Diagnostic test accuracy: application and practice using R software

18F-FDG PET or PET/CT in detecting high-grade transformation of chronic lymphocytic leukaemia and indolent lymphomas: a systematic review and meta-analysis

OBJECTIVES

To evaluate the diagnostic accuracy of 18F-FDG positron emission tomography (PET) or PET/computed tomography (CT) in detecting histological transformation (HT) of indolent lymphomas.

METHODS

A systematic search of articles up to July 2024 was performed in Embase and Medline. Eligible studies included adults with histologically proven indolent lymphoma, 18F-FDG PET or PET/CT as the index test, and sufficient data to assess diagnostic performance. Summary receiver operating characteristic curves were plotted using a bivariate model to estimate diagnostic accuracy with area under the curve (AUC).

RESULTS

Fifteen studies with 1307 participants were included. Ten studies assessed PET ability to detect Richter's transformation, and 5 studies focused on HT in follicular lymphoma and other subtypes. A meta-analysis of the former showed pooled sensitivity of 0.90 (95% CI, 0.84-0.93) and specificity of 0.54 (95% CI, 0.28-0.77) when using a maximum standardized uptake value (SUVmax) threshold of around 5. AUC was 0.89. Pooled sensitivity was 0.74 (95% CI, 0.54-0.87), and specificity was 0.84 (95% CI, 0.67-0.93) when using an SUVmax threshold of around 10. Area under the curve was 0.84. For detecting HT in follicular lymphoma, thresholds were found higher than those for Richter's transformation.

CONCLUSIONS

18F-FDG PET or PET/CT demonstrates good diagnostic accuracy to detect Richter's transformation, best when employing SUVmax ≥ 5. SUVmax thresholds may be limited in discriminating follicular lymphoma from HT, and alternatives should be sought.

ADVANCES IN KNOWLEDGE

If biopsy is feasible, SUVmax ≥ 5 can guide biopsy in patients with clinically suspicious Richter's transformation. If biopsy is infeasible, SUVmax ≥ 10 can better identify HT and guide patient management.

Diagnostic accuracy of nanopore sequencing for detecting Mycobacterium tuberculosis and drug-resistant strains: a systematic review and meta-analysis

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB) infection, remains a significant public health threat. The timeliness, portability, and capacity of nanopore sequencing for diagnostics can aid in early detection and drug susceptibility testing (DST), which is crucial for effective TB control. This study synthesized current evidence on the diagnostic accuracy of the nanopore sequencing technology in detecting MTB and its DST profile. A comprehensive literature search in PubMed, Scopus, MEDLINE, Cochrane, EMBASE, Web of Science, AIM, IMEMR, IMSEAR, LILACS, WPRO, HERDIN Plus, MedRxiv, and BioRxiv was performed. Quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Pooled sensitivity, specificity, predictive values (PV), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated. Thirty-two studies were included; 13 addressed MTB detection only, 15 focused on DST only, and 4 examined both MTB detection and DST. No study used Flongle or PromethION. Seven studies were eligible for meta-analysis on MTB detection and five for DST; studies for MTB detection used GridION only while those for DST profile used MinION only. Our results indicate that GridION device has high sensitivity [88.61%; 95% CI (83.81-92.12%)] and specificity [93.18%; 95% CI (85.32-96.98%)], high positive predictive value [94.71%; 95% CI (89.99-97.27%)], moderately high negative predictive value [84.33%; 95% CI (72.02-91.84%)], and excellent DOR [107.23; 95% CI (35.15-327.15)] and AUC (0.932) in detecting MTB. Based on DOR and AUC, the MinION excelled in detecting pyrazinamide and rifampicin resistance; however, it underperformed in detecting isoniazid and ethambutol resistance. Additional studies will be needed to provide more precise estimates for MinION's sensitivity in detecting drug-resistance, as well as DOR in detecting resistance to pyrazinamide, streptomycin, and ofloxacin. Studies on detecting resistance to bedaquiline, pretomanid, and linezolid are lacking. Subgroup analyses suggest that overall accuracy of MTB detection tends to be higher with prospective study design and use of standards other than CSTB (Chinese national standard for diagnosing TB). Sensitivity analyses reveal that retrospective study design, use of GridION, and use of Illumina whole-genome sequencing (WGS) decrease overall accuracy in detecting any drug-resistant MTB. Findings from both types of analyses, however, should be interpreted with caution because of the low number of studies and uneven distribution of studies in each subgroup.

Machine learning models and classification algorithms in the diagnosis of vestibular migraine: A systematic review and meta-analysis

OBJECTIVES

To perform a systematic review and meta-analysis to evaluate the effectiveness of machine learning (ML) algorithms in the diagnosis of vestibular migraine.

BACKGROUND

Due to the absence of defined biomarkers for diagnosing vestibular migraine (VM), it is valuable to determine which clinical, physical, and exploratory information is most crucial to diagnosing this disease. The use of artificial intelligence tools could streamline this process.

METHODS

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and searched for records from PubMed, Scopus, and Web of Science. Observational (case-control and cohort) studies were included to assess the ability of artificial intelligence (AI) to distinguish VM from other vestibular disorders. Risk of bias and applicability concerns were assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI tool.

RESULTS

A total of 14 articles were included in the systematic review, and 10 were eligible for meta-analysis. The main inputs included for the ML algorithms were anamnesis (medical history), physical examination, results from audiological and vestibular tests, and imaging. The global sensitivity was 0.85 (95% confidence interval [CI] 0.73-0.92, I2 = 96%), while the global specificity was 0.89 (95% CI 0.84-0.93, I2 = 95%). The pooled diagnostic odds ratio was 48.15 (95% CI 17.64-131.43, I2 = 97%). Using the bivariate model, the area under the curve and for the summary receiver operating characteristic curve, using the 10 available studies, was 0.94 (95% CI 0.86-0.96).

CONCLUSION

Machine learning algorithms could be used as effective tools for the diagnosis process in VM. The use of models trained with three to four inputs yield the highest accuracy, compared to other strategies. However, the design and validation of these studies could be improved to ensure the reproducibility and generalizability of results.

Machine learning radiomics for H3K27M mutation prediction in gliomas: A systematic review and meta-analysis

PURPOSE

Noninvasive prediction and identification of the H3K27M mutation play an important role in optimizing therapeutic strategies and improving outcomes in gliomas. In this systematic review and meta-analysis, we aimed to evaluate the performance of machine learning (ML)-based models in predicting H3K27M mutation in gliomas.

METHODS

Literature records were retrieved on September 16th, 2024, in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from the included studies were extracted. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software.

RESULTS

A total of 15 studies were included in our study. Our meta-analysis demonstrated a pooled AUC, sensitivity, and specificity of 0.87 (95% CI: 0.77-0.97), 92% (95% CI: 83%-96%), and 89% (95% CI: 86%-91%)), respectively. The subgroup meta-analysis revealed that despite the higher sensitivity of the deep learning (DL) models, the sensitivity is not superior to ML (P = 0.6). In contrast, the ML-based pooled specificity was significantly higher (P < 0.01). The meta-analysis revealed a 78.1 (95% CI: 33.3 - 183.5). The SROC curve indicated an AUC of 0.921, and the estimated sensitivity is 0.898 concurrent with the false positive rate of 0.126, which indicates high sensitivity with a low false positive rate.

CONCLUSION

Our systematic review and meta-analysis demonstrated that ML-based magnetic resonance imaging (MRI) radiomics models are associated with promising diagnostic performance in predicting H3K27M mutation in gliomas.

Evaluating Rabies Test Accuracy: A Systematic Review and Meta-Analysis of Human and Canine Diagnostic Methods

Background/Objectives: Rabies is almost invariably fatal once clinical symptoms manifest. Timely and accurate diagnosis is essential for effective treatment and prevention. Dogs are the principal reservoirs of the virus, particularly in developing nations, highlighting the importance of precise diagnostic and control measures to prevent human cases. This systematic review and meta-analysis assessed the accuracy of laboratory tests for diagnosing rabies in humans and dogs. Methods: The PubMed database was searched for published studies on rabies diagnosis between 1990 and 2024. Following PRISMA statement recommendations, we included 60 studies that met the selection criteria. Results: The results demonstrated the effectiveness of immunological tests like the Enzyme-Linked Immunosorbent Assay (ELISA) and molecular tests such as Reverse Transcription Polymerase Chain Reaction (RT-PCR) for both humans and dogs. In this study, the Direct Fluorescent Antibody Test (DFAT) exhibited lower diagnostic performance, with an area under the curve for false positive rates (AUCFPR = 0.887). In contrast, ELISA (AUCFPR = 0.909) and RT-PCR (AUCFPR = 0.905) provided more consistent results. Notably, the Rapid Immunochromatographic Test (RIT) showed the best performance (AUCFPR = 0.949), highlighting its superior diagnostic capabilities compared to DFAT. Conclusions: These findings underscore the need to modernize rabies diagnostic protocols by incorporating advanced methodologies to improve diagnostic accuracy, reduce transmission, and decrease mortality rates.

Diagnostic Performance of ASL-MRI and FDG-PET in Frontotemporal Dementia: A Systematic Review and Meta-Analysis

BACKGROUND

While the diagnosis of frontotemporal dementia (FTD) is based mostly on clinical features, [18F]-FDG-PET has been investigated as a potential imaging standard in ambiguous cases, with arterial spin-labeling (ASL) MRI gaining recent interest.

PURPOSE

The purpose of this study is to conduct a systematic review and meta-analysis on the diagnostic performance of ASL MRI in patients with FTD and compare it with that of [18F]-FDG-PET.

DATA SOURCES

A systematic search of PubMed, Scopus, and Embase was conducted until March 13, 2024.

STUDY SELECTION

Inclusion criteria were original articles, patients with FTD and/or its variants, use of ASL MR perfusion imaging with or without [18F]-FDG-PET, and presence of sufficient diagnostic performance data. Exclusion criteria were meeting abstracts, comments, summaries, protocols, letters and guidelines, longitudinal studies, and overlapping cohorts.

DATA ANALYSIS

The quality of eligible studies was assessed by using the Quality Assessment of Diagnostic Accuracy Studies-2. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) for [18F]-FDG-PET and ASL MRI were calculated, and a summary receiver operating characteristic curve was plotted.

DATA SYNTHESIS

Seven eligible studies were identified, which included a total of 102 patients with FTD. Aside from some of the studies showing, at worst, an unclear risk of bias in patient selection, index test, flow, and timing, all studies showed low risk of bias and applicability concerns in all categories. Data from 4 studies were included in our meta-analysis for ASL MRI and 3 studies for [18F]-FDG-PET. Pooled sensitivity, specificity, and DOR were 0.70 (95% CI: 0.59-0.79), 0.81 (95% CI: 0.71-0.88), and 8.00 (95% CI: 3.74-17.13) for ASL MRI and 0.88 (95% CI: 0.71-0.96), 0.89 (95% CI: 0.43-0.99), and 47.18 (95% CI: 10.77-206.75) for [18F]-FDG-PET.

LIMITATIONS

The number of studies was relatively small, with a small sample size. The studies used different scanning protocols as well as a mix of diagnostic metrics, all of which might have introduced heterogeneity in the data.

CONCLUSIONS

While ASL MRI performed worse than [18F]-FDG-PET in the diagnosis of FTD, it exhibited a decent diagnostic performance to justify its further investigation as a quicker and more convenient alternative.

%diag_test: a generic SAS macro for evaluating diagnostic accuracy measures for multiple diagnostic tests

BACKGROUND

Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data. We developed a SAS macro for evaluating multiple diagnostic tests using individual-level data that creates a 2 × 2 summary table, AUROC and AUPRC as part of output.

METHODS

The SAS macro presented here is automated to reduce analysis time and transcription errors. It is simple to use as the user only needs to specify the input dataset, "standard" and "test" variables and threshold values. It creates a publication-quality output in Microsoft Word and Excel showing more than 15 different accuracy measures together with overlaid AUROC and AUPRC graphics to help the researcher in making decisions to adopt or reject diagnostic tests. Further, it provides for additional variance estimation methods other than the normal distribution approximation.

RESULTS

We tested the macro for quality control purposes by reproducing results from published work on evaluation of multiple types of dried blood spots (DBS) as an alternative for human immunodeficiency virus (HIV) viral load (VL) monitoring in resource-limited settings compared to plasma, the gold-standard. Plasma viral load reagents are costly, and blood must be prepared in a reference laboratory setting by a qualified technician. On the other hand, DBS are easy to prepare without these restrictions. This study evaluated the suitability of DBS from venous, microcapillary and direct spotting DBS, hence multiple diagnostic tests which were compared to plasma specimen. We also used the macro to reproduce results of published work on evaluating performance of multiple classification machine learning algorithms for predicting coronary artery disease.

CONCLUSION

The SAS macro presented here is a powerful analytic tool for analyzing data from multiple diagnostic tests. The SAS programmer can modify the source code to include other diagnostic measures and variance estimation methods. By automating analysis, the macro adds value by saving analysis time, reducing transcription errors, and producing publication-quality outputs.

Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis

The current study aimed to systematically review the literature on the accuracy of artificial intelligence (AI) models for osteoporosis (OP) diagnosis using dental images. A thorough literature search was executed in October 2022 and updated in November 2023 across multiple databases, including PubMed, Scopus, Web of Science, and Google Scholar. The research targeted studies using AI models for OP diagnosis from dental radiographs. The main outcomes were the sensitivity and specificity of AI models regarding OP diagnosis. The "meta" package from the R Foundation was selected for statistical analysis. A random-effects model, along with 95% confidence intervals, was utilized to estimate pooled values. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was employed for risk of bias and applicability assessment. Among 640 records, 22 studies were included in the qualitative analysis and 12 in the meta-analysis. The overall sensitivity for AI-assisted OP diagnosis was 0.85 (95% CI, 0.70-0.93), while the pooled specificity equaled 0.95 (95% CI, 0.91-0.97). Conventional algorithms led to a pooled sensitivity of 0.82 (95% CI, 0.57-0.94) and a pooled specificity of 0.96 (95% CI, 0.93-0.97). Deep convolutional neural networks exhibited a pooled sensitivity of 0.87 (95% CI, 0.68-0.95) and a pooled specificity of 0.92 (95% CI, 0.83-0.96). This systematic review corroborates the accuracy of AI in OP diagnosis using dental images. Future research should expand sample sizes in test and training datasets and standardize imaging techniques to establish the reliability of AI-assisted methods in OP diagnosis through dental images.

Diagnostic Accuracy and Prognostic Value of Relative Apical Sparing in Cardiac Amyloidosis - Systematic Review and Meta-Analysis

BACKGROUND

Although the relative apical sparing (RAPS) pattern of left ventricular (LV) longitudinal strain is a hallmark of cardiac amyloidosis, recent studies have raised concerns about its accuracy. The aim of this systematic review was to investigate diagnostic test accuracy (DTA) and prognostic impact of RAPS in cardiac amyloidosis.

METHODS AND RESULTS

We searched PubMed, Embase, and Scopus for manuscripts that could potentially be used in the DTA arm and prognosis arm. Thirty-seven studies were used for DTA analysis. The pooled sensitivity, specificity, and diagnostic odds ratio were 61% (95% confidence interval [CI] 54-68%), 83% (95% CI 80-86%), and 8.9 (95% CI 6.1-13.1), respectively. These values did not differ regardless of the presence of aortic stenosis, but the diagnostic odds ratio differed significantly among analytical software packages. For the prognosis arm, 6 studies were dichotomously assessed for RAPS, and 5 were assessed quantitatively. The pooled proportion of RAPS was 49% and the pooled estimate of the RAPS ratio was 1.40. Although RAPS was associated with outcome (hazard ratio [HR] 1.87; 95% CI 1.15-3.04; P=0.011), its significance disappeared after trim and fill analysis (HR 1.42; 95% CI 0.85-2.38; P=0.184).

CONCLUSIONS

RAPS has a modest DTA with a significant vendor dependency and does not provide robust prognostic information.

Ultrasound and computed tomography in differentiating between simple and complicated appendicitis in pediatric patients

OBJECTIVES

This study aims to measure the diagnostic accuracy and reliability of US and CT in diagnosing complicated appendicitis among pediatric patients and to find the performance of the imaging modalities in detecting complication signs.

METHODS

a systematic review and meta-analysis were done on 15 eligible studies from the Medline Database concerning pediatric appendicitis and its complications. Studies either provided an overall estimate of sensitivity and specificity of the imaging modality or addressed signs of complicated appendicitis The reference standard employed was either surgical findings or histopathology reports.

RESULTS

The review included assessments of 4,497 pediatric patients, with 285 undergoing CT and 4,212 undergoing US imaging. CT demonstrated sensitivities of 62% and 68.4%, and specificities of 81% and 92.4%. US showed sensitivities of 33.9% to 51.5% and specificities ranging from 68.8% to 95%. The ultrasound's ability to detect appendiceal wall diameter > 5 mm showed the highest sensitivity (99.4%), while the conglomerate sign indicated the highest specificity (99.9%).

CONCLUSION

The findings suggest that both US and CT exhibit higher specificity than sensitivity in diagnosing complicated appendicitis in pediatric patients. Given its favorable specificity, non-invasiveness, and lack of radiation exposure, US should be the first-line imaging modality in suspected cases of pediatric appendicitis. CT, offering robust specificity, should be reserved for ambiguous cases where US results are inconclusive. These insights underscore the critical role of precise imaging modalities in enhancing diagnostic accuracy, reducing unnecessary surgeries, and improving clinical outcomes in pediatric appendicitis management.

Diagnostic Accuracy of CT for the Detection of Hepatic Steatosis: A Systematic Review and Meta-Analysis

Background CT plays an important role in the opportunistic identification of hepatic steatosis. CT performance for steatosis detection has been inconsistent across various studies, and no clear guidelines on optimum thresholds have been established. Purpose To conduct a systematic review and meta-analysis to assess CT diagnostic accuracy in hepatic steatosis detection and to determine reliable cutoffs for the commonly mentioned measures in the literature. Materials and Methods A systematic search of the PubMed, Embase, and Scopus databases (English-language studies published from September 1977 to January 2024) was performed. Studies evaluating the diagnostic accuracy of noncontrast CT (NCCT), contrast-enhanced (CECT), and dual-energy CT (DECT) for hepatic steatosis detection were included. Reference standards included biopsy, MRI proton density fat fraction (PDFF), or NCCT. In several CECT and DECT studies, NCCT was used as the reference standard, necessitating subgroup analysis. Statistical analysis included a random-effects meta-analysis, assessment of heterogeneity with use of the I2 statistic, and meta-regression to explore potential sources of heterogeneity. When available, mean liver attenuation, liver-spleen attenuation difference, liver to spleen attenuation ratio, and the DECT-derived fat fraction for hepatic steatosis diagnosis were assessed. Results Forty-two studies (14 186 participants) were included. NCCT had a sensitivity and specificity of 72% and 88%, respectively, for steatosis (>5% fat at biopsy) detection and 82% and 94% for at least moderate steatosis (over 20%-33% fat at biopsy) detection. CECT had a sensitivity and specificity of 66% and 90% for steatosis detection and 68% and 93% for at least moderate steatosis detection. DECT had a sensitivity and specificity of 85% and 88% for steatosis detection. In the subgroup analysis, the sensitivity and specificity for detecting steatosis were 80% and 99% for CECT and 84% and 93% for DECT. There was heterogeneity among studies focusing on CECT and DECT. Liver attenuation less than 40-45 HU, liver-spleen attenuation difference less than -5 to 0 HU, and liver to spleen attenuation ratio less than 0.9-1 achieved high specificity for detection of at least moderate steatosis. Conclusion NCCT showed high performance for detection of at least moderate steatosis. © RSNA, 2024 Supplemental material is available for this article.

Comparison of different diagnostic protocols for the detection of Toxocara spp. in faecal samples of cats and dogs

BACKGROUND

Toxocara canis and Toxocara cati are parasitic nematodes that occur worldwide. As embryonated Toxocara spp. eggs in the environment pose a zoonotic risk, especially for children, optimal diagnostic approaches are necessary for effective disease response and management, including surveillance. However, little is known about the performance of different diagnostic protocols for detecting Toxocara spp. in the faeces of cats and dogs, hampering movement towards an optimal diagnostic process. This study aimed to compare detection methods, including a newly developed sequential sieving protocol (SF-SSV) and a high-throughput multiplex qPCR-based method to facilitate epidemiological studies.

METHODS

Species-specific Toxocara spp. egg suspensions and canine and feline faecal samples from the field were used to estimate analytical and diagnostic sensitivity of the protocols. The performance of two automated DNA extraction protocols using enzymatic and mechanical lysis were compared by multiplex qPCR, targeting both T. canis and T. cati-specific genomic sequences. All samples were examined by microscopy-based techniques, the sedimentation flotation technique (SF) and a newly developed SF-SSV for the detection, enrichment and purification of parasite eggs. The costs and processing times necessary for all protocols were estimated and compared for both single samples and sets of 100 samples.

RESULTS

To detect Toxocara spp. eggs, SF-SSV showed the highest analytical sensitivity and a significantly higher diagnostic sensitivity than the DNA detection methods. Mechanical lysis performed better than enzymatic lysis for automated DNA extraction. In automated DNA extraction, 96-well plates performed better than 24-well plates. DNA detection and microscopy-based parasitological methods showed substantial agreement between the results generated by each method. Microscopy-based techniques required the lowest costs and least hands-on time for a single sample. However, when costs and labour were estimated for a set of 100 samples, the DNA detection protocol using 96-well plates for extraction revealed costs similar to SF-SSV and the fastest processing times.

CONCLUSIONS

SF-SSV was superior in terms of analytical and diagnostic sensitivity for the detection of Toxocara spp. eggs. For larger sets of samples, multiplex qPCR-based DNA detection represents an alternative to microscopy-based methods, based on the possibility of faster sample processing at similar costs to SF-SSV, and the ability to provide species-specific diagnoses.

Cardiac CT vs Echocardiography for Intracardiac Thrombus Detection in Ischemic Stroke: A Systematic Review and Meta-Analysis of 43 Studies

BACKGROUND AND OBJECTIVES

Ischemic stroke, a leading cause of mortality, necessitates understanding its mechanism for effective prevention. Echocardiography, especially transesophageal echocardiography (TEE), is the gold standard for detection of cardiac sources of stroke including left atrial thrombus, although its invasiveness, operator skill dependence, and limited availability in some centers prompt exploration of alternatives, such as cardiac CT (CCT). We conducted a systematic review and meta-analysis assessing the ability of CCT in the detection of intracardiac thrombus compared with echocardiography.

METHODS

We searched 4 databases up through September 8, 2023. Major search terms included a combination of the terms "echocardiograph," "CT," "TEE," "imaging," "stroke," "undetermined," and "cryptogenic." The current systematic literature review of the English language literature was reported in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. We assessed risk of bias using the QUADAS-2 tool and used random-effects meta-analysis to calculate different diagnostic metrics.

RESULTS

The meta-analysis investigating CCT vs echocardiography for intracardiac thrombus detection yielded a total of 43 studies of 9,552 patients. Risk-of-bias assessment revealed a predominantly low risk of bias in the flow and timing, index test, and patient selection domains and a predominantly unclear risk of bias in the reference standard domain. The analysis revealed an overall sensitivity of 98.38% (95% CI 89.2-99.78) and specificity of 96.0% (95% CI 92.55-97.88). Subgroup analyses demonstrated that delayed-phase, electrocardiogram-gated CCT had the highest sensitivity (100%; 95% CI 0-100) while early-phase, nongated CCT exhibited a sensitivity of 94.31% (95% CI 28.58-99.85). The diagnostic odds ratio was 98.59 (95% CI 44.05-220.69). Heterogeneity was observed, particularly in specificity and diagnostic odds ratio estimates.

DISCUSSION

CCT demonstrates high sensitivity, specificity, and diagnostic odds ratios in detecting intracardiac thrombus compared with traditional echocardiography. Limitations include the lack of randomized controlled studies, and other cardioembolic sources of stroke such as valvular disease, cardiac function, and aortic arch disease were not examined in our analysis. Large-scale studies are warranted to further evaluate CCT as a promising alternative for identifying intracardiac thrombus and other sources of cardioembolic stroke.

Feasibility and diagnostic accuracy of neonatal anthropometric measurements in identifying low birthweight and preterm infants in Africa: a systematic review and meta-analysis

BACKGROUND

Complications of prematurity are the leading cause of under-5 mortality globally and 80% of newborn deaths are of low birth weight (LBW) babies. Early identification of LBW and preterm infants is crucial to initiate timely interventions.

OBJECTIVE

To evaluate the feasibility and diagnostic accuracy of alternative neonatal anthropometric measurements in identifying LBW and preterm infants in Africa.

METHODS

In this systematic review and meta-analysis, we evaluated the diagnostic performance of infant foot length, mid-upper arm circumference (MUAC), head and chest circumferences against birth weight and gestational age. Pooled correlation between the index and the reference methods was estimated. Multiple anthropometric thresholds were considered in estimating the pooled sensitivity, specificity and area under receiver operating characteristic curve (AUC).

RESULTS

21 studies from 8 African countries met the inclusion criteria. Correlation coefficients with birth weight were 0.79 (95% CI 0.70 to 0.85) for chest circumference, 0.71 (95% CI 0.62 to 0.78) for MUAC and 0.66 (95% CI 0.59 to 0.73) for foot length. Foot length measured by rigid ruler showed a higher correlation than tape measurement. Chest circumference with 28.8 cm cut-off detects LBW babies with AUC value of 0.92 (95% CI 0.71 to 0.97). Foot length identified preterm infants, with 82% sensitivity, 89% specificity and AUC of 0.91 (95% CI 0.69 to 0.98) at a 7.2 cm optimal cut-off point. MUAC had an AUC of 0.83 (95% CI 0.47 to 0.95) for preterm detection. In identifying LBW babies, foot length and MUAC have AUC values of 0.89 (95% CI 0.70 to 0.96) and 0.91 (95% CI 0.73 to 0.97) at 7.3 cm and 9.8 cm optimal cut-off points, respectively. Foot length and MUAC are relatively simple and minimise the risk of exposing infants to cold.

CONCLUSION

Newborn foot length, MUAC, head and chest circumferences have comparable diagnostic accuracy in identifying LBW and preterm babies. Using foot length and MUAC in low-resource settings are the most feasible proxy measures for screening where weighing scales are not available.

PROSPERO REGISTRATION NUMBER

CRD42023454497.

The diagnostic performance of ultrafast MRI to differentiate benign from malignant breast lesions: a systematic review and meta-analysis

OBJECTIVES

To assess the diagnostic performance of ultrafast magnetic resonance imaging (UF-DCE MRI) in differentiating benign from malignant breast lesions.

MATERIALS AND METHODS

A comprehensive search was conducted until September 1, 2023, in Medline, Embase, and Cochrane databases. Clinical studies evaluating the diagnostic performance of UF-DCE MRI in breast lesion stratification were screened and included in the meta-analysis. Pooled summary estimates for sensitivity, specificity, diagnostic odds ratio (DOR), and hierarchic summary operating characteristics (SROC) curves were pooled under the random-effects model. Publication bias and heterogeneity between studies were calculated.

RESULTS

A final set of 16 studies analyzing 2090 lesions met the inclusion criteria and were incorporated into the meta-analysis. Using UF-DCE MRI kinetic parameters, the pooled sensitivity, specificity, DOR, and area under the curve (AUC) for differentiating benign from malignant breast lesions were 83% (95% CI 79-88%), 77% (95% CI 72-83%), 18.9 (95% CI 13.7-26.2), and 0.876 (95% CI 0.83-0.887), respectively. We found no significant difference in diagnostic accuracy between the two main UF-DCE MRI kinetic parameters, maximum slope (MS) and time to enhancement (TTE). DOR and SROC exhibited low heterogeneity across the included studies. No evidence of publication bias was identified (p = 0.585).

CONCLUSIONS

UF-DCE MRI as a stand-alone technique has high accuracy in discriminating benign from malignant breast lesions.

CLINICAL RELEVANCE STATEMENT

UF-DCE MRI has the potential to obtain kinetic information and stratify breast lesions accurately while decreasing scan times, which may offer significant benefit to patients.

KEY POINTS

• Ultrafast breast MRI is a novel technique which captures kinetic information with very high temporal resolution. • The kinetic parameters of ultrafast breast MRI demonstrate a high level of accuracy in distinguishing between benign and malignant breast lesions. • There is no significant difference in accuracy between maximum slope and time to enhancement kinetic parameters.

Diagnostic Utility of SARS-CoV-2 Nucleocapsid Antigenemia: A Meta-analysis

Background

Studies of the diagnostic performance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid antigen in blood (antigenemia) have reached variable conclusions. The potential utility of antigenemia measurements as a clinical diagnostic test needs clarification.

Methods

We performed a systematic review of Pubmed, Embase, and Scopus through July 15, 2023, and requested source data from corresponding authors.

Results

Summary sensitivity from 16 studies (4543 cases) sampled at ≤14 days of symptoms was 0.83 (0.75-0.89), and specificity was 0.98 (0.87-1.00) from 6 studies (792 reverse transcription polymerase chain reaction-negative controls). Summary sensitivity and specificity for paired respiratory specimens with cycle threshold values ≤33 were 0.91 (0.85-0.95) and 0.56 (0.39-0.73) from 10 studies (612 individuals). Source data from 1779 cases reveal that >70% have antigenemia 2 weeks following symptom onset, which persists in <10% at 28 days. The available studies suffer from heterogeneity, and Omicron-era data are scarce.

Conclusions

Nucleocapsid antigenemia currently has limited utility due to limitations of existing studies and lack of Omicron-era data. Improved study designs targeting potential clinical uses in screening, surveillance, and complex clinical decision-making-especially in immunocompromised patients-are needed.

Application of machine learning for predicting lymph node metastasis in T1 colorectal cancer: a systematic review and meta-analysis

BACKGROUND

We review and analyze research on the application of machine learning (ML) and deep learning (DL) models to lymph node metastasis (LNM) prediction in patients with T1 colorectal cancer (CRC). Predicting LNM before radical surgery is important in patients with T1 CRC. However, current surgical treatment guidelines are limited. LNM prediction using ML or DL may improve predictive accuracy. The diagnostic accuracy of LNM prediction using ML- and DL-based models for patients with CRC was assessed.

METHODS

We performed a comprehensive search of the PubMed, Embase, and Cochrane databases (inception to April 30th of 2022) for studies that applied ML or DL to LNM prediction in T1 CRC patients specifically to compare with histopathological findings and not related to radiological aspects.

RESULTS

33,199 T1 CRC patients enrolled across seven studies with a retrospective design were included. LNM was observed in 3,173 (9.6%) patients. Overall, the ML- and DL-based model exhibited a sensitivity of 0.944 and specificity of 0.877 for the prediction of LNM in patients with T1 CRC. Six different types of ML and DL models were used across the studies included in this meta-analysis. Therefore, a high degree of heterogeneity was observed.

CONCLUSIONS

The ML and DL models provided high sensitivity and specificity for predicting LNM in patients with T1 CRC, and the heterogeneity between studies was significant. These results suggest the potential of ML or DL as diagnostic tools. However, more reliable algorithms should be developed for predicting LNM before surgery in patients with T1 CRC.

Diagnostic accuracy of cytology and urine methylation test in patients with non-muscle invasive bladder cancer: a systematic review and meta-analysis

Background

Multiple clinical studies have demonstrated the numerous advantages of urine methylation test over cytology for monitoring patients with non-muscle invasive bladder cancer (NMIBC) following surgery. This research aims to provide a systematic review and meta-analysis to evaluate the efficacy and limits of urine methylation test in the clinical management of NMIBC.

Methods

This research was carried out by conducting a comprehensive search of clinical trials comparing cytology and urine methylation test for NMIBC follow-up using databases such as PubMed, Embase, Web of Science, and the Cochrane Library up until May 2023, including references from relevant articles. The study is registered on PROSPERO with ID CRD42023398969.

Result

This study comprised six studies with a total of 1676 patients. The analysis revealed that the AUC of urine methylation test had a greater AUC than that of the cytology examination (0.89 vs 0.71). In post-operative follow-up of patients with NMIBC, the urine methylation test demonstrated a significant sensitivity (0.69 vs 0.52), but with lower specificity (0.87 vs 0.93) than cytology examination.

Conclusion

The urine methylation test and cytology examination have both shown strong diagnostic performance in screening for NMIBC patients. However, urine methylation test outperforms cytology examination in terms of accuracy and sensitivity.

Systematic review registration

PROSPERO, identifier CRD42023398969.

Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis

BACKGROUND AND PURPOSE

Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT.

METHODS

A systematic search of PubMed, EMBASE, and Web of Science was conducted until February 19, 2024. Inclusion criteria were as follows: patients with AIS who received reperfusion therapy; AI/ML algorithm using imaging to predict HT; or presence of sufficient data on the predictive performance. Exclusion criteria were as follows: articles with less than 20 patients; articles lacking algorithms that operate solely on images; or articles not detailing the algorithm used. The quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model, and a summary receiver operating characteristic curve was constructed using the Reitsma method.

RESULTS

We identified six eligible studies, which included 1640 patients. Aside from an unclear risk of bias regarding flow and timing identified in two of the studies, all studies showed low risk of bias and applicability concerns in all categories. Pooled sensitivity, specificity, and DOR were .849, .878, and 45.598, respectively.

CONCLUSION

AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.

Using Dried Blood Spots to Quantitatively Detect Anti-SARS-CoV-2 IgG Antibodies by ELISA: A Validation Study

SARS-CoV-2 serological testing is useful to determine seroprevalence, epidemiological trends, and the extent of transmission. The collection and transport of serum samples can be logistically challenging, especially in remote underserved areas. Dried blood spots (DBSs) would allow easier sample collection and logistical handling compared with standard serum collection, particularly for extensive and repeated SARS-CoV-2 serosurveys. We evaluated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the IgG ELISA (Wantai, Beijing, China) using DBSs against sera for the quantitative detection of SARS-CoV-2 IgG antibody. The IgG ELISA was used to test paired sera and DBSs obtained from individuals with recent virologically confirmed COVID-19 illness and banked paired sera and DBSs collected before the COVID-19 pandemic. We found that 100/100 (100%) seropositive samples were positive using DBSs, and 193/194 (99%) seronegative samples were negative using DBSs. Compared with sera, the DBS method had a 100% sensitivity, 99% specificity, 99% PPV, and 100% NPV. Use of DBSs for SARS-CoV-2 household or population serosurveys may be considered in situations with limitations in sample collection, shipment, and storage.

The conundrum of differentiating Cushing's syndrome from non-neoplastic hypercortisolism: a systematic review and meta-analysis

CONTEXT

Once hypercortisolemia is confirmed, differential diagnosis between Cushing's syndrome (CS) due to neoplastic endogenous hypercortisolism and non-neoplastic hypercortisolism (NNH, pseudo-Cushing's syndrome) is crucial. Due to worldwide corticotropin-releasing hormone (CRH) unavailability, accuracy of alternative tests to dexamethasone (Dex)-CRH, is clearly needed.

OBJECTIVE

Assess the diagnostic accuracy of Dex-CRH test, desmopressin stimulation test, midnight serum cortisol (MSC), and late-night salivary cortisol (LNSC) levels to distinguish CS from NNH.

METHODS

Articles through March 2022 were identified from Scopus, Web of Science, MEDLINE, EMBASE, and PubMed. All steps through the systematic review were performed independently and in duplicate and strictly adhered to the updated PRISMA-DTA checklist.

DATA SYNTHESIS

A total of 24 articles (1900 patients) were included. Dex-CRH had a pooled sensitivity and specificity of 91% (95%CI 87-94%; I2 0%) and 82% (73-88%; I2 50%), desmopressin test 86% (81-90%; I2 28%) and 90% (84-94%; I2 15%), MSC 91% (85-94%; I2 66%) and 81% (70-89%; I2 71%), and LNSC 80% (67-89%; I2 57%) and 90% (84-93%; I2 21%), respectively. Summary receiver operating characteristics areas under the curve were Dex-CRH 0.949, desmopressin test 0.936, MSC 0.942, and LNSC 0.950 without visual or statistical significance. The overall risk of studies bias was moderate.

CONCLUSION

Dex-CRH, the desmopressin stimulation test, and MSC have similar diagnostic accuracy, with Dex-CRH and MSC having slightly higher sensitivity, and the desmopressin test being more specific. LNSC was the least accurate, probably due to high heterogeneity, intrinsic variability, different assays, and lack of consistent reported cutoffs. When facing this challenging differential diagnosis, the results presented here should increase clinicians' confidence when deciding which test to perform.

Diagnostic yield and safety of diagnostic techniques for pulmonary lesions: systematic review, meta-analysis and network meta-analysis

BACKGROUND

With recent advancements in bronchoscopic procedures, data on the best modality to sample peripheral pulmonary lesions (PPLs) is lacking, especially comparing bronchoscopy with computed tomography-guided transthoracic biopsy or needle aspiration (CT-TBNA).

METHODS

We performed a meta-analysis, pairwise meta-analysis and network meta-analysis on studies reporting diagnostic yield and complications with the use of CT-TBNA, radial endobronchial ultrasound (rEBUS), virtual bronchoscopy (VB), electromagnetic navigation (EMN) or robot-assisted bronchoscopy (RAB) to sample PPLs. The primary outcome was diagnostic yield and the secondary outcome was complications. We estimated the relative risk ratios using a random-effects model and used the frequentist approach for the network meta-analysis. We performed extensive analysis to assess the heterogeneity including reporting bias, publication bias, subgroup and meta-regressional analysis. We assessed the quality of the studies using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and QUADAS-Comparative (QUADAS-C).

RESULTS

We included 363 studies. The overall pooled diagnostic yield was 78.1%, the highest with CT-TBNA (88.9%), followed by RAB (84.8%) and the least with rEBUS (72%). In the pairwise meta-analysis, only rEBUS showed inferiority to CT-TBNA. The network meta-analysis ranked CT-TBNA as likely the most effective approach followed by VB, EMN and RAB, while rEBUS was the least effective, with a low-GRADE certainty. CT-TBNA had the highest rate of complications.

CONCLUSION

Although CT-TBNA is the most effective approach to sample PPLs, RAB has a comparable diagnostic yield with a lesser complication rate. Further prospective studies are needed comparing CT-TBNA and RAB.

Diagnostic and prognostic value of heparin-binding protein in sepsis: A systematic review and meta-analysis

BACKGROUND

Sepsis remains a leading cause of death worldwide. In this context, heparin-binding protein (HBP) has emerged as a possible biomarker, drawing significant attention for its diagnostic and prognostic usefulness in septic patients. Despite this advancement, the literature yields conflicting results. This study is intended to critically evaluate the diagnostic and prognostic value of HBP in critically ill septic patients.

METHODS

We searched multiple databases, including PubMed, SCOPUS, Web of Science, and EBSCO, to identify relevant studies on April 27, 2023. We included studies investigating sepsis or its severe outcomes that reported HBP levels and the required data to create 2 × 2 tables. We used R version 4.2.2 and R Studio to analyze the pooled diagnostic accuracy outcomes. The diagmeta package was utilized to calculate the optimum cutoff value.

RESULTS

In our meta-analysis, we incorporated 28 studies including 5508 patients. The analysis revealed that HBP has a sensitivity of 0.71 (95% CI: 0.60; 0.79) and a specificity of 0.68 (95% CI: 0.51; 0.81) in diagnosing sepsis, respectively. HBP demonstrated moderate prognostic accuracy for mortality at a cutoff value of 161.415 ng/mL, with a sensitivity and specificity of 72%, and for severe sepsis outcomes at a cutoff value of 58.907 ng/mL, with a sensitivity and specificity of 71%.

CONCLUSION

Our findings indicate a relatively moderate diagnostic and prognostic accuracy of HBP for sepsis. Future studies are required to verify the accuracy of HBP as a biomarker for sepsis.

Prognostic nutritional index as a predictive marker for acute kidney injury in adult critical illness population: a systematic review and diagnostic test accuracy meta-analysis

BACKGROUND

The prognostic nutritional index (PNI), integrating nutrition and inflammation markers, has been increasingly recognized as a prognostic predictor in diverse patient cohorts. Recently, its effectiveness as a predictive marker for acute kidney injury (AKI) in various clinical settings has gained attention. This study aims to assess the predictive accuracy of the PNI for AKI in critically ill populations through systematic review and meta-analysis.

METHODS

A systematic review was conducted using the databases MEDLINE, EMBASE, PubMed, and China National Knowledge Infrastructure up to August 2023. The included trials reported the PNI assessment in adult population with critical illness and its predictive capacity for AKI. Data on study characteristics, subgroup covariates, and diagnostic performance of PNI, including sensitivity, specificity, and event rates, were extracted. A diagnostic test accuracy meta-analysis was performed. Subgroup analyses and meta-regression were utilized to investigate the sources of heterogeneity. The GRADE framework evaluated the confidence in the meta-analysis's evidence.

RESULTS

The analysis encompassed 16 studies with 17 separate cohorts, totaling 21,239 patients. The pooled sensitivity and specificity of PNI for AKI prediction were 0.67 (95% CI 0.58-0.74) and 0.74 (95% CI 0.67-0.80), respectively. The pooled positive likelihood ratio was 2.49 (95% CI 1.99-3.11; low certainty), and the negative likelihood ratio was 0.46 (95% CI 0.37-0.56; low certainty). The pooled diagnostic odds ratio was 5.54 (95% CI 3.80-8.07), with an area under curve of summary receiver operating characteristics of 0.76. Subgroup analysis showed that PNI's sensitivity was higher in medical populations than in surgical populations (0.72 vs. 0.55; p < 0.05) and in studies excluding patients with chronic kidney disease (CKD) than in those including them (0.75 vs. 0.56; p < 0.01). Overall, diagnostic performance was superior in the non-chronic kidney disease group.

CONCLUSION

Our study demonstrated that PNI has practical accuracy for predicting the development of AKI in critically ill populations, with superior diagnostic performance observed in medical and non-CKD populations. However, the diagnostic efficacy of the PNI has significant heterogeneity with different cutoff value, indicating the need for further research.

Artificial intelligence in diagnosing upper limb musculoskeletal disorders: a systematic review and meta-analysis of diagnostic tests

Purpose

The integration of artificial intelligence (AI) in radiology has revolutionized diagnostics, optimizing precision and decision-making. Specifically in musculoskeletal imaging, AI tools can improve accuracy for upper extremity pathologies. This study aimed to assess the diagnostic performance of AI models in detecting musculoskeletal pathologies of the upper extremity using different imaging modalities.

Methods

A meta-analysis was conducted, involving searches on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The quality of the studies was assessed using the QUADAS-2 tool. Diagnostic accuracy measures including sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR, NLR), area under the curve (AUC), and summary receiver operating characteristic were pooled using a random-effects model. Heterogeneity and subgroup analyses were also included. All statistical analyses and plots were performed using the R software package.

Results

Thirteen models from ten articles were analyzed. The sensitivity and specificity of the AI models to detect musculoskeletal conditions in the upper extremity were 0.926 (95% CI: 0.900; 0.945) and 0.908 (95% CI: 0.810; 0.958). The PLR, NLR, lnDOR, and the AUC estimates were found to be 19.18 (95% CI: 8.90; 29.34), 0.11 (95% CI: 0.18; 0.46), 4.62 (95% CI: 4.02; 5.22) with a (P < 0.001), and 95%, respectively.

Conclusion

The AI models exhibited strong univariate and bivariate performance in detecting both positive and negative cases within the analyzed dataset of musculoskeletal pathologies in the upper extremity.

Comparative Study of Different Imaging Modalities for Diagnosis of Bone Metastases of Prostate Cancer: A Bayesian Network Meta-analysis

PURPOSE

This study aimed to compare the diagnostic performances of 8 different imaging modalities for preoperative detection of bone metastases in prostate cancer patients by performing a network meta-analysis using direct comparison studies with 2 or more imaging techniques.

PATIENTS AND METHODS

We searched PubMed, Embase, and Cochrane Library for studies evaluating the performances of 8 different imaging modalities for the preoperative detection of bone metastases in prostate cancer patients. The network meta-analysis was performed in patient-based analysis. The consistency was evaluated by examining the agreement between direct and indirect treatment effects, and the surface under the cumulative ranking curve (SUCRA) values were obtained to calculate the probability of each imaging modality being the most effective diagnostic method.

RESULTS

A total of 999 patients from 13 direct comparison studies using 8 different imaging modalities for preoperative detection or follow-up of bone metastases in prostate cancer patients were included. For the detection of bone metastases of prostate cancer, 68 Ga-PSMA-11 PET/CT showed the highest SUCRA values of sensitivity, positive predictive value, accuracy, and diagnostic odds ratio. In addition, 18 F-NaF PET/CT and SPECT/CT showed high SUCRA values.

CONCLUSIONS

68 Ga-PSMA-11 PET/CT showed the highest SUCRA values. Other imaging modalities showed complementary diagnostic roles for preoperative detection of bone metastases in patients with prostate cancer, except bone scintigraphy and MRI.

Clinical Presentation, Risk Factors, and Comparison of Laboratory Diagnostics for Seasonal Influenza Virus Among Cambodians From 2007 to 2020

Background

Despite its global significance, challenges associated with understanding the epidemiology and accurately detecting, measuring, and characterizing the true burden of seasonal influenza remain in many resource-poor settings.

Methods

A prospective observational study was conducted in Cambodia at 28 health facilities between 2007 and 2020 utilizing passive surveillance data of patients presenting with acute undifferentiated febrile illness (AUFI) to describe the prevalence of influenza A and B and characterize associated risk factors and symptoms using a questionnaire. A comparison of rapid influenza diagnostic tests (RIDTs) and real-time reverse transcription polymerase chain reaction (rRT-PCR) results was also conducted.

Results

Of 30 586 total participants, 5634 (18.4%) tested positive for either influenza A or B, with 3557 (11.6%) positive for influenza A and 2288 (7.5%) positive for influenza B during the study. Influenza A and B were strongly associated with the rainy season (odds ratio [OR], 2.30; P < .001) and being from an urban area (OR, 1.45; P < .001). Analysis of individual symptoms identified cough (OR, 2.8; P < .001), chills (OR, 1.4; P < .001), and sore throat (OR, 1.4; P < .001) as having the strongest positive associations with influenza among patients with AUFI. Analysis comparing RIDTs and rRT-PCR calculated the overall sensitivity of rapid tests to be 0.492 (95% CI, 0.479-0.505) and specificity to be 0.993 (95% CI, 0.992-0.994) for both influenza type A and B.

Conclusions

Findings from this 14-year study include describing the epidemiology of seasonal influenza over a prolonged time period and identifying key risk factors and clinical symptoms associated with infection; we also demonstrate the poor sensitivity of RIDTs in Cambodia.

Diagnostic performance of angiography-derived fractional flow reserve and CT-derived fractional flow reserve: A systematic review and Bayesian network meta-analysis

OBJECTIVE

Accumulating evidence has demonstrated that fractional flow reserves (FFRs) derived from invasive coronary angiograms (CA-FFRs) and coronary computed tomography angiography-derived FFRs (CT-FFRs) are promising alternatives to wire-based FFRs. However, it remains unclear which method has better diagnostic performance. This systematic review and meta-analysis aimed to compare the diagnostic performances of the two approaches.

METHODS

The Cochrane Library, PubMed, Embase, Medline (Ovid), the Chinese China National Knowledge Infrastructure Database (CNKI), VIP, and WanFang Data databases were searched for relevant studies that included comparisons between CA-FFR and CT-FFR, from their respective database inceptions until January 1, 2023. Studies where both noninvasive FFR (including CA-FFR and CT-FFR) and invasive FFR (as a reference standard) were performed for the diagnosis of ischemic coronary artery disease and were designed as prospective, paired diagnostic studies, were pulled. The diagnostic test accuracy method and Bayesian hierarchical summary receiver operating characteristic (ROC) model for network meta-analysis (NMA) of diagnostic tests (HSROC-NMADT) were both used to perform a meta-analysis on the data.

RESULTS

Twenty-six studies were included in this NMA. The results from both the diagnostic test accuracy and HSROC-NMADT methods revealed that the diagnostic accuracy of CA-FFR was higher than that of CT-FFR, in terms of sensitivity (Se; 0.86 vs. 0.84), specificity (Sp; 0.90 vs. 0.78), positive predictive value (PPV; 0.83 vs. 0.70), and negative predictive value (NPV; 0.91 vs. 0.89) for the detection of myocardial ischemia. A cumulative ranking curve analysis indicated that CA-FFR had a higher diagnostic accuracy than CT-FFR in the context of this study, with a higher area under the ROC curve (AUC; 0.94 vs. 0.87).

CONCLUSIONS

Although both of these two commonly used virtual FFR methods showed high levels of diagnostic accuracy, we demonstrated that CA-FFR had a better Se, Sp, PPV, NPV, and AUC than CT-FFR. However, this study provided only indirect comparisions; therefore, larger studies are warranted to directly compare the diagnostic performances of these two approaches.

Accuracy of Intraoperative Neuromonitoring in the Diagnosis of Intraoperative Neurological Decline in the Setting of Spinal Surgery-A Systematic Review and Meta-Analysis

STUDY DESIGN

Systematic review and meta-analysis.

OBJECTIVES

In an effort to prevent intraoperative neurological injury during spine surgery, the use of intraoperative neurophysiological monitoring (IONM) has increased significantly in recent years. Using IONM, spinal cord function can be evaluated intraoperatively by recording signals from specific nerve roots, motor tracts, and sensory tracts. We performed a systematic review and meta-analysis of diagnostic test accuracy (DTA) studies to evaluate the efficacy of IONM among patients undergoing spine surgery for any indication.

METHODS

The current systematic review and meta-analysis was performed using the Preferred Reporting Items for a Systematic Review and Meta-analysis statement for Diagnostic Test Accuracy Studies (PRISMA-DTA) and was registered on PROSPERO. A comprehensive search was performed using MEDLINE, EMBASE and SCOPUS for all studies assessing the diagnostic accuracy of neuromonitoring, including somatosensory evoked potential (SSEP), motor evoked potential (MEP) and electromyography (EMG), either on their own or in combination (multimodal). Studies were included if they reported raw numbers for True Positives (TP), False Negatives (FN), False Positives (FP) and True Negative (TN) either in a 2 × 2 contingency table or in text, and if they used postoperative neurologic exam as a reference standard. Pooled sensitivity and specificity were calculated to evaluate the overall efficacy of each modality type using a bivariate model adapted by Reitsma et al, for all spine surgeries and for individual disease groups and regions of spine. The risk of bias (ROB) of included studies was assessed using the quality assessment tool for diagnostic accuracy studies (QUADAS-2).

RESULTS

A total of 163 studies were included; 52 of these studies with 16,310 patients reported data for SSEP, 68 studies with 71,144 patients reported data for MEP, 16 studies with 7888 patients reported data for EMG and 69 studies with 17,968 patients reported data for multimodal monitoring. The overall sensitivity, specificity, DOR and AUC for SSEP were 71.4% (95% CI 54.8-83.7), 97.1% (95% CI 95.3-98.3), 41.9 (95% CI 24.1-73.1) and .899, respectively; for MEP, these were 90.2% (95% CI 86.2-93.1), 96% (95% CI 94.3-97.2), 103.25 (95% CI 69.98-152.34) and .927; for EMG, these were 48.3% (95% CI 31.4-65.6), 92.9% (95% CI 84.4-96.9), 11.2 (95% CI 4.84-25.97) and .773; for multimodal, these were found to be 83.5% (95% CI 81-85.7), 93.8% (95% CI 90.6-95.9), 60 (95% CI 35.6-101.3) and .895, respectively. Using the QUADAS-2 ROB analysis, of the 52 studies reporting on SSEP, 13 (25%) were high-risk, 10 (19.2%) had some concerns and 29 (55.8%) were low-risk; for MEP, 8 (11.7%) were high-risk, 21 had some concerns and 39 (57.3%) were low-risk; for EMG, 4 (25%) were high-risk, 3 (18.75%) had some concerns and 9 (56.25%) were low-risk; for multimodal, 14 (20.3%) were high-risk, 13 (18.8%) had some concerns and 42 (60.7%) were low-risk.

CONCLUSIONS

These results indicate that all neuromonitoring modalities have diagnostic utility in successfully detecting impending or incident intraoperative neurologic injuries among patients undergoing spine surgery for any condition, although it is clear that the accuracy of each modality differs.PROSPERO Registration Number: CRD42023384158.

The Efficacy and Safety of Metastasis-directed Therapy in Patients with Prostate Cancer: A Systematic Review and Meta-analysis of Prospective Studies

CONTEXT

Despite the lack of level 1 evidence, metastasis-directed therapy (MDT) is used widely in the management of metastatic prostate cancer (mPCa) patients. Data are continuously emerging from well-designed prospective studies.

OBJECTIVE

To summarise and report the evidence on oncological and safety outcomes of MDT in the management of mPCa patients.

EVIDENCE ACQUISITION

We searched the PubMed, Scopus, and Web of Science databases for prospective studies assessing progression-free survival (PFS), local control (LC), androgen deprivation therapy (ADT)-free survival (ADT-FS), overall survival (OS), and/or adverse events (AEs) in mPCa patients treated with MDT. A meta-analysis was performed for 1- and 2-yr PFS, LC, ADT-FS, OS, and rate of AEs. Meta-regression and sensitivity analysis were performed to account for heterogeneity and identify moderators.

EVIDENCE SYNTHESIS

We identified 22 prospective studies (n = 1137), including two randomised controlled trials (n = 116). Two studies were excluded from the meta-analysis (n = 120). The estimated 2-yr PFS was 46% (95% confidence interval [CI]: 36-56%) or 42% (95% CI: 33-52%) after excluding studies using biochemical or ADT-related endpoints. The estimated 2-yr LC, ADT-FS, and OS were 97% (95% CI: 94-98%), 55% (95% CI: 44-65%), and 97% (95% CI: 95-98%), respectively. Rates of treatment-related grade 2 and ≥3 AEs were 2.4% (95% CI: 0.2-7%) and 0.3% (95% CI: 0-1%), respectively.

CONCLUSIONS

MDT is a promising treatment strategy associated with favourable PFS, excellent LC, and a low toxicity profile that allows oligorecurrent hormone-sensitive patients to avoid or defer ADT-related toxicity. Integration of MDT with other therapies offers a promising research direction, in particular, in conjunction with systemic treatments and as a component of definitive care for oligometastatic PCa. However, in the absence of randomised trials, using MDT for treatment intensification remains an experimental approach, and the impact on OS is uncertain.

PATIENT SUMMARY

Direct treatment of metastases is a promising option for selected prostate cancer patients. It can delay hormone therapy and is being investigated as a way of intensifying treatment at the expense of manageable toxicity.

The diagnostic utility of heparin-binding protein among patients with bacterial infections: a systematic review and meta-analysis

BACKGROUND

Bacterial infections are considered a leading cause of hospitalization and death globally. There is still a need for a rapid and feasible biomarker for bacterial infections. Heparin-binding protein (HBP) was shown to be related to bacterial infections. The objective of the study is to investigate the diagnostic accuracy of HBP in bacterial infections.

METHODS

Articles were screened in PubMed, SCOPUS, Web of Science, and Cochrane to recognize eligible studies. We included studies investigating the diagnostic accuracy of HBP and reported the necessary data to construct 2 × 2 tables. A univariate analysis was conducted to determine the pooled sensitivity and specificity, and a bivariate diagnostic random-effects model was used to calculate the optimal cut-off point.

RESULTS

The analysis comprised sixteen studies in total. Plasma HBP showed a sensitivity of 0.90 (95% CI: [0.79, 0.96]) and a specificity of 0.87 (95% CI: [0.66, 0.96]) in diagnosing bacterial infections using blood samples. Pooling data from seven studies revealed that HBP in cerebrospinal fluid (CSF) has sensitivity and specificity of 96% (95% CI: [0.85, 0.99]), and 95% (95% CI: [0.89, 0.97]), respectively, for the diagnosis of bacterial meningitis. In urinary tract infections (UTI), urine-HBP was revealed to have a high diagnostic value in discriminating bacterial from non-bacterial UTI infection at a cut-off value of 32.868 ng/ml with sensitivity and specificity of 87%.

CONCLUSION

HBP has shown a high diagnostic accuracy of bacterial infections, including UTI and meningitis. Further studies are needed to determine its prognostic value and whether it could guide antibiotic therapy.

Diagnostic Accuracy of Deep Learning for the Prediction of Osteoporosis Using Plain X-rays: A Systematic Review and Meta-Analysis

(1) Background: This meta-analysis assessed the diagnostic accuracy of deep learning model-based osteoporosis prediction using plain X-ray images. (2) Methods: We searched PubMed, Web of Science, SCOPUS, and Google Scholar from no set beginning date to 28 February 2023, for eligible studies that applied deep learning methods for diagnosing osteoporosis using X-ray images. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. The area under the receiver operating characteristic curve (AUROC) was used to quantify the predictive performance. Subgroup, meta-regression, and sensitivity analyses were performed to identify the potential sources of study heterogeneity. (3) Results: Six studies were included; the pooled AUROC, sensitivity, and specificity were 0.88 (95% confidence interval [CI] 0.85-0.91), 0.81 (95% CI 0.78-0.84), and 0.87 (95% CI 0.81-0.92), respectively, indicating good performance. Moderate heterogeneity was observed. Mega-regression and subgroup analyses were not performed due to the limited number of studies included. (4) Conclusion: Deep learning methods effectively extract bone density information from plain radiographs, highlighting their potential for opportunistic screening. Nevertheless, additional prospective multicenter studies involving diverse patient populations are required to confirm the applicability of this novel technique.

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

BACKGROUND

Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported.

OBJECTIVE

The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm.

METHODS

We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard.

RESULTS

We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84-0.91), the summary specificity was 1.00 (95% CI 0.85-1.00), the AUC was 0.96 (95% CI 0.94-0.98), the pAUC was 0.92 (95% CI 0.89-0.96), and the DOR was 207.62 (95% CI 24.62-924.64).

CONCLUSION

Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.

Comparable performance of antigen-detecting rapid test by healthcare worker-collected and self-collected swabs for SARS-CoV-2 diagnostic: A systematic review and meta-analysis

Usage of self-screening tests has become increasingly relevant in public health perspective for early detection of SARS-CoV-2 infection in the transitioning era of the COVID-19 pandemic into an endemic. This study was designed to compare the diagnostic accuracy of self-conducted and health professional-conducted SARS-CoV-2 rapid antigen tests (Ag-RDTs) and whether the sample was taken from anterior nasal or nasal mid-turbinate. Eligible comparative Ag-RDTs accuracy studies were retrieved from electronic databases systematically, in accordance with PRISMA. Selected studies were assessed for risk of bias using QUADAS-2 and QUADAS-C. In total, we selected five out of 1952 studies retrieved using the keywords. The overall sensitivity for the self-collected nasal swab method and healthcare worker-collected nasopharyngeal swab method was 79% (95% CI 68-87; I2  = 62%) and 83% (95% CI 75-89; I2  = 32%), respectively, which was not statistically different (p = 0.499). Nasal mid-turbinate swabs have a significantly higher sensitivity compared to anterior nasal swabs (p < 0.01). Both sampling methods represent high and comparable specificity values of 98% (95% CI 97-99; I2  = 0%) and 99% (95% CI 98-99; I2  = 0%). Positive predictive value (range 90%-99%) and negative predictive value (range 87%-98%) were equivalent for both methods. Our findings indicated the accuracy of self-collected Ag-RDT on nasal swabs was comparable to those performed by healthcare worker-collected on nasopharyngeal swabs. Self-collected Ag-RDT could be considered as a transmission prevention method in the transition of COVID-19 pandemic.

Diagnostic performance of CL Detect rapid-immunochromatographic test for cutaneous leishmaniasis: a systematic review and meta-analysis

BACKGROUND

Sensitive, robust, and fast point-of-care tests are needed for cutaneous leishmaniasis (CL) diagnosis. The recently developed CL Detect rapid test (InBios) for detecting Leishmania peroxidoxin antigen has been evaluated in several studies. However, diagnostic performances were controversial. Therefore, this systematic review and meta-analysis aimed to determine the pooled sensitivity and specificity of CL Detect for CL diagnosis.

METHODS

PubMed, Scopus, EMBASE, ScienceDirect, and Google Scholar were sources of articles. We included studies reporting the diagnostic accuracy of CL Detect and CL-suspected patients in the English language. The methodological qualities of the included studies were appraised using the quality assessment of diagnostic accuracy studies-2 (QUADAS-2). Meta-analysis was conducted using Stata 14.2 and R software.

RESULTS

A total of 9 articles were included. The study sample size ranged from 11 to 274. The sensitivities of the individual studies ranged from 23 to 100%, and the specificities ranged from 78 to 100%. Pooled sensitivity and specificity were 68% (95% CI, 41-86%) and 94% (95% CI, 87-97%), respectively. AUC displayed 0.899. Pooled sensitivity was lower (47%, 95% CI, 34-61%) when PCR was used as a reference than microscopy (83%, 95% CI, 39-97%). Pooled sensitivity was lower (48%, 95% CI, 30-67%) for all lesion durations compared to ≤ 4 months (89%, 95% CI, 43-99%).

CONCLUSIONS

CL Detect has poor sensitivity and does not meet the minimal sensitivity of 95% of target product profiles designed for CL point-of-care tests. Currently, the CL Detect test looks unsuitable for CL diagnosis, despite its high specificity. Findings are limited by the low number of studies available. Further large-scale studies are recommended.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022323497.

Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study

BACKGROUND

This systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagnosis of Intracranial Hemorrhage (ICH) on computed tomography (CT) scans.

METHODS

Until May 2023, systematic searches were conducted in ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE for studies that evaluated the diagnostic precision of ML model-assisted ICH detection. Patients with and without ICH as the target condition who were receiving CT-Scan were eligible for the research, which used ML algorithms based on radiologists' reports as the gold reference standard. For meta-analysis, pooled sensitivities, specificities, and a summary receiver operating characteristics curve (SROC) were used.

RESULTS

At last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included. The overall (Diagnostic Test Accuracy) DTA of retrospective studies with a pooled sensitivity was 0.917 (95% CI 0.88-0.943, I2 = 99%). The pooled specificity was 0.945 (95% CI 0.918-0.964, I2 = 100%). The pooled diagnostic odds ratio (DOR) was 219.47 (95% CI 104.78-459.66, I2 = 100%). These results were significant for the specificity of the different network architecture models (p-value = 0.0289). However, the results for sensitivity (p-value = 0.6417) and DOR (p-value = 0.2187) were not significant. The ResNet algorithm has higher pooled specificity than other algorithms with 0.935 (95% CI 0.854-0.973, I2 = 93%).

CONCLUSION

This meta-analysis on DTA of ML algorithms for detecting ICH by assessing non-contrast CT-Scans shows the ML has an acceptable performance in diagnosing ICH. Using ResNet in ICH detection remains promising prediction was improved via training in an Architecture Learning Network (ALN).

Diagnostic accuracy of whole-body magnetic resonance imaging versus positron emission tomography-computed tomography for the staging of pediatric lymphoma: a systematic review and meta-analysis

BACKGROUND

Whole-body magnetic resonance imaging (MRI) has been investigated by multiple authors as a radiation-free alternative to positron emission tomography computed tomography (PET-CT) in children with lymphoma.

OBJECTIVE

To evaluate the sensitivity, specificity, and diagnostic odds ratio of whole-body MRI compared to PET-CT for the staging of pediatric lymphoma.

METHODS

The databases PubMed, Embase, and Scopus were searched for studies that reported the accuracy of whole-body MRI compared to PET-CT for lymphoma staging in children. Data was collected from included studies to formulate 2 × 2 contingency tables, including the number of true positive, true negative, false positive, and false negative. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated. Summary receiver operating characteristic curves were drawn and the area under the curve (AUC) calculated. In addition, the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS 2) tool was used to assess the risk of bias and applicability concerns.

RESULTS

A total of seven studies were included in the final analysis. Of these, six studies used unenhanced whole-body MRI. The pooled sensitivity of whole-body MRI-based staging was 95.8%, while the pooled specificity was 21.8%. The DOR for whole-body MRI was 1.19. For extranodal staging, the pooled sensitivity was 88.9%, specificity was 97.4%, and DOR was 25.29. The partial AUC for overall staging was 0.63, whereas that for extranodal staging stood at 0.88. Based on the QUADAS 2 tool, all seven studies were at risk of bias (six at high risk, one at unclear risk).

CONCLUSION

Whole-body MRI has high sensitivity for staging of pediatric lymphoma and may be a useful alternative to PET-CT.

The utility of long non-coding RNAs in chronic obstructive pulmonary disease: a comprehensive analysis

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is one of the main causes of morbidity and mortality in the world. However, there are some patients who are not diagnosed early and correctly through routine methods because of inconspicuous or serious symptoms. This study aims to assess the diagnostic role of long non-coding RNA (lncRNA) in COPD.

METHODS

We searched literature from electronic databases, after excluding non-COPD literature, the bibliometric analysis was performed, and VOSviewer software was used to represent the data analyzed. Literature evaluating the diagnostic test accuracy of lncRNA for COPD was eligible, and the QUADAS-2 checklist was used to evaluate the quality. The pooled sensitivity (SEN), specificity (SPE), diagnostic odds ratio (DOR), and summary receiver operating characteristic curve (sROC) were used to analyze the overall diagnostic performance. Subgroup and meta-regression analyses were performed to explore the heterogeneity, and a funnel plot was assessed for publication bias. Also, lncRNAs related to COPD were identified and explored for their potential biological function.

RESULTS

An increased annual growth rate of literature on this subject from 2016 focused on COPD, humans, RNA, and lncRNA. The meta-analysis enrolled 17 literature indicated that the SEN, SPE, and DOR differentiating COPD patients from normal controls (NCs) were 0.86 (95% CI [0.80, 0.90]), 0.78 (95% CI [0.67, 0.86]), and 21.59 (95% CI [11.39, 40.91]), respectively. Meanwhile, lncRNAs had the ability to distinguish acute exacerbations of COPD (AECOPD) patients from COPD; the SEN, SPE, and DOR were 0.75 (95% CI [0.62, 0.85]), 0.81 (95% CI [0.71, 0.89]), and 13.02 (95% CI [7.76, 21.85]), respectively. The area under the sROC were calculated to be greater than 0.8 at least. Subgroup and meta-regression analysis showed that the types of specimens and dysregulated lncRNAs might affect the diagnostic accuracy. The funnel plot showed there was a certain publication bias. 41 lncRNAs related to COPD were identified and mainly located in the nucleus and cytoplasm, associated with proliferation, invasion, and prognosis. These lncRNA-binding proteins were involved in the spliceosome, Rap1 signaling pathway, MAPK signaling pathway, and so on.

CONCLUSION

LncRNA suggests potential diagnostic biomarkers and therapeutic targets for COPD patients.

Diagnostic accuracy of biomarkers to detect acute mesenteric ischaemia in adult patients: a systematic review and meta-analysis

BACKGROUND

Acute mesenteric ischaemia (AMI) is a disease with different pathophysiological mechanisms, leading to a life-threatening condition that is difficult to diagnose based solely on clinical signs. Despite widely acknowledged need for biomarkers in diagnosis of AMI, a broad systematic review on all studied biomarkers in different types of AMI is currently lacking. The aim of this study was to estimate the diagnostic accuracy of all potential biomarkers of AMI studied in humans.

METHODS

A systematic literature search in PubMed, The Cochrane Library, Web of Science and Scopus was conducted in December 2022. Studies assessing potential biomarkers of AMI in (at least 10) adult patients and reporting their diagnostic accuracy were included. Meta-analyses of biomarkers' sensitivity, specificity, and positive and negative likelihood ratios were conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study quality was assessed with the QUADAS-2 tool.

RESULTS

Seventy-five studies including a total of 9914 patients assessed 18 different biomarkers in serum/plasma and one in urine (each reported in at least two studies), which were included in meta-analyses. None of the biomarkers reached a conclusive level for accurate prediction. The best predictive value overall (all studies with any type and stage of AMI pooled) was observed for Ischaemia-modified albumin (2 studies, sensitivity 94.7 and specificity 90.5), interleukin-6 (n = 4, 96.3 and 82.6), procalcitonin (n = 6, 80.1 and 86.7), and intestinal fatty acid-binding protein (I-FABP) measured in serum (n = 16, 73.9 and 90.5) or in urine (n = 4, 87.9 and 78.9). In assessment of transmural mesenteric ischaemia, urinary I-FABP (n = 2, 92.3 and 85.2) and D-dimer (n = 3, 87.6 and 83.6) showed moderate predictive value. Overall risk of bias was high, mainly because of selected study populations and unclear timings of the biomarker measurements after onset of symptoms. Combinations of biomarkers were rarely studied, not allowing meta-analyses.

CONCLUSIONS

None of the studied biomarkers had sufficient sensitivity and specificity to diagnose AMI, although some biomarkers showed moderate predictive accuracy. Future studies should focus on timing of measurements of biomarkers, distinguishing between early stage and transmural necrosis, and between different types of AMI. Additionally, studies on combinations of biomarkers are warranted. PROSPERO registration: CRD42022379341.

Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study

BACKGROUND

Osteoporosis is a significant health problem in the skeletal system, associated with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has been accompanied by improvements in recent years and has been in the spotlight. This study is designed to investigate the Diagnostic Test Accuracy (DTA) of ML to detect osteoporosis through the hip dual-energy X-ray absorptiometry (DXA) images.

METHODS

The ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched until June 2023 for studies that tested the diagnostic precision of ML model-assisted for predicting an osteoporosis diagnosis.

RESULTS

The pooled sensitivity of univariate analysis of seven studies was 0.844 (95% CI 0.791 to 0.885, I2 = 94% for 7 studies). The pooled specificity of univariate analysis was 0.781 (95% CI 0.732 to 0.824, I2 = 98% for 7 studies). The pooled diagnostic odds ratio (DOR) was 18.91 (95% CI 14.22 to 25.14, I2 = 93% for 7 studies). The pooled mean positive likelihood ratio (LR+) and the negative likelihood ratio (LR-) were 3.7 and 0.22, respectively. Also, the summary receiver operating characteristics (sROC) of the bivariate model has an AUC of 0.878.

CONCLUSION

Osteoporosis can be diagnosed by ML with acceptable accuracy, and hip fracture prediction was improved via training in an Architecture Learning Network (ALN).

Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis

BACKGROUND

IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR.

METHODS

We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures.

RESULTS

We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80-0.88), specificity = 0.87 (95% CI 0.83-0.90), positive predictive value = 0.78 (95% CI 0.68-0.86), negative predictive value = 0.91 (95% CI 0.86-0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34-49.59). In detail, the RF-SVM (Random Forest-Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG.

CONCLUSIONS

our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.

Clinical value of alarm features for colorectal cancer: a meta-analysis

BACKGROUND

Colorectal cancer (CRC) is a common neoplasm in Western countries. Prioritizing access to colonoscopy appears of critical relevance. Alarm features are considered to increase the likelihood of CRC. Our aim was to assess the diagnostic performance of alarm features for CRC diagnosis.

METHODS

We performed a systematic review and meta-analysis of studies reporting the diagnostic accuracy of alarm features (rectal bleeding, anemia, change in bowel habit, and weight loss) for CRC, published up to September 2021. Colonoscopy was required as the reference diagnostic test. Diagnostic accuracy measures were pooled by a bivariate mixed-effects regression model. The number needed to scope (NNS; i. e. the number of patients who need to undergo colonoscopy to diagnose one CRC) according to each alarm feature was calculated.

RESULTS

31 studies with 45 100 patients (mean age 31-88 years; men 36 %-63 %) were included. The prevalence of CRC ranged from 0.2 % to 22 %. Sensitivity was suboptimal, ranging from 12.4 % for weight loss to 49 % for rectal bleeding, whereas specificity ranged from 69.8 % for rectal bleeding to 91.9 % for weight loss. Taken individually, rectal bleeding and anemia would be the only practical alarm features mandating colonoscopy (NNS 5.3 and 6.7, respectively).

CONCLUSIONS

When considered independently, alarm features have variable accuracy for CRC, given the high heterogeneity of study populations reflected by wide variability in CRC prevalence. Rectal bleeding and anemia are the most practical to select patients for colonoscopy. Integration of alarm features in a comprehensive evaluation of patients should be considered.

A Systematic Review and Meta-Analysis Comparing the Diagnostic Accuracy Tests of COVID-19

In this paper, we present a systematic review and meta-analysis that aims to evaluate the reliability of coronavirus disease diagnostic tests in 2019 (COVID-19). This article seeks to describe the scientific discoveries made because of diagnostic tests conducted in recent years during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Between 2020 and 2021, searches for published papers on the COVID-19 diagnostic were made in the PubMed database. Ninety-nine scientific articles that satisfied the requirements were analyzed and included in the meta-analysis, and the specificity and sensitivity of the diagnostic accuracy were assessed. When compared to serological tests such as the enzyme-linked immunosorbent assay (ELISA), chemiluminescence immunoassay (CLIA), lateral flow immunoassay (LFIA), and chemiluminescent microparticle immunoassay (CMIA), molecular tests such as reverse transcription polymerase chain reaction (RT-PCR), reverse transcription loop-mediated isothermal amplification (RT-LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR) performed better in terms of sensitivity and specificity. Additionally, the area under the curve restricted to the false-positive rates (AUC_FPR) of 0.984 obtained by the antiviral neutralization bioassay (ANB) diagnostic test revealed significant potential for the identification of COVID-19. It has been established that the various diagnostic tests have been effectively adapted for the detection of SARS-CoV-2; nevertheless, their performance still must be enhanced to contain potential COVID-19 outbreaks, which will also help contain potential infectious agent outbreaks in the future.

Virotyping and genetic antimicrobial susceptibility testing of porcine ETEC/STEC strains and associated plasmid types

Introduction

Enterotoxigenic Escherichia coli (ETEC) infections are the most common cause of secretory diarrhea in suckling and post-weaning piglets. For the latter, Shiga toxin-producing Escherichia coli (STEC) also cause edema disease. This pathogen leads to significant economic losses. ETEC/STEC strains can be distinguished from general E. coli by the presence of different host colonization factors (e.g., F4 and F18 fimbriae) and various toxins (e.g., LT, Stx2e, STa, STb, EAST-1). Increased resistance against a wide variety of antimicrobial drugs, such as paromomycin, trimethoprim, and tetracyclines, has been observed. Nowadays, diagnosing an ETEC/STEC infection requires culture-dependent antimicrobial susceptibility testing (AST) and multiplex PCRs, which are costly and time-consuming.

Methods

Here, nanopore sequencing was used on 94 field isolates to assess the predictive power, using the meta R package to determine sensitivity and specificity and associated credibility intervals of genotypes associated with virulence and AMR.

Results

Genetic markers associated with resistance for amoxicillin (plasmid-encoded TEM genes), cephalosporins (ampC promoter mutations), colistin (mcr genes), aminoglycosides (aac(3) and aph(3) genes), florfenicol (floR), tetracyclines (tet genes), and trimethoprim-sulfa (dfrA genes) could explain most acquired resistance phenotypes. Most of the genes were plasmid-encoded, of which some collocated on a multi-resistance plasmid (12 genes against 4 antimicrobial classes). For fluoroquinolones, AMR was addressed by point mutations within the ParC and GyrA proteins and the qnrS1 gene. In addition, long-read data allowed to study the genetic landscape of virulence- and AMR-carrying plasmids, highlighting a complex interplay of multi-replicon plasmids with varying host ranges.

Conclusion

Our results showed promising sensitivity and specificity for the detection of all common virulence factors and most resistance genotypes. The use of the identified genetic hallmarks will contribute to the simultaneous identification, pathotyping, and genetic AST within a single diagnostic test. This will revolutionize future quicker and more cost-efficient (meta)genomics-driven diagnostics in veterinary medicine and contribute to epidemiological studies, monitoring, tailored vaccination, and management.

Diagnostic accuracy of point-of-care lung ultrasound for COVID-19: a systematic review and meta-analysis

BACKGROUND

Point-of-care (POC) lung ultrasound (LUS) is widely used in the emergency setting and there is an established evidence base across a range of respiratory diseases, including previous viral epidemics. The necessity for rapid testing combined with the limitations of other diagnostic tests has led to the proposal of various potential roles for LUS during the COVID-19 pandemic. This systematic review and meta-analysis focused specifically on the diagnostic accuracy of LUS in adult patients presenting with suspected COVID-19 infection.

METHODS

Traditional and grey-literature searches were performed on 1 June 2021. Two authors independently carried out the searches, selected studies and completed the Quality Assessment Tool for Diagnostic Test Accuracy Studies (QUADAS-2). Meta-analysis was carried out using established open-source packages in R. We report overall sensitivity, specificity, positive and negative predictive values, and the hierarchical summary receiver operating characteristic curve for LUS. Heterogeneity was determined using the I² statistic.

RESULTS

Twenty studies were included, published between October 2020 and April 2021, providing data from a total of 4314 patients. The prevalence and admission rates were generally high across all studies. Overall, LUS was found to be 87.2% sensitive (95% CI 83.6 to 90.2) and 69.5% specific (95% CI 62.2 to 72.5) and demonstrated overall positive and negative likelihood ratios of 3.0 (95% CI 2.3 to 4.1) and 0.16 (95% CI 0.12 to 0.22), respectively. Separate analyses for each reference standard revealed similar sensitivities and specificities for LUS. Heterogeneity was found to be high across the studies. Overall, the quality of studies was low with a high risk of selection bias due to convenience sampling. There were also applicability concerns because all studies were undertaken during a period of high prevalence.

CONCLUSION

During a period of high prevalence, LUS had a sensitivity of 87% for the diagnosis of COVID-19 infection. However, more research is required to confirm these results in more generalisable populations, including those less likely to be admitted to hospital.

PROSPERO REGISTRATION NUMBER

CRD42021250464.

Influence of seasonal and operator variations on diagnostic accuracy of lateral flow devices during the COVID-19 pandemic: a systematic review and meta-analysis

BACKGROUND

Lateral flow tests (LFT) are point-of-care rapid antigen tests that allow isolation and control of disease outbreaks through convenient, practical testing. However, studies have shown significant variation in their diagnostic accuracy. We conducted a systematic review of the diagnostic accuracy of LFTs for the detection of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) to identify potential factors affecting their performance.

METHODS

A systematic search of online databases was carried out to identify studies assessing the sensitivity and specificity of LFTs compared with polymerase chain reaction (PCR) tests. Data were extracted and used to calculate pooled sensitivity and specificity. Meta-regression analysis was conducted to identify covariates influencing diagnostic accuracy.

RESULTS

In total, 76 articles with 108,820 test results were identified for analysis. Pooled sensitivity and specificity were 72% (95% confidence interval (CI): 0.68-0.76) and 100% (95% CI: 0.99-1.00), respectively. Staff operation of the LFT showed a statistically significant increase in sensitivity (p=0.04) and specificity (p=0.001) compared with self-operation by the test subjects. The use of LFTs in symptomatic patient subgroups also resulted in higher test sensitivity.

CONCLUSION

LFTs display good sensitivity and extremely good specificity for SARS-CoV-2 antigen detection; they become more sensitive in patients with symptoms and when performed by trained professionals.

CA125 for the Diagnosis of Advanced Urothelial Carcinoma of the Bladder: A Systematic Review and Meta-Analysis

BACKGROUND

Urothelial carcinoma of the bladder (UCB) is the second most common genitourinary cancer. This study aims to assess the diagnostic accuracy of CA125 in advanced UCB.

METHODS

We searched prevalent studies in PubMed, the Cochrane Library, Scopus, Embase, the Web of Science China National Knowledge Infrastructure database, and Wanfang data before October 2022. Pooled sensitivity, specificity, and summary receiver operating characteristics were used to assess the diagnostic value of CA125.

RESULTS

One thousand six hundred forty-one patients from 14 studies were analyzed. UCB stage T3-4N1 was defined as advanced UCB in ten studies; T2-4 was used in three studies; and N1M1 in one study. Patients' age was between 21 to 92, and 21% to 48.6% of patients were female. The pooled sensitivity was 0.695 (95% confidence interval (CI): 0.426-0.875). The pooled specificity was 0.846 (95% CI: 0.713-0.924). The diagnostic odds ratio was 8.138 (95% CI: 4.559-14.526). The AUC was 0.797.

CONCLUSION

CA125 may provide significant diagnostic accuracy in identifying muscle-invasive, lymph node-involved, and distant metastatic tumors in patients with urothelial carcinoma of the bladder. Limited studies have been conducted on the prognostic role of CA125. More studies are needed for a meta-analysis on the prognostic role of CA125 in UCB.

Identifying the culprit artery via 12-lead electrocardiogram in inferior wall ST-segment elevation myocardial infarction: A meta-analysis

BACKGROUND

Inferior wall ST-segment elevation myocardial infarction (STEMI) is mostly caused by acute occlusion of right coronary artery (RCA) and left circumflex artery (LCX). Several methods and algorithms using 12-lead ECG were developed to localize the lesion in inferior wall STEMI. However, the diagnostic properties of these methods remain under-recognized.

AIMS

The aim of this meta-analysis is to compare the diagnostic properties among the methods of identifying culprit artery in inferior wall STEMI using 12-lead ECG.

METHODS

We performed a meta-analysis to calculate the pooled sensitive, specificity, area under the curve (AUC) and diagnostic odds ratio (DOR) of each method.

RESULTS

Thirty-three studies with 4414 participants were included in the analysis. Methods using double leads had better diagnostic properties, especially ST-segment elevation (STE) in III > II [with pooled sensitivity 0.89 (0.84-0.93), specificity 0.68 (0.57-0.79), DOR 17 (9-32), AUC 0.88 (0.85-0.91)], ST-segment depression (STD) in aVL > I [with pooled sensitivity 0.82 (0.72-0.90), specificity 0.69 (0.48-0.86), DOR 11 (4-29), AUC 0.85 (0.81-0.88)], and STD V3/STE III ≤1.2 [with pooled sensitivity 0.88 (0.78-0.95), specificity 0.59 (0.42-0.75), DOR 12 (5-27), AUC 0.82 (0.78-0.85)]. Diagnostic algorithms, including Jim score[pooled sensitivity 0.70 (0.55-0.85), specificity 0.88 (0.75-0.96)], Fiol's algorithm [pooled sensitivity 0.54 (0.44-0.62), specificity 0.92 (0.88-0.96)] and Tierala's algorithm [pooled sensitivity 0.60 (0.49-0.71), specificity 0.91 (0.86-0.96)], were not superior to these simple methods.

CONCLUSIONS

Our meta-analysis indicated that diagnostic methods using double leads had better properties. STE in III > II together with STD in aVL > I may be the most ideal method, for its accuracy and convenience.

Performance of screening tools for cervical neoplasia among women in low- and middle-income countries: A systematic review and meta-analysis

OBJECTIVE

To evaluate the performance of visual inspection with acetic acid (VIA) testing, visual inspection with Lugol's iodine (VILI), primary HPV testing, and conventional Pap smear in detecting CIN2+ among non-pregnant women aged 30-65 in LMICs between 1990 and 2020.

DESIGN

Systematic review and meta-analysis.

SETTING AND PARTICIPANTS

Low- and middle-income countries, non-pregnant women aged 30-65.

METHODS

CENTRAL (Cochrane Library), CINAHL, Embase, Global Health, PubMed, and Web of Science databases were systematically searched to identify studies evaluating the performance of cervical cancer screening methods in LMICs. A diagnostic test accuracy meta-analysis was conducted to evaluate the performance of 4 screening methods in detecting CIN2+ relative to biopsy or cytology reference standards. Pooled statistics for sensitivity, specificity, diagnostic odds ratios, and summary receiver operating characteristic curves were determined for each method. Subgroup analyses were performed to examine whether there was variation in performance based on different reference standards for defining CIN2+, specifically: colposcopy-directed biopsy, biopsy alone, colposcopy alone, or liquid-based cytology.

RESULTS

Eighteen studies were identified through systematic review. Twelve studies were included in meta-analysis; 11 were cross-sectional and 1 was a randomized controlled clinical trial. The remaining six of the eighteen studies were inclided in a narrative syntehsis. Pooled estimates for sensitivity for VIA, VILI, primary HPV testing, and conventional Pap smear were 72.3%, 64.5%, 79.5%, and 60.2%, respectively; pooled estimates for specificity were 74.5%, 68.5%, 72.6%, and 97.4%, respectively; the diagnostic odds ratios were 7.31, 3.73, 10.42, 69.48, respectively; and the area under the summary receiver operating characteristic curves were 0.766, 0.647, 0.959, and 0.818, respectively. Performance of the screening method varied based on the reference standard used; pooled estimates using either colposcopy-directed biopsy or biopsy alone as the reference standard generally reported lower estimates; pooled estimates using either colposcopy alone or liquid-based cytology as references reported higher estimates.

CONCLUSIONS AND IMPLICATIONS

This meta-analysis found primary HPV testing to be the highest performing cervical cancer screening method in accurately identifying or excluding CIN2+. Further evaluation of performance at different CIN thresholds is warranted.

The Diagnostic Power of Circulating miR-1246 in Screening Cancer: An Updated Meta-analysis

Background

MicroRNA-1246 (miR-1246), an oncomiR that regulates the expression of multiple cancer-related genes, has been attracted and studied as a promising indicator of various tumors. However, diverse conclusions on diagnostic accuracy have been shown due to the small sample size and limited studies included. This meta-analysis is aimed at systematically assessing the performance of extracellular circulating miR-1246 in screening common cancers.

Methods

We searched the PubMed/MEDLINE, Web of Science, Cochrane Library, and Google Scholar databases for relevant studies until November 28, 2022. Then, the summary receiver operating characteristic (SROC) curves were drawn and calculated area under the curve (AUC), diagnostic odds ratio (DOR), sensitivity, and specificity values of circulating miR-1246 in the cancer surveillance.

Results

After selection and quality assessment, 29 eligible studies with 5914 samples (3232 cases and 2682 controls) enrolled in the final analysis. The pooled AUC, DOR, sensitivity, and specificity of circulating miR-1246 in screening cancers were 0.885 (95% confidence interval (CI): 0.827-0.892), 27.7 (95% CI: 17.1-45.0), 84.2% (95% CI: 79.4-88.1), and 85.3% (95% CI: 80.5-89.2), respectively. Among cancer types, superior performance was noted for breast cancer (AUC = 0.950, DOR = 98.5) compared to colorectal cancer (AUC = 0.905, DOR = 47.6), esophageal squamous cell carcinoma (AUC = 0.757, DOR = 8.0), hepatocellular carcinoma (AUC = 0.872, DOR = 18.6), pancreatic cancer (AUC = 0.767, DOR = 12.3), and others (AUC = 0.887, DOR = 27.5, P = 0.007). No significant publication bias in DOR was observed in the meta-analysis (funnel plot asymmetry test with P = 0.652; skewness value = 0.672, P = 0.071).

Conclusion

Extracellular circulating miR-1246 may serve as a reliable biomarker with good sensitivity and specificity in screening cancers, especially breast cancer.