Prognostic value of Nanopore sequencing-based metagenomics next-generation sequencing in clinical infectious cases: A retrospective observational study.

This study aimed to investigate the diagnostic value of nanopore sequencing technology in non-tuberculous mycobacterial pulmonary disease (NTMPD) and compare it with traditional culture methods. A retrospective analysis was conducted on 225 suspected NTMPD patients admitted to the Fourth People's Hospital of Nanning City from January 2022 to July 2024. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), kappa coefficient, and area under the receiver operating characteristic curve (AUC) of nanopore sequencing, culture, and combined diagnostic methods were compared to evaluate their diagnostic performance. In addition, patients were divided into different groups to investigate the detection of NTMPD by nanopore sequencing technology under different pathogen concentrations, in cases of concurrent Mycobacterium tuberculosis (MTB) infection, and among the elderly (aged > 60 years). Among 139 NTMPD samples, nanopore sequencing detected positives in 113 cases, with a sensitivity of 81.3%, PPV of 99.1%, NPV of 76.6%, kappa coefficient of 0.759, and AUC of 0.901, demonstrating high specificity (98.8%) comparable to culture. The combined diagnostic approach significantly improved the sensitivity (90.6%), NPV (98.4%), kappa coefficient (0.862), and AUC (0.942) of NTMPD diagnosis. Nanopore sequencing showed superior diagnostic value in samples with various bacterial concentrations and in cases of concurrent MTB infection. Third-generation nanopore sequencing technology serves as a rapid and effective diagnostic tool, which may profoundly impact the current diagnosis of NTMPD.

Influence of Age on the Outcome of the Atopy Patch Test with Food in Children with Atopic Dermatitis

Apathy in Dementia: An Examination of the Psychometric Properties of the Apathy Evaluation Scale

Vision Screening

Many countries have seen a decline in recidivism rates over the past decades. These base rates are pertinent information for assessing the recidivism risk of offenders. They provide a foundation for clinical assessment and an empirical basis for risk assessment instrument norms, which inform expected recidivism rates. The present study explored the extent to which base rates influence the validity of risk assessment instruments. We systematically reviewed the available evidence on the discrimination ability of four well-established risk assessment instruments used to estimate the probability of recidivism for general (Level of Service Inventory-Revised [LSI-R]), violent (Violence Risk Appraisal Guide [VRAG]), sexual (Static-99R), and intimate partner violent offences (Ontario Domestic Assault Risk Assessment [ODARA]). We conducted a bivariate logit-normal random effects meta-analysis of sensitivity and false positive rates and modelled the positive and negative predictive values. We used base rates as reported in (a) the construction samples of each risk assessment instrument and (b) recent official statistics and peer-reviewed articles for different offence categories and countries. To assess the risk of bias, we used the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. We screened 644 studies and subsequently analysed 102, of which 96 were included in the systematic review and 24 in the meta-analyses. Discrimination was comparable for all four instruments (median area under the curve = 0.68-0.71). The information needed to calculate summary statistics of sensitivity and false positive rate was often not reported, and a risk of bias may be present in up to half of the studies. The largest summary sensitivity and false positive rate were estimated for the ODARA, followed by the LSI-R, the VRAG, and the Static-99R. If base rates are low, positive predictive values tend to be relatively low, while negative predictive values are higher: positive predictive value = 0.032-0.133 and negative predictive value = 0.985-0.989 for sexual offences; positive predictive value = 188-0.281 and negative predictive value = 0.884-0.964 for intimate partner violence; positive predictive value = 0.218-0.241 and negative predictive value = 0.907-0.942 for violent offences; positive predictive value = 0.335-0.377 and negative predictive value = 0.809-0.810 for general offences. When interpreting the results of individual risk assessments, it is not sufficient to provide the discrimination of the instrument; the risk statement must also address the positive predictive value and discuss its implications for the specific case. As recidivism rates are neither stable over time nor uniform across countries or samples, the primary interpretation of risk assessment instruments should rely on the percentile rank. Expected recidivism rates should be interpreted with caution. However, our results are drawn from a limited database, as studies not reporting sufficient information were excluded from analyses and it was only possible to identify current base rates for modelling positive and negative predictive values for certain countries. International standards for consistently collecting and reporting base rates are important to better identify crime trends. Future research on the validity of risk assessment instruments should follow rigorous reporting standards.

Objective To study the value of EUS-FNA cytology and fluid carcinoembryonic antigen （CEA） for differential diagnosis of malignant and benign pancreatic cystic lesions. Methods Data of 27 patients who underwent EUS-FNA were reviewed. According to Youden exponent, the optimal cut-off points for cyst fluid CEA were determined by receiver operating characteristic （ROC） curve. Compared with surgical pathology, the accuracy, sensitivity, specificity, positive predictive value （PPV） and negative pre- dictive value （NPV） of the EUS imaging, cytology as well as cyst fluid CEA were determined. Results Of the 27 cases, 14 were diagnosed as benign lesions, 13 were diagnosed as malignant or premalignant lesions. The accuracy , sensitivity, specificity, PPV and NPV of EUS imaging were 77. 8% （ 21/27 ）, 69. 2% （9/13）, 85.7% （ 12/14）, 81.8% （9/11） and 75.0% （12/16）. The accuracy , sensitivity, specificity, PPV and NPV of EUS-FNA cytology were 85.2% （23/27）, 76. 9% （10/13）, 92.9% （13/14）, 90. 9% （ 10/11 ）, and 81.3% （13/16）. The corresponding values of fluid carcinoembryonic antigen under the ROC- derived ideal cut-off were 74. 1% （20/27）, 84. 6% （11/13）, 64. 3% （9/14）, 68.8% （11/16） and 81.8% （9/11） （CEA 〉 22. 24 ng/ml）. Conclusion EUS-FNA cytology is highly accurate and specific for differential diagnosis of malignant and benign pancreatic cystic lesions. Cyst fluid CEA shows better sensitivi-ty. EUS-FNA cytology and cyst fluid CEA analysis can basically meet the requirement of differentiating the benign and （pre）malignant pancreatic cystic lesions. Key words: Pancreas; Carcinoembryonic antigen; Endoscopic ultrasound-guided fineneedleas piration

Non-invasive diagnosis of acute rejection in kidney transplants with delayed graft function

Paper 13: Correlation of Magnetic Resonance Arthrography Findings With Revision Hip Arthroscopy

Urinary IL-18 is an early predictive biomarker of acute kidney injury after cardiac surgery

The diagnostic efficacy of the umbilical arterial systolic/ diastolic ratio as a screening tool: A prospective blinded study

In volume 3, number 2, pages 48-49, we explained some screening characteristics of a diagnostic test in an educational manuscript entitled “Simple definition and calculation of accuracy, sensitivity and specificity (1). The present article was aimed to review other screening performance characteristics including positive and negative predictive values (PPV and NPV). PPV and NPV are true positive and true negative results of a diagnostic test, respectively (2). In other words, if a subject receives a certain diagnosis by a test, predictive values describe how likely it is for the diagnosis to be correct Definitions: Patient: positive for disease Healthy: negative for disease True positive (TP)= the number of cases correctly identified as patient False positive (FP) = the number of cases incorrectly identified as patient True negative (TN) = the number of cases correctly identified as healthy False negative (FN) = the number of cases incorrectly identified as healthy Positive predictive value: Positive predictive value is the proportion of cases giving positive test results who are already patients (3). It is the ratio of patients truly diagnosed as positive to all those who had positive test results (including healthy subjects who were incorrectly diagnosed as patient). This characteristic can predict how likely it is for someone to truly be patient, in case of a positive test result. Positive predictive value=TPTP+FP Negative predictive value: Negative predictive value is the proportion of the cases giving negative test results who are already healthy (3). It is the ratio of subjects truly diagnosed as negative to all those who had negative test results (including patients who were incorrectly diagnosed as healthy). This characteristic can predict how likely it is for someone to truly be healthy, in case of a negative test result. Negative predictive value=TNTN+FN SBP Total Positive Negative Ascites fluid appearance Positive TP = 15 FP = 6 21 Negative FN = 25 TN = 34 59 Total 40 40 80 Open in a separate window Lens dislocation Total Positive Negative Ultrasonography Positive TP = 11 FP = 2 13 Negative FN = 2 TN = 115 117 Total 13 117 130 Open in a separate window Predictive values and the prevalence of the disease: Since the ratio includes both healthy and patient subjects, predictive values are affected by the prevalence of the disease and can differ from one setting to another for the same diagnostic test. The lower the prevalence of the disease, the higher its negative predictive value. On the other hand, the higher the prevalence of the disease, the higher the positive predictive value. For solving these problems, positive and negative likelihood ratios were developed, which will be introduced and discussed in part three of EBM series articles of Emergency. Examples: Example 1: Imagine we have a sample population of 100 people, 50 healthy and the others patients. If the test was positive for 75 people of this population, the PPV and NPV of test are as follows: PPV: 50/75 = 0.66 or 66.6%. This means that in this population, 66.6% of people whose test result is positive, have the disease. NPV: 25/25 = 100%. This means that in this population, 100% of the people whose test result is negative, are healthy (Figure 1). Open in a separate window Figure 1 A schematic presentation of an example test with 66.6% PPV, and 100% NPV

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

Background. Postpartum hemorrhage is recognized as a leading cause of maternal mortality and morbidity in the world. Predicting postpartum hemorrhage in high-risk patients with uterine scar enables preventive measures to be scheduled and costs of treatment and rehabilitation to be reduced.Objective. To determine antenatal predictors of high postpartum hemorrhage risk in pregnant women with uterine scar in order to improve the prevention of massive blood loss.Methods. An observational cohort study involves the medical records of 4494 maternity women with uterine scar (pregnancy and delivery histories) of the Perinatal Center of Regional Clinical Hospital No. 2, Krasnodar Krai. The study sample included data of maternity women coded O34.2 according to International Classification of Diseases, 10th Edition, for the period from 2017 to 2020. The sample participants were distributed into two groups depending on the blood loss during delivery, determined in compliance with clinical recommendations: a group of patients without massive blood loss during delivery and a group of patients with massive blood loss in labor/early postpartum period. The study was mainly focused on parameters of the prediction performance of bleeding in labor and early postpartum period in pregnant women with uterine scar using a multiparametric, logistic regression models. The study considered demographic data, comorbidity, obstetric history, pregnancy course, ultrasound data, and volume of blood loss at delivery. The performance of prediction for postpartum hemorrhage was calculated using multivariate binary logistic regression. Descriptive statistical analysis was carried out by means of statistical software package SPSS Version 26 (IBM, USA). Two-sided p-value < 0.05 was taken as a statistically significant difference. A prognostic significance of predictors was determined by binary logistic regression. The Wald statistic was used to determine an observed significance. In order to define the performance of the model, the study involved calculating sensitivity, specificity, positive and negative predictive value, Nagelkerke coefficient of determination, as well as performing ROC analysis. The DeLong test was used for paired comparisons of ROC curves.Results. In the retrospective follow-up group (2017–2020) (n = 502), postpartum hemorrhage with massive blood loss occurred in 41 cases (8.17%). For the model based on clinical-anamnestic predictors, the prediction performance for postpartum hemorrhage comprised: sensitivity = 12.2% (95% confidence interval (CI) 4.1–26.2); specificity = 99.3% (95% CI 98.1–99.9); positive predictive value = 62.5% (95% CI 24.5–91.5); negative predictive value = 92.6% (95% CI 89.9–94.8); area under the ROC curve = 0.864 (95% CI 0.807–0.920), p < 0.001. The prediction performance for the model based on three ultrasound predictors (asymmetry coefficient of placental thickness, uterine wall bulging in the scar and myometrial thickness in the placentation zone) comprised: sensitivity = 85.4% (95% CI 70.8–94.4); specificity = 98.5% (95% CI 96.9–99.4); positive predictive value = 83.3% (95% CI 68.6–93.0); negative predictive value = 98.7% (95% CI 97.2–99.5); area under the ROC curve = 0.919 (95% CI 0.855–0.983), p < 0.001. No significant difference was revealed for the performance of the models ( p = 0.170). For the model based on ultrasound predictors and placenta previa, the prediction performance comprised: sensitivity = 85.4% (95% CI 70.8–94.4); specificity = 98.5% (95% CI 96.9–99.4); positive predictive value = 83.3% (95% CI 68.6–93.0); negative predictive value = 98.7% (95% CI 97.2–99.5); area under the ROC curve = 0.955 (95% CI 0.912–0.999), p < 0.001. The model based on clinical-anamnestic and ultrasound indicators predicted postpartum hemorrhage with sensitivity equal to 85.4% (95% CI 70.8–94.4); specificity — 98.9% (95% CI 97.4–99.6); positive predictive value — 87.5% (95% CI 73.2–95.8); negative predictive value — 98.7% (95% CI 97.1–99.5); area under the ROC curve — 0.984 (95% CI 0.966–1.0), р < 0,001. Thus, this model outperformed the model based on clinical-anamnestic data (p < 0.001), based on ultrasound predictors (p = 0.006) and revealed no difference with the model considering placenta previa and ultrasound predictors (p = 0.127). Using prenatal prediction of postpartum hemorrhage based on ultrasound features, the incidence of massive blood loss at delivery decreased from 6.88/1000 deliveries (2019–2020) to 4.18/1000 deliveries (2021–2022) (p < 0.001).Conclusion. Ultrasound predictors in pregnant women with uterine scar increase the sensitivity of antenatal prediction of postpartum hemorrhage based on the assessment of clinical and anamnestic risk factors, thereby enabling preventive measures to be scheduled in the risk group and incidence of massive blood loss to be reduced.

Role of an age-adjusted D-dimer cutoff level in the diagnosis algorithm of lower limb deep venous thrombosis in outpatients

For patients who present to clinicians with palpable breast lesions, the triple test: (physical examination, mammography and fine-needle aspiration cytology) can be employed to provide accurate diagnosis. In this study, the sensitivity, specificity and predictive values of triple test components were studied separately and in combination. During the study period (from January 1998 to December 1999), 140 patients with palpable breast masses were subjected to all components of the triple test followed by confirmatory open biopsy. Physical examination showed 82.6% sensitivity, 97.3% specificity and 86.4% positive predictive value. Mammography showed 87.5% sensitivity, 97.3% specificity and 87.5% positive predictive value and fine-needle aspiration cytology (FNAC) showed 91.7% sensitivity, 100% specificity and 100% positive predictive value in concordant cases (elements had either all malignant or all benign results). The triple test was 100% accurate in the diagnosis of palpable breast lesions when all three elements were concordant. Based on these results, we recommend elimination of the confirmatory open biopsy, which will result in reduced expenses and morbidity compared with open biopsy.