Diagnostic Accuracy Research Articles

Truncus arteriosus from prenatal diagnosis to clinical outcome: a single-centre experience.

The aim of this study was to review our institution's experience with truncus arteriosus from prenatal diagnosis to clinical outcome. and results: We conducted a single-centre retrospective cohort study for the years 2005-2020. Truncus arteriosus antenatal echocardiographic diagnostic accuracy within our institution was 92.3%. After antenatal diagnosis, five parents (31%) decided to terminate the pregnancy. After inclusion from referring hospitals, 16 patients were offered surgery and were available for follow-up. Right ventricle-to-pulmonary artery continuity was preferably established without the use of a valve (direct connection), which was possible in 14 patients (88%). There was no early or late mortality. Reinterventions were performed in half of the patients at latest follow-up (median follow-up of 5.4 years). At a median age of 5.5 years, 13 out of 14 patients were still without right ventricle-to-pulmonary artery valve, which was well tolerated without signs of right heart failure. The right ventricle demonstrated preserved systolic function as expressed by tricuspid annular plane systolic excursion z-score (-1.4 ± 1.7) and fractional area change (44 ± 12%). The dimensions and function of the left ventricle were normal at latest follow-up (ejection fraction 64.4 ± 6.2%, fractional shortening 34.3 ± 4.3%). This study demonstrates good prenatal diagnostic accuracy of truncus arteriosus. There was no mortality and favourable clinical outcomes at mid-term follow-up, with little interventions on the right ventricle-to-pulmonary artery connection and no right ventricle deterioration. This supports the notion that current perspectives of patients with truncus arteriosus are good, in contrast to the poor historic outcome series. This insight can be used in counselling and surgical decision-making.

Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine.

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine.

Diagnostic Identification of Acute Brain Dysfunction in Pediatric Sepsis and Septic Shock in the Electronic Health Record: A Comparison of Four Definitions in a Reference Dataset.

Acute brain dysfunction (ABD) in pediatric sepsis has a prevalence of 20%, but can be difficult to identify. Our previously validated ABD computational phenotype (CPABD) used variables obtained from the electronic health record indicative of clinician concern for acute neurologic or behavioral change. We tested whether the CPABD has better diagnostic performance to identify confirmed ABD than other definitions using the Glasgow Coma Scale or delirium scores. Diagnostic testing in a curated cohort of pediatric sepsis/septic shock patients. Quaternary freestanding children's hospital. The test dataset comprised 527 children with sepsis/septic shock managed between 2011 and 2021 with a prevalence (pretest probability) of confirmed ABD of 30% (159/527). CPABD was based on use of neuroimaging, electroencephalogram, and/or administration of new antipsychotic medication. We compared the performance of the CPABD with three GCS/delirium-based definitions of ABD-Proulx et al, International Pediatric Sepsis Consensus Conference, and Pediatric Organ Dysfunction Information Update Mandate. The posttest probability of identifying ABD was highest in CPABD (0.84) compared with other definitions. CPABD also had the highest sensitivity (83%; 95% CI, 76-89%) and specificity (93%; 95% CI, 90-96%). The false discovery rate was lowest in CPABD (1-in-6) as was the false omission rate (1-in-14). Finally, the prevalence threshold for the definitions varied, with the CPABD being the definition closest to 20%. In our curated dataset of pediatric sepsis/septic shock, CPABD had favorable characteristics to identify confirmed ABD compared with GCS/delirium-based definitions. The CPABD can be used to further study the impact of ABD in studies using large electronic health datasets.

Gastrointestinal Myoelectrical Activity (GIMA) Biomarker for Noninvasive Diagnosis of Endometriosis

Background/Objectives: Endometriosis represents substantial direct and indirect healthcare costs impacted by an absence of uniformly accurate, non-invasive diagnostic tools. We endeavored to demonstrate gastrointestinal myoelectrical activity (GIMA) biomarkers, unique to endometriosis, will allow non-invasive, uniformly accurate diagnosis or exclusion of endometriosis. Methods: Prospective open-label comparative study of 154 patients, age ≥ 18, with or without diagnosed endometriosis. Population included 62 non-endometriosis controls (Cohort 1), 43 subjects with surgically/histologically confirmed endometriosis (Cohort 2), and 49 subjects with abdominal pain and negative imaging (Cohort 3). Non-invasive electroviscerography (EVG) recorded GIMA biomarkers from three abdominal electrodes before and 30 min post water load protocol. Cohort 2 had postoperative EVG and Cohort 3 had preoperative EVG. Calculated specificity, sensitivity, negative predictive value (NPV), positive predictive value (PPV), and predictive probability or C-statistic used univariate, multivariate, linear, and logistical regression analyses of the area under the curve (AUC) at all frequency and time points, including age and pain covariants. Results: The non-endometriosis cohort differed significantly from the endometriosis cohorts (p < 0.001) for median (IQR) and AUC percent frequency distribution of power at baseline, 10 min, 20 min, and 30 min post water load at all frequency ranges: 15–20 cpm, 30–40 cpm, and 40–50 cpm. The endometriosis cohorts were statistically similar (p > 0.05). GIMA biomarker threshold scoring demonstrated 95%/91% sensitivity and PPV, 96%/95% specificity and NPV, and a C-statistic of >99%/98%, respectively, for age subsets. GIMA biomarkers in Cohort 3 predicted 47/49 subjects positive and 2/49 negative for endometriosis, confirmed surgically. Hormonal therapy, surgical stage, nor pain score affected diagnostic accuracy. Conclusions: EVG with GIMA biomarker detection distinguished participants with and without endometriosis based upon endometriosis-specific GIMA biomarkers threshold scoring.

Feedback Loop Failure Modes in Medical Diagnosis: How Biases Can Emerge and Be Reinforced.

Medical diagnosis in practice connects to research through continuous feedback loops: Studies of diagnosed cases shape our understanding of disease, which shapes future diagnostic practice. Without accounting for an imperfect and complex diagnostic process in which some cases are more likely to be diagnosed correctly (or diagnosed at all), the feedback loop can inadvertently exacerbate future diagnostic errors and biases. A feedback loop failure occurs if misleading evidence about disease etiology encourages systematic errors that self-perpetuate, compromising future diagnoses and patient care. This article defines scenarios for feedback loop failure in medical diagnosis. Through simulated cases, we characterize how disease incidence, presentation, and risk factors can be misunderstood when observational data are summarized naive to biases arising from diagnostic error. A fourth simulation extends to a progressive disease. When severe cases of a disease are diagnosed more readily, less severe cases go undiagnosed, increasingly leading to underestimation of the prevalence and heterogeneity of the disease presentation. Observed differences in incidence and symptoms between demographic groups may be driven by differences in risk, presentation, the diagnostic process itself, or a combination of these. We suggested how perceptions about risk factors and representativeness may drive the likelihood of diagnosis. Differing diagnosis rates between patient groups can feed back to increasingly greater diagnostic errors and disparities in the timing of diagnosis and treatment. A feedback loop between past data and future medical practice may seem obviously beneficial. However, under plausible scenarios, poorly implemented feedback loops can degrade care. Direct summaries from observational data based on diagnosed individuals may be misleading, especially concerning those symptoms and risk factors that influence the diagnostic process itself. Current evidence about a disease can (and should) influence the diagnostic process. A feedback loop failure may occur if biased "evidence" encourages diagnostic errors, leading to future errors in the evidence base.When diagnostic accuracy varies for mild versus severe cases or between demographic groups, incorrect conclusions about disease prevalence and presentation will result without specifically accounting for such variability.Use of demographic characteristics in the diagnostic process should be done with careful justification, in particular avoiding potential cognitive biases and overcorrection.

Diagnostic accuracy of autofluorescence-Raman spectroscopy for surgical margin assessment during Mohs micrographic surgery of basal cell carcinoma.

Autofluorescence (AF) - Raman spectroscopy is a technology that can detect residual basal cell carcinoma (BCC) on the resection margin of fresh surgically excised tissue specimens. The technology does not require tissue fixation, staining, labelling, or sectioning, and provides quantitative diagnosis maps of the surgical margins in 30 minutes. To determine the accuracy of the AF-Raman instrument to detect incomplete excisions of BCC during Mohs micrographic surgery, using histology as reference standard. Skin layers from 130 patients undergoing Mohs surgery at the Nottingham University Hospitals NHS Trust (September 2022 to July 2023) were investigated with the AF-Raman instrument. The layers were measured fresh, immediately after excision. The AF-Raman results and the intra-operative assessment by Mohs surgeons were compared to a post-operative consensus-derived reference produced by three dermatopathologists. The sensitivity, specificity, positive predictive value, and negative predictive value were calculated. The AF-Raman analysis was successfully completed for 125 out of the 130 layers. The AF-Raman analysis covered 91% of the specimen surface area on average, with the lowest being 87% for eyelid and the highest being 94% for forehead specimens. The AF-Raman instrument identified positive margins in 24 out of 36 BCC-positive cases, resulting in a 67% sensitivity (95% confidence intervals (CI): 49%-82%) and negative margins in 65 out of 89 BCC-negative cases, resulting in a 73% specificity (95% CI 63%-82%). Only one out of the 12 false negative cases was caused by misclassification by the AF-Raman algorithm. The other 11 false negatives cases were produced because no valid Raman signal was recorded at the location of the residual BCC due to either occlusion by blood or poor contact between tissue and cassette window. The intra-operative diagnosis by Mohs surgeons identified positive margins in 31 out of 36 BCC-positive cases, 86% sensitivity (95% CI: 70%-95%), and negative margins in 79 out of 89 BCC-negative cases, 89% specificity (95% CI: 81%-95%). This study shows that the AF-Raman instrument has potential for intra-operative microscopic assessment of surgical margins in surgery of BCC. Further improvements are required for tissue processing to ensure complete coverage of the surgical specimens. ClinicalTrials.gov ID NCT03482622.

Prevalence of HIV, Hepatitis C and Hepatitis B Infection Among Detainees in a French Administrative Detention Centre.

In France, migrants constitute a significant proportion of people diagnosed with HIV, hepatitis C (HCV) and B (HBV). This study estimated the prevalence of these three viruses among detainees at a French administrative detention centre (CRA), through systematic Rapid Diagnostic Test (RDT) screening. This prospective, single-centre, cross-sectional, pilot study included detainees at the Nîmes CRA from February to December 2022. The primary endpoint was HIV, HCV and HBV prevalence determined by RDT. Secondary outcomes were: co-infections; study acceptability, reasons for non-inclusion, causes of non-contributory samples; and concordance between serological tests and RDT. Among the 350 people agreeing to participate of 726 eligible, five refused the RDT, leaving 345 analysable participants for a participation rate of 47.5% (345/726). Participants were predominantly male (90%) with an average age of 31 years. The most common country of origin was Algeria (34%). Twenty (6%) had taken drugs intravenously and 240 (70%) had had unprotected sex within a median of 4.92 [1.08; 15] months. Virus prevalence was: 0% HIV; 4.64 [2.42; 6.86] % HCV; and 2.32 [1.01; 4.52] % HBV. Eleven (73%) of the RDT HCV positive cases were confirmed serologically. RDT detected one false-positive HCV case, as an anti-HCV Ac serological test was negative. Of the eight patients with positive HBV RDT, one declined the serology testing, thus 100% (7/7) of the tested RDT positive cases were confirmed by serology. The study highlighted the need to screen detainees for HIV, HCV and HBV infection and suitability of RDTs.

Targeted nanoparticles for imaging and therapy of endometriosis.

In this brief review, we discuss our efforts to validate nanoplatforms for imaging and treatment of endometriosis. We specifically highlight our use of nonhuman primates and primate tissues in this effort. Endometriosis is a painful disorder of women and nonhuman primates (NHPs) where endometrium-like tissue exists outside of the uterus. There are no reliable, specific, and noninvasive diagnostic tests for endometriosis. Laparoscopic imaging remains the gold standard for identifying small endometriotic lesions in both women and monkeys. Visualizing and surgically removing microscopic lesions remains a clinical challenge. To address this challenge, we have created nanoparticle reagents that, when administered intravenously, enter endometriotic lesions both passively and by targeting endometriotic cells. The particles can carry payloads, including near-infrared fluorescent dyes and magnetic nanoparticles. These agents can be used for imaging and thermal ablation of diseased tissues. We evaluated this approach on macaque endometriotic cells, human and macaque endometrium engrafted into immunodeficient mice, in endometrium subcutaneously autografted in macaques, and in rhesus monkeys with spontaneous endometriosis. Employing these models, we report that nanoplatform-based reagents can improve imaging and provide thermal ablation of endometriotic tissues.

Development of the Telephone-based Daily Instrumental Activities of Living (T-DIAL) to assess financial management remotely in older adults

ABSTRACT The current study evaluated the reliability and validity of a novel, performance-based banking task in 60 younger (18–34 years) and 60 older (50–85 years) adults. All participants completed the Telephone-based Daily Instrumental Activities of Living (T-DIAL) using interactive voice response technology to complete a series of mock actions with a financial institution via telephone. The T-DIAL showed strong inter-rater reliability and internal consistency. T-DIAL accuracy was significantly and independently related to better self-reported instrumental activities of daily living and executive functions at a large effect size. Findings from this study provided preliminary supportive evidence for the reliability and validity of the T-DIAL, which had robust associations with manifest everyday functioning and higher-order cognitive ability. Future work is needed on the psychometrics (e.g. test–retest reliability, normative standards), and construct validity (e.g. diagnostic accuracy) of the T-DIAL in neurocognitive disorders and under-served communities for whom remote evaluations might be particularly relevant.

Cost effectiveness and decision analysis for national airport screening options to reduce risk of COVID-19 introduction in Uganda, 2020

IntroductionEarly during the COVID-19 outbreak, various approaches were utilized to prevent COVID-19 introductions from incoming airport travellers. However, the costs and effectiveness of airport-specific interventions have not been evaluated.MethodsWe evaluated policy options for COVID-19-specific interventions at Entebbe International Airport for costs and impact on COVID-19 case counts, we took the government payer perspective. Policy options included; (1)no screening, testing, or mandatory quarantine for any incoming traveller; (2)mandatory symptom screening for all incoming travellers with RT-PCR testing only for the symptomatic and isolation of positives; and (3)mandatory 14-day quarantine and one-time testing for all, with 10-day isolation of persons testing positive. We calculated incremental cost-effectiveness ratios (ICERs) in US$ per additional case averted.ResultsExpected costs per incoming traveller were $0 (Option 1), $19 (Option 2), and $766 (Option 3). ICERs per case averted were $257 for Option 2 (which averted 4,948 cases), and $10,139 for Option 3 (which averted 5,097 cases) compared with Option I. Two-week costs were $0 for Option 1, $1,271,431 Option 2, and $51,684,999 Option 3. The per-case ICER decreased with increase in prevalence. The cost-effectiveness of our interventions was modestly sensitive to the prevalence of COVID-19, diagnostic test sensitivity, and testing costs.ConclusionScreening all incoming travellers, testing symptomatic persons, and isolating positives (Option 2) was the most cost-effective option. A higher COVID-19 prevalence among incoming travellers increased cost-effectiveness of airport-specific interventions. This model could be used to evaluate prevention options at the airport for COVID-19 and other infectious diseases with similar requirements for control.

Synthetic MRI and diffusion-weighted imaging for differentiating nasopharyngeal lymphoma from nasopharyngeal carcinoma: combination with morphological features.

To investigate the feasibility of synthetic MRI (syMRI), diffusion-weighted imaging (DWI) and their combination with morphological features for differentiating nasopharyngeal lymphoma (NPL) from nasopharyngeal carcinoma (NPC). Sixty-nine patients with nasopharyngeal tumors (NPL, n = 22; NPC, n = 47) who underwent syMRI and DWI were retrospectively enrolled between October 2020 and May 2022. syMRI and DWI quantitative parameters (T1, T2, PD, ADC), and morphological features were obtained. Diagnostic performance was assessed by independent sample t-test, chi-square test, logistic regression analysis, receiver operating characteristic curve (ROC), and DeLong test. NPL has significantly lower T2, PD, and ADC values compared to NPC (all P < 0.05), whereas no significant difference was found in T1 value between these two entities (P > 0.05). The morphological features of tumor type, skull-base involvement, Waldeyer ring involvement, and lymph nodes involvement region were significantly different between NPL and NPC (all P < 0.05). The syMRI (T2+PD) model has better diagnostic efficacy, with AUC, sensitivity, specificity, and accuracy of 0.875, 77.27%, 89.36%, and 85.51%. Compared with syMRI model, syMRI+Morph (PD+Waldeyer ring involvement+lymph nodes involvement region), syMRI+DWI (T2+PD+ADC), and syMRI+DWI+Morph (PD+ADC+skull base involvement+Waldeyer ring involvement) models can further improve the diagnostic efficiency (all P < 0.05). Furthermore, syMRI+DWI+Morph model has excellent diagnostic performance, with AUC, sensitivity, specificity, and accuracy of 0.986, 95.47%, 97.87%, and 97.10%, respectively. syMRI and DWI quantitative parameters were helpful in discriminating NPL from NPC. syMRI+DWI+Morph model has the excellent diagnostic efficiency in differentiating these two entities. syMRI+DWI+morphological feature method can differentiate NPL from NPC with excellent diagnostic performance.

Molecular and phenotypic identification of bacterial species isolated from cows with mastitis from three regions of Poland

BackgroundBovine mastitis is a widespread disease affecting dairy cattle worldwide and it generates substantial losses for dairy farmers. Mastitis may be caused by bacteria, fungi or algae. The most common species isolated from infected milk are, among others, Streptococcus spp., Escherichia coli, Staphylococcus aureus and non-aureus staphylococci and mammaliicocci. The aim of this paper is to determine the frequency of occurrence of bacterial species in milk samples from cows with mastitis from three regions of Poland: the north-east, the south-west and the south. To this end 203 milk samples taken from cows with a clinical form (CM) of mastitis (n = 100) and healthy animals (n = 103) were examined, which included culture on an appropriate medium followed by molecular detection of E. coli, S. aureus, Streptococcus agalactiae and Streptococcus uberis, as one of the most common species isolated from mastitis milk.ResultsThe results obtained indicated that S. uberis was the most commonly cultivated CM species (38%, n = 38), followed by S. aureus (22%, n = 22), E. coli (21%, n = 21) and S. agalactiae (18%, n = 18). Similar frequencies in molecular methods were obtained for S. uberis (35.1%) and S. aureus (28.0%). The variation of sensitivity of both methods may be responsible for the differences in the E. coli (41.0%, p = 0.002) and S. agalactiae (5.0%, p = 0.004) detection rates. Significant differences in composition of species between three regions of Poland were noted for E. coli incidence (p < 0.001), in both the culture and molecular methods, but data obtained by the PCR method indicated that this species was the least common in north-eastern Poland, while the culture method showed that in north-eastern Poland E. coli was the most common species. Significant differences for the molecular method were also observed for S. uberis (p < 0.001) and S. aureus (p < 0.001). Both species were most common in southern and south-western Poland.ConclusionsThe results obtained confirm the need to introduce rapid molecular tests for veterinary diagnostics, as well as providing important epidemiological data, to the best of our knowledge data on Polish cows in selected areas of Poland is lacking.

A Beginner's Guide to Artificial Intelligence for Ophthalmologists.

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice.

Multiparametric approach with synthetic MR imaging for diagnosing salivary gland lesions.

To determine whether synthetic MR imaging can distinguish between benign and malignant salivary gland lesions. The study population included 44 patients with 33 benign and 11 malignant salivary gland lesions. All MR imaging was obtained using a 3 Tesla system. The QRAPMASTER pulse sequence was used to acquire images with four TI values and two TE values, from which quantitative images of T1 and T2 relaxation times and proton density (PD) were generated. The Mann-Whitney U test was used to compare T1, T2, PD, and ADC values among the subtypes of salivary gland lesions. ROC analysis was used to evaluate diagnostic capability between malignant tumors (MTs) and either pleomorphic adenomas (PAs) or Warthin tumors (WTs). We further calculated diagnostic accuracy for distinguishing malignant from benign lesions when combining these parameters. PAs demonstrated significantly higher T1, T2, PD, and ADC values than WTs (all p < 0.001). Compared to MTs, PAs had significantly higher T1, T2, and ADC values (all p < 0.001), whereas WTs had significantly lower T1, T2, and PD values (p < 0.001, p = 0.008, and p = 0.003, respectively). T2 and ADC were most effective in differentiating between MTs and PAs (AUC = 0.928 and 0.939, respectively), and T1 and PD values for differentiating between MTs and WTs (AUC = 0.915 and 0.833, respectively). Combining T1 with T2 or ADC achieved accuracy of 86.4% in distinguishing between malignant and benign tumors. Similarly, combining PD with T2 or ADC reached accuracy of 86.4% for differentiating between malignant and benign tumors. Utilizing a combination of synthetic MRI parameters may assist in differentiating malignant from benign salivary gland lesions.

ACUTE RHABDOMYOLYSIS IN A JUVENILE WITH DENGUE FEVER: A RARE MANIFESTATION BUT BEWARE

Myalgia is a common symptom in patients with Dengue fever. However, acute rhabdomyolysis is a rare manifestation. The precise mechanism of Dengue-associated rhabdomyolysis has not been determined, it is thought likely to be caused by myotoxic cytokines released in response to viral infection, such as TNF and IFN-α. Here we report a case of a 13-year-old boy with Dengue fever, presenting with red urine on day 5 of illness. Together with a decreased platelet count (26 G/L), we initially suspected hematuria but the urinalysis showed no red blood cells. We then thought of rhabdomyolysis causing myoglobinuria, so we ordered diagnostic tests with the following results: CK 1353 U/L, GOT 361 U/L, GPT 90 U/L. The patient was given intravenous fluids and oral oresol to control urine output. Urine color faded and blood CK concentration returned to normal after 13 days. Rhabdomyolysis is rare in dengue fever but requires proper diagnosis and management to prevent acute kidney injury - a life-threatening complication.

Evaluating Diagnostic Algorithms for Heparin-Induced Thrombocytopenia using Two Combined Automated Rapid Immunoassays.

Heparin-induced thrombocytopenia (HIT) is an autoimmune disorder caused by antibodies against platelet factor 4 (PF4) and heparin complexes. Rapid immunoassays (IAs) for detection of these antibodies mark a milestone in HIT diagnosis, despite a higher false-positive rate compared with functional platelet-activation assays. However, combining different rapid IAs may help to improve their diagnostic specificity. Here, we compared the individual performance of the latex immunoturbidimetric assay (LIA; HemosIL HIT-Ab [PF4-H]; sensitivity 91.7%, specificity 68.4%) and chemiluminescence immunoassay (CLIA; HemosIL AcuStarHIT-Ab [PF4-H]; sensitivity 92.4%, specificity 85.8%) with their combined performance using two unique diagnostic algorithms in a single prospective cohort of suspected HIT patients. Using the simultaneous algorithm adapted from Warkentin et al, the combined LIA-CLIA had a sensitivity of 99.0% and specificity of 64.3%. The sequential algorithm adapted from Rittener-Ruff et al was applied in two theoretical scenarios to reflect real-world circumstances in diagnostic laboratories where access to clinical information is limited: (1) assuming all patients had an intermediate 4Ts score and (2) assuming all patients had a high 4Ts score. This algorithm correctly predicted HIT in 94.5% (high 4Ts) and 96.0% (intermediate 4Ts) and excluded HIT in 82.6% (high 4Ts) and 80.1% (intermediate 4Ts) of patients in either scenario, respectively. Although both combined algorithms improved diagnostic performance of individual IAs, the simultaneous algorithm showed fewer false predictions (7.9%) than the sequential algorithm (intermediate 4Ts: 37.6% and high 4Ts: 41.5%) and proved more practical as it does not rely on physician evaluations. Our findings highlight the importance of accounting for clinician and interlaboratory variability when evaluating diagnostic tests for HIT.

Agreement of Fractional Flow Reserve Estimated by Computed Tomography With Invasively Measured Fractional Flow Reserve: A Systematic Review and Meta-Analysis.

Fractional flow reserve (FFR) is the ratio of blood pressure measured distal to a stenosis and pressure proximal to a stenosis. FFR can be estimated noninvasively using computed tomography (CT) although the usefulness of this technique remains controversial. This meta-analysis evaluated the agreement of FFR estimated by CT (FFR-CT) with invasively measured FFR. The study also evaluated the diagnostic accuracy of FFR-CT, defined as the ability of FFR-CT to classify lesions as hemodynamically significant (invasive FFR ≤0.8) or insignificant (invasive FFR >0.8). Forty-three studies reporting on 7291 blood vessels from 5236 patients were included. A moderate positive linear relationship between FFR-CT and invasively measured FFR was observed (Spearman correlation coefficient: 0.67). Agreement between the 2 measures increased as invasively measured FFR values approached 1. The overall diagnostic accuracy, sensitivity and specificity of FFR-CT were 82.2%, 80.9%, and 83.1%, respectively. Diagnostic accuracy of 90% could be demonstrated for FFR-CT values >0.90 and <0.49. The diagnostic accuracy of off-site tools was 79.4% and the diagnostic accuracy of on-site tools was 84.1%. The agreement between FFR-CT and invasive FFR is moderate although agreement is highest in vessels with FFR-CT >0.9. Diagnostic accuracy varies widely with FFR-CT value but is above 90% for FFR-CT values >0.90 and <0.49. Furthermore, on-site and off-site tools have similar performance. Ultimately, FFR-CT may be a useful adjunct to CT coronary angiography as a gatekeeper for invasive coronary angiogram.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies

BackgroundA Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs.MethodsWe compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed.ResultsFor most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate.ConclusionsWe recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

Diagnostic performance of lactate dehydrogenase as a potential biomarker in predicting preeclampsia and associated factors

BackgroundPreeclampsia (PE), a pregnancy specific syndrome, is defined as new-onset hypertension (≥140/90 mmHg) and proteinuria diagnosed after gestational week 20 or new-onset pre-eclampsia associated signs in the absence of proteinuria, and it may tend to present as late as 4–6 weeks’ postpartum period. It is a leading cause of maternal mortality in both developed and developing countries. In order to prevent PE, the disease must be diagnosed at its earliest stage, however, the triads of high blood pressure, edema and albuminuria is neither specific nor sensitive enough for diagnosing the disease. Lactate dehydrogenase (LDH) is useful biochemical marker reflecting the occurrence of complications associated with preeclampsia. Besides, it has been suggested as potential biomarker to predict the severity of preeclampsia and as indicator of multi-organ involvement. The aim of this study was to investigate the diagnostic accuracy of LDH, which is affordable and easy to test, as a potential clinical biomarker to predict onset of preeclampsia.MethodsA hospital based cross-sectional study was conducted as of September 9 to December 24, 2022 at Debre Birhan Comprehensive Specialized Hospital (DBCSH). A total of 132 study subjects (66 preeclamptic and 66 normotensive controls) were enrolled in the study. A receiver operating characteristics (ROC) curve was used to calculate the area under the curve (AUC) and determine diagnostic accuracy of LDH. Youden’s index was used to identify an optimal cut-off point for LDH in detecting preeclampsia associated complications.ResultAUC for LDH was found to be 0.963 (95% CI, 0.91, 1.0; p = 0.000) from ROC curve analysis. An optimal cut-off point for LDH was 376.5 U/L having a sensitivity and specificity of 87.5 and 90.8%, respectively.ConclusionSerum LDH had an AUC of greater than 0.8 and showed good diagnostic accuracy in predicting development of preeclampsia. Disease duration, gestational age, systolic and diastolic blood pressure among enormous number of predictor variables had association with serum level of LDH.

The performance of the node reporting and data system 1.0 (Node-RADS) and DWI-MRI in staging patients with cervical carcinoma according to the new FIGO classification (2018).

To evaluate the diagnostic accuracy of the Node-RADS score and the utility of apparent diffusion coefficient (ADC) values in predicting metastatic lymph nodes (LNs) involvement in cervical cancer (CC) patients using magnetic resonance imaging (MRI). The applicability of the Node RADS score across three readers with different years of experience in pelvic imaging was also assessed. Among 140 patients, 68 underwent staging MRI, neoadjuvant chemotherapy and radical surgery, forming the study cohort. Node-RADS scores of the main pelvic stations were retrospectively determined to assess LN metastatic likelihood and compared with the histological findings. Mean ADC, relative ADC (rADC), and correct ADC (cADC) values of LNs classified as Node-RADS ≥ 3 were measured and compared with histological reports, considered as gold standard. Sensitivity, specificity, positive and negative predictive values (PPVs and NPVs), and accuracy were calculated for different Node-RADS thresholds. Node RADS ≥ 3 showed a sensitivity of 92.8% and specificity of 72.5%. Node RADS ≥ 4 yielded a sensitivity of 71.4% and specificity of 100%, while Node RADS 5 yielded 42.9% and 100%, respectively. The diagnostic performance of mean ADC, cADC and rADC values from 78 LNs with Node-RADS score ≥ 3 was assessed, with ADC demonstrating the highest area under the curve (AUC 0.820), compared to cADC and rADC values. The Node-RADS score provides a standardized LNs assessment, enhancing diagnostic accuracy in CC patients. Its ease of use and high inter-observer concordance support its clinical utility. ADC measurement of LNs shows promise as an additional tool for optimizing patient diagnostic evaluation.