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Gender Bias in Diagnosis, Prevention, and Treatment of Cardiovascular Diseases: A Systematic Review.Cureus Feb 2024Cardiovascular disease (CVDs) has been perceived as a 'man's disease', and this impacted women's referral to CVD diagnosis and treatment. This study systematically... (Review)
Review
Cardiovascular disease (CVDs) has been perceived as a 'man's disease', and this impacted women's referral to CVD diagnosis and treatment. This study systematically reviewed the evidence regarding gender bias in the diagnosis, prevention, and treatment of CVDs. Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were followed. We searched CINAHL, PubMed, Medline, Web of Science, British Nursing Index, Scopus, and Google Scholar. The included studies were assessed for quality using risk bias tools. Data extracted from the included studies were exported into Statistical Product and Service Solutions (SPSS, v26; IBM SPSS Statistics for Windows, Armonk, NY), where descriptive statistics were applied. A total of 19 studies were analysed. CVDs were less reported among women who either showed milder symptoms than men or had their symptoms misdiagnosed as gastrointestinal or anxiety-related symptoms. Hence, women had their risk factors under-considered by physicians (especially by male physicians). Subsequently, women were offered fewer diagnostic tests, such as coronary angiography, ergometry, electrocardiogram (ECG), and cardiac enzymes, and were referred to less to cardiologists and/or hospitalisation. Furthermore, if hospitalised, women were less likely to receive a coronary intervention. Similarly, women were prescribed cardiovascular medicines than men, with the exception of antihypertensive and anti-anginal medicines. When it comes to the perception of CVD, women considered themselves at lower risk of CVDs than men. This systematic review showed that women were offered fewer diagnostic tests for CVDs and medicines than men and that in turn influenced their disease outcomes. This could be attributed to the inadequate knowledge regarding the differences in manifestations among both genders.
PubMed: 38500942
DOI: 10.7759/cureus.54264 -
EClinicalMedicine Apr 2024Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small...
BACKGROUND
Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA).
METHODS
Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed.
FINDINGS
Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively.
INTERPRETATION
We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation.
FUNDING
The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.
PubMed: 38495518
DOI: 10.1016/j.eclinm.2024.102498 -
BMC Medical Research Methodology Mar 2024Clinical trials are of high importance for medical progress. This study conducted a systematic review to identify the applications of EHRs in supporting and enhancing...
BACKGROUND AND OBJECTIVE
Clinical trials are of high importance for medical progress. This study conducted a systematic review to identify the applications of EHRs in supporting and enhancing clinical trials.
MATERIALS AND METHODS
A systematic search of PubMed was conducted on 12/3/2023 to identify relevant studies on the use of EHRs in clinical trials. Studies were included if they (1) were full-text journal articles, (2) were written in English, (3) examined applications of EHR data to support clinical trial processes (e.g. recruitment, screening, data collection). A standardized form was used by two reviewers to extract data on: study design, EHR-enabled process(es), related outcomes, and limitations.
RESULTS
Following full-text review, 19 studies met the predefined eligibility criteria and were included. Overall, included studies consistently demonstrated that EHR data integration improves clinical trial feasibility and efficiency in recruitment, screening, data collection, and trial design.
CONCLUSIONS
According to the results of the present study, the use of Electronic Health Records in conducting clinical trials is very helpful. Therefore, it is better for researchers to use EHR in their studies for easy access to more accurate and comprehensive data. EHRs collects all individual data, including demographic, clinical, diagnostic, and therapeutic data. Moreover, all data is available seamlessly in EHR. In future studies, it is better to consider the cost-effectiveness of using EHR in clinical trials.
Topics: Humans; Data Collection; Electronic Health Records; PubMed; Research Design; Clinical Trials as Topic
PubMed: 38494497
DOI: 10.1186/s12874-024-02177-7 -
Survey of Ophthalmology 2024Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the... (Review)
Review
Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the health economics of AI in this field. We examine studies from the PubMed, Google Scholar, and Web of Science databases that employed quantitative analysis, retrieved up to July 2023. Most of the studies indicate that AI leads to cost savings and improved efficiency in ophthalmology. On the other hand, some studies suggest that using AI in healthcare may raise costs for patients, especially when taking into account factors such as labor costs, infrastructure, and patient adherence. Future research should cover a wider range of ophthalmic diseases beyond common eye conditions. Moreover, conducting extensive health economic research, designed to collect data relevant to its own context, is imperative.
Topics: Humans; Artificial Intelligence; Eye Diseases; Ophthalmology; Cost-Benefit Analysis; Health Care Costs; Mass Screening
PubMed: 38492584
DOI: 10.1016/j.survophthal.2024.03.008 -
Clinics (Sao Paulo, Brazil) 2024Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder, with main manifestations related to communication, social interaction, and behavioral... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder, with main manifestations related to communication, social interaction, and behavioral patterns. The slight dynamics of change in the child over time require that the onset of clinical manifestations presented by the child be more valued, with the aim of stabilizing the condition. Faced with a variety of methods for diagnosing ASD, the question arises as to which method should be used. This systematic review aims to recommend the best tools to perform screening and diagnosis.
METHODOLOGY
This systematic review followed the PRISMA guidelines. The databases MEDLINE, Embase, CENTRAL (Cochrane), and Lilacs were accessed, and gray and manual searches were performed. The search strategy was created with terms referring to autism and the diagnosis/broad filter. The studies were qualitatively evaluated and quantitatively. Statistical analysis was performed using Meta-diSc-2.0 software, the confidence interval was 95 %.
RESULTS
The M-CHAT-R/F tool demonstrated a sensitivity of 78 % (95 % CI 0.57‒0.91) and specificity of 0.98 (95 % CI 0.88-1.00). The diagnostic tools demonstrated sensitivity and specificity respectively of: ADOS, sensitivity of 87 % (95 % CI 0.79‒0.92) and specificity 75 % (95 % CI 0.73‒0.78); ADI-R demonstrated test sensitivity of 77 % (95 % CI 0.56‒0.90) and specificity 68 % (95 % CI 0.52‒0.81), CARS test sensitivity was 89 % (95 % CI 0.78‒0.95) and specificity 79 % (95 % CI 0.65‒0.88).
CONCLUSION
It is mandatory to apply a screening test, the most recommended being the M-CHAT-R/F. For diagnosis CARS and ADOS are the most recommended tools.
Topics: Child; Humans; Autism Spectrum Disorder; Sensitivity and Specificity; Mass Screening; Communication; Research Design
PubMed: 38484581
DOI: 10.1016/j.clinsp.2023.100323 -
PloS One 2024The Mini-Cog is a rapid screening tool that can be administered to older adults to detect cognitive impairment (CI); however, the accuracy of the Mini-Cog to detect CI... (Meta-Analysis)
Meta-Analysis
BACKGROUND
The Mini-Cog is a rapid screening tool that can be administered to older adults to detect cognitive impairment (CI); however, the accuracy of the Mini-Cog to detect CI for older patients in various healthcare settings is unclear.
OBJECTIVES
To evaluate the diagnostic accuracy of the Mini-Cog to screen for cognitive impairment in older patients across different healthcare settings.
METHODS/DESIGN
We searched nine electronic databases (including MEDLINE, Embase) from inception to January 2023. We included studies with patients ≥60 years old undergoing screening for cognitive impairment using the Mini-Cog across all healthcare settings. A cut-off of ≤ 2/5 was used to classify dementia, mild cognitive impairment (MCI), and cognitive impairment (defined as either MCI or dementia) across various settings. The diagnostic accuracy of the Mini-Cog was assessed against gold standard references such as the Diagnostic and Statistical Manual of Mental Disorders (DSM). A bivariate random-effects model was used to estimate accuracy and diagnostic ability. The risk of bias was assessed using QUADAS-2 criteria.
RESULTS
The systematic search resulted in 4,265 articles and 14 studies were included for analysis. To detect dementia (six studies, n = 4772), the Mini-Cog showed 76% sensitivity and 83% specificity. To detect MCI (two studies, n = 270), it showed 84% sensitivity and 79% specificity. To detect CI (eight studies, n = 2152), it had 67% sensitivity and 83% specificity. In the primary care setting, to detect either MCI, dementia, or CI (eight studies, n = 5620), the Mini-Cog demonstrated 73% sensitivity and 84% specificity. Within the secondary care setting (seven studies, n = 1499), the Mini-Cog to detect MCI, dementia or CI demonstrated 73% sensitivity and 76% specificity. A high or unclear risk of bias persisted in the patient selection and timing domain.
CONCLUSIONS
The Mini-Cog is a quick and freely available screening tool and has high sensitivity and specificity to screen for CI in older adults across various healthcare settings. It is a practical screening tool for use in time-sensitive and resource-limited healthcare settings.
Topics: Humans; Aged; Middle Aged; Dementia; Alzheimer Disease; Cognitive Dysfunction; Mental Status and Dementia Tests; Secondary Care; Sensitivity and Specificity
PubMed: 38483857
DOI: 10.1371/journal.pone.0298686 -
Frontiers in Oncology 2024With the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics...
BACKGROUND AND AIMS
With the rapid growth of artificial intelligence (AI) applications in various fields, understanding its impact on liver cancer research is paramount. This scientometrics project aims to investigate publication trends and topics in AI-related publications in liver cancer.
MATERIALS AND METHODS
We employed a search strategy to identify AI-related publications in liver cancer using Scopus database. We analyzed the number of publications, author affiliations, and journals that publish AI-related publications in liver cancer. Finally, the publications were grouped based on intended application.
RESULTS
We identified 3950 eligible publications (2695 articles, 366 reviews, and 889 other document types) from 1968 to August 3, 2023. There was a 12.7-fold increase in AI-related publications from 2013 to 2022. By comparison, the number of total publications on liver cancer increased by 1.7-fold. Our analysis revealed a significant shift in trends of AI-related publications on liver cancer in 2019. We also found a statistically significant consistent increase in numbers of AI-related publications over time (tau = 0.756, p < 0.0001). Eight (53%) of the top 15 journals with the most publications were radiology journals. The largest number of publications were from China (n=1156), the US (n=719), and Germany (n=236). The three most common publication categories were "medical image analysis for diagnosis" (37%), "diagnostic or prognostic biomarkers modeling & bioinformatics" (19%), and "genomic or molecular analysis" (18%).
CONCLUSION
Our study reveals increasing interest in AI for liver cancer research, evidenced by a 12.7-fold growth in related publications over the past decade. A common application of AI is in medical imaging analysis for various purposes. China, the US, and Germany are leading contributors.
PubMed: 38482208
DOI: 10.3389/fonc.2024.1355454 -
Medical Archives (Sarajevo, Bosnia and... 2024Active case finding (ACF) is an alternative strategy to accelerate the identification of TB cases among the migrant population. (Review)
Review
BACKGROUND
Active case finding (ACF) is an alternative strategy to accelerate the identification of TB cases among the migrant population.
OBJECTIVE
This study aimed to synthesize the evidence for the effectiveness of ACF TB in migrants.
METHODS
This study uses the PRISMA model as a method of searching for journal articles in the databases of Google Scholar, ProQuest, EBSCO, ScienceDirect, Elsevier, and PubMed, as well as other sources such as textbooks and reports from 2017 to 2021 with the keywords . The search revealed 371 articles, of which 26 met the criteria for further discussion.
RESULTS
Most studies show that the TB incidence among migrants is higher than in the local population. Factors leading to increased cases include lack of knowledge about the symptoms, high mobilization, social isolation, economic problems, and medication adherence that impact an advanced stage. Furthermore, it is also influenced by the low quality of health services, including accessibility, health facilities, health workers, and information. Therefore, Active Case Finding (ACF) is more effective in identifying cases of TB in the risk groups. This was conducted on migrants with increased notifications followed up with treatment.
CONCLUSION
ACF is effective approach in screening and diagnosing TB in the migrant group.
Topics: Humans; Transients and Migrants; Mass Screening; Tuberculosis; Incidence; Health Personnel
PubMed: 38481594
DOI: 10.5455/medarh.2024.78.60-64 -
Scandinavian Journal of Trauma,... Mar 2024Chest pain is responsible for millions of visits to the emergency department (ED) annually. Cardiac ultrasound can detect ischemic changes, but varying accuracy... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Chest pain is responsible for millions of visits to the emergency department (ED) annually. Cardiac ultrasound can detect ischemic changes, but varying accuracy estimates have been reported in previous studies. We synthetized the available evidence to yield more precise estimates of the accuracy of cardiac ultrasound for acute myocardial ischemia in patients with chest pain in the ED and to assess the effect of different clinical characteristics on test accuracy.
METHODS
A systematic search for studies assessing the diagnostic accuracy of cardiac ultrasound for myocardial ischemia in the ED was conducted in MEDLINE, EMBASE, CENTRAL, CINAHL, LILACS, Web of Science, two trial registries and supplementary methods, from inception to December 6th, 2022. Prospective cohort, cross-sectional, case-control studies and randomized controlled trials (RCTs) that included data on diagnostic accuracy were included. Risk of bias was assessed with the QUADAS-2 tool and a bivariate hierarchical model was used for meta-analysis with paired Forest and SROC plots used to present the results. Subgroup analyses was conducted on clinically relevant factors.
RESULTS
Twenty-nine studies were included, with 5043 patients. The overall summary sensitivity was 79.3% (95%CI 69.0-86.8%) and specificity was 87.3% (95%CI 79.9-92.2%), with substantial heterogeneity. Subgroup analyses showed increased sensitivity in studies where ultrasound was conducted at ED admission and increased specificity in studies that excluded patients with previous heart disease, when the target condition was acute coronary syndrome, or when final chart review was used as the reference standard. There was very low certainty in the results based on serious risk of bias and indirectness in most studies.
CONCLUSIONS
Cardiac ultrasound may have a potential role in the diagnostic pathway of myocardial ischemia in the ED; however, a pooled accuracy must be interpreted cautiously given substantial heterogeneity and that important patient and test characteristics affect its diagnostic performance.
PROTOCOL REGISTRATION
PROSPERO (CRD42023392058).
Topics: Humans; Echocardiography; Ultrasonography; Myocardial Ischemia; Chest Pain; Emergency Service, Hospital; Sensitivity and Specificity
PubMed: 38468316
DOI: 10.1186/s13049-024-01192-3 -
International Journal of Infectious... May 2024Early diagnosis of infectious diseases remains a challenge. This study assessed the diagnostic value of mNGS in infections and explored the effect of various factors on... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
Early diagnosis of infectious diseases remains a challenge. This study assessed the diagnostic value of mNGS in infections and explored the effect of various factors on the accuracy of mNGS.
METHODS
An electronic article search of PubMed, Cochrane Library, and Embase was performed. A total of 85 papers were eligible for inclusion and analysis. Stata 12.0 was used for statistical calculation to evaluate the efficacy of mNGS for the diagnosis of infectious diseases.
RESULTS
The AUC of 85 studies was 0.88 (95%CI, 0.85-0.90). The AUC of the clinical comprehensive diagnosis and conventional test groups was 0.92 (95%CI, 0.89-0.94) and 0.82 (95%CI, 0.78-0.85), respectively. The results of subgroup analysis indicated that the PLR and NLR were 12.67 (95%CI, 6.01-26.70) and 0.05 (95%CI, 0.03-0.10), respectively, in arthrosis infections. The PLR was 24.41 (95%CI, 5.70-104.58) in central system infections and the NLR of immunocompromised patients was 0.08 (95%CI, 0.01-0.62).
CONCLUSION
mNGS demonstrated satisfactory diagnostic performance for infections, especially for bone and joint infections and central system infections. Moreover, mNGS also has a high value in the exclusion of infection in immunocompromised patients.
Topics: Humans; High-Throughput Nucleotide Sequencing; Arthritis, Infectious; Immunocompromised Host; Metagenome; Metagenomics; Sepsis; Communicable Diseases; Sensitivity and Specificity
PubMed: 38458421
DOI: 10.1016/j.ijid.2024.106996