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Acta Obstetricia Et Gynecologica... Mar 2024Depression and anxiety are significant contributors to maternal perinatal morbidity and a range of negative child outcomes. This systematic review and meta-analysis... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
Depression and anxiety are significant contributors to maternal perinatal morbidity and a range of negative child outcomes. This systematic review and meta-analysis aimed to review and assess the diagnostic test accuracy of selected screening tools (Edinburgh Postnatal Depression Scale [EPDS], EPDS-3A, Patient Health Questionnaire [PHQ-9]-, PHQ-2, Matthey Generic Mood Question [MGMQ], Generalized Anxiety Disorder scale [GAD-7], GAD-2, and the Whooley questions) used to identify women with antenatal depression or anxiety in Western countries.
MATERIAL AND METHODS
On January 16, 2023, we searched 10 databases (CINAHL, Cochrane Library, CRD Database, Embase, Epistemonikos, International HTA Database, KSR Evidence, Ovid MEDLINE, PROSPERO and PsycINFO); the references of included studies were also screened. We included studies of any design that compared case-identification with a relevant screening tool to the outcome of a diagnostic interview based on the Diagnostic and Statistical Manual of Mental Disorders, fourth or fifth edition (DSM-IV or DSM-5), or the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10). Diagnoses of interest were major depressive disorder and anxiety disorders. Two authors independently screened abstracts and full-texts for relevance and evaluated the risk of bias using QUADAS-2. Data extraction was performed by one person and checked by another team member for accuracy. For synthesis, a bivariate model was used. The certainty of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE).
REGISTRATION
PROSPERO CRD42021236333.
RESULTS
We screened 8276 records for eligibility and included 16 original articles reporting on diagnostic test accuracy: 12 for the EPDS, one article each for the GAD-2, MGMQ, PHQ-9, PHQ-2, and Whooley questions, and no articles for the EPDS-3A or GAD-7. Most of the studies had moderate to high risk of bias. Ten of the EPDS articles provided data for synthesis at cutoffs ≥10 to ≥14 for diagnosing major depressive disorder. Cutoff ≥10 gave the optimal combined sensitivity (0.84, 95% confidence interval [CI]: 0.75-0.90) and specificity (0.87, 95% CI: 0.79-0.92).
CONCLUSIONS
Findings from the meta-analysis suggest that the EPDS alone is not perfectly suitable for detection of major depressive disorder during pregnancy. Few studies have evaluated the other instruments, therefore, their usefulness for identification of women with depression and anxiety during pregnancy remains very uncertain. At present, case-identification with any tool may best serve as a complement to a broader dialogue between healthcare professionals and their patients.
Topics: Child; Female; Humans; Pregnancy; Depressive Disorder, Major; Depression; Mass Screening; Anxiety Disorders; Anxiety; Depression, Postpartum
PubMed: 38014572
DOI: 10.1111/aogs.14734 -
Journal of Medical Internet Research Nov 2021Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life... (Review)
Review
BACKGROUND
Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.
OBJECTIVE
This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.
METHODS
The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.
RESULTS
We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning-based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.
CONCLUSIONS
This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.
Topics: Algorithms; Bipolar Disorder; Data Management; Humans; Machine Learning; Natural Language Processing
PubMed: 34806996
DOI: 10.2196/29749 -
Journal of Stroke and Cerebrovascular... Nov 2023Stroke diagnosis is dependent on lengthy clinical and neuroimaging assessments, while rapid treatment initiation improves clinical outcome. Currently, more sensitive... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Stroke diagnosis is dependent on lengthy clinical and neuroimaging assessments, while rapid treatment initiation improves clinical outcome. Currently, more sensitive biomarker assays of both non-coding RNA- and protein biomarkers have improved their detectability, which could accelerate stroke diagnosis. This systematic review and meta-analysis compares non-coding RNA- with protein biomarkers for their potential to diagnose and differentiate acute stroke (subtypes) in (pre-)hospital settings.
METHODS
We performed a systematic review and meta-analysis of studies evaluating diagnostic performance of non-coding RNA- and protein biomarkers to differentiate acute ischemic and hemorrhagic stroke, stroke mimics, and (healthy) controls. Quality appraisal of individual studies was assessed using the QUADAS-2 tool while the meta-analysis was performed with the sROC approach and by assessing pooled sensitivity and specificity, diagnostic odds ratios, positive- and negative likelihood ratios, and the Youden Index.
SUMMARY OF REVIEW
112 studies were included in the systematic review and 42 studies in the meta-analysis containing 11627 patients with ischemic strokes, 2110 patients with hemorrhagic strokes, 1393 patients with a stroke mimic, and 5548 healthy controls. Proteins (IL-6 and S100 calcium-binding protein B (S100B)) and microRNAs (miR-30a) have similar performance in ischemic stroke diagnosis. To differentiate between ischemic- or hemorrhagic strokes, glial fibrillary acidic protein (GFAP) levels and autoantibodies to the NR2 peptide (NR2aAb, a cleavage product of NMDA neuroreceptors) were best performing whereas no investigated protein or non-coding RNA biomarkers differentiated stroke from stroke mimics with high diagnostic potential.
CONCLUSIONS
Despite sampling time differences, circulating microRNAs (< 24 h) and proteins (< 4,5 h) perform equally well in ischemic stroke diagnosis. GFAP differentiates stroke subtypes, while a biomarker panel of GFAP and UCH-L1 improved the sensitivity and specificity of UCH-L1 alone to differentiate stroke.
Topics: Humans; Hemorrhagic Stroke; Stroke; Biomarkers; Ischemic Stroke; Glial Fibrillary Acidic Protein; MicroRNAs; RNA, Untranslated
PubMed: 37778160
DOI: 10.1016/j.jstrokecerebrovasdis.2023.107388 -
European Respiratory Review : An... Dec 2022Thoracentesis and thoracoscopy are used to diagnose malignant pleural effusions (MPE). Data on how sensitivity varies with tumour type is limited. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Thoracentesis and thoracoscopy are used to diagnose malignant pleural effusions (MPE). Data on how sensitivity varies with tumour type is limited.
METHODS
Systematic review using PubMed was performed through August 2020 to determine the sensitivity of thoracentesis and thoracoscopy for MPE secondary to malignancy, by cancer type, and complication rates. Tests to identify sources of heterogeneity were performed. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 and National Institutes of Health quality assessment tools. Publication bias was tested using funnel plots.
RESULTS
Meta-analyses for sensitivity of thoracentesis for MPE secondary to malignancy, mesothelioma and lung and breast cancer included 29, eight, 12 and nine studies, respectively. Pooled sensitivities were 0.643 (95% CI 0.592-0.692), 0.451 (95% CI 0.249-0.661), 0.738 (95% CI 0.659-0.836) and 0.820 (95% CI 0.700-0.917), respectively. For sensitivity of thoracoscopy for MPE secondary to malignancy and mesothelioma, 41 and 15 studies were included, respectively. Pooled sensitivities were 0.929 (95% CI 0.905-0.95) and 0.915 (95% CI 0.871-0.952), respectively. Pooled complication rates of thoracentesis and thoracoscopy were 0.041 (95% CI 0.025-0.051) and 0.040 (95% CI 0.029-0.052), respectively. Heterogeneity was significant for all meta-analyses. Funnel plots were asymmetric.
INTERPRETATION
Sensitivity of thoracentesis varied significantly per cancer type. Pooled complication rates were low. Awareness of how sensitivity of thoracentesis changes across cancers can improve decision-making when MPE is suspected.
Topics: Humans; Thoracentesis; Retrospective Studies; Pleural Effusion, Malignant; Mesothelioma; Mesothelioma, Malignant; Thoracoscopy
PubMed: 36543349
DOI: 10.1183/16000617.0053-2022 -
World Journal of Clinical Cases Aug 2019Hepatocellular carcinoma (HCC) appears in most of cases in patients with advanced liver disease and is currently the primary cause of death in this population....
BACKGROUND
Hepatocellular carcinoma (HCC) appears in most of cases in patients with advanced liver disease and is currently the primary cause of death in this population. Surveillance of HCC has been proposed and recommended in clinical guidelines to obtain earlier diagnosis, but it is still controversial and is not accepted worldwide.
AIM
To review the actual evidence to support the surveillance programs in patients with cirrhosis as well as the diagnosis procedure.
METHODS
Systematic review of recent literature of surveillance (tools, interval, cost-benefit, target population) and the role of imaging diagnosis (radiological non-invasive diagnosis, optimal modality and agents) of HCC.
RESULTS
The benefits of surveillance of HCC, mainly with ultrasonography, have been assessed in several prospective and retrospective analysis, although the percentage of patients diagnosed in surveillance programs is still low. Surveillance of HCC permits diagnosis in early stages allows better access to curative treatment and increases life expectancy in patients with cirrhosis. HCC is a tumor with special radiological characteristics in computed tomography and magnetic resonance imaging, which allows highly accurate diagnosis without routine biopsy confirmation. The actual recommendation is to perform biopsy only in indeterminate nodules.
CONCLUSION
The evidence supports the recommendation of performing surveillance of HCC in patients with cirrhosis susceptible of treatment, using ultrasonography every 6 mo. The diagnosis evaluation of HCC can be established based on noninvasive imaging criteria in patients with cirrhosis.
PubMed: 31531321
DOI: 10.12998/wjcc.v7.i16.2269 -
Diagnostics (Basel, Switzerland) Aug 2022There is a pressing demand for the development of cancer-specific diagnostic imaging tools, particularly for staging of pancreatic-, gastric- or cholangiocarcinoma, as... (Review)
Review
INTRODUCTION
There is a pressing demand for the development of cancer-specific diagnostic imaging tools, particularly for staging of pancreatic-, gastric- or cholangiocarcinoma, as current diagnostic imaging techniques, including CT, MRI and PET using FDG, are not fully adequate. The novel PET-tracer "FAPI" has the potential to visualize even small tumour deposits employing the tumour-specific expression of fibroblast-activating protein (FAP) in malignant cells.
METHODS
We performed a systematic review to select studies investigating the use of FAPI PET for staging pancreatic-, gastric- and cholangiocarcinoma (PROSPERO CRD42022329512). Patient-wise and lesion-wise comparisons were performed for primary tumour (T), lymph nodes (N), organ metastases (M) and peritoneal carcinomatosis (PC). Maximum standardized uptake values (SUVmax) and tumour-to-background ratios (TBR) were compared between PET using FAPI versus FDG (if reported).
RESULTS
Ten articles met the inclusion criteria. In all studies, FAPI PET showed superiority over FDG-PET/CT/MRI for the detection of T, N, M and PC, both in the patient-wise and in lesion-wise comparisons (when performed). Additionally, higher SUVmax and TBRmax values were reported for use of FAPI compared to FDG.
CONCLUSIONS
The positive results of this review warrant prospective clinical studies to investigate the accuracy and clinical value of FAPI PET for diagnosing and staging patients with pancreatic-, gastric- and cholangiocarcinoma.
PubMed: 36010308
DOI: 10.3390/diagnostics12081958 -
The Lancet. Microbe Oct 2023Pulmonary tuberculosis due to Mycobacterium tuberculosis can be challenging to diagnose when sputum samples cannot be obtained, which is especially problematic in... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Pulmonary tuberculosis due to Mycobacterium tuberculosis can be challenging to diagnose when sputum samples cannot be obtained, which is especially problematic in children and older people. We systematically appraised the performance characteristics and diagnostic accuracy of upper respiratory tract sampling for diagnosing active pulmonary tuberculosis.
METHODS
In this systematic review and meta-analysis, we searched MEDLINE, Cinahl, Web of Science, Global Health, and Global Health Archive databases for studies published between database inception and Dec 6, 2022 that reported on the accuracy of upper respiratory tract sampling for tuberculosis diagnosis compared with microbiological testing of sputum or gastric aspirate reference standard. We included studies that evaluated the accuracy of upper respiratory tract sampling (laryngeal swabs, nasopharyngeal aspirate, oral swabs, saliva, mouth wash, nasal swabs, plaque samples, and nasopharyngeal swabs) to be tested for microbiological diagnosis of tuberculous (by culture and nucleic acid amplification tests) compared with a reference standard using either sputum or gastric lavage for a microbiological test. We included cohort, case-control, cross-sectional, and randomised controlled studies that recruited participants from any community or clinical setting. We excluded post-mortem studies. We used a random-effects meta-analysis with a bivariate hierarchical model to estimate pooled sensitivity, specificity, and diagnostics odds ratio (DOR; odds of a positive test with disease relative to without), stratified by sampling method. We assessed bias using QUADAS-2 criteria. This study is registered with PROSPERO (CRD42021262392).
FINDINGS
We screened 10 159 titles for inclusion, reviewed 274 full texts, and included 71, comprising 119 test comparisons published between May 13, 1933, and Dec 19, 2022, in the systematic review (53 in the meta-analysis). For laryngeal swabs, pooled sensitivity was 57·8% (95% CI 50·5-65·0), specificity was 93·8% (88·4-96·8), and DOR was 20·7 (11·1-38·8). Nasopharyngeal aspirate sensitivity was 65·2% (52·0-76·4), specificity was 97·9% (96·0-99·0), and DOR was 91·0 (37·8-218·8). Oral swabs sensitivity was 56·7% (44·3-68·2), specificity was 91·3% (CI 81·0-96·3), and DOR was 13·8 (5·6-34·0). Substantial heterogeneity in diagnostic accuracy was found, probably due to differences in reference and index standards.
INTERPRETATION
Upper respiratory tract sampling holds promise to expand access to tuberculosis diagnosis. Exploring historical methods using modern microbiological techniques might further increase options for alternative sample types. Prospective studies are needed to optimise accuracy and utility of sampling methods in clinical practice.
FUNDING
UK Medical Research Council, Wellcome, and UK Foreign, Commonwealth and Development Office.
Topics: Child; Humans; Aged; Mycobacterium tuberculosis; Cross-Sectional Studies; Sensitivity and Specificity; Tuberculosis; Tuberculosis, Pulmonary; Respiratory System
PubMed: 37714173
DOI: 10.1016/S2666-5247(23)00190-8 -
Seminars in Arthritis and Rheumatism Oct 2022Giant cell arteritis (GCA) and polymyalgia rheumatica (PMR) can be concurrent diseases. We aimed to estimate the point-prevalence of concurrent GCA and PMR.... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
Giant cell arteritis (GCA) and polymyalgia rheumatica (PMR) can be concurrent diseases. We aimed to estimate the point-prevalence of concurrent GCA and PMR. Additionally, an incidence rate (IR) of GCA presenting after PMR diagnosis in patients was estimated.
METHODS
Two authors performed a systematic literature search, data extraction and risk of bias assessment independently. Studies assessing cohorts of patients presenting with both GCA and PMR were included. The outcomes were point-prevalence of concurrent GCA and PMR and IR for development of GCA after PMR diagnosis. A meta-analysis was performed to calculate a pooled prevalence of concurrent PMR and GCA.
RESULTS
We identified 29 studies investigating concurrent GCA and PMR. Only two studies applied imaging systematically to diagnose GCA and none to diagnose PMR. GCA presenting after PMR diagnosis was assessed in 12 studies but imaging was not applied systematically. The point-prevalence of concurrent GCA present at PMR diagnosis ranged from 6%-66%. The pooled estimate of the point-prevalence from the meta-analysis was 22%. The point-prevalence of PMR present at GCA diagnosis ranged from 16%-65%. The pooled estimate of the point-prevalence from the meta-analysis was 42%. The IR ranged between 2-78 cases of GCA presenting after PMR per 1000 person-years.
CONCLUSION
This review and meta-analysis support that concurrent GCA and PMR is frequently present at the time of diagnosis. Additionally, we present the current evidence of GCA presenting in patients after PMR diagnosis. These results emphasize the need for studies applying imaging modalities to diagnose GCA.
Topics: Diagnostic Imaging; Giant Cell Arteritis; Humans; Incidence; Polymyalgia Rheumatica; Prevalence
PubMed: 35858507
DOI: 10.1016/j.semarthrit.2022.152069 -
Journal of Obstetrics and Gynaecology :... Dec 2024The diagnosis of endometriomas in patients with endometriosis is of primary importance because it influences the management and prognosis of infertility and pain.... (Meta-Analysis)
Meta-Analysis Review
INTRODUCTION
The diagnosis of endometriomas in patients with endometriosis is of primary importance because it influences the management and prognosis of infertility and pain. Imaging techniques are evolving constantly. This study aimed to systematically assess the diagnostic accuracy of transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) in detecting endometrioma using the surgical visualisation of lesions with or without histopathological confirmation as reference standards in patients of reproductive age with suspected endometriosis.
METHODS
PubMed, Embase, Web of Science, Cumulative Index to Nursing and Allied Health Literature and ClinicalTrials.gov databases were searched from their inception to 12 October 2022, using a manual search for additional articles. Two authors independently performed title, abstract and full-text screening of the identified records, extracted study details and quantitative data and assessed the quality of the studies using the 'Quality Assessment of Diagnostic Accuracy Study 2' tool. Bivariate random-effects models were used to determine the pooled sensitivity and specificity, compare the two imaging modalities and evaluate the sources of heterogeneity.
RESULTS
Sixteen prospective studies (10 assessing TVUS, 4 assessing MRI and 2 assessing both TVUS and MRI) were included, representing 1976 participants. Pooled TVUS and MRI sensitivities for endometrioma were 0.89 (95% confidence interval 'CI', 0.86-0.92) and 0.94 (95% CI, 0.74-0.99), respectively (indirect comparison -value of 0.47). Pooled TVUS and MRI specificities for endometrioma were 0.95 (95% CI, 0.92-0.97) and 0.94 (95% CI, 0.89-0.97), respectively (indirect comparison p-value of 0.51). These studies had a high or unclear risk of bias. A direct comparison (all participants undergoing TVUS and MRI) of the modalities was available in only two studies.
CONCLUSION
TVUS and MRI have high accuracy for diagnosing endometriomas; however, high-quality studies comparing the two modalities are lacking.
Topics: Female; Humans; Endometriosis; Prospective Studies; Ultrasonography; Magnetic Resonance Imaging; Sensitivity and Specificity; Diagnostic Tests, Routine
PubMed: 38348799
DOI: 10.1080/01443615.2024.2311664 -
Caries Research 2022We performed a systematic review to evaluate the success of machine learning algorithms in the diagnosis and prognostic prediction of dental caries. The review protocol...
We performed a systematic review to evaluate the success of machine learning algorithms in the diagnosis and prognostic prediction of dental caries. The review protocol was a priori registered in the PROSPERO, CRD42020183447. The search involved electronic bibliographic databases: PubMed/Medline, Scopus, EMBASE, Web of Science, and grey literature until December 2020. We excluded review articles, case series, case reports, editorials, letters, comments, educational methodologies, assessments of robotic devices, and articles with less than 10 participants or specimens. Two independent reviewers selected the studies and performed the assessment of the methodological quality based on standardized scales. We summarize data on the machine learning algorithms used; software; performance outcomes such as accuracy/precision, sensitivity/recall, specificity, area under the receiver operating characteristic curve (AUC), and positive/negative predictive values related to dental caries. Meta-analyses were not performed due to methodological differences. Our review included 15 studies (10 diagnostic studies and 5 prognostic prediction studies). Cross-sectional design studies were predominant (12). The most frequently used statistical measure of performance reported in diagnostic studies was AUC value, which ranged from 0.745 to 0.987. For most diagnostic studies, data from contingency tables were not available. Reported sensitivities were higher in low risk of bias prognostic prediction studies (median [IQR] of 0.996 [0.971-1.000] vs. unclear/high risk of bias studies 0.189 [0-0.340]; p value 0.025). While there were no significant differences in the specificity between these subgroups, we concluded that the use of these technologies for the diagnosis and prognostic prediction of dental caries, although promising, is at an early stage. The general applicability of the evidence was limited given that most models were developed outside the real clinical setting with a prevalence of unclear/high risk of bias. Researchers must increase the overall quality of their research protocols by providing a comprehensive report on the methods implemented.
Topics: Humans; Prognosis; Dental Caries; Cross-Sectional Studies; Machine Learning; Algorithms
PubMed: 35636386
DOI: 10.1159/000524167