Diagnostic Data Research Articles

Validation of diagnostic wearable ECG data from high-risk patients and integration into the atrial fibrillation catheter ablation pathway

Abstract Background Wearables like the Apple Watch (AW) offer non-invasive, long-term monitoring and patient-led, single-lead ECG recordings. Although validated for Atrial Fibrillation (AF) detection in the general population, the accuracy and clinical utility of the single-lead ECG recording feature for the diagnosis of AF has not been evaluated in patients at high risk of atrial arrhythmias.(1) An evaluation of the accuracy of the Wearable-derived labels is important to determine the feasibility of integrating AW-based remote monitoring into clinical pathways. Objective To evaluate the accuracy and data burden from AW-based, patient-led ECG monitoring in patients undergoing AF catheter ablation. Methods Patients referred for first-time AF catheter ablation to the Study Institution between January 2022 and March 2023 were invited to enrol in the AF Follow-up with Apple Watch (AFFU-AW) Trial. Patients randomised to the active arm of the study received 12 months of post-ablation follow-up using a loaned Apple Watch. Participants underwent an education session and were asked to perform daily ECGs; sending through any labelled as AF, inconclusive, or in the context of symptoms. The accuracy of the automated ECG labelling was determined against blinded, independent labelling by two experienced Electrophysiologists. Results 1093 remote transmissions were emailed in for evaluation from 52 patients to the Study team. This was a median of 6 [2, 17] per patient and a receiver burden of 2 [1, 4] ECGs per day. 475 were categorised by the AW as AF with a sensitivity of 0.71 and specificity of 0.96 compared to expert labelling. 342 (31%) were not categorised by the wearable, with an ‘inconclusive’ interpretation (161 [47%]) or ‘high heart rate’ (108 [32%]) as the most cited reasons. 178 (55%) of the uncategorised ECGs were subsequently overread as AF. Accompanying symptoms were reported with 324 (30%) ECGs, most commonly palpitations. Conclusion This is the first reported evaluation of the AF diagnostic feature based on AW ECG recordings as part of a remote patient rhythm surveillance pathway after catheter ablation. The high positive predictive value of ‘Sinus Rhythm’ and ‘AF’ labels and the high specificity of the ‘AF’ label are comparable to diagnostic algorithms in implantable loop recorders.(2) The source code to automate data curation and structured databasing of wearable ECGs has been made available for care providers to integrate the service into device monitoring workflows.(3)

Inferring diagnoses from prescription data: a machine-learning approach

Abstract Estimating disease prevalence is a fundamental while challenging public-health goal: in the absence of solid diagnostic data, it typically requires expensive and time intensive studies. Analyzing existing medical records is an alternative, complementary approach. In particular, prescription records offer a glimpse into the health status of populations, prescribing patterns, and operational dynamics. We asked whether these could be used to infer diagnostics and, therefore, estimate disease prevalence. We used the Portuguese electronic medical prescriptions dataset (ADD NUMBERS) and trained a machine learning model that captures the probability that specific drugs are prescribed for the same or related medical conditions. We 1) constructed a manually-curated dataset of diseases and associated medications; 2) used this dataset to train embeddings models; 3) tested these models against human-classified data; 4) used the trained/tested model to uncover comorbidities associated with a) antibiotic prescription (to validate the model), and b) the impact of COVID-19 in prescription patterns of chronic-diseases. The model was able to accurately identify drugs used for targeting ten specific diseases, with few false positives and false negatives. Our analysis showed that the COVID-19 pandemic had almost no effect on the number of prescriptions for some chronic diseases (such as diabetes and depression), but, importantly, had a strong impact on the number of new diagnoses, with new users of diabetes medication in 2019 dropping by more than 16.8% in 2020. Medical prescriptions offer a promising tool to quickly assess disease prevalence, underdiagnosis, and allow a broad overview of the medical status of the population, especially in the face of health emergencies that strongly pressure the medical system. Key messages • We propose a new approach to infer diagnostics from prescription data with several promising applications. • We propose a new approach to infer diagnostics from prescription data with several promising applications.

Estimating the economic burden of mental and substance use disorders in Belgium

Abstract Mental and substance use disorders are the leading cause of disease burden (disability-adjusted life years, DALYs) in Belgium. Strong mental health policies are needed to reduce the burden, taking into account effectiveness and cost-effectiveness of possible interventions. Next to providing an overview of the economic burden of mental health conditions (what is), routine cost-of-illness studies also serve as a source of information for modelling the cost-effectiveness of policies and interventions to improve the mental health of populations (what could be). In the context of an extension of the Belgian Health Status Report and the Belgian National Burden of Disease Study, we calculated the direct medical costs of a range of health conditions, including mental health conditions such as depression. We used self-reported diagnostic data from the Belgian health Interview Survey linked with claims data to calculate the incremental costs attributable to mental health disorders with g-computation. We correct for confounders such as age and sex, and also look at uncertainty brought about by the data source and modelling. This presentation aims to share the experience of calculating the economic burden of mental ill-health in the context of a routine cost-of-illness study. We will present the results of the study and discuss alternative ways in which the costs of common mental health conditions such as depression could be estimated, for example based entirely on claims data using pharmaceutical cost groups.

Development and Modeling of the Internet of Things Network for Patients IT Diagnostics

The work of a network that implements algorithms for IT diagnostics of neurological diseases based on the Internet of Things technology has been developed and modeled. The network includes a smartphone, a platform, a neural network, and applications. First, the voices of sick patients are entered from the smartphone to train the neural network, and then the examined patients for IT diagnostics. Data is transferred between the smartphone and the platform (ThingSpeak) via the MQTT protocol. The smartphone’s mobile application extracts the voice functions of the examined patients and records them on the Internet of Things network platform. Recognition is performed using the trained neural network. The structure and algorithm of the ThingSpeak platform are presented. IT diagnostics data are displayed in the application on the smartphone. The patient data used in the study are taken from the ADReSS 2020 Challenge program, which contains speech data of patients with Alzheimer’s disease and healthy people.

Prognostic significance of inferior vena cava volume defined by initial polytrauma CT-imaging: single-center experience of a level-1 trauma center

BackgroundThe significance of computed tomography (CT)-based volume measurement of the inferior vena cava (IVC) in the treatment and prognosis of trauma patients is not yet fully understood. The conflicting results that have been reported may be attributable to differences in injury severity and the use of different measurement methods, including IVC index and volumetry. The purpose of this study was to determine the relationship between IVC volume and red blood cell (RBC) transfusion and mortality in intubated trauma patients who were stable enough for initial CT imaging.MethodsA retrospective analysis was conducted on all consecutive trauma patients who underwent emergency tracheal intubation and mechanical ventilation before initial whole-body CT imaging at a level-1 trauma center over a 12-year period (2008–2019). The IVC volume was determined on initial trauma CT and included in multivariable models with demographic and diagnostic data. Associations of overall RBC transfusion, massive transfusion, 24-h mortality, and 30-day mortality were assessed using logistic regression analyses and Cox proportional hazard models.ResultsA total of 438 patients (75.3% male) with a median age of 50 years, and a median injury severity score (ISS) of 26 points were included in the analysis. Most of the patients (97.5%) had suffered from blunt trauma mechanisms. Median IVC volume was 36.25 cm3, and RBC transfusion and massive transfusion were performed in 197 and 90 patients, respectively. The 24-h and 30-day mortality rates were 7.3% and 23.3%, respectively. VCI volume was found to be independently associated with the necessity of RBC transfusion and 24-h mortality (OR 0.98, 95% CI 0.96–0.99, p = 0.01 and HR 0.96, 95% CI 0.93–0.99, p = 0.025, respectively), while associations with massive transfusion and 30-day mortality were not statistically significant in multivariable analyses.ConclusionInitial IVC volume may serve as a predictor of patients at risk for overall RBC transfusion requirements and 24-h mortality, suggesting the possibility of its diagnostic efficacy in short-term outcomes. Further studies are needed to confirm these findings.

A novel intelligent fault diagnosis method for gearbox based on multi-dimensional attention denoising convolution.

In the field of intelligent fault diagnosis, particularly concerning rotating machinery, convolutional neural networks (CNNs) face significant challenges when applied to real industrial vibration data. These data are not only contaminated by various types of noise but also exhibit fault features that vary across different scales. Consequently, the effective suppression of extraneous noise and accurate extraction of multi-scale fault features are crucial issues. To address these challenges, this study proposes a novel deep neural network framework, termed the Multidimensional Fusion Residual Attention Network (MFRANet), for gearbox fault diagnosis. The MFRANet employs a multi-scale deep separable convolution module to thoroughly investigate the fundamental characteristics of the original vibration signals in both the time and time-frequency domains. To enhance the detailed analysis of diagnostic data and mitigate the risks of overfitting and noise interference, an efficient residual channel attention module is incorporated to weight and denoise the feature maps. Additionally, an external attention module is introduced to create implicit connections between the denoised multi-scale feature maps and to highlight potential correlations within the sample data, thereby improving the accuracy of fault diagnosis. Experimental evaluations on a gearbox fault dataset demonstrate that the proposed method surpasses several benchmark and state-of-the-art techniques in terms of diagnostic performance, exhibiting robust noise resilience across various noise levels. This indicates enhanced reliability and accuracy in gearbox fault diagnosis, providing an innovative and efficient solution for fault diagnosis in rotating machinery. The study underscores the contributions of artificial intelligence through the innovative structure of the method and the integration of advanced deep learning modules, while its engineering application is evidenced by addressing practical challenges in rotating machinery fault diagnosis. This work meets the urgent need for reliable diagnostic methods in industrial environments.

A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.

We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals with diabetes in primary health care (PHC) through the analysis of diagnostic data. We used a sex- and age-matched case-control design. Cases of new CHF were identified across all outpatient care settings 2015-2022 (n=9098). We included individuals 30years and above, by sex and age groups of 30-65years and >65years. The controls (five per case) were sampled from the individuals in 2015-2022 without CHF at any time between 2010 and 2022, in total 45 490. From the stochastic gradient boosting (SGB) technique model, we obtained a rank of the 10 most important factors related to newly diagnosed CHF in individuals with diabetes, with the normalized relative influence (NRI) score and a corresponding odds ratio of marginal effects (ORME). Area under curve (AUC) was calculated. For women 30-65years and >65years, we identified 488 and 3240 new cases of CHF, respectively, and men 30-65years and >65years 1196 and 4174 new cases. Among the 10 most important factors in the four groups (divided by sex and lower and higher age) for newly diagnosed CHF, we found the number of visits 12months before diagnosis (NRI 44.3%-55.9%), coronary artery disease (NRI 2.9%-7.8%), atrial fibrillation and flutter (NRI 6.6%-12.2%) and 'abnormalities of breathing' (ICD-10 code R06) (NRI 2.6%-4.4%) were predictive in all groups. For younger women, a diagnosis of COPD (NRI 2.7%) contributed to the predictive effect, while for older women, oedema (NRI 3.1%) and number of years with diabetes (NRI 3.5%) contributed to the predictive effect. For men in both age groups, chronic renal disease had predictive effect (NRI 3.9%-5.1%) The model prediction of CHF among patients with diabetes was high, AUC around 0.85 for the four groups, and with sensitivity over 0.783 and specificity over 0.708 for all four groups. An SGB model using routinely collected data about diagnoses and number of visits in primary care, can accurately predict risk for diagnosis of heart failure in individuals with diabetes. Age and sex difference in predictive factors warrant further examination.

Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure

Background: The global healthcare system faces challenges in diagnosing and managing lung and colon cancers, which are significant health burdens. Traditional diagnostic methods are inefficient and prone to errors, while data privacy and security concerns persist. Objective: This study aims to develop a secure and transparent framework for remote consultation and classification of lung and colon cancer, leveraging blockchain technology and Microsoft Azure cloud services. Dataset and Features: The framework utilizes the LC25000 dataset, containing 25,000 histopathological images, for training and evaluating advanced machine learning models. Key features include secure data upload, anonymization, encryption, and controlled access via blockchain and Azure services. Methods: The proposed framework integrates Microsoft Azure’s cloud services with a permissioned blockchain network. Patients upload CT scans through a mobile app, which are then preprocessed, anonymized, and stored securely in Azure Blob Storage. Blockchain smart contracts manage data access, ensuring only authorized specialists can retrieve and analyze the scans. Azure Machine Learning is used to train and deploy state-of-the-art machine learning models for cancer classification. Evaluation Metrics: The framework’s performance is evaluated using metrics such as accuracy, precision, recall, and F1-score, demonstrating the effectiveness of the integrated approach in enhancing diagnostic accuracy and data security. Results: The proposed framework achieves an impressive accuracy of 100% for lung and colon cancer classification using DenseNet, ResNet50, and MobileNet models with different split ratios (70–30, 80–20, 90–10). The F1-score and k-fold cross-validation accuracy (5-fold and 10-fold) also demonstrate exceptional performance, with values exceeding 99.9%. Real-time notifications and secure remote consultations enhance the efficiency and transparency of the diagnostic process, contributing to better patient outcomes and streamlined cancer care management.

Auer rod-positive acute leukemia with predominantly lymphoid immunophenotype: Report on 11 cases and review of literature.

Auer rods (AuRs) are prominent intracellular structures found almost exclusively in myeloid cell malignancies, such as acute myeloid leukemia (AML), chronic and juvenile myelomonocytic leukemia and myelodysplastic syndrome. Extremely rare AuRs have been reported in patients with acute lymphoblastic leukemia (ALL) or among ambiguous lineage leukemia patients with a dominantly lymphoblastic immunophenotype. We report diagnostic and follow-up data of an international cohort of 11 children suffering from leukemias with AuRs and with significant presence of T and myeloid markers, majority of whom categorized as early T-cell precursor (ETP, n=7); or T-ALL (ETP status unknown, n=2), ALAL (acute leukemia of ambiguous lineage, n=1), and AML reclassified from ALAL (n=1). We described other diagnostic details and treatment types and responses. Moreover, we summarize previously published data. Among the four patients who started and remained on ALL-type therapy, all were in the first complete remission, whereas both patients who started and remained on AML-type therapy relapsed and died. Of the patients who followed either a combined ALL/AML protocol (Interfant 06) or who switched from one of the two types of therapy to the other, one patient died, and the remaining four were in first complete remission at the most recent follow-up. We also searched for similar cases in the literature and found only three additional children with nonmyeloid leukemia and AuRs and 10 adults with this type of leukemia. Briefly, ALL- or combined ALL/AML-type therapy may be effective for treating AuR-positive leukemia patients with a lymphoid immunophenotype.

Artificial General Intelligence for the Detection of Neurodegenerative Disorders

Parkinson’s disease and Alzheimer’s disease are among the most common neurodegenerative disorders. These diseases are correlated with advancing age and are hence increasingly becoming prevalent in developed countries due to an increasingly aging demographic. Several tools are used to predict and diagnose these diseases, including pathological and genetic tests, radiological scans, and clinical examinations. Artificial intelligence is evolving to artificial general intelligence, which mimics the human learning process. Large language models can use an enormous volume of online and offline resources to gain knowledge and use it to perform different types of tasks. This work presents an understanding of two major neurodegenerative disorders, artificial general intelligence, and the efficacy of using artificial general intelligence in detecting and predicting these neurodegenerative disorders. A detailed discussion on detecting these neurodegenerative diseases using artificial general intelligence by analyzing diagnostic data is presented. An Internet of Things-based ubiquitous monitoring and treatment framework is presented. An outline for future research opportunities based on the challenges in this area is also presented.

Clinico-Demographic Profile of Children Attending Pediatric Outpatient Department in Tertiary Care Center in Gandaki Province, Nepal: A Prospective, Descriptive Cross-Sectional Study

Introduction: Pediatric outpatient departments (OPDs) in tertiary care centers provide a vital connection for the early diagnosis and treatment of childhood illnesses. Understanding the clinico-demographic profile of pediatric patients attending these centers provides insight into the healthcare needs and challenges faced by children in specific regions. This study aimed to evaluate the clinico-demographic characteristics of children attending the pediatric OPD.  Methods: A prospective, descriptive cross-sectional study was conducted from April 12 to December 17, 2023 at pediatric OPD at GP Koirala National Centre for Respiratory Diseases and Hospital, Tanahun, Gandaki Province, Nepal. Data were collected from the pediatric OPD during clinical history taking and examination, including 7,020 children aged 0–14 years. Verbal consent was obtained from legal guardians for using clinical history and diagnostic data. Data on age, sex, socioeconomic status, parental education, and disease distribution were analyzed using SPSS version 23.  Results: Out of 7,020 children, 42.89% were aged 1–5 years, and 28.67% were aged 6–10 years. Males (50.7%) slightly outnumbered females (49.3%). Socioeconomic analysis showed that 57.3% of children were from low-income families. Regarding parental(either father or mother or any legal gurdian who brought child at hospital OPD) education, 31.19% had secondary education, and 28.3% had primary education. Respiratory infections (19.97%) were the most common diagnosis, followed by gastroenteritis (11%), pain abdomen (9%), and malnutrition, especially undernutrition (8%). Other notable conditions included enteric fever (7.4%) and acute conjunctivitis (7.3%).  Conclusion: The study highlights a high prevalence of respiratory and gastrointestinal conditions among children, particularly from low-income families. The findings emphasize the need for targeted healthcare interventions and improved parental education to reduce the burden of childhood illnesses in Gandaki Province.

Improving thyroid cancer diagnosis with advanced deep learning techniques

The accurate diagnosis of thyroid carcinoma is a complex and critical challenge in medical practice due to the clinical and pathological diversity of thyroid tumors. This study explores the application of advanced deep learning models, specifically EfficientNet B4 and MobileNetV3, to enhance the classification of thyroid histopathological images. By leveraging transfer learning, these models were fine-tuned on a dataset of 7,272 images encompassing three types of carcinoma: Medullary, Papillary, and Vesicular. The performance of these models was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results indicate that both models are effective, with EfficientNet B4 demonstrating slightly superior accuracy, particularly in more challenging diagnostic scenarios. Despite these promising results, the study acknowledges limitations related to dataset size and variability, which could affect the generalizability of the models. To address these issues, future research should focus on expanding the dataset and incorporating additional diagnostic data, such as genetic and molecular markers, to improve model performance and clinical applicability. This study highlights the potential of deep learning models to significantly enhance the diagnostic accuracy of thyroid carcinomas, paving the way for more reliable and precise clinical decision-making.

Microsurgical Treatment of a Large Through-and-Through Periapical Lesion with Apicomarginal Defect using Guided Tissue Regeneration (GTR): A Case Report of a Two-Year Follow-Up

In case of a long-term periapical lesion, destruction of both vestibular and oral cortical plates is sometimes observed and even a through-and-through periapical lesion occurs. The success of the treatment decreases when an apicomarginal defect is added to the through-and-through periapical lesion. Large periapical lesions should be treated initially by orthograde root canal therapy. When the signs and symptoms of the infection don’t recede after the treatment, then surgical approaches should be considered. In this case report, a 22-year-old female with previously initiated therapy was referred for an endodontic microsurgery of tooth 22 (i.e., upper left lateral incisor). After the endodontic treatment the patient was referred to the oral surgeon for apicoectomy with augmentation of the bone defect. The sinus tract in the apex area of the tooth 22 remained active since the surgical intervention. Endodontic microsurgery and guided tissue regeneration were performed. The article presents diagnostic data, namely pre- and post-operative images of cone beam computed tomography (after 2 years), as well as pre-, intra- and post-operative clinical images. All pre- and intraoperative procedures and stages are detailed. In particular, separation of platelet-rich fibrin (PRF) from venous blood, retrograde preparation with an ultrasonic tip and a device using a dental operating microscope and the use of a collagen membrane. After two-year follow-up, radiographic examination revealed significant bone reconstruction and clinical signs and symptoms were absent. The patient hasn’t reported any symptoms since. The paper also analyzes scientific sources on the use of PRF and collagen membranes in bone defects of the jaws. Attention is also paid to the formation of a flap during operations of this type. The main six success factors in the treatment of such complex cases are highlighted. Rethinking the previously performed surgery (apicoectomy) in this patient, attention was paid to the main five factors that could contribute to the failure.

African American Females are More Vulnerable to Develop Biliary Injury in NAFLD (Nonalcoholic fatty liver disease) Patients – A Single Institute Experience

Abstract Introduction/Objective Chronic alcohol exposure alone may take decades to induce severe liver disease, yet when combined with elements such as the Western diet, liver pathogenesis accelerates, resulting in earlier symptoms. Identifying the specific contributions of alcohol, diet, and other factors to liver disease is challenging, despite the availability of diagnostic tools like patient histories, liver enzyme tests, imaging, and biopsies. Methods/Case Report This study seeks to detail the liver disease profile of patients who underwent liver biopsies in 2022. Comprehensive profiles were created by retrieving liver biopsy specimens and associated diagnostic data from electronic medical records. Two independent pathologists analyzed the biopsy slides, looking for liver damage associated with alcohol consumption and Western diet influences. We employed enhanced staining techniques to better associate specific types of liver damage with demographic variables. The data analysis was conducted using GraphPad Prism 9 for robust statistical evaluation. Results (if a Case Study enter NA) The research included 88 participants—41 males and 47 females, with average ages of 56 and 62 respectively. The predominant diagnosis was fatty liver disease, with non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) accounting for 26% of cases, and alcoholic liver disease (ALD) making up 10%. The NAFLD/NASH group (n=23) consisted mainly of African Americans (n=15) and Caucasians (n=8). Among these, African Americans showed a significantly greater ductular reaction and portal inflammation (p<0.05). Conversely, no notable differences in portal fibrosis and bile stasis were observed between these groups. Notably, African American females (n=11) demonstrated more severe pathological features compared to Caucasian females (n=6), including elevated levels of ductular reaction, fibrosis, portal inflammation, and bile stasis (p<0.05), suggesting a higher vulnerability to biliary injuries. Conclusion The study highlights the complex interplay between lifestyle factors and liver disease development and emphasizes the need for precise diagnostic strategies that consider these multifactorial influences. Additionally, it reveals ethnic disparities in the pathology of NAFLD, advocating for further research into their causes and implications for clinical management. Future investigations should focus on race and gender-specific factors and adapt local healthcare strategies to effectively manage the escalating burden of liver disease.

Evaluation of ventriculoperitoneal shunt infections and risk factors in children.

To evaluate the demographic, clinical, diagnostic, and treatment data of pediatric patients with ventriculoperitoneal shunt infection and risk factors for ventriculoperitoneal shunt infection and recurrence of ventriculoperitoneal shunt infection. Patients aged 0-18 years who were diagnosed with ventriculoperitoneal shunt infection at CukurovaUniversity Faculty of Medicine Hospital between 2016 and 2021 were included in the study. Demographic, clinical, laboratory, and treatment data of the patients were evaluated retrospectively. Risk factors for the development and recurrence of ventriculoperitoneal shunt infection were evaluated. Patients who underwent ventriculoperitoneal shunt but did not develop any shunt infection were selected as the control group. Eighty-five patients with a diagnosis of ventriculoperitoneal shunt infection were included in the study. Fever (58.8%), anorexia (58.8%), vomiting (56.5%), and altered consciousness (54.1%) were the most common complaints at admission. The control group consisted of 48 patients. The number of shunt revisions was statistically significantly higher in the group that developed ventriculoperitoneal shunt infection compared to the control group (p < 0.001). In patients with ventriculoperitoneal shunt infection, C-reactive protein and cerebrospinal fluid protein values before shunt insertion were found to be statistically significantly higher than the control group (p < 0.001). Ventriculoperitoneal shunt infection occurs most frequently in the first months after shunt application. Therefore, it is extremely important to prevent colonization and contamination during surgery in preventing the development of ventriculoperitoneal shunt infection. In the present study, cerebrospinal fluid protein elevation and C-reactive protein elevation before shunt application were found to be significant in terms of the development and recurrence of ventriculoperitoneal shunt infection.

Whole body magnetic resonance imaging (WB MRI) in the diagnosis and evaluation of the effectiveness of treatment of metastatic prostate cancer: Met-RADS-P system standards

INTRODUCTION: Whole-body magnetic resonance imaging (WB MRI) including diffusion-weighted imaging (DWI) is increasingly used in clinical practice. For metastatic prostate cancer, the radiologic reporting and data system Met-RADS-P (METastasis Reporting and Data System for Prostate Cancer) has been proposed to determine the prevalence of the metastatic process and subsequently evaluate the response to specific treatment based on WB MRI data.OBJECTIVE: To present an overview of the methodological aspects of WB MRI in patients with metastatic prostate cancer in relation to the Met-RADS-P system, to outline its terminology and principles for assessing study results, and to discuss the possibilities of its clinical use.MATERIALS AND METHODS: A search was conducted for publications for the period from January 1, 2017 to December 1, 2023 in Russian and English in the electronic databases eLibrary, Medline, PubMed, using the keywords: «диффузионно-взвешенная томография всего тела», «метастатический рак предстательной железы», «Met-RADS-P», «Whole Body MRI, WB MRI», «Whole Body DWI, WB DWI», «metastatic prostate cancer», supplemented by examination of references in the retrieved articles.RESULTS: Based on the literature data and our own experience, WB MRI protocols in patients with metastatic prostate cancer are described, the algorithm of MR image analysis, the criteria for evaluating the response of metastatic foci in the skeleton, lymph nodes, and internal organs, and the response assessment categories according to the RAC (response assessment categories) scale, as well as summarize the results and discuss the prospects for the clinical use of Met-RADS-P.DISCUSSION: The few publications in the literature using the Met-RADS-P system primarily concern the assessment of its reliability (inter-reader agreement). The authors conclude that it is appropriate to use it in monitoring metastases in patients with castration-resistant prostate cancer, emphasizing the clinical importance of identifying discordant responses after therapy. They also note the obstacles to the widespread introduction of WB MRI imaging into clinical practice, including the duration of the examination procedure and the large volume of diagnostic data that requires significant time for analysis and reporting, which can be overcome by automating the assessment of MR images using artificial intelligence.CONCLUSION: The Met-RADS-P system systematizes the approach to performing and describing WB MRI in patients with advanced prostate cancer, allowing the technique to be reproduced on virtually any modern scanner, from the scan protocol to a standardized report of findings. This makes the WB MRI technique reproducible in a variety of medical settings, and therefore increases its value. Nevertheless, Met-RADS-P is a relatively new system and requires large multicenter clinical trials to confirm its efficacy and representativeness, as well as to level out its shortcomings.

Development of decision support methods for efficient maintenance of urban wastewater systems

The sanitation network is a vital urban infrastructure that requires systematic and co-ordinated activities and practices to realize lowest life cycle cost. The sustainable management of this asset is based on the efficient acquisition of diagnostic data from the sanitation network in order to analyze the defects of each section, then to assess their condition to prioritize and consider the effective mode of maintenance of sections deemed failing. In this perspective, knowledge of the sanitation networks is essential for reliable classification of defects and effective maintenance. A numerical model based on weighted criticality is developed to evaluate and prioritize the defects in an urban sanitation network. It is based on the combination of two methods: Fuzzy Analytic Hierarchy Process (F-AHP) and Weighted Product Model (WPM). A case study of a real sanitation network in Bejaia city (Algeria), consisting mainly of non-visitable pipes evaluated from television inspections, is used to illustrate and validate the proposed model. The results show that the weighting of criticality parameters has a considerable influence on the classification of defects in a sanitation network. The weighted criticality results obtained in the present case are significantly better than those obtained using a method based on the notion of classical criticality. This clearly indicates the relevance of the proposed numerical model.

Potential of Chest Radiography in Primary Diagnosis of Lung Cancer

Objective: to evaluate the effectiveness of diagnostics for non-small cell lung cancer (LC) based on chest radiography/fluorography data for LC screening diagnostics during preventive medical examination and check-ups.Material and methods. The work was organized as a retrospective cohort study. The starting point of the study was chest radiography, the final point was the diagnosis of LC based on the results of low-dose computed tomography (LDCT) and tumor morphological examination. The patient sample was composed using inclusion and exclusion criteria and initially included 800 patients, then narrowed down to 788. Patients were divided into groups according to LC stage and depending on whether radiography during preventive measures made it possible to suspect LC verified within 3 months via LDCT for any reasons not related to the suspicion of a tumor process. The diagnostic coefficient and informative value of radiography were calculated for each stage of the established LC diagnosis.Results. The study showed that chest radiography does not allow establishing stage I LC diagnosis (J=0.00; p&lt;0.05), it is low informative for stage II LC (J=0.25; p&lt;0.05), and medium informative for stage III LC (J=0.80; p&lt;0.05). Chest radiography does not reliably detect LC signs at early stages in 84.3% of cases.Conclusion. The obtained results indicate an insufficient sensitivity of radiography as a screening method, primarily for early LC diagnosis. The introduction of chest LDCT instead of radiography into the procedure for preventive medical examination and check-ups of the adult population approved by the Ministry of Health of the Russian Federation will increase LC detection at early stages by 5.37 times (95% confidence interval 3,77–7,64).

Respiratory pathogen analysis in pediatric inpatients unraveled the infection pattern of Mycoplasma pneumoniae post the COVID-19 pandemic.

To counteract the COVID-19 pandemic, nonpharmaceutical interventions (NPIs) were implemented globally, exerting a profound influence on a wide spectrum of infectious diseases, encompassing respiratory tract infections (RTIs). Subsequent to the easing of NPIs, China experienced a significant outbreak of Mycoplasma pneumoniae (MP). Over a decade from 2015 to 2024, our study scrutinized 12 common infectious diseases among pediatric children. Etiologically diagnostic data and clinical outcome metrics of children with RTIs, tested for 13 pathogens, were analyzed to evaluate changes during and after the pandemic compared to pre-pandemic periods, with a notable emphasis on age profile and coinfection patterns of MP. Among 57,471 hospitalized children, 23,178 were diagnosed with infectious diseases. Under NPIs, most respiratory infections declined compared to pre-pandemic levels, rebounding by 69.64% in 2023. While the infection rate of common respiratory pathogens decreased, cases of respiratory syncytial virus increased during the period of extensive NPI implementation. In 2023, pediatric intensive care unit durations for these pathogens increased, suggesting greater severity of illness compared to 2019. MP exhibited the highest infection rate (31.38% average), with a notable outbreak post-pandemic due to severity increase in <3 year olds and rise among older children. NPIs reduced MP coinfections and mitigated their severity, while exerting a significant influence on bacterial coinfections with MP over the span of 5 years, in contrast to their impact on viral pathogens. NPIs effectively curb transmission of respiratory infections by most pathogens, resulting in increased average age of MP infections and altered patterns of coinfection post-pandemic.

Incidence of Pulmonary Hypertension in the Echocardiography Referral Population.

Incidence rates for pulmonary hypertension using diagnostic data in patients with cardiopulmonary disease are not known. To determine incidence rates of, risk factors for, and mortality hazard associated with pulmonary hypertension among patients referred for transthoracic echocardiography Methods: Retrospective cohort study using data from the Veterans Health Administration (1999-2020) and Vanderbilt University Medical Center (1994-2020). Pulmonary hypertension was defined as pulmonary artery systolic pressure >35mmHg with prevalent cases excluded. Heart failure and chronic obstructive pulmonary disease were the primary exposures of interest. The primary outcome was incident pulmonary hypertension. Secondarily, we examined mortality rate following incident diagnosis. We identified 245,067 VA patients (94% male, 20% Black) and 117,526 Vanderbilt patients (46% male, 11% Black) without pulmonary hypertension, of whom 38,882 VA patients and 8,061 Vanderbilt patients developed pulmonary hypertension. Only 18-19% of patients with echo-based pulmonary hypertension also had a diagnostic code. Hazard of pulmonary hypertension was 4-fold higher in patients with heart failure and chronic obstructive pulmonary disease compared to patients without either. Mortality rates increased from pulmonary artery systolic pressure of 35mmHg to 45mmHg then plateaued. Independent risk factors for incident pulmonary hypertension included older age, male sex, black race, and cardiometabolic comorbidities. Pulmonary hypertension incidence rates estimated by diagnostic data are higher than code-based rates. Heart failure and chronic obstructive pulmonary disease strongly associate with incident pulmonary hypertension. Pulmonary artery systolic pressure >45mmHg at diagnosis is associated with high mortality. New pulmonary hypertension on echocardiography is an important prognostic sign.