Characteristics of Pulmonary Auscultation in Patients with 2019 Novel Coronavirus in China

Assessment of Clinical Indicators Registered on Admission to the Hospital Related to Mortality Risk in Cancer Patients with COVID-19

BACKGROUND

Oncology patients are a particularly vulnerable group to the severe course of COVID-19 due to, e.g., the suppression of the immune system. The study aimed to find links between parameters registered on admission to the hospital and the risk of later death in cancer patients with COVID-19.

METHODS

The study included patients with a reported history of malignant tumor (n = 151) and a control group with no history of cancer (n = 151) hospitalized due to COVID-19 between March 2020 and August 2021. The variables registered on admission were divided into categories for which we calculated the multivariate Cox proportional hazards models.

RESULTS

Multivariate Cox proportional hazards models were successfully obtained for the following categories: Patient data, Comorbidities, Signs recorded on admission, Medications used before hospitalization and Laboratory results recorded on admission. With the models developed for oncology patients, we identified the following variables that registered on patients' admission were linked to significantly increased risk of death. They are: male sex, presence of metastases in neoplastic disease, impaired consciousness (somnolence or confusion), wheezes/rhonchi, the levels of white blood cells and neutrophils.

CONCLUSION

Early identification of the indicators of a poorer prognosis may serve clinicians in better tailoring surveillance or treatment among cancer patients with COVID-19.

Simultaneous concept analysis of nursing diagnoses related to respiratory function

AIM

To perform a simultaneous concept analysis of the concepts associated to nursing diagnoses ineffective airway clearance, ineffective breathing pattern, and impaired gas exchange.

BACKGROUND

Concepts about respiratory manifestations need to be well defined, especially in the current pandemic scenario. For that, the simultaneous concept analysis can help in the clarity and differentiation of similar concepts.

METHODS

A concept analysis using the Walker and Avant approach and an integrative review. Data were collected by a group of nurses through a literature review. The group identified 10 articles that met the inclusion criteria and complemented the understanding of the concepts analysed through the sequential description of respiratory physiology in technical books.

RESULTS

The final list included 28, 22, and 21 clinical indicators for ineffective breathing pattern, impaired gas exchange, and ineffective airway clearance, respectively. The former, the final proposal incorporated 13 indicators that were pointed out by the group and 15 defining characteristics of NANDA-International. For Impaired gas exchange, the indicator "decreased oxygen saturation" was included; among the defining characteristics of NANDA-International, "abnormal arterial blood gases" was excluded, and "abnormal breathing pattern" was subdivided into "alterations in respiratory depth," "bradypnea," "tachypnea," and "change in respiratory rhythm." The latter, only the "wide-eyed" was removed from the final list of clinical indicators, which subsequently consisted of nine indicators suggested by the group and 12 defining characteristics.

CONCLUSION

This concept analysis may aid in the process of differentiation for ineffective airway clearance, ineffective breathing pattern, and impaired gas exchange, and aid in safer diagnostic inference. This concept analysis can support the understanding of respiratory nursing diagnoses, helping nurses to identify and differentiate them more safely.

Cardiac auscultation predicts mortality in elderly patients admitted for COVID-19

INTRODUCTION

COVID-19 has had a great impact on the elderly population. All admitted patients underwent cardiac auscultation at the Emergency Department. However, to our knowledge, there is no literature that explains the implications of cardiac auscultation at the Emergency Department.

MATERIAL AND METHODS

Data collection from our hospital records. Our cohort consists of 300 admissions with a mean age of 81.6 years and 50.7% men.

RESULTS

Pathological cardiac auscultation at the Emergency Department was a risk factor for in-hospital mortality (RR = 1.9; 95% CI 1.3-2.8), heart failure (RR = 3.2; 95% CI = 1.8-5.6), respiratory failure (RR = 1.8; 95% CI = 1.3-2.5), acute kidney injury (RR = 2.6; 95% CI = 2-3.2), and ICU admission (RR = 3.3; 95% CI = 1.3-8.2). The findings in patients with pathological cardiac auscultation were that oxygen saturation in the Emergency Department, arterial pH, and HCO3^- were significantly lower, and the ALT/GPT, LDH, and lactate determinations were significantly higher, which is compatible and correlates with the fact that the main variable is indeed a risk factor for a more severe clinical course. Among the findings from pathological auscultation, arrhythmic tone/arrhythmia was the most frequent (50%) and a risk factor for in-hospital mortality (RR = 2.3; 95% CI = 1.6-3.4). Logistic regression was performed from a multivariate analysis that showed that the initial ex novo arrhythmia correlated with pathological cardiac auscultation is an independent risk factor for in-hospital mortality.

CONCLUSION

Continuous rhythm monitoring makes it possible to detect ex novo arrhythmias and act proactively, and to offer greater care and attention to these patients who have a higher risk of in-hospital mortality and a worse prognosis. Cardiac auscultation can alert us in order to perform more electrocardiograms in these patients and thus have better monitoring.

Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool

This study was sought to investigate the feasibility of using smartphone-based breathing sounds within a deep learning framework to discriminate between COVID-19, including asymptomatic, and healthy subjects. A total of 480 breathing sounds (240 shallow and 240 deep) were obtained from a publicly available database named Coswara. These sounds were recorded by 120 COVID-19 and 120 healthy subjects via a smartphone microphone through a website application. A deep learning framework was proposed herein that relies on hand-crafted features extracted from the original recordings and from the mel-frequency cepstral coefficients (MFCC) as well as deep-activated features learned by a combination of convolutional neural network and bi-directional long short-term memory units (CNN-BiLSTM). The statistical analysis of patient profiles has shown a significant difference (p-value: 0.041) for ischemic heart disease between COVID-19 and healthy subjects. The Analysis of the normal distribution of the combined MFCC values showed that COVID-19 subjects tended to have a distribution that is skewed more towards the right side of the zero mean (shallow: 0.59±1.74, deep: 0.65±4.35, p-value: <0.001). In addition, the proposed deep learning approach had an overall discrimination accuracy of 94.58% and 92.08% using shallow and deep recordings, respectively. Furthermore, it detected COVID-19 subjects successfully with a maximum sensitivity of 94.21%, specificity of 94.96%, and area under the receiver operating characteristic (AUROC) curves of 0.90. Among the 120 COVID-19 participants, asymptomatic subjects (18 subjects) were successfully detected with 100.00% accuracy using shallow recordings and 88.89% using deep recordings. This study paves the way towards utilizing smartphone-based breathing sounds for the purpose of COVID-19 detection. The observations found in this study were promising to suggest deep learning and smartphone-based breathing sounds as an effective pre-screening tool for COVID-19 alongside the current reverse-transcription polymerase chain reaction (RT-PCR) assay. It can be considered as an early, rapid, easily distributed, time-efficient, and almost no-cost diagnosis technique complying with social distancing restrictions during COVID-19 pandemic.

VECTOR: An algorithm for the detection of COVID-19 pneumonia from velcro-like lung sounds

The coronavirus disease 2019 (COVID-19) has severely stressed the sanitary systems of all countries in the world. One of the main issues that physicians are called to tackle is represented by the monitoring of pauci-symptomatic COVID-19 patients at home and, generally speaking, everyone the access to the hospital might or should be severely reduced. Indeed, the early detection of interstitial pneumonia is particularly relevant for the survival of these patients. Recent studies on rheumatoid arthritis and interstitial lung diseases have shown that pathological pulmonary sounds can be automatically detected by suitably developed algorithms. The scope of this preliminary work consists of proving that the pathological lung sounds evidenced in patients affected by COVID-19 pneumonia can be automatically detected as well by the same class of algorithms. In particular the software VECTOR, suitably devised for interstitial lung diseases, has been employed to process the lung sounds of 28 patient recorded in the emergency room at the university hospital of Modena (Italy) during December 2020. The performance of VECTOR has been compared with diagnostic techniques based on imaging, namely lung ultrasound, chest X-ray and high resolution computed tomography, which have been assumed as ground truth. The results have evidenced a surprising overall diagnostic accuracy of 75% even if the staff of the emergency room has not been suitably trained for lung auscultation and the parameters of the software have not been optimized to detect interstitial pneumonia. These results pave the way to a new approach for monitoring the pulmonary implication in pauci-symptomatic COVID-19 patients.

Human-like tele-health robotics for older adults – A preliminary feasibility trial and vision

Introduction

The global increase of the aging population presents major challenges to healthcare service delivery. Further, the COVID-19 pandemic exposed older adults’ vulnerability to rapid deterioration of health when deprived of access to care due to the need for social distancing. Robotic technology advancements show promise to improve provision of quality care, support independence for patients and augment the capabilities of clinicians to perform tasks remotely.

Aim

This study explored the feasibility and end-user acceptance of using a novel human-like tele-robotic system with touch feedback to conduct a remote medical examination and deliver safe care.

Method

Testing of a remotely controlled robot was conducted with in-person clinician support to gather ECG readings of 11 healthy participants through a digital medical device. Post-study feedback about the system and the remote examinations conducted was obtained from study participants and study clinicians.

Results

The findings demonstrated the system’s capability to support remote examination of participants, and validated the system’s perceived acceptability by clinicians and end-users who all reported feeling safe interacting with the robot and 72% preferred remote robotic exam over in-person examination.

Conclusion

This paper discusses potential implications of robot-assisted telehealth for patients including older adults who are precluded from having in-person medical visits due to geographic distance or mobility, and proposes next steps for advancing robot-assisted telehealth delivery.

Improving the early identification of COVID-19 pneumonia: a narrative review

Delayed presentation of COVID-19 pneumonia increases the risk of mortality and need for high-intensity healthcare. Conversely, early identification of COVID-19 pneumonia grants an opportunity to intervene early and thus prevent more complicated, protracted and less successful hospital admissions. To improve the earlier detection of COVID-19 pneumonia in the community we provide a narrative review of current evidence examining the clinical parameters associated with early disease progression. Through an evolving literature review, we examined: the symptoms that may suggest COVID-19 progression; the timing of deterioration; the utility of basic observations, clinical examination and chest X-ray; the value of postexertion oxygen saturations; and the use of CRP to monitor disease progression. We go on to discuss the challenges in monitoring the COVID-19 patient in the community and discuss thresholds for further assessment. Confusion, persistent fever and shortness of breath were identified as worrying symptoms suggestive of COVID-19 disease progression necessitating urgent clinical contact. Importantly, a significant proportion of COVID-19 pneumonia patients appear not to suffer dyspnoea despite severe disease. Patients with this asymptomatic hypoxia seem to have a poorer prognosis. Such patients may present with other signs of hypoxia: severe fatigue, exertional fatigue and/or altered mental status. We found duration of symptoms to be largely unhelpful in determining risk, with evidence of deterioration at any point in the disease. Basic clinical parameters (pulse, respiratory rate, blood pressure, temperature and oxygen saturations (SpO₂)) are likely of high value in detecting the deteriorating community COVID-19 patient and/or COVID-19 mimickers/complications (eg, sepsis, bacterial pneumonia and pulmonary embolism). Of these, SpO₂ carried the greatest utility in detecting COVID-19 progression. CRP is an early biochemical parameter predictive of disease progression and used appropriately is likely to contribute to the early identification of COVID-19 pneumonia. Identifying progressive COVID-19 in the community is feasible using basic clinical questions and measurements. As such, if we are to limit the mortality, morbidity and the need for complicated, protracted admissions, monitoring community COVID-19 cases for signs of deterioration to facilitate early intervention is a viable strategy.

Conditional GAN based augmentation for predictive modeling of respiratory signals

Respiratory illness is the primary cause of mortality and impairment in the life span of an individual in the current COVID-19 pandemic scenario. The inability to inhale and exhale is one of the difficult conditions for a person suffering from respiratory disorders. Unfortunately, the diagnosis of respiratory disorders with the presently available imaging and auditory screening modalities are sub-optimal and the accuracy of diagnosis varies with different medical experts. At present, deep neural nets demand a massive amount of data suitable for precise models. In reality, the respiratory data set is quite limited, and therefore, data augmentation (DA) is employed to enlarge the data set. In this study, conditional generative adversarial networks (cGAN) based DA is utilized for synthetic generation of signals. The publicly available repository such as ICBHI 2017 challenge, RALE and Think Labs Lung Sounds Library are considered for classifying the respiratory signals. To assess the efficacy of the artificially created signals by the DA approach, similarity measures are calculated between original and augmented signals. After that, to quantify the performance of augmentation in classification, scalogram representation of generated signals are fed as input to different pre-trained deep learning architectures viz Alexnet, GoogLeNet and ResNet-50. The experimental results are computed and performance results are compared with existing classical approaches of augmentation. The research findings conclude that the proposed cGAN method of augmentation provides better accuracy of 92.50% and 92.68%, respectively for both the two data sets using ResNet 50 model.

Automated lung sound analysis using the LungPass platform: a sensitive and specific tool for identifying lower respiratory tract involvement in COVID-19

Lower respiratory tract (LRT) involvement, observed in about 20% of patients suffering from coronavirus disease 2019 (COVID-19), is associated with a more severe clinical course, adverse outcomes and long-term sequelae [1, 2]. By pointing out people at risk of deterioration, early identification of LRT involvement could facilitate targeted and timely administration of treatments that could alter short- and long-term disease outcomes [3]. While imaging represents the gold standard diagnostic test for LRT involvement, it is associated with a potentially avoidable radiation burden and may not be easily accessible in some treatment settings, such as primary care [4]. Alternatively, oxygen desaturation appears to be a specific, but not sensitive marker, since ground glass changes or consolidation are often observed in the absence of hypoxia [5–7]. The sensitivity of chest auscultation in identifying LRT involvement has been evaluated in limited populations and varies [8, 9], possibly to some extent due to variable skill among the assessors.

Automated lung sound analysis using the #LungPass platform is a sensitive and specific tool for identifying lower respiratory tract involvement in COVID-19https://bit.ly/3tyAgOD

Asthma and COVID-19: Emphasis on Adequate Asthma Control

Asthmatics are at an increased risk of developing exacerbations after being infected by respiratory viruses such as influenza virus, parainfluenza virus, and human and severe acute respiratory syndrome coronaviruses (SARS-CoV). Asthma, especially when poorly controlled, is an independent risk factor for developing pneumonia. A subset of asthmatics can have significant defects in their innate, humoral, and cell-mediated immunity arms, which may explain the increased susceptibility to infections. Adequate asthma control is associated with a significant decrease in episodes of exacerbation. Because of their wide availability and potency to promote adequate asthma control, glucocorticoids, especially inhaled ones, are the cornerstone of asthma management. The current COVID-19 pandemic affects millions of people worldwide and possesses mortality several times that of seasonal influenza; therefore, it is necessary to revisit this subject. The pathogenesis of SARS-CoV-2, the virus that causes COVID-19, can potentiate the development of acute asthmatic exacerbation with the potential to worsen the state of chronic airway inflammation. The relationship is evident from several studies that show asthmatics experiencing a more adverse clinical course of SARS-CoV-2 infection than nonasthmatics. Recent studies show that dexamethasone, a potent glucocorticoid, and other inhaled corticosteroids significantly reduce morbidity and mortality among hospitalized COVID-19 patients. Hence, while we are waiting for more studies with higher level of evidence that further narrate the association between COVID-19 and asthma, we advise clinicians to try to achieve adequate disease control in asthmatics as it may reduce incidences and severity of exacerbations especially from SARS-CoV-2 infection.

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.