Hasil untuk "Diseases of the respiratory system"

L. C. Myers, S. Parodi, G. Escobar et al.

Characteristics of Hospitalized Adults With COVID-19 in an Integrated Health Care System in California Coronavirus disease 2019 (COVID-19) has resulted in increased hospital and intensive care unit (ICU) use. In the United States, few reports have characterized patients treated outside of the ICU.1 Northern California was an early epicenter of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) community transmission in the United States. We report hospitalization and ICU admissions from Kaiser Permanente Northern California (KPNC), a regional integrated health care system serving 4.4 million members, constituting 30% of the area’s insured population.

Jan Boysen, Hristina Uzunova, Heinz Handels et al.

A precise spatial delivery of the radiation dose is crucial for the treatment success in radiotherapy. In the lung and upper abdominal region, respiratory motion introduces significant treatment uncertainties, requiring special motion management techniques. To address this, respiratory motion models are commonly used to infer the patient-specific respiratory motion and target the dose more efficiently. In this work, we investigate the possibility of using implicit neural representations (INR) for surrogate-based motion modeling. Therefore, we propose physics-regularized implicit surrogate-based modeling for respiratory motion (PRISM-RM). Our new integrated respiratory motion model is free of a fixed reference breathing state. Unlike conventional pairwise registration techniques, our approach provides a trajectory-aware spatio-temporally continuous and diffeomorphic motion representation, improving generalization to extrapolation scenarios. We introduce biophysical constraints, ensuring physiologically plausible motion estimation across time beyond the training data. Our results show that our trajectory-aware approach performs on par in interpolation and improves the extrapolation ability compared to our initially proposed INR-based approach. Compared to sequential registration-based approaches both our approaches perform equally well in interpolation, but underperform in extrapolation scenarios. However, the methodical features of INRs make them particularly effective for respiratory motion modeling, and with their performance steadily improving, they demonstrate strong potential for advancing this field.

Seung Gyu Jeong, Seong-Eun Kim

Automated respiratory sound classification supports the diagnosis of pulmonary diseases. However, many deep models still rely on cycle-level analysis and suffer from patient-specific overfitting. We propose PC-MCL (Patient-Consistent Multi-Cycle Learning) to address these limitations by utilizing three key components: multi-cycle concatenation, a 3-label formulation, and a patient-matching auxiliary task. Our work resolves a multi-label distributional bias in respiratory sound classification, a critical issue inherent to applying multi-cycle concatenation with the conventional 2-label formulation (crackle, wheeze). This bias manifests as a systematic loss of normal signal information when normal and abnormal cycles are combined. Our proposed 3-label formulation (normal, crackle, wheeze) corrects this by preserving information from all constituent cycles in mixed samples. Furthermore, the patient-matching auxiliary task acts as a multi-task regularizer, encouraging the model to learn more robust features and improving generalization. On the ICBHI 2017 benchmark, PC-MCL achieves an ICBHI Score of 65.37%, outperforming existing baselines. Ablation studies confirm that all three components are essential, working synergistically to improve the detection of abnormal respiratory events.

Chen Li, Thomas W. Hsiao, Stefanie Ebelt et al.

High temperatures are associated with adverse respiratory health outcomes and increases in ambient air pollution. Limited research has quantified air pollution's mediating role in the relationship between temperature and respiratory morbidity, such as emergency department (ED) visits. In this study, we conducted a causal mediation analysis to decompose the total effect of daily temperature on respiratory ED visits in Los Angeles from 2005 to 2016. We focused on ambient ozone as a mediator because its precursors and formation are directly driven by sunlight and temperature. We estimated natural direct, indirect, and total effects on the relative risk scale across deciles of temperature exposure compared to the median. We utilized Bayesian additive regression trees (BART) to flexibly characterize the nonlinear relationship between temperature and ozone and quantified uncertainty via posterior prediction and the Bayesian bootstrap. Our results showed that ozone partially mediated the association between high temperatures and respiratory ED visits, particularly at moderately high temperatures. We also validated our modeling approach through simulation studies. This study extends the existing literature by considering acute respiratory morbidity and employing a flexible modeling approach, offering new insights into the mechanisms underlying temperature-related health risks.

Ad Rian Chong, Khai Lip Ng, Nai‐Chien Huan et al.

ABSTRACT Pulmonary aspergilloma can cause life‐threatening haemoptysis. Surgical resection and/or bronchial artery embolization (BAE) are established treatment modalities, but both can be risky in frail patients with comorbidities. Spontaneous regression of aspergilloma with antifungal agents alone is rare. We report an elderly gentleman with a background history of treated pulmonary tuberculosis, who presented with haemoptysis due to a large left upper lobe aspergilloma. He declined surgery and BAE due to perceived risks. Oral voriconazole and later isavuconazole were prescribed, which led to clinical improvement and significant reduction in the size of the aspergilloma over 6 months. Antifungal agents might be a viable therapeutic option for aspergilloma patients unsuitable or who declined more invasive procedures. Further studies are needed to validate the efficacy and safety of this approach.

Franco Oberti, Stefano Di Carlo, Alessandro Savino

The Controller Area Network (CAN) protocol, essential for automotive embedded systems, lacks inherent security features, making it vulnerable to cyber threats, especially with the rise of autonomous vehicles. Traditional security measures offer limited protection, such as payload encryption and message authentication. This paper presents a novel Intrusion Detection System (IDS) designed for the CAN environment, utilizing Hardware Performance Counters (HPCs) to detect anomalies indicative of cyber attacks. A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks, which trigger distinct HPC responses. Key HPC features are optimized through data extraction and correlation analysis to enhance classification efficiency. Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.

Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda et al.

Pre-trained deep learning models, known as foundation models, have become essential building blocks in machine learning domains such as natural language processing and image domains. This trend has extended to respiratory and heart sound models, which have demonstrated effectiveness as off-the-shelf feature extractors. However, their evaluation benchmarking has been limited, resulting in incompatibility with state-of-the-art (SOTA) performance, thus hindering proof of their effectiveness. This study investigates the practical effectiveness of off-the-shelf audio foundation models by comparing their performance across four respiratory and heart sound tasks with SOTA fine-tuning results. Experiments show that models struggled on two tasks with noisy data but achieved SOTA performance on the other tasks with clean data. Moreover, general-purpose audio models outperformed a respiratory sound model, highlighting their broader applicability. With gained insights and the released code, we contribute to future research on developing and leveraging foundation models for respiratory and heart sounds.

Paul Jones, Ashraf Alzaabi, Alejandro Casas Herrera et al.

Exacerbations of chronic obstructive pulmonary disease (COPD) are associated with loss of lung function, poor quality of life, loss of exercise capacity, risk of serious cardiovascular events, hospitalization, and death. However, patients underreport exacerbations, and evidence suggests that unreported exacerbations have similar negative health implications for patients as those that are reported. Whilst there is guidance for physicians to identify patients who are at risk of exacerbations, they do not help patients recognise and report them. Newly developed tools, such as the COPD Exacerbation Recognition Tool (CERT) have been designed to achieve this objective. This review focuses on the underreporting of COPD exacerbations by patients, the factors associated with this, the consequences of underreporting, and potential solutions.

Tabish Saeed, Aneeqa Ijaz, Ismail Sadiq et al.

Cough-based diagnosis for Respiratory Diseases (RDs) using Artificial Intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias Free Network (RBFNet), an end to end solution that effectively mitigates the impact of confounders in the training data distribution. RBFNet ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID19 dataset in this study. This approach aims to enhance the reliability of AI based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks is proposed for the feature encoder module of RBFNet. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (cGAN) which helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBFNet is demonstrated by comparing classification performance with State of The Art (SoTA) Deep Learning (DL) model (CNN LSTM) after training on different unbalanced COVID-19 data sets, created by using a large scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively

Blake Fernandino, Moein Samak Bisheh

Timely detection of illnesses is vital to prevent severe infections and ensure effective treatment, as it's always better to prevent diseases than to cure them. Sadly, many patients remain undiagnosed until their conditions worsen, resulting in high death rates. Expert systems offer a solution by automating early-stage diagnoses using a fuzzy rule-based approach. Our study gathered data from various sources, including hospitals, to develop an expert system aimed at identifying early signs of diseases, particularly heart conditions. The diagnostic process involves collecting and processing test results using the expert system, which categorizes disease risks and aids physicians in treatment decisions. By incorporating expert systems into clinical practice, we can improve the accuracy of disease detection and address challenges in patient management, particularly in areas with limited medical resources.

Yang Liu, Kayla-Jade Butkow, Jake Stuchbury-Wass et al.

Respiratory rate (RR) monitoring is integral to understanding physical and mental health and tracking fitness. Existing studies have demonstrated the feasibility of RR monitoring under specific user conditions (e.g., while remaining still, or while breathing heavily). Yet, performing accurate, continuous and non-obtrusive RR monitoring across diverse daily routines and activities remains challenging. In this work, we present RespEar, an earable-based system for robust RR monitoring. By leveraging the unique properties of in-ear microphones in earbuds, RespEar enables the use of Respiratory Sinus Arrhythmia (RSA) and Locomotor Respiratory Coupling (LRC), physiological couplings between cardiovascular activity, gait and respiration, to indirectly determine RR. This effectively addresses the challenges posed by the almost imperceptible breathing signals under daily activities. We further propose a suite of meticulously crafted signal processing schemes to improve RR estimation accuracy and robustness. With data collected from 18 subjects over 8 activities, RespEar measures RR with a mean absolute error (MAE) of 1.48 breaths per minutes (BPM) and a mean absolute percent error (MAPE) of 9.12% in sedentary conditions, and a MAE of 2.28 BPM and a MAPE of 11.04% in active conditions, respectively, which is unprecedented for a method capable of generalizing across conditions with a single modality.

Angie K. Toledo, F. León-Jimenez, S. Cavalcanti et al.

Objective: a) to assess the seroprevalence of SARS-CoV-2 at the end of the second wave; b) to determine the distribution by age group and health determinants associated with seropositivity. Material and Methodology: A study performed in a Tumbes' settlement between December 2021–January 2022 sampled individuals over 2 years old from one to every four households. We collected finger-prick blood samples and conducted symptom surveys. Results: The adjusted seroprevalence after the second wave increased by twofold (50.15%, 95% CI[45.92–54.40]), compared with the first wave (24.82 %, 95%CI [22.49–27.25]). Females maintained a higher seroprevalence (53.89; 95% CI[48.48-59.23]) vs. 45.49; 95% CI [38.98-52.12], p=0.042) compared to males. Those under 18 years of age had the highest IgG seropositivity: the 12–17 age group during the second wave (85.14%) and the 2–11 age group (25.25%) during the first wave. Nasal congestion and cough were symptoms associated with seropositivity, unlike the first wave. Conclusions: The seroprevalence of COVID-19 increased by twofold compared to the initial wave in Tumbes region. Infrastructure constraints, restricted human resources, and supply limitations in healthcare facilities made the Peruvian health system collapse. The epidemiological surveillance network should incorporate mHealth tools for real-time notifiable disease information. Working alongside the community will let us improve interventions for preventing or controlling new pandemics.

Xiaoyue Ni, O. Wei, Hyoyoung Jeong et al.

Significance Continuous measurements of health status can be used to guide the care of patients and to manage the spread of infectious diseases. Conventional monitoring systems cannot be deployed outside of hospital settings, and existing wearables cannot capture key respiratory biomarkers. This paper describes an automated wireless device and a data analysis approach that overcome these limitations, tailored for COVID-19 patients, frontline health care workers, and others at high risk. Vital signs and respiratory activity such as cough can reveal early signs of infection and quantitate responses to therapeutics. Long-term trials on COVID-19 patients in clinical and home settings demonstrate the translational value of this technology. Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.

Josh Williams, Haavard Ahlqvist, Alexander Cunningham et al.

For the one billion sufferers of respiratory disease, managing their disease with inhalers crucially influences their quality of life. Generic treatment plans could be improved with the aid of computational models that account for patient-specific features such as breathing pattern, lung pathology and morphology. Therefore, we aim to develop and validate an automated computational framework for patient-specific deposition modelling. To that end, an image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images. We evaluated the airway and lung morphology produced by our image processing framework, and assessed deposition compared to in vivo data. The 2D-to-3D image processing reproduces airway diameter to 9% median error compared to ground truth segmentations, but is sensitive to outliers of up to 33% due to lung outline noise. Predicted regional deposition gave 5% median error compared to in vivo measurements. The proposed framework is capable of providing patient-specific deposition measurements for varying treatments, to determine which treatment would best satisfy the needs imposed by each patient (such as disease and lung/airway morphology). Integration of patient-specific modelling into clinical practice as an additional decision-making tool could optimise treatment plans and lower the burden of respiratory diseases.

R. Ostan, S. Varani, A. Giannelli et al.

During the pandemic, telemedicine and telehealth interventions have been leading in maintaining the continuity of care independently of patients’ physical location. However, the evidence available about the effectiveness of the telehealth approach for advanced cancer patients with chronic disease is limited. This interventional randomized pilot study aims to evaluate the acceptability of a daily telemonitoring of five vital parameters (heart rate, respiratory rate, blood oxygenation, blood pressure, and body temperature) using a medical device in advanced cancer patients with relevant cardiovascular and respiratory comorbidities assisted at home. The purpose of the current paper is to describe the design of the telemonitoring intervention in a home palliative and supportive care setting with the objective of optimizing the management of patients, improving both their quality of life and psychological status and the caregiver’s perceived care burden. This study may improve scientific knowledge regarding the impact of telemonitoring. Moreover, this intervention could foster continuous healthcare delivery and closer communication among the physician, patient and family, enabling the physician to have an updated overview of the clinical trajectory of the disease. Finally, the study may help family caregivers to maintain their habits and professional position and to limit financial consequences.

R. L. Goossen, M. Verboom, Marielle M. J. Blacha et al.

Subcutaneous emphysema, pneumothorax and pneumomediastinum are well-known complications of invasive ventilation in patients with acute hypoxemic respiratory failure. We determined the incidences of air leaks that were visible on available chest images in a cohort of critically ill patients with acute hypoxemic respiratory failure due to coronavirus disease of 2019 (COVID-19) in a single-center cohort in the Netherlands. A total of 712 chest images from 154 patients were re-evaluated by a multidisciplinary team of independent assessors; there was a median of three (2–5) chest radiographs and a median of one (1–2) chest CT scans per patient. The incidences of subcutaneous emphysema, pneumothoraxes and pneumomediastinum present in 13 patients (8.4%) were 4.5%, 4.5%, and 3.9%. The median first day of the presence of an air leak was 18 (2–21) days after arrival in the ICU and 18 (9–22)days after the start of invasive ventilation. We conclude that the incidence of air leaks was high in this cohort of COVID-19 patients, but it was fairly comparable with what was previously reported in patients with acute hypoxemic respiratory failure in the pre-COVID-19 era.

Xinlei Deng, S. Friedman, I. Ryan et al.

Highly destructive disasters such as floods and power outages (PO) are becoming more commonplace in the U.S. Few studies examine the effects of floods and PO on health, and no studies examine the synergistic effects of PO and floods, which are increasingly co-occurring events. We examined the independent and synergistic impacts of PO and floods on cardiovascular diseases, chronic respiratory diseases, respiratory infections, and food-/water-borne diseases (FWBD) in New York State (NYS) from 2002 to 2018. We obtained hospitalization data from the NYS discharge database, PO data from the NYS Department of Public Service, and floods events from NOAA. Distributed lag nonlinear models were used to evaluate the PO/floods-health association while controlling for time-varying confounders. We identified significant increased health risks associated with both the independent effects from PO and floods, and their synergistic effects. Generally, the Rate Ratios (RRs) for the co-occurrence of PO and floods were the highest, followed by PO alone, and then floods alone, especially when PO coverage is >75th percentile of its distribution (1.72% PO coverage). For PO and floods combined, immediate effects (lag 0) were observed for chronic respiratory diseases (RR:1.58, 95%CI: 1.24, 2.00) and FWBD (RR:3.02, 95%CI: 1.60, 5.69), but delayed effects were found for cardiovascular diseases (lag 3, RR:1.13, 95%CI: 1.03, 1.24) and respiratory infections (lag 6, RR:1.85, 95%CI: 1.35, 2.53). The risk association was slightly stronger among females, whites, older adults, and uninsured people but not statistically significant. Improving power system resiliency could be a very effective way to alleviate the burden on hospitals during co-occurring floods. We conclude that PO and floods have independently and jointly led to increased hospitalization for multiple diseases, and more research is needed to confirm our findings.

Wouter A. A. de Steenhuijsen Piters, E. Sanders, D. Bogaert

Thian Chee Loh, Yong Kek Pang, Chong Kin Liam et al.

Abstract Metastatic pulmonary calcification (MPC) is characterized by deposition of calcium in the normal lung parenchyma secondary to elevation of serum calcium. Most patients are asymptomatic and routine chest radiograph is not sensitive to make the diagnosis. Further imaging is needed such as computed tomography (CT) which typically shows small centrilobular nodules in the upper lobes. We report a case of a 30‐year‐old woman with end stage kidney disease who was diagnosed with pulmonary tuberculosis which was then revised to metastatic pulmonary calcification. The CT thorax feature for this patient was atypical for metastatic pulmonary calcification where it demonstrated tree‐in‐bud nodules suggestive of infection. The final diagnosis was made based on bronchoalveolar lavage which was culture‐negative for Mycobacterium and transbronchial lung biopsy demonstrating calcium deposition in the interstitium.

O. I. Koniaieva, O. V. Matvieiev

In this article authors analyze patterns of ADRs registered in Crimea Republic in 2013. Data of ADR-reporting forms sent in 2013 by Crimean 21 doctors to Regional office of pharmacovigilance used. Information was recorded in local electronic database called “ARCADe”. 1129 reports from 89 clinics had been analyzed. Most frequently ADRs were found in patients from 46 to 60 years old (22%) and in first year babies (8%). Among adults females suffered from MP ADRs more than males (61%), but among children boys dominated (58%). Most frequent type of serious ADR (37%) was life-threating ones and those, which led to hospitalization. Two reports informed about lethal reactions caused by Ceftriaxone and combination “Pitofenon+Metamizole-sodium”. Causalityassessment revealed that bigger part of ADRs had belonged to “probable” type(43%). During risk factors analysis, we found complicated allergy anamnesis(10%) and polypharmacy (5% of cases). In 22% and 17% of reports, suspended MPswere prescribed for respiratory diseases and cardiologic pathologies treatment(respectively), and in 10% of cases for therapy of infections. Leading clinicalpresentation of ADR was skin rashes with different manifestations, severityand localization (50%), symptoms of involvement of CNS (11%), GIT and bloodcirculation system (7% both) were registered less frequently. In 5% of reports, wefound descri ption of angioneurotic edema and in 1% - symptoms of anaphylacticshock. 68% of ADRs required additional prescri ption of drugs for correctionof reaction`s symptoms. 39% of ADRs were caused by systemic antimicrobialproducts, 13% and 11% by MP influencing on functions of heart and CNS(respectively). In antibiotics group Cephalosporins prevailed and Ceftriaxonecaused most of ADRs, “Zidovudine+Lamivudine” combination was leading inantiretroviral drugs, among cardiological drugs ACE inhibitors prevailed butleading drug was Amplodi pine, and among NSAIDs most reactions were causedby Metamizole-sodium and its combinations. Conclusions. In 2013, the patterns ofADRs in Crimea region did not change and were the same as in previous years.Pharmacovigilance activity is high and amount of received reports satisfies WHOrequirements. Found patterns will ease formation of local and national strategyfor prevention of ADRs