suitable scientific articles published up to July 2021 were identified through a systematic literature search and subjected to screening and quality assessment. Fifty-three out of 1338 identified studies were eligible for data extraction and 44 were further eligible for meta-analysis. The pooled prevalence was calculated in two different meta-analytic models: a simple model based on one average prevalence estimate per study and a multilevel meta-analytic model that included all prevalence outcomes reported in each study (including different subgroups of e.g. health status and age of dairy cows). The results of the two models were significantly different with a pooled prevalence estimate of 29%, 95% CI [23-36%] and 51%, 95% CI [44-57%], respectively. The effect of sub-groups on prevalence were analyzed with a multilevel mixed-effect model which showed a significant effect of the faecal collection methods and Campylobacter species on the prevalence. A meta-analysis on concentration data could not be performed due to the limited availability of data. This systematic review highlights important data gaps and limitations in current studies and variation of prevalence outcomes between available studies. The included studies used a variety of methods for sampling, data collection and analysis of Campylobacter that added uncertainty to the pooled prevalence estimates. Nevertheless, the performed meta-analysis improved our understanding of Campylobacter prevalence in faeces of dairy cows and is considered a valuable basis for the further development of quantitative microbiological risk assessment models for Campylobacter in (raw) milk and food products thereof.
Topics: Animals; Campylobacter; Campylobacter Infections; Cattle; Feces; Female; Milk; Prevalence
PubMed: 36240215
DOI: 10.1371/journal.pone.0276018 -
Journal of Critical Care Feb 2022Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review... (Meta-Analysis)
Meta-Analysis Review
PURPOSE
Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics.
METHODS
Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed.
RESULTS
Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability.
CONCLUSIONS
The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside.
REGISTRATION
PROSPERO CRD42020180218, registered on 05-07-2020.
Topics: Bias; Humans; Pneumonia, Ventilator-Associated; Prognosis
PubMed: 34673331
DOI: 10.1016/j.jcrc.2021.10.001 -
Frontiers in Public Health 2022Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description...
Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research.
Topics: Bayes Theorem; Data Mining; Humans; Machine Learning; Natural Language Processing; Occupational Injuries
PubMed: 36187621
DOI: 10.3389/fpubh.2022.984099