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Journal of Cachexia, Sarcopenia and... Feb 2023Muscle ultrasound is an emerging tool for diagnosing sarcopenia. This review aims to summarize the current knowledge on the diagnostic test accuracy of ultrasound for... (Meta-Analysis)
Meta-Analysis Review
Muscle ultrasound is an emerging tool for diagnosing sarcopenia. This review aims to summarize the current knowledge on the diagnostic test accuracy of ultrasound for the diagnosis of sarcopenia. We collected data from Ovid Medline, Embase and the Cochrane Central Register of Controlled Trials. Diagnostic test accuracy studies using muscle ultrasound to detect sarcopenia were included. Bivariate random-effects models based on sensitivity and specificity pairs were used to calculate the pooled estimates of sensitivity, specificity and the area under the curves (AUCs) of summary receiver operating characteristic (SROC), if possible. We screened 7332 publications and included 17 studies with 2143 participants (mean age range: 52.6-82.8 years). All included studies had a high risk of bias. The study populations, reference standards and ultrasound measurement methods varied across the studies. Lower extremity muscles were commonly studied, whereas muscle thickness (MT) was the most widely measured parameter, followed by the cross-sectional area (CSA). The MTs of the gastrocnemius, rectus femoris, tibialis anterior, soleus, rectus abdominis and geniohyoid muscles showed a moderate diagnostic accuracy for sarcopenia (SROC-AUC 0.83, 8 studies; SROC-AUC 0.78, 5 studies; AUC 0.82, 1 study; AUC 0.76-0.78, 2 studies; AUC 0.76, 1 study; and AUC 0.79, 1 study, respectively), whereas the MTs of vastus intermedius, quadriceps femoris and transversus abdominis muscles showed a low diagnostic accuracy (AUC 0.67-0.71, 3 studies; SROC-AUC 0.64, 4 studies; and AUC 0.68, 1 study, respectively). The CSA of rectus femoris, biceps brachii muscles and gastrocnemius fascicle length also showed a moderate diagnostic accuracy (AUC 0.70-0.90, 3 studies; 0.81, 1 study; and 0.78-0.80, 1 study, respectively), whereas the echo intensity (EI) of rectus femoris, vastus intermedius, quadriceps femoris and biceps brachii muscles showed a low diagnostic accuracy (AUC 0.52-0.67, 2 studies; 0.48-0.50, 1 study; 0.43-0.49, 1 study; and 0.69, 1 study, respectively). The combination of CSA and EI of biceps brachii or rectus femoris muscles was better than either CSA or EI alone for diagnosing sarcopenia. Muscle ultrasound shows a low-to-moderate diagnostic test accuracy for sarcopenia diagnosis depending on different ultrasound parameters, measured muscles, reference standards and study populations. The combination of muscle quality indicators (e.g., EI) and muscle quantity indicators (e.g., MT) might provide better diagnostic test accuracy.
Topics: Humans; Middle Aged; Aged; Aged, 80 and over; Sarcopenia; Quadriceps Muscle; Ultrasonography; Rectus Abdominis; Diagnostic Tests, Routine
PubMed: 36513380
DOI: 10.1002/jcsm.13149 -
European Geriatric Medicine Oct 2022Community-acquired pneumonia (CAP) is highly common across the world. It is reported that over 90% of CAP in older adults may be due to aspiration. However, the... (Review)
Review
PURPOSE
Community-acquired pneumonia (CAP) is highly common across the world. It is reported that over 90% of CAP in older adults may be due to aspiration. However, the diagnostic criteria for aspiration pneumonia (AP) have not been widely agreed. Is there a consensus on how to diagnose AP? What are the clinical features of patients being diagnosed with AP? We conducted a systematic review to answer these questions.
METHODS
We performed a literature search in MEDLINE, EMBASE, CINHAL, and Cochrane to review the steps taken toward diagnosing AP. Search terms for "aspiration pneumonia" and "aged" were used. Inclusion criteria were: original research, community-acquired AP, age ≥ 75 years old, acute hospital admission.
RESULTS
A total of 10,716 reports were found. Following the removal of duplicates, 7601 were screened, 95 underwent full-text review, and 9 reports were included in the final analysis. Pneumonia was diagnosed using a combination of symptoms, inflammatory markers, and chest imaging findings in most studies. AP was defined as pneumonia with some relation to aspiration or dysphagia. Aspiration was inferred if there was witnessed or prior presumed aspiration, episodes of coughing on food or liquids, relevant underlying conditions, abnormalities on videofluoroscopy or water swallow test, and gravity-dependent distribution of shadows on chest imaging. Patients with AP were older, more frailer, and had more comorbidities than in non-AP.
CONCLUSION
There is a broad consensus on the clinical criteria to diagnose AP. It is a presumptive diagnosis with regards to patients' general frailty rather than in relation to swallowing function itself.
Topics: Aged; Community-Acquired Infections; Deglutition; Humans; Pneumonia; Pneumonia, Aspiration; Water
PubMed: 36008745
DOI: 10.1007/s41999-022-00689-3 -
International Journal of Molecular... Mar 2021Alzheimer's disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on...
BACKGROUND
Alzheimer's disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) levels of amyloid-β1-42 (Aβ42), total tau protein and hyperphosphorylated tau (p-tau). However, the available methods are expensive and relatively invasive. Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis.
METHODS
We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD.
METHODS
We searched PubMed, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews for reviews and trials that investigated the machine learning and novel biomarkers in diagnosis of AD.
RESULTS
In additional to Aβ and tau-related biomarkers, biomarkers according to other mechanisms of AD pathology have been investigated. Neuronal injury biomarker includes neurofiliament light (NFL). Biomarkers about synaptic dysfunction and/or loss includes neurogranin, BACE1, synaptotagmin, SNAP-25, GAP-43, synaptophysin. Biomarkers about neuroinflammation includes sTREM2, and YKL-40. Besides, d-glutamate is one of coagonists at the NMDARs. Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy controls.
CONCLUSIONS
Our results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing AD in outpatient clinics.
Topics: Aged; Alzheimer Disease; Biomarkers; Chromatography, High Pressure Liquid; Diagnosis, Computer-Assisted; Female; Humans; Machine Learning; Middle Aged
PubMed: 33803217
DOI: 10.3390/ijms22052761 -
JAMA Network Open Apr 2021Reported increases in attention-deficit/hyperactivity disorder (ADHD) diagnoses are accompanied by growing debate about the underlying factors. Although overdiagnosis is...
IMPORTANCE
Reported increases in attention-deficit/hyperactivity disorder (ADHD) diagnoses are accompanied by growing debate about the underlying factors. Although overdiagnosis is often suggested, no comprehensive evaluation of evidence for or against overdiagnosis has ever been undertaken and is urgently needed to enable evidence-based, patient-centered diagnosis and treatment of ADHD in contemporary health services.
OBJECTIVE
To systematically identify, appraise, and synthesize the evidence on overdiagnosis of ADHD in children and adolescents using a published 5-question framework for detecting overdiagnosis in noncancer conditions.
EVIDENCE REVIEW
This systematic scoping review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Extension for Scoping Reviews and Joanna Briggs Methodology, including the PRISMA-ScR Checklist. MEDLINE, Embase, PsychINFO, and the Cochrane Library databases were searched for studies published in English between January 1, 1979, and August 21, 2020. Studies of children and adolescents (aged ≤18 years) with ADHD that focused on overdiagnosis plus studies that could be mapped to 1 or more framework question were included. Two researchers independently reviewed all abstracts and full-text articles, and all included studies were assessed for quality.
FINDINGS
Of the 12 267 potentially relevant studies retrieved, 334 (2.7%) were included. Of the 334 studies, 61 (18.3%) were secondary and 273 (81.7%) were primary research articles. Substantial evidence of a reservoir of ADHD was found in 104 studies, providing a potential for diagnoses to increase (question 1). Evidence that actual ADHD diagnosis had increased was found in 45 studies (question 2). Twenty-five studies showed that these additional cases may be on the milder end of the ADHD spectrum (question 3), and 83 studies showed that pharmacological treatment of ADHD was increasing (question 4). A total of 151 studies reported on outcomes of diagnosis and pharmacological treatment (question 5). However, only 5 studies evaluated the critical issue of benefits and harms among the additional, milder cases. These studies supported a hypothesis of diminishing returns in which the harms may outweigh the benefits for youths with milder symptoms.
CONCLUSIONS AND RELEVANCE
This review found evidence of ADHD overdiagnosis and overtreatment in children and adolescents. Evidence gaps remain and future research is needed, in particular research on the long-term benefits and harms of diagnosing and treating ADHD in youths with milder symptoms; therefore, practitioners should be mindful of these knowledge gaps, especially when identifying these individuals and to ensure safe and equitable practice and policy.
Topics: Adolescent; Attention Deficit Disorder with Hyperactivity; Child; Humans; Medical Overuse; Practice Patterns, Physicians'; Severity of Illness Index
PubMed: 33843998
DOI: 10.1001/jamanetworkopen.2021.5335 -
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal.BMJ (Clinical Research Ed.) Apr 2020To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients...
OBJECTIVE
To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.
DESIGN
Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.
DATA SOURCES
PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.
STUDY SELECTION
Studies that developed or validated a multivariable covid-19 related prediction model.
DATA EXTRACTION
At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).
RESULTS
37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.
CONCLUSION
Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.
SYSTEMATIC REVIEW REGISTRATION
Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.
READERS' NOTE
This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
Topics: COVID-19; Coronavirus; Coronavirus Infections; Disease Progression; Hospitalization; Humans; Models, Theoretical; Multivariate Analysis; Pandemics; Pneumonia, Viral; Prognosis
PubMed: 32265220
DOI: 10.1136/bmj.m1328 -
Journal of Hepatology Oct 2021Vibration-controlled transient elastography (VCTE), point shear wave elastography (pSWE), 2-dimensional shear wave elastography (2DSWE), magnetic resonance elastography... (Meta-Analysis)
Meta-Analysis
BACKGROUND AND AIMS
Vibration-controlled transient elastography (VCTE), point shear wave elastography (pSWE), 2-dimensional shear wave elastography (2DSWE), magnetic resonance elastography (MRE), and magnetic resonance imaging (MRI) have been proposed as non-invasive tests for patients with non-alcoholic fatty liver disease (NAFLD). This study evaluated their diagnostic accuracy for liver fibrosis and non-alcoholic steatohepatitis (NASH).
METHODS
PubMED/MEDLINE, EMBASE and the Cochrane Library were searched for studies examining the diagnostic accuracy of these index tests, against histology as the reference standard, in adult patients with NAFLD. Two authors independently screened and assessed methodological quality of studies and extracted data. Summary estimates of sensitivity, specificity and area under the curve (sAUC) were calculated for fibrosis stages and NASH, using a random effects bivariate logit-normal model.
RESULTS
We included 82 studies (14,609 patients). Meta-analysis for diagnosing fibrosis stages was possible in 53 VCTE, 11 MRE, 12 pSWE and 4 2DSWE studies, and for diagnosing NASH in 4 MRE studies. sAUC for diagnosis of significant fibrosis were: 0.83 for VCTE, 0.91 for MRE, 0.86 for pSWE and 0.75 for 2DSWE. sAUC for diagnosis of advanced fibrosis were: 0.85 for VCTE, 0.92 for MRE, 0.89 for pSWE and 0.72 for 2DSWE. sAUC for diagnosis of cirrhosis were: 0.89 for VCTE, 0.90 for MRE, 0.90 for pSWE and 0.88 for 2DSWE. MRE had sAUC of 0.83 for diagnosis of NASH. Three (4%) studies reported intention-to-diagnose analyses and 15 (18%) studies reported diagnostic accuracy against pre-specified cut-offs.
CONCLUSIONS
When elastography index tests are acquired successfully, they have acceptable diagnostic accuracy for advanced fibrosis and cirrhosis. The potential clinical impact of these index tests cannot be assessed fully as intention-to-diagnose analyses and validation of pre-specified thresholds are lacking.
LAY SUMMARY
Non-invasive tests that measure liver stiffness or use magnetic resonance imaging (MRI) have been suggested as alternatives to liver biopsy for assessing the severity of liver scarring (fibrosis) and fatty inflammation (steatohepatitis) in patients with non-alcoholic fatty liver disease (NAFLD). In this study, we summarise the results of previously published studies on how accurately these non-invasive tests can diagnose liver fibrosis and inflammation, using liver biopsy as the reference. We found that some techniques that measure liver stiffness had a good performance for the diagnosis of severe liver scarring.
Topics: Adult; Area Under Curve; Elasticity Imaging Techniques; Humans; Magnetic Resonance Imaging; Non-alcoholic Fatty Liver Disease; ROC Curve
PubMed: 33991635
DOI: 10.1016/j.jhep.2021.04.044 -
Pediatric Emergency Care Sep 2020The aims of the study were to perform the first systematic review of pediatric syncope etiologies and to determine the most common diagnoses with credible intervals...
OBJECTIVES
The aims of the study were to perform the first systematic review of pediatric syncope etiologies and to determine the most common diagnoses with credible intervals (CredIs).
METHODS
Review was performed within Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and used Embase, Scopus, PubMed, and the Cochrane Controlled Trial databases. The following inclusion criteria for the articles were used: minimum of 10 patients, standard definition of syncope used, subjects who were 21 years or younger, and subjects who were either a consecutive retrospective group or a prospective group. No restrictions were made regarding language of the studies, but an English abstract was required. The following information was collected: purpose of the study, definition of syncope, number of patients, patient age range, inclusion/exclusion criteria, and etiologies of syncope.
RESULTS
Of the 500 articles initially identified, 11 studies met the inclusion criteria and were the basis for this review. Three thousand seven hundred patients were included, ranging in age from 3 months to 21 years. The most common etiologies identified were vasovagal (52.2%; 95% CredI, 50.6-53.9), postural orthostatic tachycardia syndrome (13.1%; 95% CredI, 12.1-14.2), and cardiac causes (4.0%; 95% CredI, 3.39-4.65). A total of 18.3% (95% CredI, 17.0-19.5) of patients were found to have syncope of unknown cause.
CONCLUSIONS
Syncope is a common pediatric complaint. Most cases seen are a result of benign causes, with only a small percentage because of serious medical conditions. In addition, most syncopal episodes in the pediatric population are diagnosed clinically or with minimally invasive testing, emphasizing the importance of a detailed history and physical examination.
Topics: Child; Diagnosis, Differential; Humans; Medical History Taking; Physical Examination; Syncope
PubMed: 32530839
DOI: 10.1097/PEC.0000000000002149 -
The Australian and New Zealand Journal... Feb 2023Autism spectrum disorders and personality disorders are spectrum conditions with shared clinical features. Despite similarities, previous attempts to synthesise... (Review)
Review
OBJECTIVES
Autism spectrum disorders and personality disorders are spectrum conditions with shared clinical features. Despite similarities, previous attempts to synthesise literature on co-existing prevalence and shared traits have employed a unidirectional focus, assessing personality characteristics of individuals with an autism spectrum disorder diagnosis. Here, we assess the prevalence of autism spectrum disorder diagnosis and/or traits among persons diagnosed with a personality disorder.
METHODS
We systematically reviewed the English-language literature following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, according to a pre-registered protocol (PROSPERO: CRD 42021264106). Peer-reviewed quantitative studies reporting the prevalence of autism spectrum disorder diagnosis or traits in persons with an established personality disorder diagnosis were included. Studies were critically appraised using the Appraisal tool for Cross-Sectional Studies.
RESULTS
Fifteen studies were identified, including 72,902 participants (median: 48, interquartile range: 30-77). Diagnoses included borderline, schizotypal and obsessive-compulsive personality disorders, and cohorts with unspecified personality disorder diagnoses. There was significant heterogeneity in diagnostic methodology and assessment tools used. We identified preliminary evidence of an increased prevalence of co-existing autism spectrum disorder diagnosis and traits among those diagnosed with a personality disorder, although significant limitations of the literature were identified.
CONCLUSION
Our research suggests clinicians should consider conducting a careful developmental assessment when assessing service-users with possible or confirmed personality disorder. Future research directions may include larger studies featuring clinical control groups, an exploration of shared and differentiating behavioural-cognitive features of the two conditions, and investigation into potentially shared aetiological factors. Research investigating demographic factors that may contribute to potential diagnostic overshadowing would also be welcomed.
Topics: Humans; Adult; Adolescent; Autism Spectrum Disorder; Prevalence; Cross-Sectional Studies; Personality Disorders; Obsessive-Compulsive Disorder
PubMed: 35986511
DOI: 10.1177/00048674221114603 -
The Journal of Clinical Endocrinology... Mar 2020Signs and symptoms of Cushing's syndrome (CS) overlap with common diseases, such as the metabolic syndrome, obesity, osteoporosis, and depression. Therefore, it can take... (Meta-Analysis)
Meta-Analysis
CONTEXT
Signs and symptoms of Cushing's syndrome (CS) overlap with common diseases, such as the metabolic syndrome, obesity, osteoporosis, and depression. Therefore, it can take years to finally diagnose CS, although early diagnosis is important for prevention of complications.
OBJECTIVE
The aim of this study was to assess the time span between first symptoms and diagnosis of CS in different populations to identify factors associated with an early diagnosis.
DATA SOURCES
A systematic literature search via PubMed was performed to identify studies reporting on time to diagnosis in CS. In addition, unpublished data from patients of our tertiary care center and 4 other centers were included.
STUDY SELECTION
Clinical studies reporting on the time to diagnosis of CS were eligible. Corresponding authors were contacted to obtain additional information relevant to the research question.
DATA EXTRACTION
Data were extracted from the text of the retrieved articles and from additional information provided by authors contacted successfully. From initially 3326 screened studies 44 were included.
DATA SYNTHESIS
Mean time to diagnosis for patients with CS was 34 months (ectopic CS: 14 months; adrenal CS: 30 months; and pituitary CS: 38 months; P < .001). No difference was found for gender, age (<18 and ≥18 years), and year of diagnosis (before and after 2000). Patients with pituitary CS had a longer time to diagnosis in Germany than elsewhere.
CONCLUSIONS
Time to diagnosis differs for subtypes of CS but not for gender and age. Time to diagnosis remains to be long and requires to be improved.
Topics: Age Factors; Cushing Syndrome; Delayed Diagnosis; Early Diagnosis; Humans; Sex Factors; Time Factors
PubMed: 31665382
DOI: 10.1210/clinem/dgz136 -
Journal of the American Medical... Jan 2022To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.
OBJECTIVE
To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.
MATERIALS AND METHODS
PubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted.
RESULTS
The clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies.
DISCUSSION
Studies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units.
CONCLUSIONS
Approaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.
Topics: Humans; Machine Learning; Natural Language Processing; Sepsis; Shock, Septic; Vital Signs
PubMed: 34897469
DOI: 10.1093/jamia/ocab236