Novel Reference Equations for Pulmonary Artery Size and Pulsatility Using Echocardiography and Their Diagnostic Value in Pulmonary Hypertension.

BACKGROUND: According to the most recent pulmonary hypertension (PH) guidelines, a main pulmonary artery (MPA) diameter > 25 mm on transthoracic echocardiography supports the diagnosis of PH. However, the size of the pulmonary artery (PA) may vary according to body size, age, and cardiac phases. RESEARCH QUESTION: (1) What are the reference limits for PA size on transthoracic echocardiography, considering differences in body size, sex, and age? (2) What is the diagnostic value of the PA size for classifying PH? (3) How does the selection of different reference groups (healthy volunteers vs patients referred for right heart catheterization [RHC]) influence the diagnostic OR (DOR)? STUDY DESIGN AND METHODS: The study included a reference cohort of 248 healthy individuals as control patients, 693 patients with PH proven by RHC, and 156 non-PH patients proven by RHC. In the PH cohort, 300 had group 1 PH, 207 had group 2 PH, and 186 had group 3 PH. MPA and right PA diameters and areas were measured in the upper sternal short-axis and suprasternal notch views. Reference limits (5th-95th percentile) were based on absolute values and height-indexed measures. Quantile regression analysis was used to derive median and 95th quantile reference equations for the PA measures. DORs and probability diagnostic plots for PH were then determined using healthy control and non-PH cohorts. RESULTS: The 95th percentile for indexed MPA diameter was 15 mm/m in diastole and 19 mm/m in systole in both sexes. Quantile regression analysis revealed a weak age effect (pseudo-R2 of 0.08-0.10 for MPA diameters). Among measures, the MPA size in diastole had the highest DOR (156.2; 95% CI, 68.3-357.5) for detection of group 1 PH. Similarly, the DORs were also high for groups 2 and 3 PH when compared with the control cohort but significantly lower compared with the non-PH cohort. INTERPRETATION: This study presents novel reference limits for MPA based on height indexing and quantile regression.

Three-dimensional sonography has satisfied accuracy for detecting rotator cuff tears.

Background: Rotator cuff injuries and tears are common causes of shoulder pain and dysfunction, necessitating accurate diagnostic methods to guide clinical decision-making. This study evaluates the diagnostic utility of three-dimensional (3D) shoulder sonography in identifying rotator cuff injury and tear patterns. Methods: A comprehensive search across seven electronic databases, which included Cochrane Library, Embase, PubMed, Cochrane Library, China Biology Medicine (CBM) database, CNKI, Wanfang, and VIP database. These databases were utilized to retrieve articles that assess the diagnostic value of 3D shoulder sonography for identifying rotator cuff injuries and tear patterns. The effectiveness of 3D shoulder sonography was assessed in terms of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). For each parameter, the 95% confidence intervals were calculated. Additionally, summary receiver operating characteristic curves (SROCs) were constructed, allowing for a comprehensive evaluation of diagnostic accuracy, which is reflected in the area under the SROC curve (AUC). Results: Screening of 8,508 identified nine literatures eligible for inclusion in the meta-analysis, encompassing a total of 366 patients. The analysis of detecting any rotator cuff tear revealed a sensitivity of 0.97 and specificity of 0.87, yielding a DOR of 90.03 and an AUC of 0.98. Furthermore, 3D shoulder sonography demonstrated satisfactory accuracy in detecting both full and partial-thickness rotator cuff tears (Sensitivity: 0.92 vs. 0.83, specificity: 0.94 vs. 097, and AUC: 0.96 vs. 0.95). Conclusion: This study indicates that three-dimensional sonography has satisfied accuracy for detecting rotator cuff tears.

Sensitive and rapid identification of pathogens by droplet digital PCR in a cohort of septic patients: a prospective diagnostic study.

BACKGROUND: There is a critical need for a rapid and sensitive pathogen detection method for septic patients. This study aimed to investigate the diagnostic efficacy of Digital droplet polymerase chain reaction (ddPCR) in identifying pathogens among suspected septic patients. METHODS: We conducted a prospective pilot diagnostic study to clinically validate the multiplex ddPCR panel in diagnosing suspected septic patients. A total of 100 sepsis episodes of 89 patients were included in the study. RESULTS: In comparison to blood culture, the ddPCR panel exhibited an overall sensitivity of 75.0% and a specificity of 69.7%, ddPCR yielded an additional detection rate of 17.0% for sepsis cases overall, with a turnaround time of 2.5 h. The sensitivity of ddPCR in the empirical antibiotic treatment and the non-empirical antibiotic treatment group were 78.6% versus 80.0% (p > 0.05). Antimicrobial resistance genes were identified in a total of 13 samples. Whenever ddPCR detected the genes beta-lactamase-Klebsiella pneumoniae carbapenemase (blaKPC) or beta-lactamase-New Delhi metallo (blaNDM), these findings corresponded to the cultivation of carbapenem-resistant gram-negative bacteria. Dynamic ddPCR monitoring revealed a consistent alignment between the quantitative ddPCR results and the trends observed in C-reactive protein and procalcitonin levels. CONCLUSIONS: Compared to blood culture, ddPCR exhibited higher sensitivity for pathogen diagnosis in suspected septic patients, and it provided pathogen and drug resistance information in a shorter time. The quantitative results of ddPCR generally aligned with the trends seen in C-reactive protein and procalcitonin levels, indicating that ddPCR can serve as a dynamic monitoring tool for pathogen load in septic patients.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

BACKGROUND: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. OBJECTIVE: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. METHODS: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. RESULTS: This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. CONCLUSIONS: Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54388.

Diagnostic test accuracy in longitudinal study settings: theoretical approaches with use cases from clinical practice.

OBJECTIVES: In this study, we evaluate how to estimate diagnostic test accuracy (DTA) correctly in the presence of longitudinal patient data (ie, repeated test applications per patient). STUDY DESIGN AND SETTING: We used a nonparametric approach to estimate the sensitivity and specificity of three tests for different target conditions with varying characteristics (ie, episode length and disease-free intervals between episodes): 1) systemic inflammatory response syndrome (n = 36), 2) depression (n = 33), and 3) epilepsy (n = 30). DTA was estimated on the levels 'time', 'block', and 'patient-time' for each diagnosis, representing different research questions. The estimation was conducted for the time units per minute, per hour, and per day. RESULTS: A comparison of DTA per and across use cases showed variations in the estimates, which resulted from the used level, the time unit, the resulting number of observations per patient, and the diagnosis-specific characteristics. Intra- and inter-use-case comparisons showed that the time-level had the highest DTA, particularly the larger the time unit, and that the patient-time-level approximated 50% sensitivity and specificity. CONCLUSION: Researchers need to predefine their choices (ie, estimation levels and time units) based on their individual research aims, estimands, and diagnosis-specific characteristics of the target outcomes to make sure that unbiased and clinically relevant measures are communicated. In cases of uncertainty, researchers could report the DTA of the test using more than one estimation level and/or time unit.

Artificial Intelligence Model Assisting Thyroid Nodule Diagnosis and Management: A Multicenter Diagnostic Study.

CONTEXT: It is not clear how to integrate artificial intelligence (AI)-based models into diagnostic workflows. OBJECTIVE: To develop and validate a deep-learning-based AI model (AI-Thyroid) for thyroid cancer diagnosis, and to explore how this improves diagnostic performance. METHODS: The system was trained using 19 711 images of 6163 patients in a tertiary hospital (Ajou University Medical Center; AUMC). It was validated using 11 185 images of 4820 patients in 24 hospitals (test set 1) and 4490 images of 2367 patients in AUMC (test set 2). The clinical implications were determined by comparing the findings of six physicians with different levels of experience (group 1: 4 trainees, and group 2: 2 faculty radiologists) before and after AI-Thyroid assistance. RESULTS: The area under the receiver operating characteristic (AUROC) curve of AI-Thyroid was 0.939. The AUROC, sensitivity, and specificity were 0.922, 87.0%, and 81.5% for test set 1 and 0.938, 89.9%, and 81.6% for test set 2. The AUROCs of AI-Thyroid did not differ significantly according to the prevalence of malignancies (>15.0% vs ≤15.0%, P = .226). In the simulated scenario, AI-Thyroid assistance changed the AUROC, sensitivity, and specificity from 0.854 to 0.945, from 84.2% to 92.7%, and from 72.9% to 86.6% (all P < .001) in group 1, and from 0.914 to 0.939 (P = .022), from 78.6% to 85.5% (P = .053) and from 91.9% to 92.5% (P = .683) in group 2. The interobserver agreement improved from moderate to substantial in both groups. CONCLUSION: AI-Thyroid can improve diagnostic performance and interobserver agreement in thyroid cancer diagnosis, especially in less-experienced physicians.

Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy.

BACKGROUND: Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)-based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images. OBJECTIVE: This diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images. METHODS: For the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. RESULTS: The proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. CONCLUSIONS: Our proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists.

Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value.

This review article provides a concise guide to interpreting receiver operating characteristic (ROC) curves and area under the curve (AUC) values in diagnostic accuracy studies. ROC analysis is a powerful tool for assessing the diagnostic performance of index tests, which are tests that are used to diagnose a disease or condition. The AUC value is a summary metric of the ROC curve that reflects the test's ability to distinguish between diseased and nondiseased individuals. AUC values range from 0.5 to 1.0, with a value of 0.5 indicating that the test is no better than chance at distinguishing between diseased and nondiseased individuals. A value of 1.0 indicates perfect discrimination. AUC values above 0.80 are generally consideredclinically useful, while values below 0.80 are considered of limited clinical utility. When interpreting AUC values, it is important to consider the 95% confidence interval. The confidence interval reflects the uncertainty around the AUC value. A narrow confidence interval indicates that the AUC value is likely accurate, while a wide confidence interval indicates that the AUC value is less reliable. ROC analysis can also be used to identify the optimal cutoff value for an index test. The optimal cutoff value is the value that maximizes the test's sensitivity and specificity. The Youden index can be used to identify the optimal cutoff value. This review article provides a concise guide to interpreting ROC curves and AUC values in diagnostic accuracy studies. By understanding these metrics, clinicians can make informed decisions about the use of index tests in clinical practice.

Development and validation of a clinical prediction model for detecting coronary heart disease in middle-aged and elderly people: a diagnostic study.

OBJECTIVE: To develop and validate a multivariate prediction model to estimate the risk of coronary heart disease (CHD) in middle-aged and elderly people and to provide a feasible method for early screening and diagnosis in middle-aged and elderly CHD patients. METHODS: This study was a single-center, retrospective, case-control study. Admission data of 932 consecutive patients with suspected CHD were retrospectively assessed from September 1, 2020 to December 31, 2021 in the Department of Integrative Cardiology at China-Japan Friendship Hospital. A total of 839 eligible patients were included in this study, and 588 patients were assigned to the derivation set and 251 as the validation set at a 7:3 ratio. Clinical characteristics of included patients were compared between derivation set and validation set by univariate analysis. The least absolute shrinkage and selection operator (Lasso) regression analysis method was performed to avoid collinearity and identify key potential predictors. Multivariate logistic regression analysis was used to construct a clinical prediction model with identified predictors for clinical practice. Bootstrap validation was used to test performance and eventually we obtained the actual model. And the Hosmer-Lemeshow test was carried out to evaluate the goodness-fit of the constructed model. The area under curve (AUC) of receiver operating characteristic (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were plotted and utilized with validation set to comprehensively evaluate the predictive accuracy and clinical value of the model. RESULTS: A total of eight indicators were identified as risk factors for the development of CHD in middle-aged and elderly people by univariate analysis. Of these candidate predictors, four key parameters were defined to be significantly related to CHD by Lasso regression analysis, including age (OR 1.034, 95% CI 1.002 ~ 1.067, P = 0.040), hemoglobin A1c (OR 1.380, 95% CI 1.078 ~ 1.768, P = 0.011), ankle-brachial index (OR 0.078, 95% CI 0.012 ~ 0.522, P = 0.009), and brachial artery flow-mediated vasodilatation (OR 0.848, 95% CI 0.726 ~ 0.990, P = 0.037). The Hosmer-Lemeshow test showed a good calibration performance of the clinical prediction model (derivation set, χ2 = 7.865, P = 0.447; validation set, χ2 = 11.132, P = 0.194). The ROCs of the nomogram in the derivation set and validation set were 0.722 and 0.783, respectively, suggesting excellent predictive power and suitable performance. The clinical prediction model presented a greater net benefit and clinical impact based on DCA and CIC analysis. CONCLUSION: Overall, the development and validation of the multivariate model combined the laboratory and clinical parameters of patients with CHD, which could be beneficial to the individualized prediction of middle-aged and elderly people, and helped to facilitate clinical assessments and decisions during treatment and management of CHD.

Missing values and inconclusive results in diagnostic studies - A scoping review of methods.

Most diagnostic studies exclude missing values and inconclusive results from the analysis or apply simple methods resulting in biased accuracy estimates. This may be due to the lack of availability or awareness of appropriate methods. This scoping review aimed to provide an overview of strategies to handle missing values and inconclusive results in the reference standard or index test in diagnostic accuracy studies. Conducting a systematic literature search in MEDLINE, Cochrane Library, and Web of Science, we could identify many articles proposing methods for addressing missing values in the reference standard. There are also several articles describing methods regarding missing values or inconclusive results in the index test. The latter encompass imputation, frequentist and Bayesian likelihood, model-based, and latent class methods. While methods for missing values in the reference standard are regularly applied in practice, this is not true for methods addressing missing values and inconclusive results in the index test. Our comprehensive overview and description of available methods may raise further awareness of these methods and will enhance their application. Future research is needed to compare the performance of these methods under different conditions to give valid and robust recommendations for their usage in various diagnostic accuracy research scenarios.

Comparison between Visual Assessment and Longitudinal Strain during Dobutamine Stress Echocardiography.

Background: The relationship between visual assessment and longitudinal strain during dobutamine stress echocardiography (DSE) remains poorly investigated. This study assessed wall motion segments visually graded as normokinetic, hypokinetic, and akinetic at baseline and the peak of DSE and compared with longitudinal strain between segments with and without induced impaired contractility and improved contractility during DSE. Methods: This study included 112 patients examined by DSE, consisting of 58 patients referred for diagnostic study and 54 patients referred for viability study. Regional left ventricular (LV) contractility was assessed visually and longitudinal strain was measured using echocardiography transthoracic. Results: At baseline, the strain of LV segments was -16.33 ± 6.26 in visually normokinetic, 13.05 ± 6.44 in visually hypokinetic, and -8.46 ± 5.69 in visually akinetic segments. During peak dose, the strain of LV segments was -15.37 ± 6.89 in visually normokinetic, -11.37 ± 5.11 in visually hypokinetic, and -7.37 ± 3.92 in visually akinetic segments. In segments with visually observed impaired contractility, the median longitudinal strain was significantly lower than in segments without impaired contractility. For segments with visually observed improved contractility, the median longitudinal strain was significantly higher than for segments without improved contractility. In diagnostic study, sensitivity of visual assessment for absolute decrease of >2% longitudinal strain was 77%, respectively. In the viability study, the sensitivity was 82% for an absolute decrease of ≥2% longitudinal strain. Conclusions: There is good association between strain analysis value and visually assessed wall motion contractility.

Sensitivity and specificity of alternative screening methods for systematic reviews using text mining tools.

OBJECTIVES: To evaluate the impact of text mining (TM) on the sensitivity and specificity of title and abstract screening strategies for systematic reviews (SRs). STUDY DESIGN AND SETTING: Twenty reviewers each evaluated a 500-citation set. We compared five screening methods: conventional double screen (CDS), single screen, double screen with TM, combined double screen and single screen with TM, and single screen with TM. Rayyan, Abstrackr, and SWIFT-Review were used for each TM method. The results of a published SR were used as the reference standard. RESULTS: The mean sensitivity and specificity achieved by CDS were 97.0% (95% confidence interval [CI]: 94.7, 99.3) and 95.0% (95% CI: 93.0, 97.1). When compared with single screen, CDS provided a greater sensitivity without a decrease in specificity. Rayyan, Abstrackr, and SWIFT-Review identified all relevant studies. Specificity was often higher for TM-assisted methods than that for CDS, although with mean differences of only one-to-two percentage points. For every 500 citations not requiring manual screening, 216 minutes (95% CI: 169, 264) could be saved. CONCLUSION: TM-assisted screening methods resulted in similar sensitivity and modestly improved specificity as compared to CDS. The time saved with TM makes this a promising new tool for SR.

Accuracy of Self-Reported COVID-19 Vaccination Status Compared With a Public Health Vaccination Registry in Québec: Observational Diagnostic Study.

BACKGROUND: The accuracy of self-reported vaccination status is important to guide real-world vaccine effectiveness studies and policy making in jurisdictions where access to electronic vaccine registries is restricted. OBJECTIVE: This study aimed to determine the accuracy of self-reported vaccination status and reliability of the self-reported number of doses, brand, and time of vaccine administration. METHODS: This diagnostic accuracy study was completed by the Canadian COVID-19 Emergency Department Rapid Response Network. We enrolled consecutive patients presenting to 4 emergency departments (EDs) in Québec between March 24, 2020, and December 25, 2021. We included adult patients who were able to consent, could speak English or French, and had a proven COVID-19 infection. We compared the self-reported vaccination status of the patients with their vaccination status in the electronic Québec Vaccination Registry. Our primary outcome was the accuracy of the self-reported vaccination status (index test) ascertained during telephone follow-up compared with the Québec Vaccination Registry (reference standard). The accuracy was calculated by dividing all correctly self-reported vaccinated and unvaccinated participants by the sum of all correctly and incorrectly self-reported vaccinated and unvaccinated participants. We also reported interrater agreement with the reference standard as measured by unweighted Cohen κ for self-reported vaccination status at telephone follow-up and at the time of their index ED visit, number of vaccine doses, and brand. RESULTS: During the study period, we included 1361 participants. At the time of the follow-up interview, 932 participants reported at least 1 dose of a COVID-19 vaccine. The accuracy of the self-reported vaccination status was 96% (95% CI 95%-97%). Cohen κ for self-reported vaccination status at phone follow-up was 0.91 (95% CI 0.89-0.93) and 0.85 (95% CI 0.77-0.92) at the time of their index ED visit. Cohen κ was 0.89 (95% CI 0.87-0.91) for the number of doses, 0.80 (95% CI 0.75-0.84) for the brand of the first dose, 0.76 (95% CI 0.70-0.83) for the brand of the second dose, and 0.59 (95% CI 0.34-0.83) for the brand of the third dose. CONCLUSIONS: We reported a high accuracy of self-reported vaccination status for adult patients without cognitive disorders who can express themselves in English or French. Researchers can use self-reported COVID-19 vaccination data on the number of doses received, vaccine brand name, and timing of vaccination to guide future research with patients who are capable of self-reporting their vaccination data. However, access to official electronic vaccine registries is still needed to determine the vaccination status in certain susceptible populations where self-reported vaccination data remain missing or impossible to obtain. TRIAL REGISTRATION: Clinicaltrials.gov NCT04702945; https://clinicaltrials.gov/ct2/show/NCT04702945.

Circulating Tumor Cells as Diagnostic Markers of Early Gastric Cancer and Gastric Precancerous Lesions.

INTRODUCTION: Circulating tumor cells (CTCs) may be potential diagnostic biomarkers of various malignancies including gastric cancer. This study aimed to evaluate whether CTCs could be used to facilitate the diagnosis of early gastric cancer (EGC) or precancerous gastric lesions. METHODS: The diagnostic study included consecutive patients with EGC, gastric precancerous lesions, or fundic gland polyps admitted to the Gastroenterology Department, Beijing Friendship Hospital Affiliated to Capital Medical University (National Center for Digestive Diseases) between October 2016 and January 2018. RESULTS: A total of 92 patients were enrolled, including 57 patients with EGC, 14 patients with gastric precancerous lesions, and 21 patients with fundic gland polyps (control group). CTCs were detected in 47.89% (34/71) of patients with EGC/gastric precancerous lesions and 4.76% (1/21) of patients with fundic gland polyps (p < 0.001). CTC detection distinguished EGC/precancerous lesions from fundic gland polyps with an area under the receiver operating characteristic curve of 0.740 (95% confidence interval, 0.640-0.840; p = 0.001), a sensitivity of 49.10%, a specificity of 95.00%, a positive predictive value of 97.00%, and a negative predictive value of 64.90%. CONCLUSIONS: Peripheral blood CTCs are more common in patients with EGC or gastric precancerous lesions than in patients with fundic gland polyps. Measurement of CTCs may be a useful tool to aid in the diagnosis of EGC and precancerous lesions.

Development of a diagnostic score using FeNO and symptoms to predict asthma.

BACKGROUND: Fractional exhaled nitric oxide (FeNO) is known as effective for ruling-in asthma. The diagnostic value might be increased in combination with clinical signs and symptoms (CSS). The aim was to develop a new model for ruling-in and ruling-out asthma. METHODS: Diagnostic multi-centre study in three practices of pneumologists in Germany. Whole-body plethysmography was combined with bronchodilation tests or bronchial provocation as diagnostic reference standard. Follow-up was performed after 3 months. An expert committee evaluated test results, symptoms, and course of disease for the final diagnosis. Relevant CSS known from guidelines were used to enable combinatorial development of decision rules. Outcomes of multiple logistic regression modeling were translated into a diagnostic score and internally validated by ten-fold cross validation. RESULTS: 308 patients with complete follow-up were included. 186 (60.4%) were female, average age was 44.7 years and 161 (52.5%) had asthma. The average area under the receiver operating curve (AUC) of the diagnostic score was 0.755 (interquartile range 0.721-0.814). Allergic rhinitis, wheezing, dyspnea on exertion, coughing attacks at night, and awakening by shortness of breath were leading symptoms for ruling-in asthma. Frequent coughing and frequent respiratory infections were leading symptoms for ruling-out. The combination of FeNO and CSS allowed ruling-in asthma with a probability of up to 99%, and ruling-out with a post-test probability down to 9%. CONCLUSION: The diagnostic scoring model increased the diagnostic value of FeNO in combination with CSS. The new decision rule allowed to rule-in asthma with high certainty, and also to rule-out with acceptable certainty.

A detection model for cognitive dysfunction based on volatile organic compounds from a large Chinese community cohort.

INTRODUCTION: We explored whether volatile organic compound (VOC) detection can serve as a screening tool to distinguish cognitive dysfunction (CD) from cognitively normal (CN) individuals. METHODS: The cognitive function of 1467 participants was assessed and their VOCs were detected. Six machine learning algorithms were conducted and the performance was determined. The plasma neurofilament light chain (NfL) was measured. RESULTS: Distinguished VOC patterns existed between CD and CN groups. The CD detection model showed good accuracy with an area under the receiver-operating characteristic curve (AUC) of 0.876. In addition, we found that 10 VOC ions showed significant differences between CD and CN individuals (p < 0.05); three VOCs were significantly related to plasma NfL (p < 0.005). Moreover, a combination of VOCs with NfL showed the best discriminating power (AUC = 0.877). DISCUSSION: Detection of VOCs from exhaled breath samples has the potential to provide a novel solution for the dilemma of CD screening.

Combination of ultrasound and serological tests for detecting occult lateral lymph node metastases in medullary thyroid cancer.

PURPOSE: To investigate the value of ultrasound and serum marker tests in detecting lateral lymph node metastasis in medullary thyroid cancer (MTC). METHODS: Data of 105 patients diagnosed with MTC and admitted to the Department of General Surgery in Peking Union Medical College Hospital from June 2010 to August 2020 were collected and retrospectively analyzed. RESULTS: Ultrasound examination alone had a sensitivity of 89.36% and a specificity of 70.69%. For surveillance of postoperative carcinoembryonic antigen and calcitonin, cut-off values of 7.115 ng/mL and 13.185 pg/mL, respectively, were shown to discriminate the presence of cervical lymph node metastasis. Combining ultrasound and postoperative serum levels of both carcinoembryonic antigen and calcitonin as serial tests increased the specificity to 91.38% and 87.93%, with a sensitivity of 95.45%. Multivariate logistic analysis identified the following risk factors for lateral lymph node metastasis in MTC: suspicious lymph nodes detected by ultrasound and postoperative calcitonin above 13.185 pg/mL. CONCLUSION: The combination of ultrasound and serological tests achieved higher sensitivity and specificity to identify MTC cases with potential occult lateral cervical lymph node metastasis compared with single tests.

Presepsin as a promising biomarker for early detection of post-operative infection in children.

Background: Post-operative systemic inflammation response syndrome (SIRS) is an event that results from surgical trauma, white blood cells contact activation, and intra-surgical bacterial translocation, which is difficult to distinguish from sepsis. Presepsin is a novel biomarker that is increased since the early stages of bacterial infection and can be used to confirm the diagnosis of post-operative infectious complications. This study aimed to investigate the diagnostic performance of presepsin for post-operative infectious complications compared to other well-known biomarkers. Method: This cross-sectional study included 100 post-operative patients admitted to Cipto Mangunkusumo National Hospital and Bunda Hospital in Jakarta, Indonesia. The objective was to identify the optimal cutoff and trend of plasma presepsin concentration on the first and third day after surgery and to compare them with other biomarkers. Result: Plasma presepsin level was higher in the infection group compared to the non-infection group (median 806.5 pg/ml vs. 717 pg/ml and 980 pg/ml vs. 516 pg/ml on the first and third day, respectively). Presepsin levels tended to increase on the third post-operative day (median + 252 pg/ml) in children with infection. The opposite trend was observed in the non-infection group from the first to the third day (median -222.5 pg/ml). Presepsin delta, a three-day difference between the first and third post-operative day, had the best diagnostic performance compared to other biomarkers (Area Under the Curve 0.825). The optimal cutoff for presepsin delta to diagnose post-operative infection was +90.5 pg/ml. Conclusion: Serial assessments of presepsin levels on the first and third days post-surgery and their trends are helpful diagnostic markers for clinicians to detect post-operative infectious complications in children.

Three to six tissue specimens for histopathological analysis are most accurate for diagnosing periprosthetic joint infection.

AIMS: The aim of this study was to evaluate the optimal deep tissue specimen sample number for histopathological analysis in the diagnosis of periprosthetic joint infection (PJI). METHODS: In this retrospective diagnostic study, patients undergoing revision surgery after total hip or knee arthroplasty (n = 119) between January 2015 and July 2018 were included. Multiple specimens of the periprosthetic membrane and pseudocapsule were obtained for histopathological analysis at revision arthroplasty. Based on the Infectious Diseases Society of America (IDSA) 2013 criteria, the International Consensus Meeting (ICM) 2018 criteria, and the European Bone and Joint Infection Society (EBJIS) 2021 criteria, PJI was defined. Using a mixed effects logistic regression model, the sensitivity and specificity of the histological diagnosis were calculated. The optimal number of periprosthetic tissue specimens for histopathological analysis was determined by applying the Youden index. RESULTS: Based on the EBJIS criteria (excluding histology), 46 (39%) patients were classified as infected. Four to six specimens showed the highest Youden index (four specimens: 0.631; five: 0.634; six: 0.632). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of five tissue specimens were 76.5% (95% confidence interval (CI) 67.6 to 81.4), 86.8% (95% CI 81.3 to 93.5), 66.0% (95% CI 53.2 to 78.7), and 84.3% (95% CI 79.4 to 89.3), respectively. The area under the curve (AUC) was calculated with 0.81 (as a function of the number of tissue specimens). Applying the ICM and IDSA criteria (excluding histology), 40 (34%) and 32 (27%) patients were categorized as septic. Three to five specimens had the highest Youden index (ICM 3: 0.648; 4: 0.651; 5: 0.649) (IDSA 3: 0.627; 4: 0.629; 5: 0.625). CONCLUSION: Three to six tissue specimens of the periprosthetic membrane and pseudocapsule should be collected at revision arthroplasty and analyzed by a pathologist experienced and skilled in interpreting periprosthetic tissue.Cite this article: Bone Joint J 2023;105-B(2):158-165.

Revisiting sample size planning for receiver operating characteristic studies: A confidence interval approach with precision and assurance.

Estimation of areas under receiver operating characteristic curves and their differences is a key task in diagnostic studies. Here we develop closed-form sample size formulas for such studies with a focus on estimation rather than hypothesis testing, by explicitly incorporating pre-specified precision and assurance, with precision denoted by the lower limit of confidence interval and assurance denoted by the probability of achieving that lower limit. For sample size estimation purposes, we introduce a normality-based variance function for valid estimation allowing for unequal variances of observations in the disease and non-disease groups. Simulation results demonstrate that the proposed formulas produce empirical assurance probability close to the pre-specified assurance probability and empirical coverage probability close to the nominal level. Compared with a frequently used existing variance function, the proposed function provides more accurate and efficient sample size estimates. For an illustration of the proposed formulas, we present real-world worked examples. To facilitate implementation, we have developed an online calculator openly available at https://dishu.page/calculator/.