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International Journal of Cancer Apr 2024While previous reviews found a positive association between pre-existing cancer diagnosis and COVID-19-related death, most early studies did not distinguish long-term... (Meta-Analysis)
Meta-Analysis
While previous reviews found a positive association between pre-existing cancer diagnosis and COVID-19-related death, most early studies did not distinguish long-term cancer survivors from those recently diagnosed/treated, nor adjust for important confounders including age. We aimed to consolidate higher-quality evidence on risk of COVID-19-related death for people with recent/active cancer (compared to people without) in the pre-COVID-19-vaccination period. We searched the WHO COVID-19 Global Research Database (20 December 2021), and Medline and Embase (10 May 2023). We included studies adjusting for age and sex, and providing details of cancer status. Risk-of-bias assessment was based on the Newcastle-Ottawa Scale. Pooled adjusted odds or risk ratios (aORs, aRRs) or hazard ratios (aHRs) and 95% confidence intervals (95% CIs) were calculated using generic inverse-variance random-effects models. Random-effects meta-regressions were used to assess associations between effect estimates and time since cancer diagnosis/treatment. Of 23 773 unique title/abstract records, 39 studies were eligible for inclusion (2 low, 17 moderate, 20 high risk of bias). Risk of COVID-19-related death was higher for people with active or recently diagnosed/treated cancer (general population: aOR = 1.48, 95% CI: 1.36-1.61, I = 0; people with COVID-19: aOR = 1.58, 95% CI: 1.41-1.77, I = 0.58; inpatients with COVID-19: aOR = 1.66, 95% CI: 1.34-2.06, I = 0.98). Risks were more elevated for lung (general population: aOR = 3.4, 95% CI: 2.4-4.7) and hematological cancers (general population: aOR = 2.13, 95% CI: 1.68-2.68, I = 0.43), and for metastatic cancers. Meta-regression suggested risk of COVID-19-related death decreased with time since diagnosis/treatment, for example, for any/solid cancers, fitted aOR = 1.55 (95% CI: 1.37-1.75) at 1 year and aOR = 0.98 (95% CI: 0.80-1.20) at 5 years post-cancer diagnosis/treatment. In conclusion, before COVID-19-vaccination, risk of COVID-19-related death was higher for people with recent cancer, with risk depending on cancer type and time since diagnosis/treatment.
Topics: Humans; COVID-19; COVID-19 Testing; Neoplasms
PubMed: 38083979
DOI: 10.1002/ijc.34798 -
Cornea Dec 2023There are no defined diagnostic criteria and severity classification for Fuchs endothelial corneal dystrophy (FECD), which are required for objective standardized...
PURPOSE
There are no defined diagnostic criteria and severity classification for Fuchs endothelial corneal dystrophy (FECD), which are required for objective standardized assessments. Therefore, we performed a systematic literature review of the current diagnosis and severity classification of FECD.
METHODS
We searched the Ovid MEDLINE and Web of Science databases for studies published until January 13, 2021. We excluded review articles, conference abstracts, editorials, case reports with <5 patients, and letters.
RESULTS
Among 468 articles identified, we excluded 173 and 165 articles in the first and second screenings, respectively. Among the 130 included articles, 61 (47%) and 99 (76%) mentioned the diagnostic criteria for FECD and described its severity classification, respectively. Regarding diagnosis, slitlamp microscope alone was the most frequently used device in 31 (51%) of 61 articles. Regarding diagnostic findings, corneal guttae alone was the most common parameter [adopted in 23 articles (38%)]. Regarding severity classification, slitlamp microscopes were used in 88 articles (89%). The original or modified Krachmer grading scale was used in 77 articles (78%), followed by Adami's classification in six (6%). Specular microscopes or Scheimpflug tomography were used in four articles (4%) and anterior segment optical coherence tomography in one (1%).
CONCLUSIONS
FECD is globally diagnosed by the corneal guttae using slitlamp examination, and its severity is predominantly determined by the original or modified Krachmer grading scale. Objective severity grading using Scheimpflug or anterior segment optical coherence tomography can be applied in the future innovative therapies such as cell injection therapy or novel small molecules.
Topics: Humans; Fuchs' Endothelial Dystrophy; Tomography, Optical Coherence; Slit Lamp Microscopy; Endothelium, Corneal
PubMed: 37603692
DOI: 10.1097/ICO.0000000000003343 -
Neurological Sciences : Official... Jun 2024The diagnostic criteria for adult-onset Alzheimer's disease (AD) in patients with Down syndrome (DS) have not been standardised. This study investigated the specific... (Meta-Analysis)
Meta-Analysis Review
The diagnostic criteria for adult-onset Alzheimer's disease (AD) in patients with Down syndrome (DS) have not been standardised. This study investigated the specific symptoms of AD in the prodromal stage of DS, the mean age at diagnosis at each stage of dementia, and the relationship between intellectual disability (ID) and dementia. PubMed, Web of Science, and Embase were searched for studies on DS, AD, early-stage disease, initial symptoms, and prodromal dementia registered between January 2012 and January 2022. We also performed a meta-analysis of the differences between the mean age at prodromal symptoms and AD diagnosis and the proportion of mild cognitive impairment in patients with mild and moderately abnormal ID. We selected 14 articles reporting the behavioural and psychological symptoms of dementia (BPSD) and memory- and language-related impairments as early symptoms of AD in patients with DS. The specific symptoms of BPSD were classified into five categories: irritability (agitation), apathy, abnormal behaviour, adaptive functioning, and sleep disturbance. The mean age at the diagnosis of prodromal symptoms and AD dementia was 52.7 and 56.2 years, respectively (mean difference, + 3.11 years; 95% CI 1.82-4.40) in the meta-analysis. The diagnosis of mild dementia tended to correlate with ID severity (odds ratio [OR], 1.38; 95% CI 0.87-2.18). The features of behaviour-variant frontotemporal dementia may be clinically confirmed in diagnosing early symptoms of DS-associated AD (DSAD). Moreover, age-appropriate cognitive assessment is important. Further studies are required to evaluate DSAD using a combination of biomarkers and ID-related data.
Topics: Down Syndrome; Humans; Alzheimer Disease; Prodromal Symptoms; Cognitive Dysfunction
PubMed: 38228941
DOI: 10.1007/s10072-023-07292-9 -
Aging Clinical and Experimental Research Nov 2023Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Alzheimer's disease (AD) is a debilitating neurodegenerative disease. Early diagnosis of AD and its precursor, mild cognitive impairment (MCI), is crucial for timely intervention and management. Radiomics involves extracting quantitative features from medical images and analyzing them using advanced computational algorithms. These characteristics have the potential to serve as biomarkers for disease classification, treatment response prediction, and patient stratification. Of note, Magnetic resonance imaging (MRI) radiomics showed a promising result for diagnosing and classifying AD, and MCI from normal subjects. Thus, we aimed to systematically evaluate the diagnostic performance of the MRI radiomics for this task.
METHODS AND MATERIALS
A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to August 5, 2023. Original studies discussing the diagnostic performance of MRI radiomics for the classification of AD, MCI, and normal subjects were included. Method quality was evaluated with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools.
RESULTS
We identified 13 studies that met the inclusion criteria, involving a total of 5448 participants. The overall quality of the included studies was moderate to high. The pooled sensitivity and specificity of MRI radiomics for differentiating AD from normal subjects were 0.92 (95% CI [0.85; 0.96]) and 0.91 (95% CI [0.85; 0.95]), respectively. The pooled sensitivity and specificity of MRI radiomics for differentiating MCI from normal subjects were 0.74 (95% CI [0.60; 0.85]) and 0.79 (95% CI [0.70; 0.86]), respectively. Also, the pooled sensitivity and specificity of MRI radiomics for differentiating AD from MCI were 0.73 (95% CI [0.64; 0.80]) and 0.79 (95% CI [0.64; 0.90]), respectively.
CONCLUSION
MRI radiomics has promising diagnostic performance in differentiating AD, MCI, and normal subjects. It can potentially serve as a non-invasive and reliable tool for early diagnosis and classification of AD and MCI.
Topics: Humans; Alzheimer Disease; Neurodegenerative Diseases; Cognitive Dysfunction; Magnetic Resonance Imaging; Sensitivity and Specificity
PubMed: 37801265
DOI: 10.1007/s40520-023-02565-x -
Journal of Medical Internet Research Jul 2023Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence-based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB.
OBJECTIVE
This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]).
METHODS
In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results.
RESULTS
Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist's report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC; n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions.
CONCLUSIONS
Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects.
TRIAL REGISTRATION
PROSPERO CRD42021277155; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277155.
Topics: Humans; Artificial Intelligence; COVID-19; Deep Learning; Radiography; Reproducibility of Results; Tuberculosis; X-Rays
PubMed: 37399055
DOI: 10.2196/43154 -
The Journal of Laryngology and Otology Sep 2023Sudden sensorineural hearing loss is considered idiopathic in up to 90 per cent of cases. This study explored the role of blood tests as biomarkers for the diagnosis and... (Review)
Review
OBJECTIVE
Sudden sensorineural hearing loss is considered idiopathic in up to 90 per cent of cases. This study explored the role of blood tests as biomarkers for the diagnosis and prognosis of sudden sensorineural hearing loss.
METHOD
Two researchers filtered 34 papers into the final review. This review was pre-registered on the Prospero database and conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines.
RESULTS
Raised inflammatory markers are almost universal in sudden sensorineural hearing loss, suggesting an inflammatory or autoimmune process. The most useful biomarkers are neutrophil-lymphocyte ratio, platelet-lymphocyte ratio and fibrinogen level. Focused investigations should be deployed on a case-by-case basis to identify underlying metabolic, infective and autoimmune conditions.
CONCLUSION
A full blood count and coagulation screen (fibrinogen) is recommended in all cases of sudden sensorineural hearing loss. These are inexpensive, accessible and offer as much diagnostic and prognostic information as any other biomarker. There is emerging evidence regarding specific biomarkers for sudden sensorineural hearing loss prognosis, with heat shock protein-70, anti-endothelial cell antibody and prestin demonstrating potential; investigation of their validity through prospective, controlled research is recommended.
Topics: Humans; Adult; Prospective Studies; Prognosis; Biomarkers; Hearing Loss, Sudden; Hearing Loss, Sensorineural; Hematologic Tests; Fibrinogen
PubMed: 36794400
DOI: 10.1017/S0022215123000282 -
International Journal of Surgery... Dec 2023Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities.
METHODS
Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized.
RESULTS
A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity ( P <0.05) in the Cochrane's Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane's Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane's Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%).
CONCLUSION
AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.
Topics: Adult; Humans; Artificial Intelligence; Sensitivity and Specificity; Pancreas; Endosonography; Pancreatic Neoplasms
PubMed: 37800594
DOI: 10.1097/JS9.0000000000000717 -
Pediatrics Apr 2024Effective treatment of attention-deficit/hyperactivity disorder (ADHD) is essential to improving youth outcomes.
CONTEXT
Effective treatment of attention-deficit/hyperactivity disorder (ADHD) is essential to improving youth outcomes.
OBJECTIVES
This systematic review provides an overview of the available treatment options.
DATA SOURCES
We identified controlled treatment evaluations in 12 databases published from 1980 to June 2023; treatments were not restricted by intervention content.
STUDY SELECTION
Studies in children and adolescents with clinically diagnosed ADHD, reporting patient health and psychosocial outcomes, were eligible. Publications were screened by trained reviewers, supported by machine learning.
DATA EXTRACTION
Data were abstracted and critically appraised by 1 reviewer and checked by a methodologist. Data were pooled using random-effects models. Strength of evidence and applicability assessments followed Evidence-based Practice Center standards.
RESULTS
In total, 312 studies reported in 540 publications were included. We grouped evidence for medication, psychosocial interventions, parent support, nutrition and supplements, neurofeedback, neurostimulation, physical exercise, complementary medicine, school interventions, and provider approaches. Several treatments improved ADHD symptoms. Medications had the strongest evidence base for improving outcomes, including disruptive behaviors and broadband measures, but were associated with adverse events.
LIMITATIONS
We found limited evidence of studies comparing alternative treatments directly and indirect analyses identified few systematic differences across stimulants and nonstimulants. Identified combination of medication with youth-directed psychosocial interventions did not systematically produce better results than monotherapy, though few combinations have been evaluated.
CONCLUSIONS
A growing number of treatments are available that improve ADHD symptoms and other outcomes, in particular for school-aged youth. Medication therapies remain important treatment options but are associated with adverse events.
Topics: Child; Adolescent; Humans; Attention Deficit Disorder with Hyperactivity; Central Nervous System Stimulants; Treatment Outcome; Complementary Therapies
PubMed: 38523592
DOI: 10.1542/peds.2024-065787 -
Journal of Affective Disorders Jun 2024The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD.
METHODS
We conducted a comprehensive search (from database creation date to March 2024) for relevant English articles on sMRI or/and fMRI-based ML techniques for diagnosing ADHD. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were calculated to assess the diagnostic value of sMRI or/and fMRI-based ML techniques. The I test was used to assess heterogeneity and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test.
RESULTS
Forty-three studies were included in the systematic review, 27 of which were included in our meta-analysis. The pooled sensitivity and specificity of sMRI or/and fMRI-based ML techniques for the diagnosis of ADHD were 0.74 (95 % CI 0.65-0.81) and 0.75 (95 % CI 0.67-0.81), respectively. SROC curve showed that AUC was 0.81 (95 % CI 0.77-0.84). Based on these findings, the sMRI or/and fMRI-based ML techniques have relatively good diagnostic value for ADHD.
LIMITATIONS
Our meta-analysis specifically focused on ML techniques based on sMRI or/and fMRI studies. Since EEG-based ML techniques are also used for diagnosing ADHD, further systematic analyses are necessary to explore ML methods based on multimodal medical data.
CONCLUSION
sMRI or/and fMRI-based ML technique is a promising objective diagnostic method for ADHD.
Topics: Humans; Attention Deficit Disorder with Hyperactivity; Magnetic Resonance Imaging; Sensitivity and Specificity; ROC Curve; Machine Learning
PubMed: 38580035
DOI: 10.1016/j.jad.2024.03.111 -
Theranostics 2023Recent studies suggest that Ga-FAPI PET/CT demonstrated superiority over F-FDG PET/CT in the evaluation of various cancer types, especially in gastric cancer (GC). By... (Meta-Analysis)
Meta-Analysis
Recent studies suggest that Ga-FAPI PET/CT demonstrated superiority over F-FDG PET/CT in the evaluation of various cancer types, especially in gastric cancer (GC). By comprehensively reviewing and analysing the differences between Ga-FAPI and F-FDG in GC, some evidence is provided to foster the broader clinical application of FAPI PET imaging. In this review, studies published up to July 3, 2023, that employed radionuclide labelled FAPI as a diagnostic radiotracer for PET in GC were analysed. These studies were sourced from both the PubMed and Web of Science databases. Our statistical analysis involved a bivariate meta-analysis of the diagnostic data and a meta-analysis of the quantitative metrics. These were performed using R language. The meta-analysis included 14 studies, with 527 patients, of which 358 were diagnosed with GC. Overall, Ga-FAPI showed higher pooled sensitivity (0.84 [95% CI 0.67-0.94] 0.46 [95% CI 0.32-0.60]), specificity (0.91 [95% CI 0.76-0.98] 0.88 [95% CI 0.74-0.96]) and area under the curve (AUC) (0.92 [95% CI 0.77-0.98] 0.52 [95% CI 0.38-0.86]) than F-FDG. The evidence showed superior pooled sensitivities of Ga-FAPI PET over F-FDG for primary tumours, local recurrence, lymph node metastases, distant metastases, and peritoneal metastases. Furthermore, Ga-FAPI PET provided higher maximum standardized uptake value (SUVmax) and tumour-to-background ratios (TBR). For bone metastases, while Ga-FAPI PET demonstrated slightly lower patient-based pooled sensitivity (0.93 1.00), it significantly outperformed F-FDG in the lesion-based analysis (0.95 0.65). However, SUVmax (mean difference [MD] 1.79 [95% CI -3.87-7.45]) and TBR (MD 5.01 [95% CI -0.78-10.80]) of bone metastases showed no significant difference between Ga-FAPI PET/CT and F-FDG PET/CT. Compared with F-FDG, Ga-FAPI PET imaging showed improved diagnostic accuracy in the evaluation of GC. It can be effectively applied to the early diagnosis, initial staging, and detection of recurrence/metastases of GC. Ga-FAPI may have the potential of replacing F-FDG in GC in future applications.
Topics: Humans; Stomach Neoplasms; Positron Emission Tomography Computed Tomography; Fluorodeoxyglucose F18; Gallium Radioisotopes; Positron-Emission Tomography
PubMed: 37649615
DOI: 10.7150/thno.88335