Role of telemedicine in anesthesia: Are we ready yet?

Telemedicine is a modality which utilizes technology to provide and support health care across large distances. It has redefined the practices of medicine in many specialties and continues to be a boon for clinicians on many frontiers. Its role in the branch of anesthesia remains largely unexplored but has shown to be beneficial in all the three phases: pre-operative, intra-operative, and post-operative. Now time has come that anesthesiologists across the globe reassess their strategies and utilize the telemedicine facilities in the field of anesthesia.Copyright © 2021 EDP Sciences. All rights reserved.

PREDICTION OF MORTALITY USING CLUSTERS BASED ON CLINICAL AND LAB PARAMETERS: DATA FROM 2072 PATIENTS FROM INSPIRE COHORT

BackgroundLupus is a heterogenous diseases which results in significant premature mortality. Most studies have evaluated risk factors for lupus mortality using regression models which considers the phenotype in isolation. Identifying clusters of patients on the other hand may help overcome the limitations of such analyses.ObjectivesThe objectives of this study were to describe the causes of mortality and to analyze survival across clusters based on clinical phenotype and autoantibodies in patients of the Indian SLE Inception cohort for Research (INSPIRE)MethodsOut of all patients, enrolled in the INSPIRE database till March 3st 2022, those who had <10% missing variables in the clustering variables were included in the study. The cause of mortality and duration between the recruitment into the cohort and mortality was calculated. Agglomerative unsupervised hierarchical cluster analysis was performed using 25 variables that define SLE phenotype in clinical practice. The number of clusters were fixed using the elbow and silhouette methods. Survival rates were examined using Cox proportional hazards models: unadjusted, adjusted for age at disease onset, socio-economic status, steroid pulse, CYC, MMF usage and cluster of the patients.ResultsIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting.Out of 2211 patients in the cohort, 2072 were included into the analysis. The median (IQR) age of the patients was 26 (20-33) years and 91.7% were females. There were 288 (13.1%) patients with juvenile onset lupus. The median (range) duration of follow up of the patients was 37 (6-42) months. There were 170 deaths, with only 77 deaths occurring in a health care setting. Death within 6 months of enrollment occured in in 80 (47.1%) patients. Majority (n=87) succumbed to disease activity, 23 to infections, 24 to coexisting disease activity and infection and 21 to other causes. Pneumonia was the leading cause of death (n=24). Pneumococcal infection led to death in 11 patients and SARS-COV2 infection in 7 patients. The hierarchical clustering resulted in 4 clusters and the characteristics of these clusters are represented in a heatmap (Figure-1A,B). The mean (95% confidence interval [95% CI] survival was 39.17 (38.45-39.90), 39.52 (38.71-40.34), 37.73 (36.77-38.70) and 35.80 (34.10-37.49) months (p<0.001) in clusters 1, 2, 3 and 4, respectively with an HR (95% CI) of 2.34 (1.56, 3.49) for cluster 4 with cluster 1 as reference(Figure 1C). The adjusted model showed an HR (95%CI) for cluster 4 of 2.22 (1.48, 3.22) with an HR(95%CI) of 1.78 (1.29, 2.45) for low socioeconomic status as opposed to a high socioeconomic status (Table 1).ConclusionIndian patients with lupus have significant early mortality and the majority of deaths occurs outside the hospital setting. Disease activity as determined by the traditional activity measures may not be sufficient to understand the true magnitude of organ involvement resulting in mortality. Clinically relevant clusters can help clinicians identify those at high risk for mortality with greater accuracy.Table 1.Univariate and multivariate Cox regression models predicting mortalityUnivariateMultivariateVariablesHazard ratio (95% Confidence interval)P valueHazard ratio (95% Confidence interval)P valueCluster1Reference-Reference-20.87 (0.57, 1.34)0.5320.89 (0.57, 1.38)0.59831.22 (0.81, 1.84)0.3371.15 (0.76, 1.73)0.51342.34 (1.56, 3.49)<0.0012.22(1.48, 3.22)<0.001Socioeconomic statusLower1.78 (1.29, 2.45)<0.001Pulse steroidYes1.6 (0.99, 2.58)0.051MMFYes0.71 (0.48, 1.05)0.083CYCYes1.42 (0.99, 2.02)0.052Proliferative LNYes0.99 (0.62, 1.56)0.952Date of birth age0.99 (0.98, 1.01)0.657CYC- cyclophosphamide, MMF- Mycophenolate mofetilFigure 1.A. Agglomerative clustering dendrogram depicting the formation of four clusters. B.Heatmap depicting distribution of variables used in clustering C. Kaplan-Meier curve showing the survival function across the 4 clusters[Figure omitted. See PDF]REFERENCES:NIL.Acknowledgements:NIL.Disclosure of InterestsNone eclared.

COVID bell - A smart doorbell solution for prevention of COVID-19

The article introduces a novel strategy for efficiently mitigating COVID-19 distribution at the local level due to contact with any surfaces. Our project aims to be a critical safety shield for the general people in the fight against the epidemic. An ultrasonic sensor is integrated with the automated doorbell system to ring the doorbell with a hand motion. A temperature sensor Mlx90614 is also included in the system, which records the temperature of the person standing in front of the door. The device also includes a camera module that captures the image of the person standing at the front entrance. The captured image is processed through an ML model which runs at over 30 fps to detect whether or not the person is wearing a mask. The image and the temperature of the person standing outside are sent to the owner through the configured iOS application. If the person outside is wearing a mask, one can open the door through the app itself and permit the entry of the person standing outside thereby integrating the edge device with an app for a better user experience. The system helps in reducing physical contact, and the results obtained are at par with the already existing solutions and provide a few advantages over them. © 2023 the author(s), published by De Gruyter.

Impact of Ayush Alternative Medicine Interventions in the COVID-19 Pandemic

The five Indian systems of alternative and complementary medicine (Ayurveda, Yoga, Naturopathy, Unani, Siddha, and Homeopathy) are frequently used to treat "COVID-19" throughout the country (referred to simply as "AYUSH"). At the outset of the pandemic, "The Ministry of AYUSH" (the governing authority of the Indian system of medicine) issued recommendations for the use of particular herbs and acts to improve immunity, based on the advice of an advisory council of "AYUSH" doctors and scant evidence. Some of these suggestions involved using traditional herbs and practices, which have been used for centuries and are effective in treating a wide range of conditions, including fever, sore throat, and breathing difficulties, as well as acting as a non-specific leniency enhancer thanks to their anti-viral, anti-bacterial, and anti-microbial properties. A number of the formulations recommended here-including AYUSH 64, Chyawanprash, Guduchi Ghanavati, Arsenic Album, Kabasur Kudineer, and Nilavembu Kudineer-have been the subject of scientific research into their putative prophylactic or therapeutic benefits.

COVID-19: Clinical Spectrum—It's Multiorgan Syndrome

COVID-19 outbreak caused by SARS-CoV-2 has emerged as a global challenge for the entire health care system worldwide. It has affected the developed as well as developing countries markedly. What began as pneumonia-like illness later evolved into a multiorgan disease leading to severe morbidity and even death. The clinical data and global literature explicitly suggest that in addition to respiratory symptoms, the COVID-19 patients may present with hematological, cardiovascular, renal, gastrointestinal, neurological, ocular, and skin manifestations. The underlying mechanism for multisystem involvement is the expression of angiotensin-converting enzyme 2 (ACE2) receptors at multiple extrapulmonary tissues. Injury to various organs may be attributed to cytokine storms or to disturbances of coagulation and vascular endothelium. The aim of this review is to emphasize the impact of SARS CoV-2 infection on not only the lungs but other organ systems too. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021.

Remdesivir Reduces Mortality in Immunocompromised Patients Hospitalized for Covid-19

Background: There is limited information on effectiveness of COVID-19 therapies in immunocompromised patients, who are at higher risk of hospitalizations, complications, and mortality due to COVID-19. We examined hospital all-cause mortality for early RDV use vs. no RDV use among immunocompromised COVID-19 patients across several distinct dominant variants of concern (VOC) periods: pre-Delta (Dec'20-Apr'21), Delta (May-Nov'21) and Omicron (Dec'21-Apr'22). Method(s): Using the Premier Healthcare Database, we identified adults with an immunocompromised condition (cancer, solid organ and hematopoietic stem cell transplant, hematologic malignancies, primary immunodeficiencies, asplenia, bone marrow failure/aplastic anemia, severe combined immunodeficiencies or HIV), hospitalized with a primary diagnosis of COVID-19. Patients treated with RDV in first 2 days of admission vs. those not treated with RDV during the hospitalization were matched using 1:1 preferential withinhospital propensity matching with replacement. Patients were excluded if discharged within 3 days of RDV initiation. Cox Proportional Hazards Model was used to examine time to 14-and 28-day mortality. Result(s): Overall (Dec'20-Apr'22), 14,169 RDV-treated patients were matched to 5,341 unique non-RDV patients. Post-matching balance was achieved with 59% being 65+ years, 40.5% with no supplementary oxygen charges, 39% received low-flow oxygen, 19% on high-flow oxygen/non-invasive ventilation and 1.5% on invasive mechanical ventilation/ECMO at baseline. During the study period, unadjusted mortality rate was significantly lower for RDV patients at 14 days (11% [95% CI: 11%-12%] vs 15% [15%-16%];p< .0001) and 28 days (18% [17%-18%];p< .0001 vs 22% [22%-23%];p< .0001) as compared to patients that did not receive RDV. After adjusting for baseline and clinical covariates, 14-day results showed that RDV had significantly lower mortality risk compared to non-RDV across all VOC periods [overall (30% lower risk), pre-delta (41%), Delta (23%), Omicron (25%)]. Similarly, 28-day results showed that RDV had significantly lower mortality risk compared to non-RDV across all VOC periods [overall (25%), pre-delta (35%), Delta (21%), Omicron (16%)] (Fig). Conclusion(s): Timely initiation of RDV in first two days of hospital admission demonstrated significant mortality reduction in immunocompromised patients hospitalized with primary diagnosis of COVID-19. RDV demonstrated consistent benefit in an immunocompromised cohort across all variant periods of the pandemic.

Histomorphological Spectrum of Lung Lesions at Autopsy-A Tertiary Care Centre Experience

Background and aims: A wide variety of pathological conditions involve the lungs. In autopsy, the lungs are examined for disease, injury and other findings suggesting cause of death or related changes.Aims & Objectives: The present study aimed to study the histomorphological spectrum of lung lesions at autopsy and to assess the frequency of different types of lesions;and to associate histomorphological changes with cause of death.Material and Methods: It was a one-year observational study conducted in the Department of Pathology, Govt. Medical College, Jammu. Lung tissue pieces from all medicolegal autopsies received were fixed, examined grossly, processed;paraffin embedded sections obtained were stained with Hematoxylin and Eosin stain and examined under microscope. Findings were recorded and tabulated. Result(s): Out of 264 cases, males were predominantly affected (84%);median age was 38 years. The various changes observed were congestion (68%), edema (45.4%), pneumonia (5%), granulomatous inflammation (3%), diffuse alveolar damage (1.5%), haemorrhage (14.4%), interstitial changes (60%), malaria (0.4%) and malignancy (0.4%). Natural deaths were the commonest cause (75, 28%) followed by asphyxial deaths (65, 24.6%). Conclusion(s): Histopathological examination of lung autopsies highlights many incidental findings, establishes underlying cause of death, serves as a learning tool and also holds scope for detection of newer diseases.Copyright © 2023 JK Science.

Incidence of Long Covid Symptoms in Patients Previously Hospitalized for Covid-19

Background: Long COVID, also known as post-acute sequelae of COVID (PASC), affects more than 144 million people globally. While there is no broadly accepted consensus on a definition for the term "long COVID," studies have found symptoms persist or begin weeks or months after the end of SARS-CoV-2 infection. This study assessed the incidence of codes found in medical claims and hospital chargemasters that were consistent with long COVID symptoms commonly found in the literature. Method(s): Using the HealthVerity database, which provides closed claims and linked hospital chargemaster data on more than 25 million US patients, we examined patients aged 12 and above hospitalized between May 1, 2020 and September 30, 2021 with a diagnosis of COVID-19 who had at least 365 days of closed medical claims enrollment prior to index hospitalization admission and 90 days after admission, and did not have a long COVID diagnosis (ICD-10- CM U09.9) prior to the index hospitalization. Patients were allowed to have symptoms prior to hospitalization. The assessment period for the outcomes, which included 10 symptoms, was 90 days to 270 days after the date of hospitalization. Incidence rate per 100 person-years was calculated as the number of patients with the outcome divided by total person-time contributed (90 days after admission to the minimum of the following: outcome, inpatient death, disenrollment, end of data (April 30, 2022), or 270 days after admission). Result(s): The dataset included 3,661,303 patients with an inpatient hospitalization during the study period. The final study cohort included 44,922 patients hospitalized with COVID-19, 20,627 of whom experienced at least one of the long COVID symptoms. Anosmia and dysgeusia were the rarest events captured in medical claims. More commonly found symptoms were joint pain, fatigue and breathlessness (see table). Conclusion(s): This study examined diagnosed symptoms commonly found posthospitalization among COVID-19 patients and reported the incidence of these symptoms in a representative population. The start period of long COVID used in this study (90 days post hospitalization) is consistent with the WHO definition of long COVID. In the absence of an understanding of the pathophysiology of long COVID, the use of diagnosed symptoms to define long COVID has the advantage of ease of use and availability of data. Further studies of additional symptoms and predictors of long COVID are needed. (Figure Presented).

Impacts of COVID-19 on Future Preferences Toward Telework

This paper presents a study in capturing the impacts of the mandatory pandemic-induced telework practice on workers' perceptions of the benefits, challenges, and difficulties associated with telecommuting and how those might influence their preference for telework in the future. Data was collected through an online survey conducted in South Florida in May 2020. Survey data showed that telework indices (either measured through actual behavior or stated preference) before, during, and after the pandemic were heterogeneous across socio-economic, demographic, and attitudinal segments. Before the outbreak, males, full-time students, those with PhD degrees, and high-income people showed higher percentages of involvement in jobs with a telework option. They also had higher pro-technology, pro-online education, workaholic, and pro-telework attitudes. During the pandemic, professional/managerial/technical jobs as well as jobs with lower physical-proximity measures showed the highest telework frequency. In view of future telework preferences, our analysis showed that those who were more pro-telework, pro-technology, and showed less dislike of telework dislike preferred higher telework frequency. A structural equation model was developed to assess the impacts of different predictors on telework behavior before the pandemic and preferences after the pandemic. While telework frequency before the pandemic was highly affected by the pro-telework attitude, the after-pandemic preferences were influenced by several other attitudes such as dislike telework, enjoy interaction, workaholic, as well as productivity factors. This might confirm the assumption that the mandatory practice through the pandemic has provided employees more experiences with work-from-home arrangements, which could reshape decisions and expectations around telework adoption in the future. © National Academy of Sciences: Transportation Research Board 2021.

Eco-Friendly Hierarchical Nanoporous Microfiber Respirator Filters Fabricated Using Rotary Jet Spinning Technology (RJS)

The current global health crisis caused by the SARS-CoV-2 virus (COVID-19) has increased the use of personal protective equipment, especially face masks, leading to the disposal of a large amount of plastic waste causing an environmental crisis due to the use of non-biodegradable and non-recyclable polymers, such as polypropylene and polyester. In this work, an eco-friendly biopolymer, polylactic acid (PLA), was used to manufacture hierarchical nanoporous microfiber biofilters via a single-step rotary jet spinning (RJS) technique. The process parameters that aid the formation of nanoporosity within the microfibers were discussed. The microstructure of the fibers was analyzed by scanning electron microscopy (SEM) and a noninvasive X-ray microtomography (XRM) technique was employed to study the three-dimensional (3D) morphology and the porous architecture. Particulate matter (PM) and aerosol filtration efficiency were tested by OSHA standards with a broad range (10-1000 nm) of aerosolized saline droplets. The viral penetration efficiency was tested using the phi X174 bacteriophage (similar to 25 nm) with an envelope, mimicking the spike protein structure of SARS-CoV-2. Although these fibers have a similar size used in N95 filters, the developed biofilters present superior filtration efficiency (similar to 99%) while retaining better breathability (<4% pressure drop) than N95 respirator filters.

Marching Ahead through the Pandemic: Continuing Anesthesia Services in COVID Era-Our Experience from a Tertiary Care Cancer Centre

Introduction This paper aims to provide an overview of the administrative and clinical preparations done in a tertiary care cancer hospital in continuing operation theatre (OT) services through the COVID pandemic.Methods Retrospective data collection, data for the past 1.5 years (COVID period) March 2020 to August 2021 were compared to surgical output for a similar duration of time before the COVID era (September 2018-February 2020).Results A total of 1,022 surgeries were done under anesthesia in the COVID period as against 1,710 surgeries done in a similar time frame in the pre-COVID era. Overall, we saw a 40%drop in the total number of cases. Thorax, abdominal, and miscellaneous surgeries (soft tissue sarcomas, urology, and gyneconcology) saw a maximum fall in numbers;however, head and neck cases saw an increase in numbers during the pandemic. Surgical morbidity and mortality were similar in the COVID and pre-COVID era. No cases of severe COVID infection were reported among the healthcare staff working in OT.Discussion We could successfully continue our anesthesia services with minimal risk to healthcare staff throughout the pandemic by adopting major guidelines in a pragmatic and practical approach with minor changes to suit our setup.

Covid-19 Pandemic Predictive System Using Machine Learning

Corona -virus disease commonly known as COVID-19 that outbreak in late December 2019 is continuously spreading worldwide and infecting people due to which it's required to analysis research on the expansion of CODID-19.In this research, a more improved model. HYBRID ARTIFICAL MODEL (AI) is suggested for prediction. In conventional model, it treats similar infection rate for all people, an improvised ISI (improved susceptible-infected) is suggested to gauge the infection rate to calculate the development mode. We have build the hybrid AI model by using natural language processing(NLP) model and long short-term memory(LSTM) network modules inside ISI module.According to the attentive results, it represents more infections from three to eight days.In comparison to both the models , our developed new AI model can remarkably reduces the prediction result's error and prevail the mean percentage errors with different percentage for the six consecutive days in different countries.For example-China , Italy, France, etc. © 2022 IEEE.

BEHAVIORAL INSIGHT FOR COVID-19 AMONG STAFFS OF A TEACHING HOSPITAL IN KUMAUN REGION, UTTARAKHAND

Introduction: COVID-19 is caused by a novel virus and is known to be an infectious respiratory disease affecting more than 200 countries and union territories of the world. 80% of the patients are shown to have mild to moderate disease. Illness prevention is the mainstay of getting away from the condition with the non-availability of vaccines or medicine. Methods: The aim & objective of this study is to describe the behavioral insight (knowledge, attitude & practice) regarding preventive measures of COVID-19 among the teaching hospital staff. An institutional-based descriptive study was conducted among 168 staff, including lab technicians, doctors, staff nurses, and other supporting staff of a tertiary hospital using a convenient sampling technique. The data were obtained by interviewing staff with a pre-tested semi-structured schedule. Result: The study results showed that most of the respondents were found to have good knowledge, attitude, and practice towards preventive measures such as social distancing, hand hygiene, face mask, and PPE Kit. 88% of participants were using 3 layered medical masks, and most of them (63%) were using a regular wash of face mask. Among most respondents, no gap between knowledge and practice was seen;however, proper use, handling, and disposal of face masks and other PPE was a concern. Conclusion: Therefore, there is a need to improve the knowledge of COVID-19 among healthcare workers, which is possible with the availability of good quality Information Education and Communication (IEC) materials. © 2022 IJPH.

Hollowing of Delayed Surgical Obturator in Maxillectomy Defects Secondary to Mucormycosis in Post-COVID-19 Era: A Case Series

During the second wave of the coronavirus disease 2019 (COVID-19) pandemic in India, there was an increase in the surge of mucormycosis cases secondary to COVID-19 infection. Aggressive surgical debridement is the most common treatment modality opted for its treatment that leads to extended maxillary defects. Obturating such defects may be very challenging from a prosthodontic point of view, as larger defect sizes and fewer retentive areas make it difficult to retain the prosthesis. A delayed surgical obturator is a prosthesis that is placed 6–10 days after the surgery, mainly used to minimize postoperative complications. It reproduces the contour of the palate and allows the patient to resume a regular diet. It also assists in normal speech. But in large surgical defects, the increased obturator's weight makes it uncomfortable and nonretentive for the patient, compromising its function. Consequently, in this case series, hollow bulb obturators are fabricated to decrease the weight of the prosthesis and to improve the function by establishing palatal contour. In case 1, hollowing was done using thermoplastic polyvinyl chloride (PVC) sheets and in case 2 acrylic shim was used. In both cases two-layer techniques were used, as in large defects if we use a single-layer technique it will either increase the weight of the prosthesis or may fail to create a palatal contour that further compromises the function. The techniques followed here are easy to use and less time-consuming. © The Author(s). 2022.

Association of Angiotensin Converting Enzyme 2 (ACE2) Gene Polymorphisms with Disease Severity in Indian Covid-19 Patients: A Next-gen Sequencing Approach

The clinical picture of COVID-19 ranges from asymptomatic to mild, moderate or severe disease sometimes leading to death. Differences in the interaction between SARS-CoV-2 Spike (S) protein and angiotensin converting enzyme 2 (ACE2) protein may lead to differences in disease severity. We studied whether ACE2 polymorphisms are associated with disease severity and outcome. We recruited 114 patients between July 2020 - March 2022 confirmed positive by RTPCR for COVID-19 with different degrees of severity (21 mild, 29 moderate, 34 severe, 30 death) and 30 controls (10 non-vaccinated+ 20 vaccinated) who were RT-PCR negative inspite of high-risk contact. Next-gen sequencing was done on MiSeq (Illumina) using amplicon-based targeted sequencing approach using a custom-designed panel to sequence all the exons of ACE2 gene. SPSS ver.26 was used for analysis. The following ACE2 variants were identified on the Local Run Manager (LRM) software from Illumina: (i) rs2285666 (c.439+4G>A) splice region variant, in controls (60%) and Patients (45.8%), (ii) rs4646140 (c.802+24G>A) intronic variant in 4/114 patients and 1/30 controls, (iii) rs41303171 ( c.2158A>G) missense variant in 2/114 patients, (iv) rs536749578 (c.2114+9T>C) intron variant, (v) rs763994205 (c.868A>C) missense variant and (vi) rs7595907 (c.656G>A) missense variant in 1/114 patient each only. rs2285666 was observed in equal frequency ( 60%) in vaccinated and non-vaccinated controls. rs2285666 was observed amongst different severity groups: Mild (80.95%), Moderate (37.93%), Severe (44.11%), and Death (56.67%) revealing association with disease severity, probably having a protective effect. However, these results need to be confirmed on larger sample sizes.

Modern Mortuary Complex Plan in Covid-19 Era

The COVID-19 pandemicis the most burning health emergency worldwide now a days and all health professionals are called to give support in the diagnosis, treatment of patients and management of dead bodies affected by this disease. Different modes of transmission of corona virus is well established, however aerosol generating procedure has significant importance for mortuary during autopsy. The risk of infection can be minimized by using standard universal precautions for autopsy dissection procedure, applying high bio-safety levels, using negative pressure and laminar flow system for ventilation, UV irradiation for sterilization, virtual autopsy etc. The aim can be achieved by constructing the mortuary complex in more planned way like area division, ventilation planning, environmental disinfection, individual protection, autopsy procedure, virtual autopsy, preservation and transportation of dead body and waste disposal management. The present manuscript describes in detail about modern mortuary complex during the pandemic era of covid-19 © 2022, Journal of Indian Academy of Forensic Medicine.All Rights Reserved.

Post-acute sequelae of SARS-CoV-2 among previously hospitalised individuals with COVID-19: a systematic literature review and meta-analysis

Introduction: Many individuals hospitalised with COVID-19 experience persistent symptoms following acute infection, known as long COVID or post-acute sequelae of COVID-19 (PASC). Objective(s): To conduct a de novo SLR and meta-analysis to identify PASC-associated symptoms in patients that required hospitalisation, and to determine frequency and temporal nature of PASC. Method(s): Searches of MEDLINE, Embase, the Cochrane Library (from 2019-2021), WHO ICTRP and reference lists were performed. Articles were assessed by two reviewers against eligibility criteria and a risk-of-bias tool. Symptom data were synthesised by random-effects meta-analyses. Result(s): Of 6,942 records found, 52 studies with >100 patients were included in this analysis, ~70% of which were Europe-based. Most collected data from the first wave of the pandemic. PASC symptoms were analysed from 28 days to 1 year after hospital discharge. At 1-4 months post-acute COVID, the most frequent symptoms were fatigue (29%;95% CI: 20-41) and dyspnea (20%;13-29). Symptom burden persisted at 4-8 and 8-12 months (Fig 1). Conclusion(s): Lasting symptoms of COVID-19 can result in clinical and societal burden long after acute infection. Further research with longer follow-up is needed to better define the duration of PASC and whether factors such as vaccination, hospitalisation status and prior treatments have an impact on PASC.

Cough-based COVID-19 Detection with Multi-head Deep Neural Network

Clinicians have long used audio signals created by the human body as indications to diagnose sickness or track disease progression. Preliminary research indicates promise in detecting COVID-19 from voice and coughing acoustic signals. In this paper, various popular convolutional neural networks (CNN) are employed to detect COVID-19 from cough sounds available in the Coughvid opensource dataset. The CNN models are given input in the form of hand-crafted features or raw signals represented using spectrograms. The CNN architectures for both the types of inputs has been optimized to enhance performance. COVID-19 could be detected from cough sounds with an accuracy of 77.5% using CNN on handcrafted features, and 72.5% using VGG16 on spectrograms. However, result show that the concatenation of the two in a multi-head deep neural network yield higher accuracy as compared to just using hand-extracted features or spectrograms of raw signals as input. The classification improved to 81.25% when ResNet50 was employed in the multi-head deep neural network, which was higher than that obtained with VGG16 and MobileNet. © 2022 IEEE.