Diagnostic Odds Ratio Research Articles

Value of bronchial amylase level for predicting ventilator associated pneumonia in intubated adults: a systematic review and meta-analysis.

The ability of bronchial amylase level for predicting ventilator associated pneumonia (VAP) has been extensively studied with conflicting results. This meta-analysis aimed to explore the value of bronchial amylase for predicting VAP in intubated adults. PubMed, Embase, and Cochrane Central Register of Controlled Trials were searched up to November 2023. The diagnostic odds ratio (DOR), sensitivity, and specificity were calculated. The summary receiver operating characteristic curve was estimated, and the area under the curve (AUROC) was calculated. Overall, six studies including 769 patients were included in this review, of whom 273 (36%) were developed VAP. The cutoff values of bronchial amylase level were ranged from 8.1 U/L to 4681.5U/L. Heterogeneity between studies was assessed with an overall Q = 1.99, I2 = 0, and P = 0.185, The pooled sensitivity and specificity for the overall population were 0.78 [95% confidence interval (CI) 0.67-0.86] and 0.75(95% CI 0.56-0.88) respectively. The DOR was 11(95% CI 3.0-40.0). The pooled AUROC was 0.83 (95%CI 0.80-0.86). The bronchial amylase is a helpful marker for predicting VAP in intubated adults. However, it cannot be recommended as the single definitive test for VAP, but rather it must be interpreted in context with information from careful medical history, physical examination, and when feasible, microbiological assessment.

Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis

IntroductionAdverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data.MethodsA systematic search of PubMed, Web of Science, Embase, and IEEE Xplore was conducted to identify relevant articles published from the inception to 20 May 2024. Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.ResultsA total of 59 studies met the inclusion criteria, covering 15 drugs and 15 ADEs. In total, 38 machine learning algorithms were reported, with random forest (RF) being the most frequently used, followed by support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), and light gradient boosting machine (LightGBM). The performance of the ML models was generally strong, with an average area under the curve (AUC) of 76.68% ± 10.73, accuracy of 76.00% ± 11.26, precision of 60.13% ± 24.81, sensitivity of 62.35% ± 20.19, specificity of 75.13% ± 16.60, and an F1 score of 52.60% ± 21.10. The combined sensitivity, specificity, diagnostic odds ratio (DOR), and AUC from the summary receiver operating characteristic (SROC) curve using a random effects model were 0.65 (95% CI: 0.65–0.66), 0.89 (95% CI: 0.89–0.90), 12.11 (95% CI: 8.17–17.95), and 0.8069, respectively. The risk factors associated with different drugs and ADEs varied.DiscussionFuture research should focus on improving standardization, conducting multicenter studies that incorporate diverse data types, and evaluating the impact of artificial intelligence predictive models in real-world clinical settings.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024565842, identifier CRD42024565842.

Diagnostic and predictive value of radiomics-based machine learning for intracranial aneurysm rupture status: a systematic review and meta-analysis.

Currently, the growing interest in radiomics within the clinical practice has prompted some researchers to differentiate the rupture status of intracranial aneurysm (IA) by developing radiomics-based machine learning models. However, systematic evidence supporting its performance remains scarce. The purpose of this meta-analysis and systematic review is to assess the diagnostic performance of radiomics-based machine learning for the early detection of IA rupture and to offer evidence-based recommendations for the application of radiomics in this area.PubMed, Cochrane, Embase, and Web of Science databases were searched systematically up to March 2, 2024. The Radiomics Quality Score (RQS) was employed to assess the risk of bias in all included primary studies. We separately discussed the diagnostic or predictive performance of machine learning for IA rupture status based on task type (diagnosis or prediction).We finally included 15 original studies covering 9,111 IA cases. In the validation cohort, radiomics demonstrated a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, as well as SROC curve of 0.84 (95% CI: 0.76-0.90), 0.82 (95% CI: 0.77-0.86), 4.7 (95% CI: 3.7-5.8), 0.19 (95% CI: 0.13-0.29), and 24 (95% CI: 15-40), respectively, for the diagnostic task of aneurysm rupture status. Only 2 studies (3 models) addressed predictive tasks, with sensitivity and specificity ranging from 0.77 to 0.89 and from 0.69 to 0.87, respectively.Radiomics-based machine learning exhibits promising accuracy for early identification of IA rupture status, whereas evidence for its predictive capability is limited. Further research is needed to validate predictive models and provide insights for developing specialized strategies to prevent aneurysm rupture.

Abstract 4115654: Assessing Diagnostic Performance: A Systematic Review and Meta-Analysis of Machine Learning-Based Fractional Flow Reserve versus Traditional Invasive Fractional Flow Reserve for Coronary Artery Stenosis Evaluation

Introduction: Fractional Flow Reserve (FFR) is a stenosis-specific index for coronary artery stenosis (CAS) detection and its effect on myocardial perfusion. Visualizing stenosis through an invasive coronary angiography (iFFR) lacks consistency and is highly subjective. Recently, non-invasive techniques have been developed using artificial intelligence (AI) and Machine learning (ML) to measure FFR (ML-FFR). We aimed to analyze the diagnostic performance of ML-FFR in comparison to iFFR in the detection of CAS. Methods: A search was carried out in PubMed, IEEE Xplore digital library, and clinicaltrials.gov where studies evaluating the risk of CAS in patients undergoing ML-FFR and iFFR were considered in terms of FFR values from 2014 to 2024. OpenMetaAnalyst was used to evaluate the diagnostic performance in terms of pooled Sensitivity(SN), Specificity(SP), Negative Likelihood Ratio(NLR), Positive Likelihood Ratio(PLR), and Diagnostic Odds Ratio(DOR) at 95% confidence interval (CI). P<0.05 was considered significant. Results: A total of 9 studies including 1358 vessel lesions were analyzed. The pooled SN, SP, NLR, and PLR were 81.1%(74.3%-86.4,p=0.014), 91.8%(84.9%-95.7%, p<0.001), 0.187 (0.151-0.232, p=0.575) and 9.898(5.492-17.839,p<0.001), respectively with >75% heterogeneity in SP and PLR. After excluding two studies, a “leave-out” analysis showed pooled SN, SP, NLR, and PLR as 79.9% (71.4%-86.3%, p=0.007), 94.4% (90.2%-96.9%, p=0.002), 0.187 ( 0.140-0.250, p=0.540) and 14.307 (8.094-25.289, p=0.004) with <70% heterogeneity. The pooled DOR was 53.42 (28.26-100.99, p=0.005) at random effect model. Conclusion: ML-FFR showed significant diagnostic values signifying that it can be used as a potential non-invasive alternative to iFFR for detecting lesion severity and guiding clinical decision-making in patients with suspected CAD, especially for those who are considered high-risk or ineligible for invasive angiography.

The Role of High-Resolution Ultrasound in the Diagnosis of Nerve Trauma New Perspective: A Preliminary Systematic Review and Meta-Analysis of the Recent Evidence.

Precise localization of peripheral nerve injuries and evaluation of their prognosis based on clinical and electrodiagnostic examinations are particularly challenging in the acute phase. High-resolution ultrasound (HRUS) may offer a viable and cost-effective imaging option for assessing the morphology of nerve injuries. Consequently, a systematic review and meta-analysis of studies on the use of ultrasound for diagnosing traumatic nerve injuries were conducted. A total of 15 studies were included, reporting the most recent findings on using HRUS in the diagnosis of traumatic nerve injury. These studies assessed the diagnostic test accuracy of ultrasound for the detection of traumatic nerve injury in 272 participants, with the cross-sectional area at the site of traumatic nerve injury also reported in 1,249 participants. The pooled sensitivity and specificity of the included studies were 92% confidence interval (CI) (0.89-0.95) and 86% CI (0.82-0.89), respectively. The positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 13.76 CI (1.41-134.34), 0.08 CI (0.03-0.18), and 286.23 CI (21.22-3,860.40), respectively. In the summary of the receiver operating characteristic curve, the area under the curve was 0.986, and the Q* index was 0.949. Based on the current literature, HRUS has shown promising results in addition to its availability and feasibility. HRUS can serve as a valuable complement to clinical and electrodiagnostic examinations for diagnosing traumatic peripheral nerve injuries. Further research is recommended to better understand the ultrasound characteristics of these injuries.

Can KOOS-PS be replaced with a simple anchor question in patients after total knee arthroplasty?: an agreement study of 2,478 primary surgeries.

Physical function and pain are the most important outcomes following total knee arthroplasty (TKA). These can be evaluated by patient-reported outcome measures (PROMs), or by an anchor question. The primary aim of the study was to evaluate whether a simple anchor question can replace KOOS-PS in assessing postoperative knee function until 1-year follow-up, evaluated by analyzing the agreement between the 2 methods using the diagnostic odds ratio (DOR). Secondary aims were pain (NRS) at rest and during mobilization. This is a diagnostic accuracy study with primary TKAs performed between 2010 and 2022. The surgeries were categorized as improved (I) or worsened (W) based on a dichotomized anchor question related to self-perceived change in physical function, and the dichotomized change in KOOS-PS until 1-year follow-up. This led to 4 groups: (II, IW, WI, and WW). Agreement was found with a DOR of 11.3 (CI 7.9-16.2). 2,335 (94%) reported improved function on the anchor question and 143 (6%) worsened function. Among those with improved anchor 2,132 (91%) had improved KOOS-PS, but among those with worsened anchor only 74 (52%) had worsened KOOS-PS. Pain at 1-year follow-up was lower in the groups reporting improved anchor. The KOOS-PS can be replaced with an anchor question to assess change in function until 1 year. However, the KOOS-PS might be a valuable supplement in patients reporting worsened anchor as only half of those had worsened KOOS-PS.

Expression of Salivary miRNAs, Clinical, and Demographic Features in the Early Detection of Gastric Cancer: A Statistical and Machine Learning Analysis.

Gastric cancer ranks as one of the top five deadliest cancers worldwide and is often diagnosed at late stages. Analysis of saliva may provide a non-invasive approach for detection of malignancies in organs associated with the oral cavity. This research aims to analyze salivary microRNA expression together with clinical and demographic features with the aim of diagnosing gastric cancer. The study included 19 patients with early-stage gastric cancer and 19 healthy controls. Saliva samples were collected and processed for RNA isolation. Salivary expression of miR-223-3p and miR-21-5p were measured using quantitative reverse-transcription polymerase chain reaction (RT-qPCR). Receiver operating characteristic (ROC) curves were generated to evaluate the accuracy of diagnostic models. Machine learning algorithms, multiple logistic regression, and principal component analysis (PCA) were used to assess the predictive power of miRNAs in conjunction with clinical-demographic features. Significant upregulation of miR-223-3p and downregulation of miR-21-5p in saliva were observed in patients with gastric cancer. The area under ROC curve (AUC) values for salivary miR-21-5p, salivary miR-223-3p, and their multiple logistic regression were determined to be 0.723, 0.791, and 0.850, respectively. The AUC for multiple logistic regression model was 0.919. The PCA model led to the highest diagnostic odds ratio (DOR) of 134.33 (sensitivity = 0.785, specificity = 1.00, AUC = 903). Application of machine learning methods, and in particular a random forest algorithm, showed high accuracy in diagnosing patients with gastric cancer (sensitivity = 1.00, specificity = 0.857, AUC = 0.93). The application of validated salivary diagnostics in clinical practice could help facilitate earlier diagnosis of gastric cancer and improve medical outcome. Expression of miR-21 and miR-223-3p in saliva together with clinical and demographic features, appears promising in screening for GC.

The performance of machine learning for predicting the recurrent stroke: a systematic review and meta-analysis on 24,350 patients.

Stroke is a leading cause of death and disability worldwide. Approximately one-third of patients with stroke experienced a second stroke. This study investigates the predictive value of machine learning (ML) algorithms for recurrent stroke. This study was prepared according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. PubMed, Scopus, Embase, and Web of Science (WOS) were searched until January 1, 2024. The quality assessment of studies was conducted using the QUADAS-2 tool. The diagnostic meta-analysis was conducted to calculate the pooled sensitivity, specificity, diagnostic accuracy, positive and negative diagnostic likelihood ratio (DLR), diagnostic accuracy, diagnostic odds ratio (DOR), and area under of the curve (AUC) by the MIDAS package in STATA V.17. Twelve studies, comprising 24,350 individuals, were included. The meta-analysis revealed a sensitivity of 71% (95% CI 0.64-0.78) and a specificity of 88% (95% confidence interval (CI) 0.76-0.95). Positive and negative DLR were 5.93 (95% CI 3.05-11.55) and 0.33 (95% CI 0.28-0.39), respectively. The diagnostic accuracy and DOR was 2.89 (95% CI 2.32-3.46) and 18.04 (95% CI 10.21-31.87), respectively. The summary ROC curve indicated an AUC of 0.82 (95% CI 0.78-0.85). ML demonstrates promise in predicting recurrent strokes, with moderate to high sensitivity and specificity. However, the high heterogeneity observed underscores the need for standardized approaches and further research to enhance the reliability and generalizability of these models. ML-based recurrent stroke prediction can potentially augment clinical decision-making and improve patient outcomes by identifying high-risk patients.

Diagnostic Accuracy of Magnetic Resonance Imaging in the 120° Flexed-Knee Position for Detecting and Classifying Meniscal Ramp Lesion.

Detection of meniscal ramp lesions concomitant with anterior cruciate ligament (ACL) injury using conventional magnetic resonance imaging (MRI) has low sensitivity, and these lesions are currently difficult to diagnose preoperatively. The purpose of this study was to assess the accuracy of MRI in detecting the presence of meniscal ramp lesions in the 120° flexed-knee position compared with that in the near-extended-knee position. It was hypothesized that the diagnostic performance of MRI in the 120° flexed-knee position would be better than that in the extended-knee position. Cohort study (Diagnosis); Level of evidence, 2. This retrospective study of prospectively collected data between February 2019 and January 2024 included 154 consecutive patients undergoing ACL reconstruction. All patients underwent 3-T MRI examination in the near extended- and 120° flexed-knee positions preoperatively. The presence and Thaunat classification of ramp lesions were separately detected on each MRI scan and confirmed via arthroscopy during ACL reconstruction. Diagnostic sensitivity, specificity, and conditional relative odds ratios for detecting ramp lesions and the classification accuracy were compared between 2 MRI modalities. The accuracies of these MRI scans in acute and chronic cases were also evaluated. This study included 154 patients (79 male and 75 female; mean age, 29.0 ± 14.2 years). A total of 62 ramp lesions (40.3%) were observed on arthroscopy. The sensitivity and specificity of MRI in near extension were 69.4% and 77.2%, respectively, and those in flexion were 91.9% and 94.6%, respectively, with significant superiority in MRI at knee flexion (P = .003 and P < .001, respectively). The conditional relative odds ratio between the MRI examinations at these 2 positions was 10.3 (95% CI, 4.82-21.8). The classification accuracy of MRI in flexion was significantly higher than that of MRI in near extension (accuracy, 49 vs 11; P < .001). The diagnostic accuracy of MRI in the 120° flexed-knee position was significantly higher than that in the near-extended-knee position in the acute cases (P < .05); however, no significant differences were observed in the chronic cases. The diagnostic accuracy of MRI in the 120° flexed-knee position for detecting and classifying meniscal ramp lesions was superior to that of MRI in the near-extended-knee position.

Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis

ObjectiveOral cancer is a widespread global health problem characterised by high mortality rates, wherein early detection is critical for better survival outcomes and quality of life. While visual examination is the primary method for detecting oral cancer, it may not be practical in remote areas. AI algorithms have shown some promise in detecting cancer from medical images, but their effectiveness in oral cancer detection remains Naïve. This systematic review aims to provide an extensive assessment of the existing evidence about the diagnostic accuracy of AI-driven approaches for detecting oral potentially malignant disorders (OPMDs) and oral cancer using medical diagnostic imaging.MethodsAdhering to PRISMA guidelines, the review scrutinised literature from PubMed, Scopus, and IEEE databases, with a specific focus on evaluating the performance of AI architectures across diverse imaging modalities for the detection of these conditions.ResultsThe performance of AI models, measured by sensitivity and specificity, was assessed using a hierarchical summary receiver operating characteristic (SROC) curve, with heterogeneity quantified through I2 statistic. To account for inter-study variability, a random effects model was utilized. We screened 296 articles, included 55 studies for qualitative synthesis, and selected 18 studies for meta-analysis. Studies evaluating the diagnostic efficacy of AI-based methods reveal a high sensitivity of 0.87 and specificity of 0.81. The diagnostic odds ratio (DOR) of 131.63 indicates a high likelihood of accurate diagnosis of oral cancer and OPMDs. The SROC curve (AUC) of 0.9758 indicates the exceptional diagnostic performance of such models. The research showed that deep learning (DL) architectures, especially CNNs (convolutional neural networks), were the best at finding OPMDs and oral cancer. Histopathological images exhibited the greatest sensitivity and specificity in these detections.ConclusionThese findings suggest that AI algorithms have the potential to function as reliable tools for the early diagnosis of OPMDs and oral cancer, offering significant advantages, particularly in resource-constrained settings.Systematic Review Registrationhttps://www.crd.york.ac.uk/, PROSPERO (CRD42023476706).

Evaluation of Fungitell (1,3)-β-D-glucan assay in tear samples for rapid diagnosis of fungal keratitis.

Fungal keratitis (FK) is a serious suppurative and ulcerative corneal infection leading to blindness and vision loss. Rapid diagnosis of FK can contribute to prompt clinical management with early recovery. The study aimed to standardize the detection of (1,3)-β-D-glucan (BDG) and establish the diagnostic cut-off concentration in tears of suspected FK patients along with non-infected controls. This prospective multicentric study was conducted between the period of August 2022 and April 2024. Samples were collected from three tertiary eye-care facilities across India. All suspected FK patients were enrolled in the study. Prior to tear collection, the eye and eyelid were gently cleansed using sterile normal saline (NS) and lint-free tissue paper. Subsequently, 50 µL of sterile NS was instilled into the eye, followed by tear sample collection after 60 s using sterile fine microtips. Tear samples were collected from the contralateral eyes of the FK patients, and those from healthy volunteers served as controls. The concentration of BDG in tears in varying dilutions was quantitatively measured using the Fungitell Assay Kit (Associates of Cape Cod, East Falmouth, Massachusetts). A total of 53 tear samples were analyzed at 1:10 and 1:20 dilutions. The receiver operating curve revealed an area under the curve (AUC) of 0.919 for the 1:10 dilution, with a cut-off value of 123 pg/mL, yielding a sensitivity of 100% and specificity of 84.85%. The corresponding Youden Index was 0.798. At the 1:20 dilution, the AUC was 0.898 with a cut-off of 84 pg/mL, achieving a sensitivity of 70% and specificity of 96.88% with a Youden Index of 0.670. Given the higher specificity at the 1:20 dilution, it was further validated in 145 tear samples. The validation cohort demonstrated a sensitivity of 95.56%, specificity of 83%, positive predictive value (PPV) of 71.67%, negative predictive value (NPV) of 97.65%, and diagnostic odds ratio of 112.4. Notably, a significantly higher BDG concentration (P < 0.0001) was observed in infected tear samples compared to controls (270.6 ± 128.5 vs 37.31 ± 31.32). Detection of BDG in tear samples is a new, non-invasive, and rapid technique showing excellent performance and can be effectively implemented for diagnosing FK in laboratories.

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

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

Effects of amide proton transfer imaging in diagnosis, grading and prognosis prediction of cervical cancer: a systematic review and meta-analysis

PurposeTo assess the effectiveness of Amide Proton Transfer (APT) imaging in predicting the histopathological characteristics of cervical cancer. MethodsA comprehensive literature search was conducted across multiple databases, covering studies until December 27, 2023. The meta-analysis was performed using Stata 15 and Review Manager 5.4 software. Key metrics analyzed included pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (sROC) curves. The analysis focused on differentiating cervical cancer types, squamous carcinoma differentiation, and lymph node involvement. Meta-regression was employed to investigate heterogeneity. ResultsThirteen studies involving 868 patients were included in the meta-analysis. For differentiating adenocarcinoma from squamous carcinoma, the pooled sensitivity was 0.82 (95%CI: 0.71-0.90), specificity was 0.65 (95%CI: 0.48-0.79), and DOR was 9 (95%CI: 1.6-3.5). When distinguishing poorly differentiated from moderately/well-differentiated squamous carcinoma, the sensitivity was 0.74 (95%CI: 0.66-0.81), specificity was 0.83 (95%CI: 0.75-0.89), and DOR was 14 (95%CI: 8-23). For identifying lymph node involvement, the sensitivity was 0.87 (95%CI: 0.78-0.92), specificity was 0.66 (95%CI: 0.59-0.73), and DOR was 13 (95%CI: 7-26). No publication bias was detected. ConclusionsAPT imaging demonstrates high sensitivity and specificity in distinguishing between cervical cancer types, grading squamous carcinoma, and detecting lymph node involvement. It can be considered a reliable technique for predicting the pathological features of cervical cancer in clinical practice.

Diagnostic value of circulating IL-6 in adult sepsis: a meta-analysis.

In recent years, numerous studies have been published on the diagnostic performance of circulating IL-6 levels in adult sepsis, but the conclusions are not consistent. This meta-analysis aims to systematically evaluate the accuracy of circulating IL-6 in the diagnosis of adult sepsis. Literature on the diagnostic value of circulating IL-6 in adult sepsis published on CNKI, Wanfang Data, PubMed, Embase, Cochrane Library, and Web of Science were searched up to December 2023. QUADAS-2 scale was used to evaluate the quality of the included literature. Stata 15.0 statistical software was used for data processing. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were calculated. The summary receiver operating characteristic curve (SROC) was drawn, with the area under the curve (AUC) calculated. A total of 31 articles were included in this meta-analysis. The results of the meta-analysis showed that the combined sensitivity, specificity, DOR, PLR, and NLR of circulating IL-6 for the diagnosis of adult sepsis were 0.74 (95%CI: 0.69-0.78), 0.73 (95%CI: 0.68-0.78), 7.71 (95%CI: 5.66-10.52), 2.77 (95%CI: 2.29-3.35), 0.36 (95%CI: 0.30-0.43), respectively. The AUC was 0.80 (95%CI: 0.76-0.83). Circulating IL-6 has a good diagnostic value in adult sepsis, with moderate sensitivity and specificity. Therefore, IL-6 can provide a basis for the diagnosis of adult sepsis.

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

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

Minimum Fascia-Tumour Distance for Differentiating Deep Lobe From Superficial Lobe Benign Parotid Tumours: A Retrospective Study and Meta-Analysis.

This study was designed to evaluate the diagnostic efficacy of the minimum fascia-tumour distance (MFTD) in distinguishing deep-lobe benign parotid tumours from superficial-lobe tumours through both an original study and a meta-analysis. In this study, we performed a retrospective analysis of data from 91 patients who had been diagnosed with benign parotid tumours. The MFTD values were sourced from preoperative ultrasound examinations. The locations of these tumours were confirmed through surgical findings. We assessed the diagnostic accuracy of MFTD by utilising receiver operating characteristic (ROC) curves. Additionally, we conducted a systematic review of the pertinent literature and performed a diagnostic meta-analysis to ascertain the overall diagnostic efficacy of MFTD in identifying benign parotid tumours. Patients with tumours in the deep lobe had a significantly greater MFTD than patients with tumours in the superficial lobe. Using a cutoff value of 3.50 mm for MFTD, we found an AUC of 0.93, a sensitivity of 81.8%, and a specificity of 98.8%. Our meta-analysis included seven studies covering a total of 1689 tumours. The pooled values for sensitivity, specificity, and diagnostic odds ratio (OR) of MFTD were 81.0%, 89.0%, and 32.2, respectively. The AUC of the summarised ROC curve of MFTD was 0.90. The MFTD demonstrated reliable diagnostic accuracy in identifying deep-lobe benign parotid tumours and may be incorporated into standard evaluations before parotidectomy.

Diagnostic Accuracy of Lung Ultrasonography Compared to Chest Radiography, BNP and Physical Examination in Patients with Dyspnea Suggestive of Pulmonary Edema: A Systematic Review and Meta-Analysis

Background: Pulmonary edema (PE) is the result of an abrupt increase in hydrostatic pressure in the pulmonary capillaries that leads to leakage of fluid through microvascular endothelial cells. This leads to a disruption of gas exchange in the lungs. Aims: This meta-analysis aimed to determine the diagnostic accuracy of lung ultrasonography (LUS) in pulmonary edema. Methods: A systematic search was conducted using a strategy based on these search terms (Lung ultrasonography, pulmonary edema, diagnostic accuracy); we searched PubMed, Google Scholar, and the Cochrane Library. Out of 1029, 14 prospective cross-sectional and observational studies with 2239 patients who reported the sensitivity and specificity of lung ultrasonography in diagnosing pulmonary edema were selected. For inclusion and data extraction, an independent review of citations was carried out. The data obtained were analyzed using SPSS, RevMan 5.3, and Stata 14.0 software. A quality assessment was conducted using the QUADAS-2 tool. The reference gold standard was the final clinical diagnosis according to chest radiography, B-type natriuretic peptide, and/or physical examination in dyspneic patients. Results: The overall sensitivity and specificity of lung ultrasonography in the diagnosis of pulmonary edema were 0.86 (95% CI, 0.81-0.90) and 0.91 (95% CI, 0.90-0.93), respectively, with a Younden index of 77.8%. The area under the receiver operating characteristic (ROC) curve was 0.889. Conclusion: The overall diagnostic odds ratio was 68.86. The results of this meta-analysis suggest that lung ultrasonography is an effective non-invasive technique in the diagnosis of acute pulmonary edema with rapid bedside examination and immediate interpretation.

Diagnostic performance of CCTA and CTP imaging for clinically suspected in-stent restenosis: A meta-analysis

AimsThe objective of this study is to conduct a meta-analysis to assess the diagnostic performance of Coronary Computed Tomography Angiography (CCTA) and a hybrid approach that incorporates Computed Tomography Perfusion (CTP) in addition to CCTA (CCTA ​+ ​CTP) for the detection of in-stent restenosis (ISR), as defined by angiography. MethodsA comprehensive search of articles identified 18,513 studies. After removing duplicates, title/abstract screening, and full-text review, 17 CCTA and 3 CCTA ​+ ​CTP studies were included. Only studies using ≥64-slices multidetector computed tomography (CT) were considered eligible. ResultsThe per-patient ISR prevalence was 43 ​%, with 92 ​% of stents fully interpretable with CCTA. Meta-analysis exhibited a per-stent CCTA (n ​= ​2674) sensitivity of 90 ​% (95 ​% CI; 84–94 ​%), specificity of 89 ​% (95 ​% CI; 86–92 ​%), positive likelihood ratio of 7.17 (95 ​% CI; 5.24–9.61), negative likelihood ratio of 0.17 (95 ​% CI; 0.10–0.25), and diagnostic odds ratio of 45.7 (95 ​% CI; 22.71–82.43). Additional sensitivity analyses revealed no influence of stent diameter or strut thickness on the diagnostic yield of CCTA. The per-stent diagnostic performance of CCTA ​+ ​CTP (n ​= ​752) did not show differences compared to CCTA. ConclusionsWith currently utilized scanners, CCTA and CCTA ​+ ​CTP demonstrated high diagnostic performance for in-stent restenosis evaluation. Consequently, a history of previous stent implantation should not be an argument to preclude using these methods in clinically suspected patients.

The Evidence Base for Circulating Tumor DNA-Methylation in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis.

Background: Non-Small Cell Lung Cancer (NSCLC) remains a challenging disease to manage with effectiveness. Early detection and precise monitoring are crucial for improving patient outcomes. Circulating tumor DNA (ctDNA) offers a non-invasive cancer detection and monitoring method. Emerging biomarkers, such as ctDNA methylation, have shown promise in enhancing diagnostic accuracy and prognostic assessment in NSCLC. In this review, we examined the current evidence regarding ctDNA methylation's role in NSCLC detection through a systematic review of the existing literature and meta-analysis. Methods: We systematically searched PubMed, Medline, Embase, and Web of Science databases up to 26 June 2024 for studies on the role of ctDNA methylation analysis in NSCLC patients. We included studies from 2010 to 2024 on NSCLC patients. We excluded case reports, non-English articles, studies on cell lines or artificial samples, those without cfDNA detection, prognostic studies, and studies with non-extractable data or mixed cancer types. Funnel plots were visually examined for potential publication bias, with a p value < 0.05 indicating bias. Meta-analysis was conducted using R packages (meta, forestplot, and mada). Combined sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive and negative predictive values, diagnostic odds ratio (DOR), and 95% confidence intervals (95% CI) were calculated. A summary receiver operating characteristic curve (SROC) and area under the curve (AUC) with related Standard Error (SE) were used to evaluate the overall diagnostic performance. Additionally, RASSF1A, APC, SOX17, SEPT9, and RARβ2 were analyzed, since their methylation was assessed in two or more studies. Results: From 38 candidate papers, we finally identified 12 studies, including 472 NSCLC patients. The pooled sensitivity was 0.62 (0.47-0.77) and the specificity was 0.90 (0.85-0.94). The diagnostic odds ratio was 15.6 (95% CI 9.36-26.09) and the area under the curve was 0.249 (SE = 0.138). The positive and negative predictive values were 5.38 (95% CI 3.89-7.44) and 0.34 (95% CI 0.22-0.54), respectively. For single genes, the specificity reached 0.83~0.96, except for RARβ2, but the sensitivity was relatively low for each gene. Significant heterogeneity across the included studies, the potential publication bias for specificity (p = 0.0231), and the need to validate the clinical utility of ctDNA methylation for monitoring treatment response and predicting outcomes in NSCLC patients represent the main limitations of this study. Conclusions: These results provide evidence of the significant potential of ctDNA methylation as a valuable biomarker for improving the diagnosis of NSCLC, advocating for its integration into clinical practice to enhance patient management.

Prostate-Specific Antigen (PSA) as a Predictor of Prostate Cancer on Transrectal Biopsy: A Meta-Analysis

Prostate cancer is a leading cause of cancer-related death in men globally. Early detection is critical for improving patient outcomes. Prostate-specific antigen (PSA) testing is a widely used screening tool, but its accuracy in predicting prostate cancer on transrectal biopsy remains a topic of debate. This meta-analysis aims to evaluate the diagnostic accuracy of PSA in predicting prostate cancer on transrectal biopsy. A systematic search of electronic databases (PubMed, Scopus, Web of Science) was conducted to identify relevant studies published between 2013 and 2024. Studies reporting the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of PSA for predicting prostate cancer on transrectal biopsy were included. Data were extracted, and the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated using a random-effects model. Six studies met the inclusion criteria, comprising a total of 1,245 patients. The pooled sensitivity and specificity of PSA for predicting prostate cancer on transrectal biopsy were 0.77 (95% CI, 0.75 - 0.82) and 0.68 (95% CI, 0.62-0.73), respectively. The pooled PLR, NLR, and DOR were 3.27 (95% CI, 2.45-4.37), 0.20 (95% CI, 0.14-0.28), and 16.39 (95% CI, 10.27-26.21), respectively. The pooled AUC was 0.87 (95% CI, 0.84-0.90). In conclusion, PSA demonstrates good diagnostic accuracy in predicting prostate cancer on transrectal biopsy. However, it is essential to consider its limitations, including false positives and negatives. Further research is needed to identify strategies to improve the accuracy of PSA testing and reduce unnecessary biopsies.