Meta-analysis Of Diagnostic Accuracy Research Articles

Accuracy of transabdominal ultrasound to diagnose functional constipation and fecal impaction in children: a systematic review and meta-analysis.

Functional constipation is common in children and accurate diagnostic methods are essential for early diagnosis and effective management. The diagnostic accuracy of transabdominal ultrasound to diagnose functional constipation is unclear. To evaluate the diagnostic accuracy of transverse rectal diameter measurement via transabdominal ultrasound in diagnosing children with functional constipation and in identifying fecal impaction. Electronic databases were searched from inception to March 2023. Original studies investigating the diagnostic accuracy of measuring transverse rectal diameter via transabdominal ultrasound, including children with and without functional constipation, or with and without fecal impaction were included. Data extraction and quality assessment were performed independently by two reviewers. Sixteen studies were included (n = 1,801 children, 0-17years). Thirteen studies investigated the diagnostic accuracy for functional constipation, and five for fecal impaction. High risk of bias was found across the majority of studies mainly due to un-blinded case-control designs. Cut-off transverse rectal diameter values to diagnose functional constipation ranged from 2.4cm to 3.8cm. Meta-analysis (seven studies, n = 509 children) estimated mean sensitivity and specificity to diagnose functional constipation were 0.68 (95% confidence interval (CI) 0.55-0.78) and 0.81 (95% CI 0.71-0.88), respectively. Meta-analysis of diagnostic accuracy of identifying fecal impaction was not feasible. Studies reported a sensitivity and specificity ranging between 68-100% and 83-100%, respectively. Transabdominal ultrasound may be a valuable non-invasive diagnostic tool to diagnose functional constipation by measuring transverse rectal diameter and identifying fecal impaction in children. Heterogeneous study methods and lack of age-dependent normal values impair current clinical recommendations. Future research should focus on separating age groups and developing a standardized protocol.

Skin Cancer Diagnosis by Lesion, Physician, and Examination Type: A Systematic Review and Meta-Analysis.

Skin cancer is the most common cancer in the US; accurate detection can minimize morbidity and mortality. To assess the accuracy of skin cancer diagnosis by lesion type, physician specialty and experience, and physical examination method. PubMed, Embase, and Web of Science. Cross-sectional and case-control studies, randomized clinical trials, and nonrandomized controlled trials that used dermatologists or primary care physicians (PCPs) to examine keratinocytic and/or melanocytic skin lesions were included. Search terms, study objectives, and protocol methods were defined before study initiation. Data extraction was performed by a reviewer, with verification by a second reviewer. A mixed-effects model was used in the data analysis. Data analyses were performed from May 2022 to December 2023. Meta-analysis of diagnostic accuracy comprised sensitivity and specificity by physician type (primary care physician or dermatologist; experienced or inexperienced) and examination method (in-person clinical examination and/or clinical images vs dermoscopy and/or dermoscopic images). In all, 100 studies were included in the analysis. With experienced dermatologists using clinical examination and clinical images, the sensitivity and specificity for diagnosing keratinocytic carcinomas were 79.0% and 89.1%, respectively; using dermoscopy and dermoscopic images, sensitivity and specificity were 83.7% and 87.4%, and for PCPs, 81.4% and 80.1%. Experienced dermatologists had 2.5-fold higher odds of accurate diagnosis of keratinocytic carcinomas using in-person dermoscopy and dermoscopic images compared with in-person clinical examination and images. When examining for melanoma using clinical examination and images, sensitivity and specificity were 76.9% and 89.1% for experienced dermatologists, 78.3% and 66.2% for inexperienced dermatologists, and 37.5% and 84.6% for PCPs, respectively; whereas when using dermoscopy and dermoscopic images, sensitivity and specificity were 85.7% and 81.3%, 78.0% and 69.5%, and 49.5% and 91.3%, respectively. Experienced dermatologists had 5.7-fold higher odds of accurate diagnosis of melanoma using dermoscopy compared with clinical examination. Compared with PCPs, experienced dermatologists had 13.3-fold higher odds of accurate diagnosis of melanoma using dermoscopic images. The findings of this systematic review and meta-analysis indicate that there are significant differences in diagnostic accuracy for skin cancer when comparing physician specialty and experience, and examination methods. These summary metrics of clinician diagnostic accuracy could be useful benchmarks for clinical trials, practitioner training, and the performance of emerging technologies.

Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy.

To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in lung cancer through a systematic review and meta-analysis. Relevant studies were identified through a systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from October 2003 to December 2023. Additional studies were located by reviewing bibliographies and relevant websites. Two independent researchers screened titles, abstracts, and full-text articles according to predefined inclusion and exclusion criteria. Data extraction was performed using standardized forms, and study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The primary outcomes, including combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC), were calculated using STATA MP-64 software(Stata Corporation LLC, College Station, USA) with a random-effects model. Meta-analysis was conducted to synthesize diagnostic accuracy measures, and analyses of heterogeneity and publication bias were performed. A total of 1,406 patients with primary lung cancer were included in this systematic review, drawing data from 9 studies. The pooled analysis revealed a sensitivity of 0.74 [0.58-0.85] and a specificity of 0.91 [0.87-0.95] for machine learning models in diagnosing radiation pneumonitis. The positive likelihood ratio (PLR) was 8.69 [5.21-14.50], the negative likelihood ratio (NLR) was 0.28 [0.16-0.49], and the diagnostic odds ratio (DOR) was 30.73 [11.96-78.97]. The area under the curve (AUC) was 0.93 [0.90-0.95], indicating excellent diagnostic performance. Meta-regression analysis identified that the number of machine learning models, year of publication, and study design contributed to heterogeneity among studies. No evidence of publication bias was found. Overall, machine learning models incorporating multimodal characteristics demonstrated 75% accuracy in predicting moderate to severe radiation pneumonitis. In conclusion, by integrating the current machine learning (ML) algorithm's ability in big data mining, a predictive model can be constructed by combining multi-modal features such as genetics, imaging, and cell factors. By selecting multiple machine learning algorithm frameworks and competing for the best combination model based on research goals, the reliability and accuracy of the radiation pneumonitis prediction model can be greatly improved. PROSPERO (CRD42024497599).

Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.

This systematic review aims to evaluate the quality and accuracy of ML algorithms in predicting ATRX and IDH mutation status in patients with glioma through the analysis of radiomic features extracted from medical imaging. The potential clinical impacts and areas for further improvement in non-invasive glioma diagnosis, classification and prognosis are also identified and discussed. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic and Test Accuracy (PRISMA-DTA) statement. Databases including PubMed, Science Direct, CINAHL, Academic Search Complete, Medline, and Google Scholar were searched from inception to April 2024. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to assess the risk of bias and applicability concerns. Additionally, meta-regression identified covariates contributing to heterogeneity before a subgroup meta-analysis was conducted. Pooled sensitivities, specificities and area under the curve (AUC) values were calculated for the prediction of ATRX and IDH mutations. Eleven studies involving 1,685 patients with grade I-IV glioma were included. Primary contributors to heterogeneity included the MRI modalities utilised (conventional only vs. combined) and the types of ML models employed. The meta-analysis revealed pooled sensitivities of 0.682 for prediction of ATRX loss and 0.831 for IDH mutations, specificities of 0.874 and 0.828, and AUC values of 0.842 and 0.948, respectively. Interestingly, incorporating semantics and clinical data, including patient demographics, improved the diagnostic performance of ML models. The high AUC in the prediction of both mutations demonstrates an overall robust diagnostic performance of ML, indicating the potential for accurate, non-invasive diagnosis and precise prognosis. Future research should focus on integrating diverse data types, including advanced imaging, semantics and clinical data while also aiming to standardise the collection and integration of multimodal data. This approach will enhance clinical applicability and consistency.

Meta-Analysis of Diagnostic Accuracy Studies With Multiple Thresholds: Comparison of Approaches in a Simulation Study.

The development of methods for the meta-analysis of diagnostic test accuracy (DTA) studies is still an active area of research. While methods for the standard case where each study reports a single pair of sensitivity and specificity are nearly routinely applied nowadays, methods to meta-analyze receiver operating characteristic (ROC) curves are not widely used. This situation is more complex, as each primary DTA study may report on several pairs of sensitivity and specificity, each corresponding to a different threshold. In a case study published earlier, we applied a number of methods for meta-analyzing DTA studies with multiple thresholds to a real-world data example (Zapf etal., Biometrical Journal. 2021; 63(4): 699-711). To date, no simulation study exists that systematically compares different approaches with respect to their performance in various scenarios when the truth is known. In this article, we aim to fill this gap and present the results of a simulation study that compares three frequentist approaches for the meta-analysis of ROC curves. We performed a systematic simulation study, motivated by an example from medical research. In the simulations, all three approaches worked partially well. The approach by Hoyer and colleagues was slightly superior in most scenarios and is recommended in practice.

Delirium detection in the emergency department: A diagnostic accuracy meta-analysis of history, physical examination, laboratory tests, and screening instruments.

Geriatric emergency department (ED) guidelines emphasize timely identification of delirium. This article updates previous diagnostic accuracy systematic reviews of history, physical examination, laboratory testing, and ED screening instruments for the diagnosis of delirium as well as test-treatment thresholds for ED delirium screening. We conducted a systematic review to quantify the diagnostic accuracy of approaches to identify delirium. Studies were included if they described adults aged 60 or older evaluated in the ED setting with an index test for delirium compared with an acceptable criterion standard for delirium. Data were extracted and studies were reviewed for risk of bias. When appropriate, we conducted a meta-analysis and estimated delirium screening thresholds. Full-text review was performed on 55 studies and 27 were included in the current analysis. No studies were identified exploring the accuracy of findings on history or laboratory analysis. While two studies reported clinicians accurately rule in delirium, clinician gestalt is inadequate to rule out delirium. We report meta-analysis on three studies that quantified the accuracy of the 4 A's Test (4AT) to rule in (pooled positive likelihood ratio [LR+] 7.5, 95% confidence interval [CI] 2.7-20.7) and rule out (pooled negative likelihood ratio [LR-] 0.18, 95% CI 0.09-0.34) delirium. We also conducted meta-analysis of two studies that quantified the accuracy of the Abbreviated Mental Test-4 (AMT-4) and found that the pooled LR+ (4.3, 95% CI 2.4-7.8) was lower than that observed for the 4AT, but the pooled LR- (0.22, 95% CI 0.05-1) was similar. Based on one study the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) is the superior instrument to rule in delirium. The calculated test threshold is 2% and the treatment threshold is 11%. The quantitative accuracy of history and physical examination to identify ED delirium is virtually unexplored. The 4AT has the largest quantity of ED-based research. Other screening instruments may more accurately rule in or rule out delirium. If the goal is to rule in delirium then the CAM-ICU or brief CAM or modified CAM for the ED are superior instruments, although the accuracy of these screening tools are based on single-center studies. To rule out delirium, the Delirium Triage Screen is superior based on one single-center study.

Systematic Review and Meta-Analysis of Diagnostic and Predictive Accuracy of the Clinical Frailty Scale in Older Adults in Emergency Department

Abstract Background Older adults attending the Emergency Department (ED) should be assessed using a frailty screening tool as older adults living with frailty are more likely to experience adverse outcomes. The Clinical Frailty Scale (CFS) is commonly used for this. This systematic review and meta-analysis synthesise the literature studying predictive accuracy of the CFS for identifying older adults at risk of adverse outcomes following ED visit, and the diagnostic accuracy of the CFS for identifying older adults living with frailty attending the ED. Methods A systematic literature search was conducted to identify studies that attempted to validate the diagnostic and predictive accuracy of the CFS for older adults in the ED setting. The population included were older adults over 65 years of age who presented to the ED with undifferentiated medical complaints. All included studies assessed the diagnostic accuracy of the CFS in comparison to a reference standard: Frailty Index, Comprehensive Geriatric Assessment or Frailty Phenotype. The adverse outcomes included were mortality, falls, functional decline, ED re-attendance, hospital admission, length of stay and institutionalisation. Results Nineteen studies were included with sixteen individual cohorts. All studies investigated predictive accuracy and four studies assessed diagnostic accuracy for the CFS. The CFS showed predictive accuracy for the outcomes of mortality at all timepoints (RR 3.05 (CI 2.22, 4.21)), ED re-attendance at 30 days (RR 1.47 (CI 1.12, 1.94)), hospital admission at index visit (RR 1.72 (CI 1.26, 2.34) and institutionalisation (RR 3.63 (CI 1.41, 9.36). The studies included had an overall low risk of bias. Conclusion The CFS successfully identifies those at risk of adverse outcomes of death, ED revisit, hospitalisation and institutionalisation. However, there is a paucity of studies which assess its diagnostic accuracy. Overall, it is a useful screening tool to identify those who may benefit from comprehensive multidisciplinary assessment in the ED setting.

Bedside-focused transthoracic echocardiography in acute atraumatic thoracic aortic syndrome: a systematic review and meta-analysis of diagnostic accuracy.

The objective of this review is to assess the diagnostic accuracy of bedside-focused transthoracic echocardiography (TTE) in acute atraumatic thoracic aortic syndrome in adults. We performed a systematic review and meta-analysis of publications that described the use of bedside-focused TTE on adults presenting to the emergency care setting with suspected atraumatic thoracic aortic syndrome. Studies were identified using keyword and Medical Subject Heading searches on databases and grey literature, followed by abstract screening and study selection by two independent reviewers. Sixteen studies over six decades were included in the meta-analysis (n = 4569 patients). The prevalence of Type A thoracic aortic dissection was 11% (range 1.4-45.7%) and Type B dissection was 7% (range 1.8-30.55%). Type A dissection through direct visualisation of an intimal flap on TTE (i.e. direct sign) pooled sensitivity and specificity were 89% [95% confidence interval (CI), 82-94%] and 92% (95% CI, 88-95%) respectively. For Type B dissection, the pooled sensitivity was 65% (95% CI, 45-80%) and specificity was 100% (95% CI, 0.69-100%). TTE indirect signs for dissection showed a pooled sensitivity of 64% (95% CI, 5.2-98.2%) and specificity of 94% (95% CI, 92-96.1%) for aortic valve regurgitation, a pooled sensitivity of 92% (95% CI 54-99.2%) and specificity of 87% (95% CI, 62-97%) for thoracic aortic aneurysm and a pooled sensitivity of 39% (95% CI 33.8-45%) and a specificity of 94% (95% CI, 92-95%) for pericardial effusion. In this systematic review and meta-analysis, bedside-focused TTE has a good specificity for Type A and B dissection, but poor sensitivity for Type B, and unclear for intramural haematoma and penetrating aortic ulcer.

Diagnostic Accuracy of Conjunctival Rt-Pcr in Sars-Cov-2: A Systematic Review and Diagnostic Accuracy Meta-Analysis

ABSTRACT Purpose To study the positivity rate of conjunctival realtime polymerase chain reaction (RT-PCR) testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Design Systematic review and diagnostic accuracy meta-analysis. Methods MEDLINE and EMBASE were queried using medical subject headings terms. Diagnostic accuracy meta-analyses and forest plots were obtained using the RevMan software. Results After deduplication, appraisal of abstract titles and full-text analysis of 1441 articles, 42 articles with 3351 COVID-19 patients were included in this review. Of these, 412 conjunctival swabs/Schirmer paper strips tested positive for SARS-CoV-2 by RT-PCR. The pooled sensitivity of the RT-PCR tests across the 24 studies with laboratory-confirmed COVID-19 patients was 10.3%. Conclusions Only 1 in 10 RT-PCR tests performed on conjunctival swabs were positive for SARS-CoV-2. Although this suggests that SARS-CoV-2 is likely present and detectable in the conjunctiva, this detection method has low diagnostic potential.

Imaging modalities for characterising T1 renal tumours: A systematic review and meta-analysis of diagnostic accuracy.

International guidelines recommend resection of suspected localised renal cell carcinoma (RCC), with surgical series showing benign pathology in 30%. Non-invasive diagnostic tests to differentiate benign from malignant tumours are an unmet need. Our objective was to determine diagnostic accuracy of imaging modalities for detecting cancer in T1 renal tumours. A systematic review was performed for reports of diagnostic accuracy of any imaging test compared to a reference standard of histopathology for T1 renal masses, from inception until January 2023. Twenty-seven publications (including 2277 tumours in 2044 participants) were included in the systematic review, and nine in the meta-analysis. Forest plots of sensitivity and specificity were produced for CT (seven records, 1118 participants), contrast-enhanced ultrasound (seven records, 197 participants), [99mTc]Tc-sestamibi SPECT/CT (five records, 263 participants), MRI (three records, 220 participants), [18F]FDG PET (four records, 43 participants), [68Ga]Ga-PSMA-11 PET (one record, 27 participants) and [111In]In-girentuximab SPECT/CT (one record, eight participants). Meta-analysis returned summary estimates of sensitivity and specificity for [99mTc]Tc-sestamibi SPECT/CT of 88.6% (95% CI 82.7%-92.6%) and 77.0% (95% CI 63.0%-86.9%) and for [18F]FDG PET 53.5% (95% CI 1.6%-98.8%) and 62.5% (95% CI 14.0%-94.5%), respectively. A comparison hierarchical summary receiver operating characteristic (HSROC) model did not converge. Meta-analysis was not performed for other imaging due to different thresholds for test positivity. The optimal imaging strategy for T1 renal masses is not clear. [99mTc]Tc-sestamibi SPECT/CT is an emerging tool, but further studies are required to inform its role in clinical practice. The field would benefit from standardisation of diagnostic thresholds for CT, MRI and contrast-enhanced ultrasound to facilitate future meta-analyses.

Hyperventilation testing in the diagnosis of vasospastic angina: A clinical review and meta-analysis.

Given the limited access to invasive vasospastic reactivity testing in Western Countries, there is a need to further develop alternative non-invasive diagnostic methods for vasospastic angina (VSA). Hyperventilation testing (HVT) is defined as a class IIa recommendation to diagnose VSA by the Japanese Society of Cardiology. In this systematic review and meta-analysis reported according to the PRISMA statement, we review the mechanisms, methods, modalities and diagnostic accuracy of non-invasive HVT for the diagnostic of VSA. A total of 106 articles published between 1980 and 2022 about VSA and HVT were included in the systematic review, among which 16 were included in the meta-analysis for diagnostic accuracy. Twelve electrocardiogram-HVT studies including 804 patients showed a pooled sensitivity of 54% (95% confidence intervals [CI]; 30%-76%) and a pooled specificity of 99% (95% CI; 88%-100%). Four transthoracic echocardiography-HVT studies including 197 patients revealed a pooled sensitivity of 90% (95% CI; 82%-94%) and a pooled specificity of 98% (95% CI; 86%-100%). Six myocardial perfusion imaging-HVT studies including 112 patients yielded a pooled sensitivity of 95% (95% CI; 63%-100%) and a pooled specificity of 78% (95% CI; 19%-98%). Non-invasive HVT resulted in a low rate of adverse events, ventricular arrhythmias being the most frequently reported, and were resolved with the administration of nitroglycerin. Non-invasive HVT offers a safe alternative with high diagnostic accuracy to diagnose VSA in patients with otherwise undiagnosed causes of chest pain.

Abstract TMP56: Accuracy of Dual-Energy Computed Tomography for Differentiation Between Cerebral Hemorrhage and Contrast Extravasation Following Mechanical Revascularization in Acute Ischemic Stroke: A Diagnostic Accuracy Meta-Analysis

Background: Mechanical revascularization has become an essential treatment for acute ischemic stroke patients. Post-treatment imaging frequently displays hyperdense regions, prompting concerns regarding possible cerebral hemorrhage. Dual-energy computed tomography (DECT), as an advanced imaging modality, offers the potential to discern between hemorrhage and harmless contrast leakage. Aim: This study aims to assess the diagnostic precision of DECT in distinguishing cerebral hemorrhage from contrast extravasation post-mechanical revascularization in acute ischemic stroke cases. Methods: A comprehensive search was performed for PubMed, EMBASE, and the Cochrane Library until January 2023. We focused on studies that focused on the diagnostic efficacy of DECT for this specific application. R software (version 4.0.3) coupled with mada package facilitated the analyses pooling metrics such as sensitivity, specificity, false-positive rate estimates, diagnostic odds ratio, and positive and negative Likelihood Ratios. These metrics were reported with a 95% Confidence Interval (CI). Results: Our analysis included 5 studies involving 241 patients. DECT demonstrated a pooled sensitivity of 94.5% (95% CI: 25.9% to 99.9%, I 2 =0%) and a specificity of 99% (95% CI: 77.3% to 100%, I 2 =0%). The false-positive rate was minimal at 1% (95% CI: 0% to 22.7%). The positive likelihood ratio was calculated as 98.94 (95% CI: 3.34 to 2929.22), while the negative likelihood ratio was 0.05 (95% CI: 0.001 to 2.21). The diagnostic odds ratio was substantial at 1795.17 (95% CI: 10.04 to 321055.24), signifying a robust performance of DECT in making the differentiation. Conclusion: While DECT manifests impressive accuracy in differentiating cerebral hemorrhage from contrast extravasation after mechanical revascularization in acute ischemic stroke cases, the wide confidence intervals signify a degree of uncertainty in these estimates. Nevertheless, given its high diagnostic metrics, DECT can substantially influence post-intervention evaluations, paving the way for more informed clinical decisions and potentially reducing patient adverse outcomes. However, further studies with larger sample sizes might help refine these diagnostic estimates.

P299 Ulcerative Colitis Severity Assessment through Abdominal Ultrasonography: A Diagnostic Accuracy Meta-analysis

Abstract Background Ulcerative colitis, a prevalent form of inflammatory bowel disease, necessitates a precise evaluation of disease severity. While effective, traditional diagnostic methods like colonoscopy are invasive and uncomfortable for patients. Abdominal ultrasonography offers a non-invasive alternative for this assessment, but its differential accuracy in evaluating disease severity in the right and left colon is not fully explored.This study aims to assess the diagnostic accuracy of abdominal ultrasonography in determining the severity of ulcerative colitis, focusing on its performance in the right and left colon. Methods A systematic review was conducted in PubMed, EMBASE, and the Cochrane Library, targeting studies up to June 2023. The selection criteria included studies that used abdominal ultrasonography for ulcerative colitis severity assessment, compared against histopathology or combined clinical, endoscopic, and radiological findings. Data analysis was performed using R software (version 4.0.3) with the mada package. This analysis focused on calculating sensitivity, specificity, diagnostic odds ratio, and positive and negative Likelihood Ratios for both the right and left colon, each with a 95% Confidence Interval. Results The search identified 5 studies with a total of 383 ulcerative colitis patients. In the right colon, the pooled sensitivity was 73.7% (95% CI, 57.6-85.2%, I2=0%), and specificity was 99.5% (95% CI, 1.6-100%, I2=0%). The diagnostic odds ratio was extremely high at 560.1 (95% CI, 0.04-6966551), with a positive Likelihood Ratio of 148.14 (95% CI, 0.01-1713586.1) and a negative Likelihood Ratio of 0.26 (95% CI, 0.15-0.45). In the left colon, sensitivity was higher at 93.7% (95% CI, 87.2-97%, I2=49.1%) with a specificity of 73.3% (95% CI, 41.8-91.3%, I2=70.9%). The diagnostic odds ratio for the left colon was 40.96 (95% CI, 8.67-193.62), with a positive Likelihood Ratio of 3.51 (95% CI, 1.31-9.42) and a negative Likelihood Ratio of 0.08 (95% CI, 0.04-0.19). Conclusion Abdominal ultrasonography shows promising results in assessing disease severity in ulcerative colitis, particularly in the left colon. The high specificity in the right colon is notable, but the wide confidence intervals suggest caution in interpretation, likely due to the small sample sizes and study variability. The left colon's more consistent and reliable performance indicates its suitability for routine clinical use in this region. This study highlights the need for further research with larger patient cohorts and standardized methodologies to solidify these findings and optimize ultrasonography’s role in ulcerative colitis management.

Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis.

Atrial fibrillation (AF) is a common arrhythmia that can result in adverse cardiovascular outcomes but is often difficult to detect. The use of machine learning (ML) algorithms for detecting AF has become increasingly prevalent in recent years. This study aims to systematically evaluate and summarize the overall diagnostic accuracy of the ML algorithms in detecting AF in electrocardiogram (ECG) signals. The searched databases included PubMed, Web of Science, Embase, and Google Scholar. The selected studies were subjected to a meta-analysis of diagnostic accuracy to synthesize the sensitivity and specificity. A total of 14 studies were included, and the forest plot of the meta-analysis showed that the pooled sensitivity and specificity were 97% (95% confidence interval [CI]: 0.94-0.99) and 97% (95% CI: 0.95-0.99), respectively. Compared to traditional machine learning (TML) algorithms (sensitivity: 91.5%), deep learning (DL) algorithms (sensitivity: 98.1%) showed superior performance. Using multiple datasets and public datasets alone or in combination demonstrated slightly better performance than using a single dataset and proprietary datasets. ML algorithms are effective for detecting AF from ECGs. DL algorithms, particularly those based on convolutional neural networks (CNN), demonstrate superior performance in AF detection compared to TML algorithms. The integration of ML algorithms can help wearable devices diagnose AF earlier.

Ultrasound-guided transperineal vs transrectal prostate biopsy: A meta-analysis of diagnostic accuracy and complication rates.

We conducted a systematic review to compare the diagnostic utility of ultrasound-guided transperineal (TP) and transrectal (TR) prostate biopsy methods for prostate cancer detection. We searched PubMed, Embase, Web of Science, and Cochrane databases up to October 30, 2023, for relevant studies, screening the literature and assessing bias independently. Eleven trials were analyzed using relative risk and 95% confidence intervals, with no evidence of publication bias. Diagnostic rates showed no significant difference between TP and TR biopsies (mean difference [MD]: 1.03, 95% confidence interval [CI]: 0.91-1.14, P = 0.56). Prostate volume analysis also showed no significant difference (MD: -0.07, 95% CI: -0.73 to 0.59, P < 0.0001, combined effect size P = 0.83). Similarly, PSA levels were comparable between TP and TR biopsies (MD: 0.93, 95% CI: -0.44 to 2.30, P < 0.0001, combined effect size P = 0.18). Both biopsy methods exhibit similar diagnostic accuracy; however, TP has a lower risk of biopsy.

Meta-analysis of diagnostic accuracy of nucleic acid amplification tests for abdominal tuberculosis.

Abdominal tuberculosis (TB) is a severe extrapulmonary TB, which can lead to serious complications. Early diagnosis and treatment are very important for the prognosis and the diagnosis of abdominal TB is still difficult. We searched PubMed, the Cochrane Library, Embase, China National Knowledge Infrastructure, and the Wanfang database for studies evaluating the diagnostic accuracy of NAATs for abdominal TB until August 2020. Any types of study design with full text were sought and included. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. Subgroup analysis, meta-regression analysis and sensitivity analysis were used to explore the sources of heterogeneity. Stata version 15.0 with the midas command packages was used to carry out meta-analyses. We included a total of 78 independent studies from 53 articles; 64 with CRS as the reference standard, and 14 with culture as the reference standard. The pooled sensitivity, specificity, and the areas under summary receiver operating characteristic (SROC) curves (AUC) were 58% (51%-64%; I2 = 87%), 99% (97%-99%; I2 = 81%), and 0.92 (0.89-0.94) compared with CRS, respectively. The pooled sensitivity, specificity, and the AUC values of the SROC were 80% (66%-90%; I2 = 56%), 96% (92%-98%; I2 = 84%), and 0.97 (0.95-0.98) compared with culture, respectively. The heterogeneity of sensitivity and specificity was significant. NAATs had excellent efficacy in the diagnosis of abdominal TB regardless of the reference standard and regardless of the subtype of abdominal TB. Multiplex PCR with multiple target genes may improve diagnostic sensitivity, and stool specimens may also be used for the diagnosis of abdominal TB in addition to tissue and ascites.

Abstract 275: Point-of-Use Ultrasound in Out-of-Hospital Cardiac Arrest: A Meta-Analysis of Diagnostic Accuracy and Outcome Prediction

Background: Out-of-hospital cardiac arrest (OHCA) is a significant global health issue where accurate and rapid decision-making can often be the difference between life and death. Point-of-use ultrasound has been proposed as a potential tool to assist in such decisions by predicting patient outcomes. However, the current body of literature presents diverse results, thus emphasizing the need for an all-encompassing meta-analysis to assess the diagnostic accuracy of point-of-use ultrasound. Aim: This study aimed to determine the diagnostic accuracy of point-of-use ultrasound in predicting the outcomes of adult OHCA. Methods: We undertook a search of databases, including PubMed, Embase, and Cochrane Library, for studies published until November 2022 that investigated the role of point-of-use ultrasound in adult OHCA and its relation to patient outcomes. We utilized R software (version 4.0.3) equipped with the mada package for our statistical analysis, which allowed for pooling sensitivity, specificity, and false-positive rate estimates. Results: Nine studies were included in the final analysis. For survival to hospital discharge (four studies with a total of 1144 patients), the pooled sensitivity was 6.4% (95% Confidence Interval (CI): 1.2%-27.2%, I2=97.5%), specificity was 1.9% (95% CI: 0.9%-4.2%, I2=58.2%), the false-positive rate was 98.1% (95% CI: 95.8%-99.1%), the diagnostic odds ratio was 0.001 (95% CI: 0-0.009), positive LR was 0.065 (95% CI: 0.013-0.319), and negative LR was 49.15 (95% CI: 22.08-109.37). For the return of spontaneous circulation (ROSC) (six studies with a total of 1476 patients), the pooled sensitivity was 26.1% (95% CI: 17%-37.8%, I2=91.2%), specificity was 76.1% (95% CI: 42.1%-93.3%, I2=94.1%), the false-positive rate was 23.9% (95% CI: 6.7%-57.9%), diagnostic odds ratio was 1.13 (95% CI: 0.23-5.44), positive LR was 1.09 (95% CI: 0.33-3.61), and negative LR was 0.97 (95% CI: 0.66-1.42). Conclusion: Our study suggests that point-of-use ultrasound's predictive value for survival and ROSC in adult OHCA may be lower than anticipated, given high false-positive rates and low diagnostic odds ratios. These results underscore the need for further research before integrating ultrasound into OHCA decision-making.

Abstract 208: Evaluating the Predictive Power of Serum Biomarkers Neuron-Specific Enolase, S-100B Protein, and Neurofilament Light Chain for Neurological Outcomes Following Cardiac Arrest: A Diagnostic Accuracy Meta-Analysis

Introduction: The assessment of serum biomarkers, specifically Neuron-Specific Enolase, S-100B protein, and Neurofilament Light Chain, at 24 hours post-cardiac arrest is under investigation for its ability to predict brain injury. Aim: This study aims to evaluate the accuracy of serum biomarkers NSE, S-100B protein, and NFL predicting post-cardiac arrest neurological outcomes. Methods: A systematic search up to December 2022 across PubMed, Embase, and Scopus identified studies examining the association between 24-hour serum levels of NSE, S-100B protein, or NFL and neurological outcomes post-cardiac arrest. Statistical analyses were conducted using R software with mada package to pool sensitivity, specificity, false-positive rates, diagnostic odds ratios, and positive and negative likelihood ratios; each expressed with a 95% Confidence Interval. Results: We incorporated 3 studies, with 759 patients in S-100B protein group, 753 in NSE group, and 799 in NFL group. At 24 hours post-cardiac arrest, NSE showed a pooled sensitivity of 37.7% (95% CI: 24.4-53.2%, I2=85.7%) and specificity of 85.5% (95% CI: 81.6-88.7%, I2=0%). S-100B protein demonstrated a pooled sensitivity of 54.7% (95% CI: 32.1-75.5%, I2=94.8%) and specificity of 81% (95% CI: 66.9-90.1%, I2=82.3%). NFL exhibited a pooled sensitivity of 88.1% (95% CI: 47.7-98.4%, I2=93.8%) and specificity of 93.6% (95% CI: 85.4-97.3%, I2=77.7%). The diagnostic odds ratios were 3.57 (95% CI: 1.79-7.15) for NSE, 5.15 (95% CI: 1.55-17.14) for S-100B protein, and 108.65 (95% CI: 11.02-1071.24) for NFL. The positive Likelihood Ratios were 2.61 (95% CI: 1.64-4.14) for NSE, 2.88 (95% CI: 1.37-6.06) for S-100B protein, and 13.87 (95% CI: 5.64-33.69) for NFL, while the negative Likelihood Ratios were 0.73 (95% CI: 0.57-0.93) for NSE, 0.19 (95% CI: 0.9-0.33) for S-100B protein, and 0.06 (95% CI: 0.03-0.15) for NFL. Conclusion: Brain biomarkers NSE, S-100B protein, and NFL, especially NFL, with its superior sensitivity and specificity, are promising in predicting neurological outcomes post-cardiac arrest, aiding clinicians in patient management and family communication. The limited number of studies and heterogeneity in results necessitate further validation and research of their predictability.

Abstract 15577: Diagnostic Performance of Cadmium-Zinc-Telluride Single-Photon Emission Computed Tomography versus Coronary Angiography Fractional Flow Reserve in Estimating Myocardial Blood Flow and Flow Reserve: A Diagnostic Accuracy Meta-Analysis

Introduction: Precisely estimating myocardial blood flow and flow reserve is crucial in diagnosing and managing ischemic heart disease. Coronary angiography fractional flow reserve (FFR) has been a standard tool. However, the recent emergence of Cadmium-Zinc-Telluride Single-Photon Emission Computed Tomography (CZT-SPECT) has provided an innovative diagnostic approach. Consequently, a comprehensive comparative analysis of their efficacies in evaluating myocardial blood flow and flow reserve is necessary. Hypothesis: This study aims to determine the diagnostic accuracy of CZT-SPECT compared to coronary angiography FFR in estimating myocardial blood flow and flow reserve. Methods: A systematic search until July 2022 was conducted across databases such as PubMed, Embase, and Web of Science, focusing on studies comparing CZT-SPECT and coronary angiography FFR in the estimation of myocardial blood flow and flow reserve. Utilizing the mada package in R software (version 4.0.3), we conducted diagnostic accuracy analysis by pooling metrics like sensitivity, specificity, false-positive rate estimates, diagnostic odds ratio, and positive and negative Likelihood Ratios (LR), all of which were presented with a 95% Confidence Interval (CI). Results: The meta-analysis incorporated 6 studies with a total patient population of 346. Our findings reveal that CZT-SPECT exhibited a pooled sensitivity of 79.1% (95% CI: 71.8-84.9%, I^2=0) and a specificity of 78.5% (95% CI: 70.4-84.8%, I^2=25.3%). The false-positive rate was 21.5% (95% CI: 15.2-29.6%). Moreover, CZT-SPECT demonstrated a diagnostic odds ratio of 13.75 (95% CI: 7.67-24.65), while the positive likelihood ratio was 3.67 (95% CI: 2.59-5.19), and the negative likelihood ratio stood at 0.26 (95% CI: 0.19-0.37). Conclusions: This study provides valuable insights into the diagnostic accuracy of CZT-SPECT compared to coronary angiography FFR in estimating myocardial blood flow and flow reserve. These findings indicate that while CZT-SPECT shows promising diagnostic potential, its performance is similar to coronary angiography FFR. The results will be instrumental in informing clinical decision-making and shaping future research directions in the field of cardiovascular imaging.

A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography.

Background: Subarachnoid hemorrhage resulting from cerebral aneurysm rupture is a significant cause of morbidity and mortality. Early identification of aneurysms on Computed Tomography Angiography (CTA), a frequently used modality for this purpose, is crucial, and artificial intelligence (AI)-based algorithms can improve the detection rate and minimize the intra- and inter-rater variability. Thus, a systematic review and meta-analysis were conducted to assess the diagnostic accuracy of deep-learning-based AI algorithms in detecting cerebral aneurysms using CTA. Methods: PubMed (MEDLINE), Embase, and the Cochrane Library were searched from January 2015 to July 2023. Eligibility criteria involved studies using fully automated and semi-automatic deep-learning algorithms for detecting cerebral aneurysms on the CTA modality. Eligible studies were assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A diagnostic accuracy meta-analysis was conducted to estimate pooled lesion-level sensitivity, size-dependent lesion-level sensitivity, patient-level specificity, and the number of false positives per image. An enhanced FROC curve was utilized to facilitate comparisons between the studies. Results: Fifteen eligible studies were assessed. The findings indicated that the methods exhibited high pooled sensitivity (0.87, 95% confidence interval: 0.835 to 0.91) in detecting intracranial aneurysms at the lesion level. Patient-level sensitivity was not reported due to the lack of a unified patient-level sensitivity definition. Only five studies involved a control group (healthy subjects), whereas two provided information on detection specificity. Moreover, the analysis of size-dependent sensitivity reported in eight studies revealed that the average sensitivity for small aneurysms (<3 mm) was rather low (0.56). Conclusions: The studies included in the analysis exhibited a high level of accuracy in detecting intracranial aneurysms larger than 3 mm in size. Nonetheless, there is a notable gap that necessitates increased attention and research focus on the detection of smaller aneurysms, the use of a common test dataset, and an evaluation of a consistent set of performance metrics.