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Mayo Clinic Proceedings. Digital Health Jun 2024This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future...
This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023. For included studies, we extracted the NLP method (eg, rule-based, machine learning, deep learning, or hybrid), NLP application (eg, identification, classification, and automation), thyroid condition (eg, thyroid cancer, thyroid nodule, and functional or autoimmune disease), data source (eg, electronic health records, health forums, medical literature databases, or genomic databases), performance metrics, and stages of development. We identified 24 eligible NLP studies focusing on thyroid-related conditions. Deep learning-based methods were the most common (38%), followed by rule-based (21%), and traditional machine learning (21%) methods. Thyroid nodules (54%) and thyroid cancer (29%) were the primary conditions under investigation. Electronic health records were the dominant data source (17/24, 71%), with imaging reports being the most frequently used (15/17, 88%). There is increasing interest in NLP applications for thyroid-related studies, mostly addressing thyroid nodules and using deep learning-based methodologies with limited external validation. However, none of the reviewed NLP applications have reached clinical practice. Several limitations, including inconsistent clinical documentation and model portability, need to be addressed to promote the evaluation and implementation of NLP applications to support patient care in thyroidology.
PubMed: 38938930
DOI: 10.1016/j.mcpdig.2024.03.007 -
Frontiers in Nutrition 2024The controlling nutritional status score (CONUT) has been widely used for ascertaining the prognosis of various cancers. However, its use in patients with hematological... (Review)
Review
OBJECTIVE
The controlling nutritional status score (CONUT) has been widely used for ascertaining the prognosis of various cancers. However, its use in patients with hematological malignancies remains unclear. This review examined evidence on the utility of CONUT as a prognostic marker for patients with hematological malignancies.
METHODS
All cohort studies that examined the association between CONUT and outcomes of hematological malignancies and were published on the databases of Embase, Scopus, CENTRAL, Web of Science, and PubMed were searched from the inception of the databases to 30 January 2024. The primary outcome was overall survival (OS), and the secondary outcome was progression-free survival (PFS).
RESULTS
A total of 23 studies were available for review. A meta-analysis of 22 studies showed that high CONUT was significantly associated with poor OS in patients with hematological malignancies (HR: 1.95 95% CI: 1.62, 2.35 = 89%). The results remained unchanged on sensitivity and subgroup analyses based on study location, sample size, diagnosis, CONUT cutoff, and the Newcastle-Ottawa Scale score. Only six studies reported data on PFS, and the pooled analysis found that high CONUT was a significant marker for poor PFS in patients with hematological malignancies [hazards ratio (HR): 1.64 95% CI: 1.21, 2.20 = 70%]. These results, too, maintained significance in the sensitivity analysis.
CONCLUSION
CONUT is an independent predictor of poor OS in patients with hematological malignancies. The results appear to be valid across different cancer types and with different CONUT cutoffs. Scarce data also suggest that CONUT could predict PFS.
PubMed: 38938670
DOI: 10.3389/fnut.2024.1402328 -
Systematic Reviews Jun 2024The steep rise in substance use and substance use disorder (SUD) shows an urgency to assess its prevalence using valid measures. This systematic review summarizes the... (Review)
Review
BACKGROUND
The steep rise in substance use and substance use disorder (SUD) shows an urgency to assess its prevalence using valid measures. This systematic review summarizes the validity of measures to assess the prevalence of substance use and SUD in the US estimated in population and sub-population-based surveys.
METHODS
A literature search was performed using nine online databases. Studies were included in the review if they were published in English and tested the validity of substance use and SUD measures among US adults at the general or sub-population level. Independent reviews were conducted by the authors to complete data synthesis and assess the risk of bias.
RESULTS
Overall, 46 studies validating substance use/SUD (n = 46) measures were included in this review, in which 63% were conducted in clinical settings and 89% assessed the validity of SUD measures. Among the studies that assessed SUD screening measures, 78% examined a generic SUD measure, and the rest screened for specific disorders. Almost every study used a different survey measure. Overall, sensitivity and specificity tests were conducted in over a third of the studies for validation, and 10 studies used receiver operating characteristics curve.
CONCLUSION
Findings suggest a lack of standardized methods in surveys measuring and reporting prevalence of substance use/SUD among US adults. It highlights a critical need to develop short measures for assessing SUD that do not require lengthy, time-consuming data collection that would be difficult to incorporate into population-based surveys assessing a multitude of health dimensions.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO CRD42022298280.
Topics: Humans; Substance-Related Disorders; United States; Reproducibility of Results; Prevalence; Health Surveys; Surveys and Questionnaires; Sensitivity and Specificity
PubMed: 38937847
DOI: 10.1186/s13643-024-02536-x -
Lipids in Health and Disease Jun 2024The final decision to fast or not fast for routine lipid profile examination in a standard, healthy population is unclear. Whereas the United States and European... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The final decision to fast or not fast for routine lipid profile examination in a standard, healthy population is unclear. Whereas the United States and European protocols state that fasting for regular lipid analysis is unnecessary, the North American and Chinese guidelines still recommend fasting before routine lipid testing.
AIM
This study aimed to unravel the contradiction between the different protocols of lipid profile testing worldwide and clarify the effect of diet on lipid profile testing only in a regular, healthy population.
METHODS
A literature search was conducted through May 2024. The analyses included studies performed from the date 2000 until now because the contradiction of guidelines for lipid profile testing appeared for the first time in this period. A planned internal validity evaluation was performed using the National Institute of Health (NIH) quality measurement tools for observational cohort, case‒control, controlled interventional, and cross-sectional studies. The data were synthesized according to RevMan 5.3.
RESULTS
Eight studies with a total of 244,665 participants were included. The standardized mean difference in cholesterol in six studies showed significant differences in overall effect among fasting and nonfasting states (P < 0.00001), as did high-density lipoprotein cholesterol (P < 0.00001). At the same time, with respect to triglycerides and low-density lipoprotein cholesterol, there were notable variations in the overall effect between the fasted and nonfasted states (P < 0.00001 and P ≤ 0.001, respectively).
CONCLUSIONS
This meta-analysis concluded that fasting for lipid profile testing is preferred as a conservative model to reduce variability and increase consistency in patients' metabolic status when sampling for lipid testing.
Topics: Humans; Fasting; Triglycerides; Cholesterol, LDL; Cholesterol, HDL; Lipids; Female; Male; Adult
PubMed: 38937752
DOI: 10.1186/s12944-024-02169-y -
Journal of Clinical Anesthesia Jun 2024Depression is a common cause of long-lasting disability and preoperative mental health state that has important implications for optimizing recovery in the perioperative... (Review)
Review
STUDY OBJECTIVE
Depression is a common cause of long-lasting disability and preoperative mental health state that has important implications for optimizing recovery in the perioperative period. In older elective surgical patients, the prevalence of preoperative depression and associated adverse pre- and postoperative outcomes are unknown. This systematic review and meta-analysis aimed to determine the prevalence of preoperative depression and the associated adverse outcomes in the older surgical population.
DESIGN
Systematic review and meta-analysis.
SETTING
MEDLINE, MEDLINE Epub Ahead of Print and In-Process, In-Data-Review & Other Non-Indexed Citations, Embase/Embase Classic, Cochrane CENTRAL, and Cochrane Database of Systematic Reviews, ClinicalTrials.Gov, the WHO ICTRP (International Clinical Trials Registry Platform) for relevant articles from 2000 to present.
PATIENTS
Patients aged ≥65 years old undergoing non-cardiac elective surgery with preoperative depression assessed by tools validated in older adults. These validated tools include the Geriatric Depression Scale (GDS), Hospital Depression and Anxiety Scale (HADS), Beck Depression Inventory-II (BDI), Patient Health Questionnaire-9 (PHQ-9), and the Centre for Epidemiological Studies Depression Scale (CESD).
INTERVENTIONS
Preoperative assessment.
MEASUREMENT
The primary outcome was the prevalence of preoperative depression. Additional outcomes included preoperative cognitive impairment, and postoperative outcomes such as delirium, functional decline, discharge disposition, readmission, length of stay, and postoperative complications.
MAIN RESULTS
Thirteen studies (n = 2824) were included. Preoperative depression was most assessed using the Geriatric Depression Scale-15 (GDS-15) (n = 12). The overall prevalence of preoperative depression was 23% (95% CI: 15%, 30%). Within non-cancer non-cardiac mixed surgery, the pooled prevalence was 19% (95% CI: 11%, 27%). The prevalence in orthopedic surgery was 17% (95% CI: 9%, 24%). In spine surgery, the prevalence was higher at 46% (95% CI: 28%, 64%). Meta-analysis showed that preoperative depression was associated with a two-fold increased risk of postoperative delirium than those without depression (32% vs 23%, OR: 2.25; 95% CI: 1.67, 3.03; I: 0%; P ≤0.00001).
CONCLUSIONS
The overall prevalence of older surgical patients who suffered from depression was 23%. Preoperative depression was associated with a two-fold higher risk of postoperative delirium. Further work is needed to determine the need for depression screening and treatment preoperatively.
PubMed: 38936304
DOI: 10.1016/j.jclinane.2024.111532 -
International Journal of Surgery... Jun 2024Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data.
METHODS
Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models' diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted.
RESULTS
Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022-2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78-0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001).
CONCLUSION
Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.
Topics: Humans; Colorectal Neoplasms; Lymphatic Metastasis; Lymph Nodes; Radiomics
PubMed: 38935817
DOI: 10.1097/JS9.0000000000001239 -
PloS One 2024The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine...
BACKGROUND
The elderly is commonly susceptible to depression, the symptoms for which may overlap with natural aging or other illnesses, and therefore miss being captured by routine screening questionnaires. Passive sensing data have been promoted as a tool for depressive symptoms detection though there is still limited evidence on its usage in the elderly. Therefore, this study aims to review current knowledge on the use of passive sensing data via smartphones and smartwatches in depressive symptom screening for the elderly.
METHOD
The search of literature was performed in PubMed, IEEE Xplore digital library, and PsycINFO. Literature investigating the use of passive sensing data to screen, monitor, and/or predict depressive symptoms in the elderly (aged 60 and above) via smartphones and/or wrist-worn wearables was included for initial screening. Studies in English from international journals published between January 2012 to September 2022 were included. The reviewed studies were further analyzed by a narrative analysis.
RESULTS
The majority of 21 included studies were conducted in Western countries with a few in Asia and Australia. Most studies adopted a cohort study design (n = 12), followed by cross-sectional design (n = 7) and a case-control design (n = 2). The most popular passive sensing data was related to sleep and physical activity using an actigraphy. Sleep characteristics, such as prolonged wakefulness after sleep onset, along with lower levels of physical activity, exhibited a significant association with depression. However, cohort studies expressed concerns regarding data quality stemming from incomplete follow-up and potential confounding effects.
CONCLUSION
Passive sensing data, such as sleep, and physical activity parameters should be promoted for depressive symptoms detection. However, the validity, reliability, feasibility, and privacy concerns still need further exploration.
Topics: Humans; Smartphone; Depression; Aged; Mass Screening; Wearable Electronic Devices; Sleep; Middle Aged; Exercise; Female
PubMed: 38935797
DOI: 10.1371/journal.pone.0304845 -
Environmental Monitoring and Assessment Jun 2024Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics...
Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics may act as vectors for the dissemination of other pollutants and the transmission of microorganisms into the water environment. The currently available literature reviews focus on analysing the occurrence, environmental effects and methods of microplastic detection, however lacking a wide-scale systematic review and classification of the mathematical microplastic modelling applications. Thus, the current review provides a global overview of the modelling methodologies used for microplastic transport and fate in water environments. This review consolidates, classifies and analyses the methods, model inputs and results of 61 microplastic modelling studies in the last decade (2012-2022). It thoroughly discusses their strengths, weaknesses and common gaps in their modelling framework. Five main modelling types were classified as follows: hydrodynamic, process-based, statistical, mass-balance and machine learning models. Further, categorisations based on the water environments, location and published year of these applications were also adopted. It is concluded that addressed modelling types resulted in relatively reliable outcomes, yet each modelling framework has its strengths and weaknesses. However, common issues were found such as inputs being unrealistically assumed, especially biological processes, and the lack of sufficient field data for model calibration and validation. For future research, it is recommended to incorporate macroplastics' degradation rates, particles of different shapes and sizes and vertical mixing due to biofouling and turbulent conditions and also more experimental data to obtain precise model inputs and standardised sampling methods for surface and column waters.
Topics: Environmental Monitoring; Microplastics; Models, Chemical; Models, Theoretical; Water Pollutants, Chemical
PubMed: 38935176
DOI: 10.1007/s10661-024-12731-x -
Journal of Diabetes and Metabolic... Jun 2024Metabolic syndrome (MetS) is a constellation of coexisting cardiovascular risk factors. This study aimed to assess the evidence for the association between the... (Review)
Review
OBJECTIVES
Metabolic syndrome (MetS) is a constellation of coexisting cardiovascular risk factors. This study aimed to assess the evidence for the association between the apolipoprotein B/A1 ratio, apolipoprotein B, and apolipoprotein A1, and the MetS in children and adolescents.
METHODS
The English electronic databases including PubMed, Embase, Web of Science, and Scopus were searched up to February 28, 2022. To ascertain the validity of eligible studies, modified JBI scale was used. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were pooled using the random-effects model to evaluate the association between the apolipoprotein B/A1 ratio, apolipoprotein B, and apolipoprotein A1 and the MetS. Heterogeneity amongst the studies was determined by the use of the Galbraith diagram, Cochran's Q-test, and I test. Publication bias was assessed using Egger's and Begg's tests.
RESULTS
From 7356 records, 5 studies were included in the meta-analysis, representing a total number of 232 participants with MetS and 1320 participants as control group. The results indicated that increased levels of apolipoprotein B/A1 ratio (SMD 1.26; 95% CI: 1.04, 1.47) and apolipoprotein B (SMD 0.75; 95% CI: 0.36, 1.14) and decreased levels of apolipoprotein A1 (SMD -0.53; 95% CI: -0.69, -0.37) are linked to the presence of MetS. The notable findings were, children and adolescents with MetS had elevated levels of the apolipoprotein B/A1 ratio, apolipoprotein B, and decreased levels of apolipoprotein A1.
CONCLUSIONS
Our results suggest the need to evaluate the levels of apolipoproteins for detecting the risk of MetS in children and adolescents.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s40200-023-01235-z.
PubMed: 38932877
DOI: 10.1007/s40200-023-01235-z -
Frontiers in Public Health 2024Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature. (Meta-Analysis)
Meta-Analysis
BACKGROUND
Uncertainty and inconsistency in terminology regarding the risk factors (RFs) for in-hospital falls are present in the literature.
OBJECTIVE
(1) To perform a literature review to identify the fall RFs among hospitalized adults; (2) to link the found RFs to the corresponding categories of international health classifications to reduce the heterogeneity of their definitions; (3) to perform a meta-analysis on the risk categories to identify the significant RFs; (4) to refine the final list of significant categories to avoid redundancies.
METHODS
Four databases were investigated. We included observational studies assessing patients who had experienced in-hospital falls. Two independent reviewers performed the inclusion and extrapolation process and evaluated the methodological quality of the included studies. RFs were grouped into categories according to three health classifications (ICF, ICD-10, and ATC). Meta-analyses were performed to obtain an overall pooled odds ratio for each RF. Finally, protective RFs or redundant RFs across different classifications were excluded.
RESULTS
Thirty-six articles were included in the meta-analysis. One thousand one hundred and eleven RFs were identified; 616 were linked to ICF classification, 450 to ICD-10, and 260 to ATC. The meta-analyses and subsequent refinement of the categories yielded 53 significant RFs. Overall, the initial number of RFs was reduced by about 21 times.
CONCLUSION
We identified 53 significant RF categories for in-hospital falls. These results provide proof of concept of the feasibility and validity of the proposed methodology. The list of significant RFs can be used as a template to build more accurate measurement instruments to predict in-hospital falls.
Topics: Accidental Falls; Humans; Risk Factors; Proof of Concept Study; Hospitalization
PubMed: 38932769
DOI: 10.3389/fpubh.2024.1390185